New reasoning algorithm based on EFALC

The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reasoning algorithm is given. This algorithm is designed in the style of tableau algorithms, which is usually used in classical description logics. The transformation rules and the process of this algorithm is described and optimized with three main techniques： recursive procedure call, branch cutting and introducing sets of mesne results. The optimized algorithm is proved sound, complete and with an EXPTime complexity, and the satisfiability problem is EXPTime-complete.

Threat Assessment Method Based on Intuitionistic Fuzzy Similarity Measurement Reasoning with Orientation

The aim of this paper is to propose a threat assessment method based on intuitionistic fuzzy measurement reasoning with orientaion to deal with the shortcomings of the method proposed in [Ying-Jie Lei et al., Journal of Electronics and Information Technology 29(9)(2007)2077-2081] and [Dong-Feng Chen et al., Procedia Engineering 29(5)(2012)3302-3306] the ignorance of the influence of the intuitionistic index's orientation on the membership functions in the reasoning, which caused partial information loss in reasoning process. Therefore, we present a 3D expression of intuitionistic fuzzy similarity measurement, make an analysis of the constraints for intuitionistic fuzzy similarity measurement, and redefine the intuitionistic fuzzy similarity measurement. Moreover, in view of the threat assessment problem, we give the system variables of attribute function and assessment index, set up the reasoning system based on intuitionistic fuzzy similarity measurement with orientation, and design the reasoning rules, reasoning algorithms and fuzzy-resolving algorithms. Finally, through the threat assessment, some typical examples are cited to verify the validity and superiority of the method.

Signed Directed Graph and Qualitative Trend Analysis Based Fault Diagnosis in Chemical Industry

In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.

A variable weight approach for evidential reasoning

A variable weight approach was proposed to handle the probability deficiency problem in the evidential reasoning （ER） approach. The probability deficiency problem indicated that the inadequate information in the assessment result should be less than that in the input. However, it was proved that under certain circumstances, the ER approach could not solve the probability deficiency problem. The variable weight approach was based on two assumptions： 1） the greater weight should be given to the rule with more adequate information; 2） the greater weight should be given to the rules with less disparate information. Assessment results of two notional case studies show that 1） the probability deficiency problem is solved using the proposed variable weight approach, and 2） the information with less inadequacy and more disparity is provided for the decision makers to help reach a consensus.

AN APPROXIMATE REASONING METHOD IN DEZERT-SMARANDACHE THEORY

With the increment of focal elements number in discernment framework,the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide application and development of DSmT. Aiming at this difficulty,in this paper,a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently,this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hyper-power set are forced to group through bintree or tri-tree technologies. At the same time,the assignments of singletons in those different groups corresponding to each source are added up respectively,in order to realize the mapping from the refined hyper-power set to the coarsened one. And then,two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then,all assignments of singletons in different groups will be normalized respectively. Tree depth is set,in order to decide the iterative times in hierarchical system. Finally,by comparing new method with old one from different views,the superiority of new one over old one is testified well.

Emotional inference by means of Choquet integral and λ-fuzzy measurement in consideration of ambiguity of human mentality

Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.

Inference of Global HIV-1 Sequence Patterns and Preliminary FeatureAnalysis

The epidemiology of HIV-1 varies in different areas of the world, and it is possible that this complexity may leave unique footprints in the viral genome. Thus, we attempted to find significant patterns in global HIV-1 genome sequences. By applying the rule inference algorithm RIPPER (Repeated Incremental Pruning to Produce Error Reduction) to multiple sequence alignments of Env sequences from four classes of compiled datasets, we generated four sets of signature patterns. We found that these patterns were able to distinguish southeastern Asian from non- southeastern Asian sequences with 97.5% accuracy, Chinese from non-Chinese sequences with 98.3% accuracy, African from non-African sequences with 88.4% accuracy, and southern African from non-southern African sequences with 91.2% accuracy. These patterns showed different associations with subtypes and with amino acid positions. In addition, some signature patterns were characteristic of the geographic area from which the sample was taken. Amino acid features corresponding to the phylogenetic clustering of HIV-1 sequences were consistent with some of the deduced patterns. Using a combination of patterns inferred from subtypes B, C, and all subtypes chimeric with CRF01_AE worldwide, we found that signature patterns of subtype C were extremely common in some sampled countries (for example, Zambia in southern Africa), which may hint at the origin of this HIV-1 subtype and the need to pay special attention to this area of Africa. Signature patterns of subtype B sequences were associated with different countries. Even more, there are distinct patterns at single position 21 with glycine, leucine and isoleucine corresponding to subtype C, B and all possible recombination forms chimeric with CRF01_AE, which also indicate distinct geographic features. Our method widens the scope of inference of signature from geographic, genetic, and genomic viewpoints. These findings may provide a valuable reference for epidemiological research or vaccine design.

Signal Path Reckoning Localization Method in Multipath Environment

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

Three-dimensional Reconstruction of Geological Solids Based on Section Topology Reasoning

In order to solve the dynamic reconstruction and local updating problem of three-dimensional geological solids, topology reasoning is used for three-dimensional geological modeling. This can advance the level of the corresponding section automation in implementing the 3D geological solid dynamical reconstruction by the construction of and reasoning on topology on the 3D curved surface. This method has been successfully used in the Nanjing city geological modeling and the Zijin gold mine modeling. The results prove that this method adapts to coplanar section and noncoplanar section data, and improves the efficiency of 3D geological modeling.

Application of uncertainty reasoning based on cloud model in time series prediction

Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Efficient Path Query and Reasoning Method Based on Rare Axis

A new concept of rare axis based on statistical facts is proposed, and an evaluation algorithm is designed thereafter. For the nested regular expressions containing rare axes, the proposed algorithm can reduce its evaluation complexity from polynomial time to nearly linear time. The distributed technique is also employed to construct the navigation axis indexes for resource description framework(RDF) graph data. Experiment results in Drug Bank and Bio GRID show that this method can improve the query efficiency significantly while ensuring the accuracy and meet the query requirements on Web-scale RDF graph data.

A NEURAL FUZZY INFERENCE SYSTEM

This paper proposes a new neural fuzzy inference system that mainly consists of four parts. The first part is about how to use neural network to express the relation within a fuzzy rule. The second part is the simplification of the first part, and experiments show that these simplifications work. On the contrary to the second part, the third part is the enhancement of the first part and it can be used when the first part cannot work very well in the fuzzy inference algorithm, which would be introduced in the fourth part. Finally, the fourth part "neural fuzzy inference algorithm" is been introduced. It can inference the new membership function of the output based on previous fuzzy rules. The accuracy of the fuzzy inference algorithm is dependent on neural network generalization ability. Even if the generalization ability of the neural network we used is good, we still get inaccurate results since the new coming rule may not be related to any of the previous rules. Experiments show this algorithm is successful in situations which satisfy these conditions.

Deduction Theorem and Hypothetical Syllogism Rule on Fuzzy Logic System

Cancelled the first axiom L1) or the third axiom L3) of the classical formal logic system we established two kinds of quasi-formal deductive system, LG-R^* and LG^* respectively. In LG-R^* we proved that neither the deduction theorem nor the hypothetical syllogism (HS) rule held but a deduction theorem and an HS rule are obtained in a weak sense. We also proved that both the deduction theorem and the hypothetical syllogism(HS) rule hold in LG^*.

Discussion on incentives of Physical Education Teaching Methods

The use of literature, logical reasoning and other research methods are used in this thesis, classification and analysis of the definition of incentive methods, motivation methods are also stated in this paper, and this article also combine with students＇ psychological characteristics, different teaching content, teaching methods together, discussed how to stimulate students＇ interest in sports and mobilize students＇ enthusiasm, initiative by motivation methods in the sports teaching, thus promoting teaching effectiveness, improve teaching quality, provide some theoretical reference for improving the quality of teaching physical education. The results showed that： we have already made some researches in the stimulation methods in teaching sports, but few are studied combining with the psychological characteristics of students

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

An Autonomous Navigation Methodology for a Pioneer 3DX Robot

Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic environments. To solve this, the paper introduces a Case-Based Reasoning methodology to endow robots with an efficient decision structure aiming of selecting the best maneuver to avoid collisions. In particular, Manhattan Distance was implemented to perform the retrieval process in CBR method. Four scenarios were depicted to run a set of experiments in order to validate the functionality of the implemented work. Finally, conclusions emphasize the advantages of CBR methodology to perform autonomous navigation in unknown and uncertain environments.

Some Insights in Novel Risk Modeling of Liquefied Natural Gas Carrier Maintenance Operations

This study discusses the analysis of various modeling approaches such as genetic algorithms, fuzzy logic and evidential reasoning, and maintenance techniques applicable to the liquefied natural gas （LNG） carrier operations in the maritime environment. The usefulness of these algorithms in the LNG carrier industry in the areas of risk assessment and maintenance modeling as a standalone or hybrid algorithm are identified. This is evidenced with illustrative case studies.

Transported into the Bosom of the Empire？ Rethinking How Arthur Conan Doyle Persuades His Readers of British Imperialism in ＂The Speckled Band＂

Melanie C. Green and Timothy C. Brock have proposed their transportation-imagery model of narrative persuasion. They argue that a narrative can evoke imagery in readers＇ mind and then transport readers into the narrative world the author has created, in the course of which the author can persuade readers of the beliefs he＇s put into the narrative. This paper employs this model to rethink how Arthur Conan Doyle persuades his readers of British imperialism in his ＂The Speckled Band＂. First, this model considers the vividness of the narrative and readers＇ participatory response as key factors in readers＇ transportation. The narratives of ＂The Speckled Band＂ are picturesque, and Sherlock Holmes＇ inferential process has reinforced readers＇ participatory response. On the other hand, detective fiction usually has the theme of how the detective/law and order beats the criminal/chaos. That is, Doyle persuades his readers that the British Empire will prevail in ＂The Speckled Band＂ when Holmes, representing the British imperialism, solves the case and kills Dr. Roylott, who represents the evil and savagery in the British colony. According to Green and Brock＇s model, it simply means that there are two narratives （i.e., one is about how the law of the British Empire is challenged, and the other is about how the British Empire＇s stability is restored）, and two complementary transportations before Doyle can successfully persuades his readers. However, if we closely read ＂＇The Speckled Band＂, we can learn that Holmes, who should have represented British law, is guilty of trespassing or taking the law into his own hands. In other words, the complementarity of the two transportations is compromised. Thus, we can conclude that it is highly questionable whether or not Doyle can transport his readers into the bosom of the British Empire.