29270156
A Fuzzy Decision Agent for Meeting Scheduling Supported System
9595362
Genetic-based fuzzy image filter and its application to image processing
In this paper, we propose a Genetic-based Fuzzy Image Filter (GFIF) to remove additive identical independent distribution (i.i.d.) impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a fuzz filtering process, a genetic learning process, and an image knowledge base. First, the fuzzy number construction process receives sample images or the noise-free image and then constructs an image knowledge base for the fuzzy filtering process. Second, the fuzzy filtering process contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy decision process to perform the task of noise removal. Finally, based on the genetic algorithm, the genetic learning process adjusts the parameters of the image knowledge base. By the experimental results, GFIF achieves a better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, GFIF also results in a higher quality of global restoration.
20301861
Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition
In recent years, the population has been aging gradually, and the number of patients with chronic respiratory disease has grown increasingly; therefore the respiratory healthcare plays an important role in the clinical care. This paper presents an ontology-based intelligent healthcare agent for the respiratory waveform recognition to assist the medical staff in judging the meaning of the graph reading from ventilators. The intelligent healthcare agent contains three modules, including the respiratory waveform ontology, ontology construction mechanism, and fuzzy recognition agent, to classify the respiratory waveform. The respiratory waveform ontology represents the respiratory domain knowledge, which will be utilized to classify and recognize the respiratory waveform by the intelligent healthcare agent. The ontology construction mechanism will infer the fuzzy numbers of each respiratory waveform from the patient or respiratory waveform repository. Next, the fuzzy recognition agent will classify and recognize the respiratory waveform into different types of respiratory waveforms. Finally, after the confirmation of medical experts, the classified and recognized results are stored in the classified waveform repository. The experimental results show that our approach can classify and recognize the respiratory waveform effectively.
17825977
Ontology-based fuzzy event extraction agent for Chinese e-news summarization
Abstract An Ontology-based Fuzzy Event Extraction (OFEE) agent for Chinese e-news summarization is proposed in this article. The OFEE agent contains Retrieval Agent (RA), Document Processing Agent (DPA) and Fuzzy Inference Agent (FIA) to perform the event extraction for Chinese e-news summarization. First, RA automatically retrieves Internet e-news periodically, stores them into the e-news repository, and sends them to DPA for document processing. Then, the DPA will utilize the Chinese Part-of-speech (POS) tagger provided by Chinese knowledge information processing group to process the retrieved e-news and filter the Chinese term set by Chinese term filter. Next, the FIA and Event Ontology Filter (EOF) extract the e-news event ontology based on the Chinese term set and domain ontology. Finally, the Summarization Agent (SA) will summarize the e-news by the extracted-event ontology. By the simulation, the proposed method can summarize the Chinese weather e-news effectively.
206793088
A fuzzy ontology and its application to news summarization
In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.
33337266
A genetic fuzzy agent using ontology model for meeting scheduling system
A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.
14189714
Time-parameterized sensing task model for real-time tracking
This paper proposes a novel task model in which its physical and temporal parameters are specified as time-parameterized functions and their values are finally determined at the actual dispatch time. This model is clearly differentiated from the classical task model where parameters are fixed at the job release time. The new model better suits sensing tasks in tracking applications, since the sensor parameters such as field-of-view and measurement duration can be properly adjusted at the actual sensing time. The new model, however, creates the cyclic dependency between task parameters and scheduling behavior, that is, the task parameters depend on scheduling behavior and the latter in turn depends on the former. This cyclic dependency makes the schedulability check even more difficult. We handle this difficulty by iterative convergence and probabilistic schedulability envelope, which provides an efficient online schedulability check. The experimental study shows that the new model significantly improves the effective capacity of tracking systems without losing track accuracy
8596907
Multiple multicast tree allocation in IP network
The multicasting is defined as the distribution of the same information stream from one to many nodes concurrently. There has been an intensive research effort to design protocols and construct multicast routing graphs for a single multicast group. In this paper, the multiple multicast tree allocation problem is discussed and algorithms are proposed to solve the congestion problem in the IP network. As the congestion measure the minimum residual capacity is considered. Two phase algorithm MMTA is investigated for multiple multicast tree allocation both for identical and different bandwidth requirement by the multicast groups. The central and distributed implementation of the multiple multicast tree is discussed for the deployment in the real IP network. The performance of the proposed MMTA is compared with other procedures. Computational results show that the two-phase MMTA outperforms other procedures. Approximately 3-7% improvement in the residual capacity is obtained by the MMTA. The solution gap from the upper bound by the well-known branch and bound is within 2-11% depending on the problem size.
38422944
Neural-Network-Based Fuzzy Logic Control and Decision System
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
8167367
Online QoS Optimization Using Service Classes in Surveillance Radar Systems
Many application level qualities are functions of available computation resources. Recent studies have handled the computation resource allocation problem to maximize the overall application quality. However, such QoS problems are fundamentally multi-dimensional optimization problems that require extensive computation. Therefore, online usage of optimization procedures may significantly reduce the computation resource available for applications. This raises the question of how to best use the optimization procedures for dynamic real-time task sets. In dynamic real-time systems, it is important to improve the performance by re-allocating the resources adapting to dynamic situations. However, the overhead of changing task parameters (i.e., algorithms and frequencies) for resource re-allocation is non-negligible in many applications. Thus, too frequent change of resource allocation may not be desirable. This paper proposes a method called service classes configuration to address the QoS problem with dynamic arrival and departure of tasks. The method avoids online usage of optimization procedures by offline designing templates (called service classes) of resource allocation, which will be adaptively used depending on online situations. The service classes are designed by best trading-off the accuracy of dynamic adaptation against the overhead of resource re-allocation. A simplified radar application is used as an illustrative example.
1173830
A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks
BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions