Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems

Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase activity.More specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics.Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains.Three experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and WM-FIS.The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.

Mapping of Freshwater Lake Wetlands Using Object-Relations and Rule-based Inference

Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.

RTTRS: Rule-based Train Traffic Rescheduling Simulator

Train traffic rescheduling is a complicated and large-scaled combinatorial problem. According to the characteristics of China railway system and from the point of practical use, this paper introduces a rule-based train traffic reschedule interactive simulator. It can be used as a powerful training tool to train the dispatcher and to carry out experimental analysis. The production rules are used as the basic for describing the processes to be simulated. With the increase of rule, users can easily upgrade the simulator by adding their own rules.

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Identity verification using authenticity evaluation of handwritten signatures is an important issue.There have been several approaches for the verification of signatures using dynamics of the signing process.Most of these approaches extract only global characteristics.With the aim of capturing both dynamic global and local features,this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system(NRVS)and Genetic NRVS(GNRVS)models.The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values:truth,indeterminacy,and falsity.These three values are determined by neutrosophic membership functions.The proposed model also is able to deal with all features without the need to select from them.In the GNRVS model,the neutrosophic rules are automatically chosen by Genetic Algorithms.The performance of the proposed system is tested on the MCYT-Signature-100 dataset.In terms of the accuracy,average error rate,false acceptance rate,and false rejection rate,the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key task.Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature.Rule based approaches,like dependency-based rules,are quite popular and effective for this purpose.However,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency parser.Another popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s behavior.However,in general,the sequential rule-based approaches have poor generalization capability.Moreover,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb aspects.In this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects.Additionally,after rigorous analysis,we have also constructed rules for the extraction of verb aspects.These verb rules primarily based on the association between verb and opinion words.The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets.The F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing approaches.These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.

Representing the Knowledge of Public Construction Project Cost Estimator by Using Rule-Based Method

Despite the presence of various construction project cost estimate softwares, human experience and knowledge cannot be disregarded. This fact has been proven in practice, where the success of construction cost estimate process is mainly based on knowledge of human estimator. The main question concerns what human knowledge determines the success of the construction cost estimation process. To address this question we have applied Delphi technique and the output is eleven factors that are enough to precisely represent construction cost estimator knowledge. Then we have used First Order Logic (FOL) to represent these factors in terms of predicates and rules. These FOL rules could be used for evaluating construction cost estimator knowledge in five classes: fail, pass, acceptable, good, and very good. As a validation process we have done experiments using history data and the results have proved the accuracy of our proposed method.

Applying Fuzzy Rule-Based System on FMEA to Assess the Risks on Project-Based Software Engineering Education

Project-based learning has been in widespread use in education. However, project managers are unaware of the students’ lack of experience and treat them as if they were professional staff. This paper proposes the application of a fuzzy failure mode and effects analysis model for project-based software engineering education. This method integrates the fuzzy rule-based system with learning agents. The agents construct the membership function from historical data. Data are processed by a clustering process that facilitates the construction of the membership function. It helps students who lack experience in risk assessment to develop their expertise in that skill. The paper also suggests a classification technique for a fuzzy rule-based system that can be used to judge risk based on a fuzzy inference system. The student project will thus be further enhanced with respect to risk assessment. We then discuss the design of experiments to verify the proposed model.

Applying DNA Computation to Error Detection Problem in Rule-Based Systems

As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.

Analysis of the concept of the negligence abettor

In Germany, Japan and in the Taiwan Region of our country, there is the long-standing debate about the joint negligence crime, and in the mainland of China, there are many scholars having discussed, but because the provisions on the joint crimes in the criminal law of our country exclude the joint negligence, the relevant discussions are not very sufficient. This paper talks about the views on the relevant issues of the joint negligence crimes, with the provisions on the traffic accident accomplice in the ＂Explanation of several issues concerning the specific application of law in the trial of criminal cases of traffic accidents＂ by the Supreme People＇s Court in 2000 as the starting point, in recognition of the negligent offender, the author further analyzes the constitution of the concept, in order to more clearly define the instigator of the negligent offender.

The success of the prediction of Haicheng earthquake and the negligence of the Tangshan earthquake

The success of the prediction of Haicheng earthquake and the failure of the prediction of Tangshan earthquake were both well known in the world. What happened, why such a strong earthquake as occurred in Haicheng had been predicted successfully and with a small loss of lives and property? Why a successively strong earthquake about a year later in a region not so further was failure in the imminent stage of prediction and there were so many fatalities and a great degree of property? The author addresses these points based on these true experiences including the first hand experiences leading up to, during, and following these two earthquarter. In addition, he also introduced some seimic phenomena which he had seen after Chi-chi earthquake in Taiwan.

A method of rule-base optimization based on evaluation

For improving the translation quality of transfer-based MT system,a new metric for rule evaluation was proposed and applied to rule-base optimization.At the same time,a frequency filter was used to delete redundance before new acquired rules were added into rule-base.The new optimization method was applied to a general MT system.Experimental results show that the frequency filter is helpful to provide the knowledge expansion space of MT system for new acquired rules.The translation assessment score of open test corpus (including 2500 Chinese sentences) obtained is increased by 3.58% under 5-gram Nist metric,which is two times of that obtained by previous methods.

个人信息侵权中过错的认定及对侵权责任的影响

过错对判定个人信息侵权责任成立与明确损害赔偿金额具有重要意义。就归责原则而言,《个人信息保护法》与《民法典》体系衔接不畅,引发了个人信息处理者与非个人信息处理者适用过错推定责任抑或过错责任的法律适用难题。就过错程度而言,非法收集、使用、获取、买卖、公开、泄露个人信息等单纯侵害个人信息权益的行为,行为人主观上多为故意,过失侵权多发生于网络信息侵权及信息公开侵权;将个人信息用于刑事犯罪、商业营销及人格侵权等以个人信息为媒介侵害其他民事权益的行为,上游个人信息侵权人对下游损害承担责任以故意为要件,但上下游信息处理者之间存在合同勾连关系时,即便上游个人信息侵权人仅为过失,也需对下游损害承担相应责任。

论污染环境罪的罪过形式“包括过失”——层级罪过说之提倡

在我国刑法理论界,关于污染环境罪的罪过形式主要有故意说、混合罪过说和择一罪过说。各种学说从不同角度对污染环境罪的罪过形式作出了较为合理的解释,但均存在不足。不论从刑法相关条文的客观含义还是污染环境罪的处罚必要性来看,均应当将污染环境罪的罪过形式解释为“包括过失”,而且过失与故意之间系层级关系,即故意是建立在过失基础之上的、更高层级的罪过形式。此种解释结论所引出的主要问题是,如何根据罪过形式的不同实现刑罚个别化,进而贯彻罪刑相适应和责任主义原则。为此,应当提倡根据故意从重处罚,即将故意的功能拓展到量刑领域。此解释结论可以运用于指导所有罪过形式“包括过失”之具体犯罪的量刑。

论智能辅助场景下交通事故的刑事责任

智能辅助驾驶催生了技术缺陷和人机交互错误这两种可能导致交通事故的新风险。由于智能系统技术缺陷风险现实化,人工智能服务提供者、汽车制造者可能要承担过失致人重伤罪或过失致人死亡罪等刑事责任,但不构成交通肇事罪。在认定过失时,应以“事后”的视角来判断技术缺陷的风险与原因,以“事前”的视角根据现有的技术水平并结合容许风险的法理判断技术缺陷及交通事故的预见可能性。由于人机交互错误风险现实化,驾驶人可能要承担交通肇事罪的刑事责任。在认定过失时,应合理适用信赖原则来界定注意义务的范围,结合过失不作为犯罪的基本原理认定驾驶人是否违反了接管义务。在人工智能服务提供者、汽车制造者和驾驶人共同过失的场合,应参照交通肇事罪的罪量要素,限缩人工智能服务提供者、汽车生产者构成过失犯罪的范围。

表见代理证明责任分配论略

《民诉法解释》第91条以规范说为蓝本确立了我国证明责任分配的基本原则,而规范说旨在对实体规范进行分类的基础上确立证明责任的分配。《民法典》第172条规定了表见代理下相对人请求被代理人履行合同内容的请求权基础,奠定了表见代理证明责任分配的基准,但相关司法解释的规定存在不完善及矛盾之处。从实现制度设立的意旨出发,立足于相关实体规范所内含的风险分配,并基于体系化解释,应将被代理人可归责性作为表见代理的构成要件之一;对于可归责性与善意且无过失的证明责任分配,应遵循规范说的分配路径,明确可归责性属于权利妨碍规范、善意且无过失属于权利发生规范,分别由被代理人与相对人承担证明责任。

危险作业罪罪过形式的确定——过失危险犯理论的再思考

危险作业罪作为新增罪名,其主观罪过存在着过失说、故意说以及混合罪过说之争。危险作业罪罪过争议问题是过失危险犯理论争议问题在具体各罪上的表现,对过失危险犯理论的正本清源是解决危险作业罪罪过问题的必要条件。过失危险犯否定论者所认为的过失危险犯无法律明文规定、有违结果过失原则、与司法实践相冲突以及无益于犯罪人的特殊预防等理由是存在可商之处的。在刑法中设立过失危险犯,既满足社会治理的需要而具有目正当性,也有助于规范司法实践而践行刑法的基本理念。在承认过失危险犯的基础上,无论是通过主观主义刑法解释还是客观主义刑法解释,都能得出危险作业罪主观过为过失的结论。