2587320
Dynamic Flexible Constraint Satisfaction
Existing techniques for solving constraint satisfaction problems (CSPs) are largely concerned with a static set of imperative, inflexible constraints. Recently, work has addressed these shortcomings of classical constraint satisfaction in the form of two separate extensions known as flexible and dynamic CSP. Little, however, has been done to combine these two approaches in order to bring to bear the benefits of both in solving more complex problems. This paper presents a new integrated algorithm, Flexible Local Changes, for dynamic flexible problems. It is further shown how the use of flexible consistency-enforcing techniques can improve solution re-use and hence the efficiency of the core algorithm. Empirical evidence is provided to support the success of the present approach.
44572042
Rough Set-Based Dimensionality Reduction for Multivariate Adaptive Regression Splines
Dimensionality is an obstacle for many potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies exhibit relatively high complexity. This limits their applicability to real world applications. Friedman's Multivariate Adaptive Regression Splines (MARS) is a function approximator that produces continuous models of multi-dimensional functions using recursive partitioning and multidimensional spline curves that are automatically adapted to the data. Despite this technique's many strengths, it, too, suffers from the dimensionality problem. Each additional dimension of a hyperplane requires the addition of one dimension to the approximation model, and an increase in the time and space required to compute and store the splines. Rough set theory can reduce dataset dimensionality as a preprocessing step to training a learning system. This paper investigates the applicability of the Rough Set Attribute Reduction (RSAR) technique to MARS in an effort to simplify the models produced by the latter and decrease their complexity. The paper describes the techniques in question and discusses how RSAR can be integrated with MARS. The integrated system is tested by modelling the impact of pollution on communities of several species of river algae. These experimental results help draw conclusions on the relative success of the integration effort.
14492081
Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics
Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be used to infer the probability of defence and prosecution statements based on forensic evidence. This is an important development as it helps to quantify the meaning of forensic expert testimony during court proceedings, for example, that there is "strong support" for the defence or prosecution position. Due to the lack of experimental data, inferred probabilities often rely on subjective probabilities provided by experts. Because these are based on informed guesses, it is very difficult to express them accurately with precise numbers. Yet, conventional BNs can only employ probabilities expressed as real numbers. To address this issue, this paper presents a novel extension of probability theory. This allow the expression of subjective probabilities as fuzzy numbers, which more faithfully reflect expert opinion. By means of practical a example, it will be shown that the accurate representation of this lack of precision in reasoning with subjective probabilities has important implications for the overall result.
5566757
Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring
One of the main obstacles facing current intelligent pattern recognition applications is that of dataset dimensionality. To enable these systems to be efiective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantization of the underlying numerical features. This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss. The current work retains dataset semantics, allowing for the creation of clear, readable fuzzy models. Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-based, PCA-based and random-based methods.
6529692
Probabilistic abductive computation of evidence collection strategies in crime investigation
This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for crime investigation: symbolic crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing crime scenarios. A running example is used to demonstrate the theoretical developments.
1977195
Fault identification through the combination of symbolic conflict recognition and Markov chain-aided belief revision
Fault identification is a search for possible behaviors that would explain the observed behavior of a physical system. During this search, different possible models are considered and information about the interaction between possible behaviors is derived. Much of this potentially useful information is generally ignored in conventional pure symbolic approaches to fault diagnosis, however. A novel approach is presented in this paper that exploits uncertain information on the behavioral description of system components to identify possible fault behaviors in physical systems. The work utilizes the standard conflict recognition technique developed in the framework of the general diagnostic engine (GDE) to support diagnostic inference through the production of both rewarding and penalizing evidence. In particular, Markov matrices are derived from the given evidence, thereby enabling the use of Markov chains to implement the diagnostic process. This work has resulted in a technique, which maximizes the use of derived information, for identifying candidates for multiple faults that is demonstrated to be very effective.
7724198
Admission Control for Providing QoS in Wireless Mesh Networks
An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.
45281264
Optimum Power Assignment for Maximum Throughput in CDMA Personal Communication Systems with Integrated Voice/Data Traffic
A CDMA personal communication system with integrated voice/data traffic is considered, in which the link error performance is controlled according to the voice error rate requirement, and the acceptable data traffic error rate is ensured by ARQ. Optimum power assignment (or allocation) between voice and data users is investigated to maximize the total system throughput. A graphical method (the tangent method) is described to obtain this optimum power allocation. The maximum throughput is expressed as a function of other system parameters. The tangent method is further used to measure the impact of transmission quality on the maximum data throughput. Numerical results and a design example are given for a power controlled wideband IS-95 type wireless personal communication system.
15141835
Design of Range Correction Fuze Trajectory Calculation and Control Device
For a range correction fuze of a rocket range-extended projectile, a DSP-based trajectory calculation and correction controller that is cooperated with MCU is designed to examine the axial acceleration during the flying of a projectile, and to perform the trajectory calculation and the start-up time calculation of a correction mechanism, and to control the action of a resistance-correction mechanism. When designing the hardware and the software, the tasks for DSP and MCU are reasonably assigned. The DSP mainly completes the collection of the acceleration data, the trajectory calculation and the start-up time calculation of the resistance-correction mechanism, while MCU mainly completes the time counting and controls the startup of the resistance-correction mechanism according to the time calculated by DSP. This device also can store the flying acceleration measurement data of the projectile, the trajectory calculation results, etc., which can be adopted as the basic data for analyzing the working correctness of the range correction fuse at the research stage. It is proved in the range test that this device can resist the high-impact overloading during the emission, and can normally complete the tasks such as the trajectory calculation and the resistance-correction mechanism startup control during the flying of the projectile.
4966601
Power controlled network protocols for Multi-Rate ad hoc networks
In this paper, we propose for multi-rate ad hoc networks a cross-layer design using power control, called MRPC. MRPC consists of two parts. First, we propose a multi-rate power controlled MAC protocol, called MRPC-MAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a multi-rate power controlled routing protocol, called MRPC-Routing. Different from traditional routing protocols, MRPC-Routing is not intended to find end-to-end paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the effective transport capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPC-MAC, MRPC-routing, and especially MRPC, can improve the network throughput significantly.