16338653
Fuzzy-rough data reduction with ant colony optimization
Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. To alleviate this difficulty, a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, has been developed recently and has been shown to be effective. However, this method is still not able to find the optimal subsets regularly. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring and experimentally compared with the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparisons with the use of a support vector classifier are also included.
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.
37663323
Prioritising Behaviours in Qualitative Simulation
1646072
A preliminary specification methodology for model-based diagnosis
This paper sets out to provide a basis for a specification methodology for modelbased diagnostic systems (MBDS). The purpose of the methodology is to provide a mapping from the problem space of possible diagnostic applications to the solution space provided by the various approaches to MBDS. Therefore, given the major characteristics of a diagnostic problem, the methodology should provide guide-lines by which the specification of a suitable MBDS may be determined. As a first stage in the development of this methodology we provide taxonomies of the problem and solution spaces. The former is characterised via a set ofProblem requirements, divided intoTask, Fault andModel requirements, while the latter is classified as a set ofSystem specifications, with reference to the major functional blocks of MBDS: namely theDiagnostic Strategist, thePredictor and theCandidate Proposer. The last part of this paper proposes a mapping between these two multi-dimensional spaces. This mapping is preliminary, and therefore neither exhaustive nor exclusive, but together with the taxonomies of the problem requirements and system specification will provide a catalyst for the development of a more extensive methodology.
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.
17008570
A rough-fuzzy approach for generating classification rules
The generation of effective feature pattern-based classification rules is essential to the development of any intelligent classifier which is readily comprehensible to the user. This paper presents an approach that integrates a potentially powerful fuzzy rule induction algorithm with a rough set-assisted feature reduction method. The integrated rule generation mechanism maintains the underlying semantics of the feature set. Through the proposed integration, the original rule induction algorithm (or any other similar technique that generates descriptive fuzzy rules), which is sensitive to the dimensionality of the dataset, becomes usable on classifying patterns composed of a moderately large number of features. The resulting learned ruleset becomes manageable and may outperform rules learned using more features. This, as demonstrated with successful realistic applications, makes the present approach effective in handling real world problems.
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.
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.
15138853
A Cluster Warhead Projection-Time Self-Adaptive Algorithm and Simulation
This paper proposes a self-adaptive algorithm for the projection-time of the cluster warhead, which is applicable to the realization of the engineering project with low cost. On the basis of keeping the simplified correction strategy that keeps the constant distance of the projection points and through detecting the deviation of the actual trajectory data at a specific time with respect to the pre-determined trajectory data, this algorithm adopts the simple functional relationship to calculate the correction of the projection time. It adopts Monte Carlo computer simulation method to verify. The circular probable error of the projection point, caused by this algorithm, at the horizontal distance is 8.2 meters. The working content and the flow procedures for the setter and the fuze projection-time self-adaptive controller, which adopts this algorithm, are finally concluded. While being free from influencing the precision, this algorithm can effectively simplify the design of the projection-time self-adaptive projection controller, can reduce the volume and the circuit power consumption, and is applicable to be used on the fuze.
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.
16916124
Improving throughput by tuning carrier sensing in 802.11 wireless networks
In this paper, we study the impacts of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, we show that by adopting different carrier sensing thresholds for the RTS and CTS transmissions, the blocking problem caused by exposed terminals can be greatly alleviated. In 802.11 wireless networks with this modification, the spatial reuse ratio under certain channel rates can be increased to 13, which is the highest value to our best knowledge. Secondly, in multi-rate networks, we demonstrate that 13 is still the best value of spatial reuse ratio in terms of maximizing the achievable data rate under certain conditions. Thirdly, this paper proposes a new method to address the intra-flow contention by decreasing the carrier sensing threshold of the source node. This method requires less response time than that of the traditional method which adjusts the backoff window size. Finally, extensive simulations are implemented in NS2 and the results show that our scheme significantly improves the throughput of 802.11 wireless networks with chain topology.