An Energy Efficient Data Collection Using Multiple UAVs in Wireless Sensor Network: A Survey Study

Today, with scientific and technological advances in robotics, artificial intelligence, control and computers, land, air and sea vehicles, they have been considered. Unmanned aerial vehicles (UAVs) have also significantly improved and are very useful for many important applications in the business, urban and military environment. One of the important uses of UAVs in Wireless Sensor Networks (WSNs) is that devices with low energy and may not be able to communicate in large areas. Nevertheless, a UAV can provide a tool for collecting the data of WSN from one device and transmitting it to another device. This article focuses on the field of research on practical applications of UAVs as mobile collectors for wireless sensor networks. First, the investigations of the proposed UAV were studied and compared their weaknesses with each other. Then, the technical challenges of the applications of UAVs in the wireless sensor network were explored. 1) Introduction In the near future, millions UAVs, also known as drone, are expected to quickly operate in different parts of our everyday lives and provide massive services [1]. In fact, drones can play a key role in Wireless Sensor Networks (WSNs), which consist of devices with a limited battery size, like health monitors and sensors [2]. Because of their energy constraints, these devices can not normally be transmitted over long distances. In this WSN cases, UAV can dynamically move towards devices of WSN, gather data of WSN and transmit it to other devices which are out of the communication ranges of the transmitters.See Figure 1. The main applications of communications supported by the UAV can be divided into three categories [3]: 1) Coverage everywhere with the UAV, in which UAVs are used to provide integrated telecommunication coverage in the targeted area to assist existing ground telecommunication infrastructures. In this case, UAVs act as aeriel base stations (BS) [4]. The UAV-based wireless communications have their unique ability to connect quickly, reliably and cost-effectively to areas covered by poorly-grounded networks [5]. 2) One of other promising applications is UAV aided relaying where UAVs are sent for providing reliable wireless connection between two or more distant users or group of users in enemy environment, like communication between front and base for emergency answers or army operations [6],[7]. 3) UAV systems can also be used to collect data / disseminate information using UAV. This is attractive especially for periodic application or wireless sensor networks (WSNs) in which UAVs can fly through sensors for communications, greatly reducing the operational capacity of the sensors, thereby increasing the lifespan Increase the network [8-9]. Data collection with UAVs not only has the elasticity of a mobile data set appropriate for a WSN large scale network, but also has the following benefits [10]: 1. The deployment environment is different. Aerial data collection using the UAV can be automatically guided as a moving data collector. There is no mobility constraint on land transport and can be used in specially monitored areas that humans can not access. 2. Aerial collectors of data sets collect information much faster. Compared with the collection of terrestrial data sets, aerial data collection can be controlled using an UAV that has faster movement. This can increase the search speed and visit nodes, and reduce the lifecycle of WSN data when network has a large scale sensor network. 3. The delay is lower and the bandwidth is higher. Compared to air to ground data gathering, aerial data sets have less obstacles and bigger wireless signal coverage, which can reduce communication delays and increase bandwidth. Journal of University of Babylon for Pure and Applied Sciences (JUBAS) by University of Babylon is licened under a Creative Commons Attribution 4.0 International License. 2018.

1. The deployment environment is different. Aerial data collection using the UAV can be automatically guided as a moving data collector. There is no mobility constraint on land transport and can be used in specially monitored areas that humans can not access. 2. Aerial collectors of data sets collect information much faster. Compared with the collection of terrestrial data sets, aerial data collection can be controlled using an UAV that has faster movement. This can increase the search speed and visit nodes, and reduce the lifecycle of WSN data when network has a large scale sensor network. 3. The delay is lower and the bandwidth is higher. Compared to air to ground data gathering, aerial data sets have less obstacles and bigger wireless signal coverage, which can reduce communication delays and increase bandwidth.

Figure 1: The communication between UAVs and WSN devices
Drones play the significant role of mobile collector for WSN networks. Nevertheless, for the effective use of UAVs for WSN communications, there are several challenges such as optimal positioing, optimal trajectory, data routing, user participation, air-to-ground modeling, and the efficient use of energy for UAVs and WSN nodes must be considered. The main goal of this article is to provide outline on these callenges of UAVs as mobile collectors for WSN.
The reminder of this article is as follow: in section 2, related works on using UAV in wireless sensor networks are examined. Lastly, conclusion and future works are presented in third section.

2) Efficient Data Gathering in WSN Using Multiple UAV: Challenges
In spite of several practical advantages and applications of UAVs as air base stations, many practical challenges such as optimal trajectory, optimal positioning, data routing, air-2-ground modeling, user participation, hovering time optimization and use of energy efficient UAVs and WSNs. Related articles in this area can be classified according to the previous challenges, which we will examine in each of these categories.

Optimal Trajectory:
One of the most significant challenges in UAV aided communications is the optimal route. Feng et al. [11] have presented a multi criteria optimization model for UAV ttajectory to monitor the road sector, aimed at minimizing the UAV distance and minimizing the no. of used UAVs. An evolutionary algorithm based on the Pareto optimization method for solving a multi-objective UAV path planning problem. The issue of scheduling UAV routing was a single-objective optimization problem, without considering the difference between road sections and road nodes. In fact, the length of the road segment, the no. of UAVs available, the maximum spacing of UAV, and multi-objective optimization can be influenced jointly by the UAV route planning results, but this is rarely taken into consideration. Therefore, in this paper, the above considerations are considered, a multi-purpose UAV path planning model is constructed, and then an evolutionary algorithm is proposed for solving a multi-objective routing programming problem for the UAV.
Zeng et al. [12] study the point-2-point link between the ground user and an UAV with the aim of improving the trajectory of UAV. A new strategy model that takes into accounts the UAV's power and communications capability together. To this end, the theoretical model for the propulsion of UAVs was fixed as a purpose of the flight velocity, direction, and acceleration of the UAV. Based on the proposed model and by disregarding the energy consumption of the signal processing, the efficiency of the energy of the UAV communications is defined. In case of unconstrained trajectory optimization mode, they show that both maximizing and minimizing energy leads to a reduction in energy efficiency, resulting in overall inefficient energy. Next, they present a UAV trajectory, below which the flight range and UAV velocity are improved to maximize the efficiency of UAV energy. Additionally, an efficient method is suggested maximize the efficiency of UAV energy with overall directions for the trajectory, including its start and end locations, and its speed, in addition to maximum/minimum velocity. As a result, the proposed projects will significantly increase the UAV energy efficiency. This article is intended for single UAVs and does not consider multiple UAVs.

Optimal Deployment:
One important design aspect of UAVs is UAV deployment. For multiple UAV-aided WSN communications in particular, appropriate UAV deployment may significantly shorten the communication distance and thus is crucial for high-capacity performance. Unfortunately, finding the optimal deployment for UAV is a challenging task in general, Yakici in [13], studied the problem of positioning and roting of UAVs at a planned level with a target function. They traverse the problem as an ILP (Integer Linear Program) with the goal of maximizing the overall score gathered from points of interest, visited by aircraft trajectories. These paths are set from designated stations that are assigned concurrently with the order of visiting points of interest for each path. They create an approach to an ant colony optimization (ACO) designed for the design of a compound problem. But in this article, only preallocated points are considered and do not use deadline.
Ladosz et al. [14] proposed a method to find the optimal location of UAVs as a relay node to improve the connectivity of network and the communication purpose of a team of vehicle/ground nodes. A complex 3dimensional complex consisting of many constructions is measured to have often been blocked by the LOS between the nodes of the earth. The PSO (particle swarm optimization) method is applied to find the optimal UAV location using three criteria of performance of communication, based on the need. A simulation is done to demonstrate the advantage of using UAVs as relay and specific criteria in typical scenarios. A stationary, internal concept test is also being executed to illustrate the possibility of the proposed method in real time. This article did not take into account the deadline time in their analysis.

Data Routing:
Routing is the only issue that remains active on all types of networks. For multi-UAV networks, robust and error-tolerant routing protocols are required that can provide a minimum delay during route selection, efficient path reconnection, fast recovery, improved control over delays and jitter. Yu et al. [15], an ant colony optimization based polymorphism based routing strategies is suggested for the APAR strategy to solve problems. This strategy combines the ACO strategy and the DSR routing strategy, the level of pheromone in the paths found in the routing discovery process, is selected as the standard for selecting the route, and by route distance calculation, the loading level of a route, and Track steadiness. A fresh pheromone evaporation algorithm is also presented into the strategy. Meanwhile, the strategy can change the formation of the air force to avoid compromise the performance of network. The results of simulation show that the APAR strategy is based on traditional strategies in PDR, end-2end ending, overhead, and reliability in the battlefield surveillance.
Rosati et al. [16] compared the performance of POLSR and OLSR in FANET consisting of fixed drones. Like networks are described by a high degree of mobility that challenges the routing protocol. protocols designed for MANETs have often failed in tracing the network topologies evolution. They solve this problem by creating the extension of OLSR called POLSR: the use of GPS data to forecast the quality of wireless links evolves. Simulation of networks and field experiments confirms our expectations. With POLSR, routing is looking for uninterrupted topology changes, which is not the case with OLSR.

Efficient Data Gathering.
Another major challenge in UAV-based communications is the effective data collection method. Wang et al. [10] have developed a novel framework for aerial data gathering, which have the following five components: networks deployment, node positions, endpoint searches, rapid route planning for the UAV and Collect data from the network. They identify important challenges in each of componets and propose effective solutions. This involves proposing a FPPWR (Fast Launch Route with Rule) algorithm based on the network segment, increasing the route planning efficiency, while ensuring the path length is relatively short. This UAV article did not consider the deadline time and the optimal number of UAVs in their analysis.
Ho et al., in [17], has chosen the WSN connection and the use of UAVs for data gathering. Typical wireless sensor networks (LEACHs) are usually used to select clusters for energy conservation purposes. Energy saving is very difficult to deploy on a large scale. PSO (Particle swarm optimization) has been suggested as an optimization method to find an best topology for reducing the consumption of energy, travel time of UAV, and BER (bit error rate). The PSO is compared using a LEACH simulation file and the simulation results show that the PSO of LEACH is bigger to BER and energy consumption, while the travel time of UAV is the same. Further results indicate that the gap of performance between them increases when no.of cluster head nodes increaased. Because of energy consumption reduction, network lifetime can be significantly increased while the amount of data received increasing from the entire network. Considering the effect of wind in the PSO design, it has been displayed that this effect affects the time of travel for the UAV, but energy consumption and BER do not meaningfully increase. Data collection has not been carried out with a multiple UAVs and did not take deadline time.

Energy Efficient of UAV and WSN
Another major challenge in UAV-based communications, saving energy from UAVs and WSN. Zorbas et al. [18] addressed the issue of efficiency of energy of the drone in the tracking of target states by setting no. of active drills. Where drone aided cameras are capable of detecting and tracking moving events that occur on the earth. A mathematical formulation has been devoted to minimize the overall energy consumption of UAV, which covers all events. Because of the very complexity of the optimization problem, the optimuim solution for even small specimens can not be achieved. In contrast, LAS is a local solution to the problem, given the ability of drones to fly at low altitudes to maintain energy. In this article, the authors assumed that the position of the targets was already identified and the randomness of the network was not taken.
Li et al. [19] proposed an effective energy saving relay scheme that could extend the life span of cooperative UAVs in unfriendly environments. A difficult NP-hardness optimization problem is designed to ensure the success of packaging and energy balance. A useful sub-optimal solution was created by separating energy balance and speed adaptability, and doing these two parts alternately. The results of simulation indicate that our sub-optimal method can decrease computational complexity with different values, with a significant reduction in performance and network lifespane compared to optimal solutions. The sub-optimal method can also preserve energy by 50%, increase network performance by 15%, and increase network life expectancy by 33%, compared to current algorithms. The above article did not consider the UAV's optimal deployment, the deadline, and the optimal number of UAVs in their analysis.

Air to Ground Modeling:
In the following articles, writers conducted air-2-ground modeling for UAV-aided communications. Jeong et al. [20] studied a UAV-aided MCC system in which a UAV moving with computational abilities has been limited to providing computational offloading process to mobile users with local processing abilities. This system aims to minimize total energy consumption. Offloading is activated by using downlink and uplink communications between mobile usres and UAVs. The problem of optimizing bit allocation for downlink and uplink communications, also solved for successive convex approximation strategies (SCA), has also been solved for calculating the UAV along with the super-small path line under the UAV energy budget constraints and the latency. The advantage of this method is the important energy savings that can be enhanced by the common optimization provided by the binary allocation and the super-small path, compared with the implementation of local animations, in addition to optimization approaches that only designate super-short path mapping. This was limited to a UAV and did not consider the deadline time and optimal number of UAVs in their analysis.
Hourani et al. [24] suggest a statistical diffusion model to predict the loss of the air-2-ground route between a low altitude platform and a ground device. The forecast is based on the characteristics of the urban environment and depends on the elevation angle between the device and the platform. The model displays that the loss of the air route to the ground, there are two main propagation sets identified with two different profiles of the tarajectory. They provided a generic AM-RF radio release model for low-end operating systems that could significantly simplify the efforts planning of air wireless services, because RF could be done based on simple urban parameters -Models that are unlikely to be simply accessible and up to date. The loss of the air-to-ground route had a full tendency toward two separate release groups, which were fully illustrated for outdoor recipients.

User (Device) Association.
Another major challenge in UAV-based communications is UAV and WSN devices association. The work in [9] has studied how to use the UAV to improve connectivity to the MANET network. They presneted four types of connection: k connection, network connection, worst case, and global message. They formulated positioning and movement issues for drone and designed adaptation algorithms to provide a good performance in addition to simple solution. They provided a analysis for a simple case of a dual UAV and showed that an increase in the UAV provides a 240% upgrading in connectivity of global message. For general network settings, a UAV can improve the connectivity of global message and worst case connectivity by 109% and 60%. They showed that the network connection and k connection were also enhanced by adding a drone to the network. The data collection has not taken place with the UAV, and they did not take deadline time.
In [21], a new framework for connecting optimally optimized cells to cellular networks with the UAV is presented. Specifically, in order to minimize the interference of the average network in any arbitrary space distribution from users, optimal partitions of the cells and ground stations (BSs) are determined. To this end, using the powerful mathematical transport theory, the solution to the problem of cellular connectivity is proven, and the solution space is fully characterized. The analytical and simulation results indicate that the proposed approach will significantly improve the network latency on average.The proposed cell-line communication approach reduces network latency compared to a SNR-based communication.

Flight Time Optimization.
The drones' flight time offers a design challenge for UAV-based communication systems. Mozaffari et al. [22] is an effective use of UAV (unmanned aerial vehicle) flight as a flight BS that can examine wireless services to land users. Specifically, a new framework for optimizing system performance, including UAV based drone, is presented in terms of data services (average number of bits) conveyed to users in addition to UAV hovering. They considered two practical scenarios are. In the scenario1, based on the hovering rate, UAV may be the average of data services under a resource allocation plan that is fairly straightforward for users to maximize by finding the optimal companion patches associated with the UAV. In the scenario2, according to the load condition of the land users, the average hovering time in which the UAV is fully required to provide services to its land users. They did not consider the deadline and the optimal number of UAVs in their analysis Henchey et al. in [23] allow the approximate flight time model to create a large set of estimated loads among the possible stations in a real time scenario. The model may be combined with operations for the UAV and the effects of wind on the UAV should bound their computing power, in spite of the obvious combination of increasing the average walking frequency. Then he presents the current art mode for approximate approaches. It shows arguments and methods for adding wind effects to approximate models. It describes these three variables for purposes including the effects of wind to the approximate the model of flight. Finally, providing a runtime analysis of the model comes with comparing real-time and approximate flight rates.
In Table 1, we compare the previous articles for the UAV-based WSN network based on deadline time parameters, optimal deployment, UAV optimal number, multiple UAVs, mobile nodes, and predefined locations. The table clearly shows that the existing approaches do not address the following issues:  Deadline timeout has not been reviewed.  The optimal deployment of UAVs was not investigated.  Do not check optimal number of UAVs.  Do not consider a multiple UAVs.  Do not consider mobile nodes.  Dependent on predefined locations.

3-Conclusions:
In this survey we attempt to focus on research in the areas of optimal trajectory, optimal positioning, data routing, air-to-ground modeling, user participation, hovering time optimization and use of energy efficient UAVs and WSNs. The reason to undertake this survey was lack of a survey focusing on these issues.
By identifying the key challenges of the UAV in the WSN network, accurate investigations of wireless communications using the UAV were carried out, which constitute the main features of this paper.
Three interesting directions are recommended for future research. Recommending distributed algorithm to achieve optimal route planning for UAVs based on clustering is the first direction. Second direction can suggest a framework for supporting mobile wireless sensor network. Last direction is extending suggested framework to support deadline based application.

CONFLICT OF INTERESTS
There are no conflicts of interest.


extfieldsofstudy:
Computer Science
Engineering

text:
Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.

INTRODUCTION
With societies aging globally, hearing loss is becoming a very common problem worldwide. The development of a hearing loss is typically accompanied by increasing difficulty to discriminate speech from noise in challenging situations. Consequently, besides amplification, modern hearing instruments deliver a great spectrum of algorithmic possibilities to enhance hearing, especially hearing of speech signals in noisy environments.
The signal-to-noise ratio is typically improved in hearing instruments by making use of directional microphones [1,2]. Directional processing, however, has the inherent side-effect, that the target speaker needs to be in a defined direction, often in the frontal hemisphere, and that sounds emitted from * M. Aubreville is also affiliated with Sivantos GmbH, Erlangen, Germany.
† A. Maier is the last author of this paper. ‡ H. Puder is also affiliated with TU Darmstadt, Graduate School of Computational Engineering, Darmstadt, Germany. other angles will likely be attenuated. While, for many situations, this assumption can be safely made, it can also be disturbing in other situations, where the target source cannot be assumed to be coming from the front. Furthermore, for some instruments directional processing is not an option due to limitations in size and power consumption, e.g. devices to be inserted deeply into the ear canal.

STATE OF THE ART
Single-channel noise reduction aims to solve this problem, making use of a single microphone signal only. Most noise reduction schemes, however, use limited signal properties of noisy environments, effectively only exploiting first and second order statistics and targeting at steady state noises, resulting in difficulties when dealing with non-stationary background signals. To overcome this, codebook-based noise reduction was proposed by Kuropatwinski [3]. This approach, however, lacked robustness -a property very important for industry-grade medical products, and was combined with a recursive noise tracker by Rosenkranz and Puder to overcome this limitation [4]. The use of longer context audio segments with neural networks has been proposed by Hermansky and Sharma [5] and has found applications in automatic speech recognition [6]. In recent years, deep learning methods and deep neural networks (DNNs) have become increasingly popular in speech recognition [7] and speech enhancement. Since deep networks are able to learn a complex, nonlinear mapping function, this makes them ideal candidates for noise reduction tasks, where complex priors (speech and distortion signal properties) must be modeled.
Auto-encoder networks have been used by multiple authors also in the field of audio denoising. Lu et al. were amongst the first to report about successful use of denoising auto-encoders for speech recognition systems [8]. Xia et al. used a denoising auto-encoder to calculate an estimate of clean speech which is then used in a traditional Wiener filtering approach [9]. To cope with missing loss sensitivity at high frequencies, they introduce a frequency-dependent weight, which effectively adjusts the learning rate at the last layer of the autoencoder. However, as Kumar et al. have highlighted, these studies lack realistic noise scenarios, as the same kind of noise was used for training as for testing.
When the network is used for direct speech signal estimation, a high correlation between the loss function and human perception should be demanded. This common problem was addressed by Pascual et al., who proposed to use generative adversarial networks, in effect using discrimination loss instead of a minimum squared error that is typically used in other works [10].
Besides auto-encoders, traditional fully-connected network topologies have been used for audio denoising. Xu et al. have proposed a DNN-based regression approach for frequency-bin wise enhanced signal amplitude prediction in a filter bank setting, using the noisy phase for signal reconstruction [11]. In their work, they have also shown that the DNN can profit from the inclusion of a longer acoustic context.
A very important property of a hearing assistance device is its overall latency, especially when it comes to mild to moderate hearing losses, where acoustic coupling is typically open and thus a strong component of the direct sound is reaching the ear drum. Here, comb filter effects are introduced by the superposition of processed signal and direct signal. Latencies of below 10 ms are typically tolerated in this regard, with lower latencies being tolerated better by subjects. This places high demands on delay on every part of the signal processing chain, especially the filter banks and noise reduction schemes.
This, together with the constraint for online processing, was not given for any of the state of the art, which builds the motivation for this work.

MATERIAL AND METHODS
We used 49 real-world noise signals, including non-stationary signals, recorded at various places in Europe using hearing aid microphones in a receiver in the canal-type hearing instrument shell (Signia Pure 312, Sivantos GmbH, Erlangen, Germany) using calibrated recording equipment at a sampling rate of 24 kHz. The signals have been mixed with German sentences (N=260) from the EUROM database [12], which has been upsampled to 24 kHz for this purpose.

Dataset Generation
Since the noise conditions in our dataset have been recorded in real situations, levels should not be modified significantly. The signal mixing process can be expressed as: where the original speech signal s 0 is adjusted in level to reach a defined SNR using g S . For data augmentation, we adapt the idea of Kumar et al. [13] and combine up to four noises with different offsets within the original files into the noise mixture n 0 . The noise mixture is adjusted in level (g L ∈ The train-validation-test split for our machine learning approach was done on original signal level, i.e. no speech or noise signal that is contained in the training set is part of the validation or test set.

Signal Processing Toolchain
Our toolchain, as depicted in Fig. 1, starts with analysis filter banks (AFB) that are used to process the clean speech signal s, the noise signal n and the noisy mixture x. A standard uniform polyphase filter bank with 48 frequency bins designed for hearing aid applications is utilized for this [14], yielding signals X(k, f ), S(k, f ) and N (k, f ) with time-index k and frequency index f .
As a first subsequent step after the filter bank, the log power spectrum is calculated, followed by a normalization step. Then, the signal including the temporal context is fed into a fully connected network topology with 3 hidden layers and 2048 nodes per layer. Finally, a 48 channel gain vector G w is being predicted and applied to the noisy signal X(k, f ), which is in turn synthesized again (SFB). This structure is especially suited for hearing instrument applications, as gain application can well be combined with other algorithms working on the signal chain, such as automatic gain control for hearing loss compensation, and could be interpreted as known operator learning [15].

Asymmetric Temporal Context
As previously stated, one constraint in hearing instrument signal processing is the limited latency a device is allowed to produce [14]. Besides the filter bank design, this constraint also strongly restricts the algorithmic lookahead for noise reduction schemes. We assumed an overall latency of 8 ms to be tolerable, where approx. ing constraints of the instrument, which typically scales with available chip technology. The time context of this work can thus be divided into 2 components: where τ 1 is the look back time constant, and τ 2 is the lookahead time constant. Together with the current frame they define the input matrix to the network. For our setup, τ 1 >> τ 2 .

Normalization
Normalization plays a key role in deep learning, since it positively influences convergence behavior and aids generalization. Given the filter bank representation X(k, f ) of the input signal x(n), the signal is normalized as: This frequency bin-wise normalization is thus calculated on the available buffer, given the temporal context. This normalization scheme requires no global information and thus enables on-line processing. The calculated mean value vector µ is, alongside with the frequency bin-wise standard deviation σ, enhancing the model's input as further contextual information.

Network Details and Training
We observed that network convergence as well as RMSE loss was positively influenced by using rectified linear unit activation functions within the hidden layers. The system was trained on a Nvidia Titan Xp GPU using TensorFlow and the Adam optimizer with an initial learning rate of 10 −5 for 10 epochs.

RESULTS AND DISCUSSION
When comparing the loss for different time constants of the asymmetric context (Fig. 2), we find that the network profits from increased information from both past, and future. The benefit w.r.t. past context saturates at around 200 . . . 300 ms. This is in line with the findings of Xu et al., and could hint at the rate of speech syllables (≈ 4 Hz) [16]. Looking at the example of a fricative (Fig. 3) further hints towards this interpretation: With almost no energy at the low frequencies, the network with greater context is still able to differentiate between the noisy fricative and a real noise signal. We compared the results by our method with a state-ofthe-art noise reduction scheme, based on recursive minimum tracking [17] that is being applied in commercially available hearing instruments. Another comparison was made against an idealized scheme, where the optimal Wiener gain is applied, and as anchor a badly tuned minimum statistics estimator. For all settings, the maximum attenuation of the algorithms was limited to 14 dB.

Objective Metrics
For numeric assessment of our complete test data set, we use the short-term objective intelligibility metric from Taal et al. [18]. To improve visibility of the results, we present the difference between the original noisy signal and the enhanced signal, denoted as ∆STOI (see Fig. 4). For the DNN approach in low SNR conditions, we see that ∆STOI is on average close to zero, indicating no gains in intelligibility. For SNR closer to real world conditions, the value improves with saturating effects for higher SNRs. For the baseline approach, the average value is consistently negative.
Following the formulation of Chen et al., we further include the noise reduction (NR) and speech distortion (SD) metrics for our evaluation [19]. Comparing the baseline to the predicted gain using the DNN, we find that a higher noise reduction can be achieved while at the same time producing lower speech distortion.

Subjective Evaluation
Even though objective figures like STOI or others are designed to correlate well with perception, the validity of such measures can always be questioned for the actual noise reduction scheme that is being applied. Further, quality ratings of a noise reduction system should always be made relative to an upper boundary, limited by the SNR and the achievable optimum performance given the general approach.
In audio coding, the evaluation is subject to a similar problem, in this case that the original audio signal might be of a mediocre quality or might have signal parts that are perceived as better in the processed than the original signal given a nonoptimal encoding. For this domain, the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) [20] was developed and is being widely used.
We apply this test also for the domain of noise reduction, because we find a similar setup: We also have a potentially perceived non-optimal reference signal, we can derive an anchor signal that should be perceived as being of worse quality, and we want to compare multiple signals relative to each other. For our case, especially the comparison with the reference is of great importance, since a Wiener filter-based scheme will always have the noisy phase in the output signal and is thus restricted in quality for poor SNR conditions. The test was carried out at Sivantos R&D site in Erlangen, Germany. The vast majority of all (N=20) subjects participating in the test were audio engineering professionals and thus highly qualified for signal quality assessment. Signals were presented over a web interface (webMUSHRA implementation of Schoeffler et al.. [21]) using headphones in a calm office environment setting. For each SNR condition, 4 input signals of 12 s length were randomly picked from the test dataset and processed for all test conditions. To not include initialization of the recursive minimum tracker baseline, all signals were cut after initialization.
Looking at the results (Fig.5), we find that the median subjective quality rating improves in all conditions over the baseline. Further, we find ceiling effects for the DNN-generated signal, indicating signals with equal quality to the reference for some conditions (cf. 5 dB and 10 dB).

SUMMARY
In this work we presented a deep learning-based approach for noise reduction that is able to work with the restrictive conditions of hearing instrument signal processing. The subjective and objective evaluation showed improvements over a recursive minimum tracking approach in a wide range of SNR conditions using realistic noise scenarios.


extfieldsofstudy:
Physics
Computer Science

text:
Automated complex for research of electric drives control

In the article, the automated complex intended for research of various control modes of electric motors including the inductor motor of double-way feed is described. As a basis of the created complex, the National Instruments platform is chosen. The operating controller built in a platform is delivered with an operating system of real-time for creation of systems of measurement and management. The software developed in the environment of LabVIEW consists of several connected modules which are in different elements of a complex. Besides the software for automated management by experimental installation, the program complex is developed for modelling of processes in the electric drive. As a result there is an opportunity to compare simulated and received experimentally transitional characteristics of the electric drive in various operating modes.

Introduction
Modern electromechanical systems of responsible installations, such as spacecraft, are subject to increasingly high demands on reliability, accuracy, weight and size and functionality [1][2][3]. This leads to the search for new directions in the design of electric drives using a modern element base and control methods [4][5][6][7]. One of such directions is the use of alternating current motors, including motors of double-way feed. There are two types of motors of double-way feed: on the basis of the induction motor with a wound rotor and inductor motors of double-way feed. For motors of doubleway feed, all known methods of control of AC motors, as well as methods specific to double-way feed and providing new properties to the electric drive, can be used. One of these methods of control is phase control, which gives new capabilities for the drive in position systems and provides high dynamic and precision values of the electric drive [8][9][10][11]. At the Institute of Space and Information Technologies of the Siberian Federal University, on the basis of the National Instruments equipment, a hardware-software automated complex was created. It allows one to carry out research of various methods of electric drives control.

Hardware-software automated complex for research of electric drives control
For research of characteristics of electric motors and methods of their control, the hardware-software automated complex was created that includes an electric motor, the digital angular position sensor, current sensors, power inverter modules, mechanical load and National Instruments equipment (Fig. 1). The modular platform NI PXI-1042 was chosen as the basis of the created hardware-software complex. It allows one to easily change the structure using the modules according to the needs of the user and has a high performance for processing large data sets. NI PXI-1042 embedded controller NI PXI-8106RT is supplied with a real-time operating system for the creation of measurement and control systems.  The NI PXI-8106 RT controller uses a PXI bus to communicate with modules installed on the chassis, such as the NI PXI-7833R multifunction RIO device (Fig. 2). This module is hardwareimplemented on a programmable logic integrated circuit and has 8 channels of analog input, 8 channels of analog output and 96 digital input/output lines. The LabVIEW FPGA software module in the national Instruments LabVIEW graphical programming environment was used to develop the management and data collection program for the NI PXI-7833R module. The software developed in the LabVIEW environment ( Fig.3) consists of several connected modules that are located on the operator's PC, the NI PXI-8106 RT controller and the NI PXI-7833R module. The operator's workplace uses a module with a graphical user interface. Here it is possible to select the desired mode of operation of the automated complex and set the parameters of the operating mode of the electric drive, such as frequencies, amplitudes and phase shifts of supply voltages. Software modules executed on the NI PXI-8106 RT controller and NI PXI-7833R device ensure realtime operation of the system. They allow one to form sinusoidal currents in one or two three-phase windings of electric motors (for motors of double-way feed) by means of pulse-width modulation. It is possible to independently change various settings of the PWM and investigate transients.
One of the features of motors of double-way feed is the presence of two groups of three-phase windings, i.e. to control of such motor requires two three-phase inverters.
The formation of sinusoidal currents in the motor windings occurs by means of a sinusoidal centered pulse width modulation. PWM allows a simple way to change the basic parameters of control signals: frequencies, amplitudes and phase shifts.
The control signal generation diagram for three-phase inverters is shown in Fig. 4. The initial period of the sine is divided into n parts, each of which is represented by a single PWM pulse with a duty ratio proportional to the amplitude of the sine taken at the middle of this segment of the sine period. In the real-time system of the NI PXI given amplitude and number of pulses. The table registers in the PWM impulse selector. The selector chooses from the table the necessary impulse, based on a present situation of time, frequency and a phase. The PWM impulse selector receives a fill factor value and sends opening and closing signals to the inverters via the output ports.
As a result, sinusoidal currents of a predetermined amplitude and frequency windings of the motor (Fig. 5).

Experimental research of electric drive control
The interface of the user of the automated complex displays the data obtained from sensors, for example, of the sensor of angular position (Fig. 6).
For further analysis of transients, data from sensors is exported to a file. experimental data, it becomes possi models in developing new control methods. Experimental research of electric drive control user of the automated complex displays the data obtained from sensors, for example, of the sensor of angular position (Fig. 6).
For further analysis of transients, data from sensors is exported to a file. experimental data, it becomes possible to refine the mathematical models of the motor and use these models in developing new control methods.
Data visualization of the angular position sensor 1042 platform, a table of duty factors is calculated for a given amplitude and number of pulses. The table registers in the PWM impulse selector. The selector chooses from the table the necessary impulse, based on a present situation of time, frequency and a phase. The PWM impulse selector receives a fill factor value and sends opening and closing signals to oidal currents of a predetermined amplitude and frequency are formed in the Oscillogram of PWM signals and current form of one of phases user of the automated complex displays the data obtained from sensors, for For further analysis of transients, data from sensors is exported to a file. After obtaining ble to refine the mathematical models of the motor and use these Along with the software for management of the automated complex, the program complex for modeling of processes in the electric drive was developed. It includes a system for the automated generation of analytical mathematical models, implemented in the MathCAD program using its character processor, as well as a numerical modeling system using the obtained models. As a result, it is possible to compare the simulated and experimentally obtained transient characteristics of an electric drive in different modes of operation. To do this, first an experiment is carried out, for which all initial conditions, mode parameters, control signals and transient characteristics for the currents and the rotation angle of the rotor are fixed (from which then the flux connections, angular velocity and load angle are calculated). Then, the data stored in the file are transferred to a simulation program that uses the initial conditions and mode parameters recorded in the experiment to model the transient characteristics. After this, it is possible to derive the experimental and model characteristics for the general graph and compare them under identical test conditions.

Conclusion
Some electric motors, such as motors of double-way feed, have a large number of control options. This requires their experimental verification. The created hardware-software automated complex for experimental research of electric drives allows implementing various control methods and investigating transients in the electromechanical system with high accuracy. Technical capabilities of the complex allow flexible implementation of control laws for closed and open systems, as well as quickly analyzing the data obtained from experiments.


A Factoid Question Answering System for Vietnamese

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied"as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.

INTRODUCTION
Question Answering (QA) has been an important line of research in natural language processing in general and human-machine interface in particular. The ultimate goal of a QA system is to provide a concise and exact answer to a question asked in a natural language. For example, the answer to the question "Which French city has the largest population?" should be "Paris".
Open-domain QA is a challenging task because the research and validation of a precise answer to a question require a good understanding of the question itself and of the text containing the potential answer. Typically we need to carry out both syntactic and semantic analyses in order to fully understand a question and pinpoint an answer. This is much more difficult than the task of common information retrieval, where one only needs to present a ranked list of documents in response to a question, which can be efficiently performed by available search engines.
The state-of-the-art techniques in open-domain QA can be classified into two main categories, namely semantic parsing based techniques and information retrieval based techniques [2]. Semantic parsing systems try to interpret the meaning of a question correctly by semantic analysis. A correct interpretation converts the question into an exact database query that returns a correct answer. On the other hand, information retrieval based systems first transform a question into a valid query, then retrieve a set of candidate answers by querying a corpus and/or a knowledge base, and finally use fine-grained heuristics to identify the exact answer.
Although both kinds of system require human expertise to handcraft linguistic resources including lexicons, grammars and knowledge bases, the information retrieval based approach is more suitable to less-resourced languages since many advanced natural language processing tools such as syntactic and semantic parsers are not readily available. Furthermore, as shown in many previous studies on building QA systems, existing methods developed for well-studied languages are not easily and conveniently applied or scaled up to natural languages other than English.
In this paper we present a QA system for the Vietnamese language which combines both statistical models and knowledge-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language such as Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with a promising accuracy on a test set. The system is released as open-source software in the hope that it will serve as a baseline for future developments of question answering systems for Vietnamese.
The remainder of this paper is structured as follows. First, the next section gives a survey of existing work in the line of this research. Next, we describe the methodology that we use to develop our QA system. Then, we present our experiments and evaluation results. Finally, the last section concludes the paper and suggests some directions for future work.

RELATED WORK
There have been some existing studies on building and evaluating QA system for Vietnamese. In this section, we present a survey of existing work, compare and hightlight the difference between them and this work.
Tran [21] discussed a specific QA system for Vietnamese person named entity which focuses on only "who", "whom" and "whose" questions. To this end, the diversity of answerable questions are rather limited. A prior work of the same research group [22] presented an experimental study of a QA system for Vietnamese which utilized a search engine to search for answers. This system is restricted to travelling domain and was tested on only a small test set containing one hundred questions. Duong [5] presented a QA system for use in Vietnamese legal documents which is able to answer simple questions about procedures and sanctions in law on business. This system uses a similarity-based model and the Lucene document search engine to retrieve candiate documents and extract answers. Compared to these works, our QA system differs in three aspects. First, it is open domain which can provide answers to a much wider range of questions other than a specific domain or person named entity question types. Second, our system does not use a search engine to retrieve and rank documents but relies on a large knowledge base. And third, our system is evaluated on a test set of about ten times larger which covers a wide variety of questions, resulting in a promising accuracy.
Most recently, Nguyen [17] presented a QA system for Vietnamese which uses semantic web information to provide answers to user's queries. Together with a series of previous publications in the same line of research, this group developed the KbQAS system which is claimed to be the first knowledge-based QA system for the language. 1 A key component of their system is a knowledge acquisition module which utilizes the single classification ripple down rules method for question analysis. This is a typical rule-based system. Although their method is able to acquire rules in a consistent and systematic manner, the knowledge bases are required to be built from scratch and an adaptation to a new domain or language still requires time and effort of human expertise. As reported, this system contains 92 manual rules and was tested in a test set of 74 Vietnamese questions. In contrast to this work, our system utilizes both statistical and rule-based approaches, a large ontology base (DBPedia), and the Cypher query language -a high-performance declarative language for query graph database. Our system is also validated on a much larger test set of diverse questions, totaling nearly 900 question and answer pairs.

METHODOLOGY 3.1 DBPedia and Graph Model
Our QA system uses an ontology developed by the DBPedia project [13]. 2 DBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web for a wide number of languages, including Vietnamese. The DBPedia knowledge bases have become an important source of structured information on the emerging Web of Data [16].
DBPedia is an ontology according to the definition of W3C 3 in that it defines the terms used to describe and represent an area of knowledge. Figure 1 shows an excerpt of the DBPedia ontology. This ontology says that there is a class called Writer which is a subclass of Artist which is in turn a subclass of Person. There is a property relating an instance of the class Work to an instance of the class Person. For example, the novel titled "Angel and Daemon" is an instance of class Work and related via property author to its author "Dan Brown".
The DBPedia ontology can also be viewed as a property graph model made up of nodes, relationships and properties [20]. Nodes contain properties in the form of key-value pairs; the keys are strings and the values are arbitrary data types. Relationships connect and structure nodes. A relationship always has a direction, a label, a start node and an end node; the direction and label add 1 In their work, the term "knowledge base" may lead to confusion in that it really refers to a set of rules rather than an ontology base of entities and relations. semantic clarity to the structuring of nodes. It is noted that like nodes, relationships can also have properties which provide not only additional semantics but also metadata for graph algorithms and help constrain queries at runtime. We have constructed a graph database from the dump files of the Vietnamese DBPedia ontology. The total size of these files is about 5 GB. The database is of size 1.5GB, consisting of one million nodes, 2.5 million links and 7.5 million properties.

Query Language
We use Cypher to query the DBPedia ontology. Cypher is an expressive and compact graph database query language. This language is specific to Neo4j 4 which is a good and well-known graph database used by many organizations in production applications. The major advantages of Cypher are that it is easy to learn, easy to use and ideal for describing graphs programmatically in a precise fashion.
It is noted that other graph databases have other means of querying data. Many graph databases support the RDF query language SPARQL. However, in building a QA system, we are interested in the expressive power of a property graph combined with an advantageous delarative query language. For this reason we chose Cypher to query the database to find data matching a specific pattern.
Cypher enables a user (or an application) to ask the database to find data that matches a specific pattern. Figure 2 shows an example of a simple pattern. This pattern describes three mutual friends.
Like most query languages, Cypher is composed of clauses. The simplest queries consist of a START clause followed by a MATCH and a RETURN clause. An example of a Cypher query that uses these three clauses to find the mutual friends of user named Michael is: The other clauses that we can use in a Cypher query include WHERE, CREATE and CREATE UNIQUE, DELETE, SET, FOREACH, UNION and WITH. These clauses allow for expressive and efficient querying and updating of the graph database. For details please refer to the documentation page of the Cypher query language. 5 In the following subsections we present briefly some important Vietnamese language processing modules which are integrated in our QA system. These modules deal with basic processing tasks of Vietnamese including word segmentation, part-of-speech tagging and question classification. Due to space restriction, we do not present in this paper the general characteristics of the Vietnamese language which are discussed in detail in [10].

Word Segmentation
Word segmentation or tokenization is the problem of dividing a string of written language into its component words. In English and many occidental languages, the space is a good approximation of a word delimiter. However, many languages do not have a trivial word segmentation process. For example, in Chinese or Japanese, sentences but not words are delimited; in Thai and Lao, phrases and sentences but not words are delimited, and as presented in the previous section, in Vietnamese syllables but not words are delimited. Word segmentation is in fact a difficult problem for these languages.
In particular, there are two types of ambiguities that we must deal with in Vietnamese word segmentation. The first ambiguity is called overlap ambiguity where some adjacent syllables can have different word segmentations and their validity cannot be determined completely without resorting to a syntactic or semantic resolution of the entire sentence. For example, the three-syllable phrase "thuộc địa bàn" can have two word segmentations, either "(thuộc địa) (bàn)" or "(thuộc) (địa bàn)", depending on context. A more complicated example is with a four-syllable phrase "tổ hợp âm tiết" where all the words tổ hợp, hợp âm, and âm tiết are valid, and hence all possibly different overlapping word segmentations are plausible. The second ambiguity is called the combination ambiguity where two adjacent syllables can either be divided or combined to make words. For example, two syllables "chanh chua" can form an adjective which means to have a sharp tongue, or they can form two words chanh and chua, a noun phrase which means a sour lemon.
Although Vietnamese word segmentation is difficult, there exists efficient approaches to solve this problem which have been published by the Vietnamese language processing community. In this work we adopt the approach of [9] which is consistent and has a good accuracy in the range of 96%-98% on different test sets.

Part-of-Speech Tagging
Part-of-speech (POS) tagging, also called grammatical tagging or word-category disambiguation, is the problem of automatically determining each word in a sentence as corresponding to a particular part-of-speech such as noun, verb, adjective, adverb, etc. POS tagging is not an easy problem since many words can represent more than one part of speech on different occasions.
For well-studied languages like English or certain other occidental languages, POS tagging is a solved problem with very high accuracy, about 97.3%, which is believed to be as high as human performance [15]. However the accuracy of Vietnamese POS tagging is much lower than that of English. The combination of the best machine learning algorithms and the best features in discriminative sequence models has achieved an accuracy of about 93.5% [11]. As presented in the previous section, an important reason for the inferior accuracy of Vietnamese POS tagging is its inherent difficulty. It is not easy to determine a clear syntactic function of many Vietnamese words while syntactic category mutation is a frequent phenomenon. Furthermore, POS tagging depends heavily on word segmentation, which is a difficult task as presented in the previous section.

Question Classification
The first step of understanding a question is to perform question analysis. Question classification is an important task of question analysis which detects the answer type of the question. Question classification helps not only to filter out a wide range of candidate answers but also to determine answer selection strategies. For example, if one knows that the answer type is city, one can restrict candidate answers as cities instead of consider every noun phrase of a document providing the answer.
At first glance, one may think that question classification can be framed as a text classification task. However, there exists characteristics of question classification that distinguish it from the common task. Firstly, a question is relatively short and contains less wordbased information than an entire text. Secondly, a short question needs deeper analysis to reveal its hidden semantics. Therefore application of text classification algorithms per se to question classification cannot produce good results. Furthermore natural languages are inherently ambiguous, thus the question classification is not trivial, especially for what and which type questions. For example "What is the capital of France?" is of location (city) type, while "What is the Internet of things?" is of definition type. Consider also these examples: (1) What tourist attractions are there in Reims? (2) What do most tourists visit in Reims? (3) What are the names of the tourist attractions in Reims? (4) What attracts tourists to Reims? (5) What is worth seeing in Reims? [14]; all these questions are of the same answer type: location. Different wording and syntactic structures classification difficult [6].
With the increasing popularity of statistical approaches to natural language processing in general and to question classification in particular, recent years have seen many machine learning approaches which have been applied to the problem of question classification. The main advantage of machine learning approaches is that one can learn a statistical model using useful features extracted from a sufficiently large set of labeled questions and then use it to automatically classify new questions.
We use the method proposed by [12] in our question classification module. In contrast to many existing approaches for question classification which make use of very rich feature spaces or handcrafted rules, this method proposes a compact yet effective feature set. In particular it uses typed dependencies as semantic features. It has been shown that by integrating only two simple dependencies of types nominal subject and prepositional object, one can improve the accuracy of question classification by over 8.0% using common statistical classifiers over two benchmark datasets, the UIUC dataset for English and a recently introduced FPT question dataset for Vietnamese. With unigram feature and typed dependency feature, one can obtain accuracy of 87.6% and 80.5% using maximum entropy classification for the UIUC and FPT question dataset respectively. It is worth noting that the best question classification accuracy on the UIUC dataset is 89.00% by [6], where important features like head words and their hypernyms are included. Such semantic features are not readily available for less-resourced languages such as Vietnamese, where a WordNet is still in its first stage of construction.

Textual Question to Cypher Query Transformation
An important module of our knowledge-based QA system is the module that transforms textual questions in Vietnamese into equivalent Cypher queries. The queries are then executed to search for answers to the questions. This section describes the main processing steps of this module. First, a textual question is processed by the NLP chain presented above, ranging from word segmentation to part-of-speech tagging and question classification. For example a question such as "Thành viên chủ chốt của tập đoàn FPT là những ai?" ("Who are the most important people of FPT Corporation?") will be analysed as follows: Word Segmentation: The output is a sequence of words: [Thành_viên, chủ_chốt, của, tập_đoàn, FPT, là_những_ai] . Here the underscore character is used to connect the syllables of a word and words are separated by commas. Part-of-speech Tagging: The output is a sequence of tagged words: [Thành_viên/ N , chủ_chốt/ N, của/ E, tập_đoàn/ N, FPT/ Np, là_những_ai?/ QW] . In this step, each word of the question is tagged as a part-of-speech, where N denotes a common noun, Np denotes a proper noun, E denotes a preposition and so on. Key Word Extraction: In this step stop words or unimportant words are stripped out of the question. Only key words are retained. In the example above, the word của/ E is removed. Question Classification: This step determines the answer type of the question, that is the information type we need to find. In this example the answer type is HUM (human) since the question asks for a person (or a group of people). Some other answer types are NUM (number), DTIME (datetime), YESNO (yes/no), etc. Details of the question types, statistical models and classification techniques are presented in [12]. Entity Construction: Since we are querying a graph model which is made up of entities (nodes), relationships and properties, we need to construct a set of entities, relationships and properties which are implied in the query at hand. This step is crucial in building a good corresponding Cypher query for a textual question. In essence: • The remaining words are classified as either properties or relationships, depending on their probabilities on a datasets, using a built-in dictionary. More specifically, our approach combines a rule-based extractor and a statistical-based classifier to perform entity construction. A rule-based extractor is used to extract named entities such as persons, organizations or locations by relying on the output of a part-of-speech tagger. A logistic regression model is used to predict the likelihood of being a property or a relationship for each remaining keyword in the query. To continue with the example above, this step determines FPT as entity, and thànhViênChủChốt as relationship, and there is no property for this question. Cypher Query Construction: In this last step we first build a syntactic tree representing a cypher query which corresponds to the textual input question. The nodes in the syntactic tree correspond to either the Cypher clauses or the operators ("=", ">", "<", . . . ), starting from the root node whose name is START. The leaf nodes of the tree correspond to key words or values. Figure 3 shows an example of a syntactic tree. Once the syntactic tree has been built, we search for appropriate replacements of leaf nodes with the elements determined in the previous step. Since there may be multiple plausible replacements for leaf nodes, a syntactic tree may generate multiple Cypher queries. This is expected because in the Vietnamese DBPedia graph database a key word can either be a link of a node or be a property of another node. A Cypher query for the original question is START x=node:DBPediaIndex(key="FPT") RETURN DISTINCT x.thànhViênChủChốt As another complete example, consider the following question: "Dân số và diện tích của Hà Nội là bao nhiêu? " (What is the population and area of Hanoi?). This question is analysed by the processing chain above, where the intermediate results and the final Cypher query are as follows: (1) Word Segmentation and Part-of-Speech Tagging:  START n=node:DBPedia(key="Hà_Nội") RETURN n.dânSố, n.diệnTích In the next section, we present experimental results of our QA system and discussion.

EXPERIMENTS
Our aim is to build a QA system which is able to answer Vietnamese factoid questions on a broad range of topics from the DBPedia ontology with high accuracy. We have developed an algorithm to transform different questions to corresponding Cypher queries following the methodology described above. The system can answer a wide variety of questions of diverse types, which are shown in the Table 1.
In this table E, p and r represents an entity, a property and a relationship respectively, and the vertical character '|' is used to represent alternative choices. The last row of the table shows a complicated question type where we seek the same relationship of two different entities (here, the capital). It is also extended further to account for more complicated questions where a user wants to seek for some comparative information, such as in the following example question where the area and population of two different Vietnamese provinces are queried: "Diện tích và dân số của Hà Nội và Thái Bình bằng bao nhiêu? " (What are the area and population of Hanoi and Thaibinh?) It is worth noting that the system can effectively deal with different variants of the same question since different syntactically correct word orders are identified and analysed. For example, to query the population of Hanoi, one can use either of the two following paraphrases: Dân số của Hà Nội là bao nhiêu? Hà Nội có dân số bằng bao nhiêu? (What is the population of Hanoi?) Or to ask for the country whose capital is Bangkok, a Vietnamese speaker can use either of the two following choices: Bangkok là thủ đô của nước nào? Nước nào có thủ đô là Bangkok? (What country's capital is Bangkok?) To evaluate the performance of the system, we have manually built a dataset of 879 question-answer pairs about person, location and other facts where the answers can be found in the Vietnamese Wikipedia. 6 To understand the performance further, in addition to the accuracy of the final answer, the accuracy of query transformation is also evaluated. The accuracy of our system is shown in Table 2.
The system is able to give correct answers for 76.70% of the questions in the test set. If the system does not find an answer to a question, it is counted as an incorrect result for that question.
The current test set contains the following types of questions of different difficulty levels: • Questions about an entity of the form "Who/What/. . . is E".
For example, "Who is Barack Obama?" or "Where is Paris?" (in Vietnamese language).
• Questions about a feature or property of an entity, for example "Who is the spouse of Barack Obama?", "What is the population density of Hanoi?", or a trickier question such as "What is the population of the capital of Argentina?.
• Questions about the same relationship of two different entities, for example "What are the capitals of France and Germany?. Here, France and Germany are two entities and the same relationship is the capital. The correct answer for this question should be "Paris" and "Berlin". Table 1 gives some more examples of these types of questions along with their English translation. Our manual test set also contains the correct Cypher query for each question so that the automatic query construction module can also be evaluated. Table 3 shows two samples in our test set.
Our QA system is designed to provide a short answer to factoid questions. However by querying the DBPedia graph database built on top of the Wikipedia it can also answer many questions in great details in the sense that it can also show the answer text for the question whenever it is available. For example, the following snippet shows the answer to the question "Tác giả của Truyện Kiều là ai? " (Who is the author of The Tale of Kieu?). Once the system finds the short answer "Nguyễn Du" for this question, it can retrieve and show the summary of the corresponding entry in the Wikipedia, giving the following result: Table 3: Some samples in our test set <question>Tác giả của Truyện Kiều là ai?</question> <answer>Nguyễn Du tên chữ Tố Như, hiệu Thanh Hiên, biệt hiệu Hồng Sơn lạp hộ, là một nhà thơ nổi tiếng thời Lê mạt, Nguyễn sơ ở Việt Nam. Ông là một nhà thơ lớn của Việt Nam, được người Việt kính trọng gọi ông là "Đại thi hào dân tộc". Năm 1965, Nguyễn Du được Hội đồng hòa bình thế giới công nhận là danh nhân văn hóa thế giới và ra quyết định kỷ niệm trọng thể nhân dịp 200 năm năm sinh của ông. </answer> Our QA system has a good speed in that it can answer a question in average 0.04 second on a personal computer. Our system will be released as an open-source project and freely available for research purpose. We believe that our system will be useful for the Vietnamese language processing community. At the moment, our demo system is available for testing at http://124.158.5.68:8080/wiki-qa/.

CONCLUSION
This paper presented the development of an open-domain question answering system for the Vietnamese language. The system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. It can answer a wide range of general knowledge questions with promising accuracy on a test set. It is released as an open-source software project in the hope that it will serve as a baseline for future development of question answer systems for Vietnamese. 7 .
With the rise of available large scale structured knowledge bases, we think that the most promising approach to open-domain question answering is the ability to query efficiently such databases in natural languages. In this work we concentrated on exploiting DBPedia, a freely available database of facts which are extracted from the Wikipedia. Nevertheless there exists other good knowledge bases such as Freebase [1], an open shared database of the world's knowledge, which has been shown to be very useful for many applications including question answering. We plan to investigate how we can use the Vietnamese section of this knowledge base in our system in a future work. We also plan to perform some comparisons with other approaches that can find answers directly from Wikipedia texts to show the benefit of quering an ontology.
Current good question answering systems make use of additional natural language processing modules such as dependency parsing or semantic role labelling [4]. We would like to improve further the performance of our system by integrating recently available dependency parser [8], semantic role labeller [18] and named entity recognizer for Vietnamese [7].
Finally recent works on open-domain question answering [2,3] have shown the efficiency of embedding models, which learn low dimensional vector representations of words and knowledge bases constituents to achieve better accuracy. How these models can be used to improve our current system is another interesting line of research that we would like to research in a future work, following some recent results [19].


extfieldsofstudy: Physics Mathematics

text:
Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Introduction
Outlier is one of the problems in linear regression analysis that gives a very big influence on the model if it is used the Least Squares method (LS) [8]. The existence of the outliers in the data will cause the regression line to shift toward the outliers. This will cause the resulting error to be large so that the Gaussian assumption of normality may not be met. In LS method, outliers are usually set aside from the data set. Setting aside this data is not a wise procedure because sometimes the data provides information that other data cannot provide. To deal with this, the robust method is used as an alternative of LS method. The robust procedure is intended to accommodate the data weirdness, as well as eliminate the identification of data outliers and also automatic in tackling outliers [2].
Some of the robust methods that have been introduced include M estimator proposed by Huber in 1973, Least Median Square (LMS) and Least Trimmed Square (LTS) proposed by Rousseeuw in 1984 [6]. Birch introduced the robust method that was a generalization of the M estimator [1] called the Bounded-Influence estimator (B-I estimator). Furthermore, the regression of Minimum Covariance Determinant (MCD) proposed by Rousseeuw in 2004 and further deepened by Hubert [6]. The latest method is the TELBS method [11]. Similar to the MCD method, the TELBS method can deal with the problem of calling either in space X or space Y.
Based on the similarity in its ability to handle the problem of outliers, this paper examines TELBS and MCD methods applied to secondary data on the productivity of phytoplankton. The model obtained from both of these methods will be compared based on the robust coefficient of determination to determine the best model. The data centralize measurement used in robust determinant coefficients is median in accordance with the nature of data containing the outliers.

Linear regression analysis
Regression analysis is a statistical application that can build the relationship model between the independent variable ( X ) and the dependent variable (Y ) . In the linear model, the commonly used method for estimating the parameter is the Least Squares method (LS). The LS method is the easiest method to perform with certain assumptions called the Gaussian assumption [4]. If all Gaussian assumptions are met then is a linear, non-biased and has minimum variance estimator.
Model of linear regression sample in matrix form, expressed by: where y is the dependent variable observation vector (n x 1), X is the independent variable observation matrix (n x (k + 1)), is the parameter to be estimated ((k + 1) x 1) and e is a vector residual (n x 1 ). Using the LS method, the component estimated by the principle is to minimize the sum of the residual squares to produce the following estimates: The use of the LS method in estimating parameter parameters is a very well known method because it is easy to understand and to calculate. However, the use of this method is usually influenced by the presence of outliers. Outlier is the furthest observation of data centre that may have a major effect on regression coefficients [10]. The condition will arise if the sample data presented is varied and extreme. Every time there is an outlier, the model will usually change, so if used as a prediction model will get poor results and give a wrong interpretation. The existence of outliers can cause errors and variance of data to be large. Consequently, the parameter interval becomes large.
Outlier is rejected if after searching it results in errors in measurement or analysis, inaccuracy of data recording, and damage to the measuring tool. If it is not the result of such mistakes, careful investigation must be made [3]. An investigation or diagnosis in regression analysis is one way to monitor problems arising in relation to data or models. One way of diagnosing data related to outlier is to use a hat matrix [7] defined as follows: The matrix hat essentially transforms the observed value y vector to the estimated value y vector. The main diagonal of the hat matrix is that hii will show a value greater than 2p / n, if the data is the outlier where p = k + 1.

Minimum Covariance Determinant (MCD) method
The Minimum Covariance Determinant (MCD) estimator is one of the first affine equivariant and highly robust estimators of multivariate location and scatters [6]. MCD method begins by determining the subset of matrix X as much as h observations are: So there will be a combination of observations as much as The subsets of the matrix X are denoted by:

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IORA-ICOR 2017 IOP Publishing IOP Conf. Series: Materials Science and Engineering 332 (2018) 012037 doi:10.1088/1757-899X/332/1/012037 (5) From each Hb, call as Hbm, the average vector and the covariance matrix are determined by: where v is the vector column is worth 1 (h x 1). Next for each b is calculated the value of det(Cbm). For m = 1, if det(Cbm) ≠ 0, then the distance of the Mahalanobis, is the element in the diagonal of the matrix, is determined by: where X * is the matrix of the observed independent variables (n x k), v * is column vector is worth 1 (n x 1). is the inverse of the covariance matrix (k x k), and X * is the average matrix of each row (1 x k). The distance of the mahalanobis is sorted from the smallest value and taken as h, then the selected number of observations is identified. After that, a new X * matrix is formed as Hbm (m = m + 1) and the new tbm and Cbm values are determined. The next step is to compare the value of det(Cbm) and det(Cbm-1). If: 1. det(Cbm) ≠ det(Cbm-1), using equation (8) recovered the value of the distance of the new mahalanobis, then new X * is formed to obtain new Hbm matrix; 2. det(Cbm) = det(Cbm-1), data processing is done from by taking another subset. This process is done until all of the Hb subsets are out. Further, the smallest determinant of covariance of all determinants of the subsets of Hb is selected. The subset with the smallest covariance determinant is called HMCD, so further can be also determined tMCD and CMCD. The HMCD matrix element is the independent variable of the selected sample unit based on the smallest order of the mahalanobis distance so that it can be determined also the pair of the dependent variable. The variance-covariance matrix of the variables X and Y variables whose chosen probes can be obtained by the following formula: The main principle in the MCD method is to calculate the weights wii = 1 if with so W MCD matrix (n x n) is (11) with the matrix entries w ij = 0 for i ≠ j. This matrix is the robust weight matrix and is used to obtain sensitivity parameter estimates with the following equations: (12)

TELBS method
One of the regression methods to estimate regression parameters when there are outliers is the TELBS method. TELBS estimator is better than the least squares method, M and MM estimator and its performed by minimizing objective function [11]: ω are positive real numbers called constants of constancy (value 0.628). Estimator value σ can be obtained from the equation as follows: (17) The selection of the constant 1.1926 makes σ a near-unbiased approximation of the limited sample [9].
To minimize equation (13) it must be derived to the parameter and equal to zero, so that equation (13) can be expressed as: The weighted function is defined (11): then equation (18) can be written to be: Equation (20) if it is derived to β 0 and β j will produce the matrix form so we obtain an estimate of TELBS methods are: where W * is the square matrix with the diagonal element is w ii * in equation (19) and the matrix entry w ij * = 0,for i ≠ j. In the TELBS method the estimated value obtained by the iteration process until the convergence is achieved. The robust determination coefficient is then determined to see the suitability of the model as follows [11]:

Data
The secondary data used is the result of research on the primary productivity of phytoplankton with the physicochemical factors of aquatic in aqua culture with floating net [5]. From the result of the research, there are twenty data pair, with primary productivity (gr.L/m 2 ) as an independent variable (Y) and three independent variables namely light intensity (kkal/m 2 ) as X1, PH (X2), and phytoplankton density (ind/mL) as X3.

Results and discussion
The hat matrix is used to detect the presence of the outlier. The main diagonal of the hat matrix is shown in Figure 1. It appears that the 17 th , 18 th , and 19 th data are the outliers represented by a value greater than 2p/n = 0.3. If forced by using LS, the value of determination coefficient is 0.57762, meaning that only 57.762% variability of primary production of phytoplankton can be explained by the factors that influence it. The small coefficient of determination in LS method becomes the reason for modeling using MCD method and TELBS method. Using Maple 15, the results are presented in the following table: The results of Table 1 above show that based on the coefficient of determination, MCD produces a better model than TELBS method. This is reinforced by the residues values illustrated in figure below which shows that the residues generated by MCD tend to be less than the TELBS model.

Figure 2. Residues based on MCD and TLBS methods
Thus the best model for the phytoplankton productivity is as follows: (23) The coefficient of determination with MCD method gives a value of 0.8994. This means that 89.94% of the primary production variability of phytoplankton can be explained by the factors of light intensity, pH, and phytoplankton density. The remaining 10.06% is determined by other factors, which are not included in the study.

Conclusion
In this paper, we develop a linear model of the productivity of phytoplankton. It has been shown, that based on robust coefficient determinant, the MCD model is better than TELBS when data is contaminated by outlier.