A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios

Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision—in some studies, approaching statistically optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show that such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, “correcting-out” the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.

The Impact of Artificial Intelligence on Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis

The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the open world assumption has been discovered as a new type of uncertain information. The classical Dempster’s combination rules are difficult to solve the related problems of incomplete information under the open world assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the open world assumption. Therefore, this paper proposes a new method framework to process uncertain information and fuse incomplete data. This method is based on an extension to the Deng entropy in the open world assumption, negation of basic probability assignment (BPA), and the generalized combination rule. The proposed method can solve the problem of incomplete information under the open world assumption, and obtain more uncertain information through the negative processing of BPA, which improves the accuracy of the results. The results of applying this method to fault diagnosis of electronic rotor examples show that, compared with the other uncertain information processing and fusion methods, the proposed method has wider adaptability and higher accuracy, and is more conducive to practical engineering applications.

Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

Map-Based Exploratory Evaluation of Non-Medical Determinants of Population Health

Multi‐criteria evaluation (MCE) and decision‐making are increasingly combined with interactive tools to assist users with visual thinking and exploring decision strategies. The interactive control of criterion combination rules and the simultaneous observation of geographic space and criterion space provide a means of investigating the sensitivity of the decision outcome to the decision‐maker's preferences. The Analytic Hierarchy Process (AHP) is an MCE method that has been successfully implemented in management processes including those addressed by Geographic Information Systems. In this paper, we present a map‐based, interactive AHP implementation, which provides a link between a well‐understood decision support method and exploratory geographic visualization. Using a case study with public health data for the Province of Ontario, Canada, we demonstrate that exploratory map use increases the effectiveness of the AHP‐based evaluation of population health.

Map-Based Exploratory Evaluation of Non-Medical Determinants of Population Health

Multi‐criteria evaluation (MCE) and decision‐making are increasingly combined with interactive tools to assist users with visual thinking and exploring decision strategies. The interactive control of criterion combination rules and the simultaneous observation of geographic space and criterion space provide a means of investigating the sensitivity of the decision outcome to the decision‐maker's preferences. The Analytic Hierarchy Process (AHP) is an MCE method that has been successfully implemented in management processes including those addressed by Geographic Information Systems. In this paper, we present a map‐based, interactive AHP implementation, which provides a link between a well‐understood decision support method and exploratory geographic visualization. Using a case study with public health data for the Province of Ontario, Canada, we demonstrate that exploratory map use increases the effectiveness of the AHP‐based evaluation of population health.

MolMod – an open access database of force fields for molecular simulations of fluids

The MolMod database is presented, which is openly accessible at http://molmod.boltzmann-zuse.de andcontains intermolecular force fields for over 150 pure fluids at present. It was developed and is maintainedby the Boltzmann-Zuse Society for Computational Molecular Engineering (BZS). The set of molecularmodels in the MolMod database provides a coherent framework for molecular simulations of fluids.The molecular models in the MolMod database consist of Lennard-Jones interaction sites, pointcharges, and point dipoles and quadrupoles, which can be equivalently represented by multiple pointcharges. The force fields can be exported as input files for the simulation programmes ms2 and ls1mardyn, GROMACS, and LAMMPS. To characterise the semantics associated with the numericaldatabase content, a force field nomenclature is introduced that can also be used in other contexts inmaterials modelling at the atomistic and mesoscopic levels. The models of the pure substances thatare included in the database were generally optimised such as to yield good representations ofexperimental data of the vapour–liquid equilibrium with a focus on the vapour pressure and thesaturated liquid density. In many cases, the models also yield good predictions of caloric, transport,and interfacial properties of the pure fluids. For all models, references to the original works in whichthey were developed are provided. The models can be used straightforwardly for predictions ofproperties of fluid mixtures using established combination rules. Input errors are a major source oferrors in simulations. The MolMod database contributes to reducing such errors