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.
36534120
Combining rough sets and data-driven fuzzy learning for generation of classification rules
3023204
Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning
This paper presents an integrated knowledge-based system, which combines fuzzy rule-based reasoning with case-based reasoning, for turbomachinery diagnosis. By incorporating a case-based reasoning sub-system in a fuzzy rule-based system, the integrated system allows past experience to be applied in a more direct way. This helps improve the diagnostic accuracy of the rule-based system. This approach has been implemented for the specific task of identifying possible causes of observed vibrations in rotating machines, based on the initial work presented in [18]. The ability that the case-based sub-system brings to the integrated system in improving the diagnostic efficacy of the original rule-based system is demonstrated with test results on real cases.
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.
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.
36670732
Selection of features in transparent fuzzy modelling
The paper discusses a methodology for feature selection in the context of fuzzy modelling that simplifies domains, all the while retaining the expressive power of linguistic fuzzy rules. The approach is described with respect to a few example applications.
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.
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.
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.
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.