An extended framework for evidential reasoning systems

Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems

Is Use of Optional Attributes and Associations in Conceptual Modeling Always Problematic? Theory and Empirical Tests

Prior research has argued that use of optional properties in conceptual models results in loss of information about the semantics of the domains represented by the models. Empirical research undertaken to date supports this argument. Nevertheless, no systematic analysis has been done of whether use of optional properties is always problematic. Furthermore, prior empirical research might have deliberately or unwittingly employed models where use of optionality always causes problems. Accordingly, we examine analytically whether use of optional properties is always problematic. We employ our analytical results to inform the design of an experiment where we systematically examined the impact of optionality on users’ ability to understand domains represented by different types of conceptual models. We found evidence that use of optionality undermines users’ ability to understand the domain represented by a model but that this effect weakens when use of mandatory properties to replace optional properties leads to more-complex models.

Establishing Metrics for Assessing the Performance of Directional Modulation Systems

In this paper metrics for assessing the performance of directional modulation (DM) physical-layer secure wireless systems are discussed. In the paper DM systems are shown to be categorized as static or dynamic. The behavior of each type of system is discussed for QPSK modulation. Besides EVM-like and BER metrics, secrecy rate as used in information theory community is also derived for the purpose of this QPSK DM system evaluation.

Selective Hydrogenation of Acetylene over Pd-Boron Catalysts: A Density Functional Theory Study

Boron-modified Pd catalysts have shown excellent performance for the selective hydrogenation of alkynes experimentally. In the current work, we investigated the hydrogenation of acetylene on boron-modified Pd(111) and Pd(211) surfaces, utilizing density functional theory calculations. The activity of acetylene hydrogenation has been studied by estimating the effective barrier of the whole process. The selectivity of ethylene formation is investigated from a comparison between the desorption and the hydrogenation of ethylene as well as comparison between the ethylene and the 1,3-butadiene formation. Formation of subsurface carbon and hydrogen on both boron-modified Pd(111) and Pd(211) surfaces has also been evaluated, since these have been reported to affect both the activity and the selectivity of acetylene hydrogenation to produce ethylene on Pd surfaces. Our results provide some important insights into the Pd B catalysts for selective hydrogenation of acetylene and also for more complex hydrogenation systems, such as stereoselective hydrogenation of longer chain alkynes and selective hydrogenation of vegetable oil.

Voltage over-scaling:A cross-layer design perspective for energy efficient systems

Today's multi-media electronic era is driven by the increasing demand for small multifunctional devices able to support diverse services. Unfortunately, the high levels of transistor integration and performance required by such devices lead to an unprecedented increase of on-chip power that significantly limits the battery lifetime and even poses reliability concerns. Several techniques have been developed to address the power increase, but voltage over-scaling (VOS) is considered to be one of the most effective ones due to the quadratic dependence of voltage on dynamic power consumption. However, VOS may not always be applicable since it increases the delay in all paths of a system and may limit high performance required by today's complex applications. In addition, application of VOS is further complicated since it increases the variations in transistor characteristics imposed by their tiny size which can lead to large delay and leakage variations, making it difficult to meet delay and power budgets. This paper presents a review of various cross-layer design options that can provide solutions for dynamic voltage over-scaling and can potentially assist in meeting the strict power budgets and yield/quality requirements of future systems. © 2011 IEEE.

What’s information got to do with it? Third-party voting in plurality systems

Although strategic voting theory predicts that the number of parties will not exceed two in single-member district plurality systems, the observed number of parties often does. Previous research suggests that the reason why people vote for third parties is that they possess inaccurate information about the parties’ relative chances of winning. However, research has yet to determine whether third-party voting persists under conditions of accurate information. In this article, we examine whether possessing accurate information prevents individuals from voting for third-placed parties in the 2005 and 2010 British elections. We find that possessing accurate information does not prevent most individuals from voting for third-placed parties and that many voters possess reasonably accurate information regarding the viability of the parties in their constituencies. These findings suggest that arguments emphasizing levels of voter information as a major explanation for why multiparty systems often emerge in plurality systems are exaggerated.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.

Some current problems in perovskite nano-ferroelectrics and multiferroics: kinetically-limited systems of finite lateral size

We describe some unsolved problems of current interest; these involve quantum critical points in
ferroelectrics and problems which are not amenable to the usual density functional theory, nor to
classical Landau free energy approaches (they are kinetically limited), nor even to the Landau–
Kittel relationship for domain size (they do not satisfy the assumption of infinite lateral diameter)
because they are dominated by finite aperiodic boundary conditions.

Application of Evidence Theory and Discounting Techniques to Aerospace Design

Critical decisions are made by decision-makers throughout
the life-cycle of large-scale projects. These decisions are crucial as they
have a direct impact upon the outcome and the success of projects. To aid
decision-makers in the decision making process we present an evidential
reasoning framework. This approach utilizes the Dezert-Smarandache
theory to fuse heterogeneous evidence sources that suffer from levels
of uncertainty, imprecision and conflicts to provide beliefs for decision
options. To analyze the impact of source reliability and priority upon
the decision making process, a reliability discounting technique and a
priority discounting technique, are applied. A maximal consistent subset
is constructed to aid in dening where discounting should be applied.
Application of the evidential reasoning framework is illustrated using a
case study based in the Aerospace domain.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Application of Asymmetric Marcus-Hush Theory to Voltammetry in Room-Temperature Ionic Liquids

Asymmetric MarcusHush (AMH) theory is applied for the first time in ionic solvents to model the voltammetric reduction of oxygen in 1-butyl-1-methylpyrrolidinium bis-(trifluoromethylsulfonyl)-imide and of 2-nitrotoluene (2-NT), nitrocyclopentane (NCP), and 1-nitro-butane (BuN) in trihexyltetradecylphosphonium tris(pentafluoroethyl)trifluorophosphate on a gold microdisc electrode. An asymmetry parameter, gamma, was estimated for all systems as -0.4 for the reduction of oxygen and -0.05, 0.25, and 0 +/- 0.05 for the reductions of 2-NT, NCP, and BuN, respectively, which suggests equal force constants of reactants and products in the case of 2-NT and BuN and unequal force constants for oxygen and NCP where the force constants of the oxidized species are greater than the reduced species in the case of oxygen and less than the reduced species in the case of NCP. Previously measured values for a, the Butler-Volmer transfer coefficient, reflect this in each case. Where appreciable asymmetry occurs, AMH theory was seen to parametrize the experimental data better than either Butler-Volmer or symmetric Marcus-Hush theory, allowing additionally the extraction of reorganization energy. This is the first study to provide key physical insights into electrochemical systems in room-temperature ionic liquids using AMH theory, allowing elucidation of the reorganization energies and the relative force constants of the reactants and products in each reaction.

Density functional theory study on the activation of molecular oxygen on a stepped gold surface in an aqueous environment: a new approach for simulating reactions in solution

The activation of oxygen molecules is an important issue in the gold-catalyzed partial oxidation of alcohols in aqueous solution. The complexity of the solution arising from a large number of solvent molecules makes it difficult to study the reaction in the system. In this work, O-2 activation on an Au catalyst is investigated using an effective approach to estimate the reaction barriers in the presence of solvent. Our calculations show that O-2 can be activated, undergoing OOH* in the presence of water molecules. The OOH* can readily be formed on Au(211) via four possible pathways with almost equivalent free energy barriers at the aqueous-solid interface: the direct or indirect activation of O-2 by surface hydrogen or the hydrolysis of O-2 following a Langmuir-Hinshelwood mechanism or an Eley-Rideal mechanism. Among them, the Eley-Rideal mechanism may be slightly more favorable due to the restriction of the low coverage of surface H on Au(211) in the other mechanisms. The results shed light on the importance of water molecules on the activation of oxygen in gold-catalyzed systems. Solvent is found to facilitate the oxygen activation process mainly by offering extra electrons and stabilizing the transition states. A correlation between the energy barrier and the negative charge of the reaction center is found. The activation barrier is substantially reduced by the aqueous environment, in which the first solvation shell plays the most important role in the barrier reduction. Our approach may be useful for estimating the reaction barriers in aqueous systems.