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%.

Synthesis of refrigeration system based on generalized disjunctive programming model

Refrigeration system holds an important role in process industries. The optimal synthesis cannot only reduce the energy consumption, but also save the production costs. In this study, a general methodology is developed for the optimal design of refrigeration cycle and heat exchanger network(HEN) simultaneously. Taking the heat integration between the external heat sources/sinks and the refrigeration cycle into consideration, a superstructure with sub-coolers is developed. Through defining logical variables that indicate the relative temperature positions of refrigerant streams after sub-coolers, the synthesis is formulated as a Generalized Disjunctive Programming(GDP) problem based on LP transshipment model, with the target of minimizing the total compressor shaft work in the refrigeration system. The GDP model is then reformulated as a Mixed Integer Nonlinear Programming(MINLP) problem with the aid of binary variables and Big-M Constraint Method. The efficacy of the process synthesis model is demonstrated by a case study of ethylene refrigeration system. The result shows that the optimization can significantly reduce the exergy loss as well as the total compression shaft work.

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.

Path prediction of flexible needles based on Fokker-Planck equation and disjunctive Kriging model

Path prediction of flexible needles based on the Fokker-Planck equation and disjunctive Kriging model is proposed to improve accuracy and consider the nonlinearity and anisotropy of soft tissues.The stochastic differential equation is developed into the Fokker-Planck equation with Gaussian noise,and the position and orientation probability density function of flexible needles are then optimized by the stochastic differential equation.The probability density function obtains the mean and covariance of flexible needle movement and helps plan puncture paths by combining with the probabilistic path algorithm.The weight coefficients of the ordinary Kriging are extended to nonlinear functions to optimize the planned puncture path,and the Hermite expansion is used to calculate nonlinear parameter values of the disjunctive Kriging optimization model.Finally,simulation experiments are performed.Detailed comparison results under different path planning maps show that the kinematics model can plan optimal puncture paths under clinical requirements with an error far less than 2 mm.It can effectively optimize the path prediction model and help improve the target rate of soft tissue puncture with flexible needles through data analysis and processing of the mean value and covariance parameters derived by the probability density and disjunctive Kriging algorithms.

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.

Symphyseal Disjunction after Dystocic Vaginal Delivery: About Two Cases at the SourôSanou University Hospital of Bobo-Dioulasso (CHUSS)

Diastasis of the symphysis pubis is a rare postpartum complication. We report two cases of symphysis diastasis after dystocic delivery, in two patients who presented in the postpartum period with functional impotence, pubalgia and acute urine retention. A frontal radiograph of the pelvis confirmed the diagnosis, showing a symphyseal separation of 12 mm and 18 mm. In our case, management was mainly medical, with a favorable clinical outcome.

Disjunction of the Pubic Symphysis in the Chu Gabriel Toure: Therapeutic Aspects

Symphyseal dissection results from high-energy trauma and usually occurs in poly trauma context. The treatment is only orthopedic in our department. The aim of this work was to study the current therapeutic aspects management of the disjunction of the pubic symphysis in the department of ortho-traumatology of CHU Gabriel Touré of Bamako MALI. This was a prospective and analytical study;from 1 July 2021 to 30 June 2022, within 15 patients classified according to Young and Burgess, with a functional evaluation according to Majeed and a minimum follow-up of 4 months. We report symphyseal disconnections accounted for 20.83% of the traumas of the pelvic ring, and 1.84% of patients hospitalized for fracture in the department during the period of the study. We noted a predominance of gender male in 87% of cases with a ratio of 2.75 and the average age in our series was 32 years, with extremes ranging from 18 to 63 and a SD of 13.96. The most common etiologies are APR with 66.7% and traditional mine slide cases with 20%, and the anteroposterior compression mechanism is most frequently encountered at 73.3%. The APCI types: 20%, APCII: 40% and VC: 20% of the Young and Burgess classification are the most found. The treatment of these patients was surgical in 53% cases by locked screw plate. The surgical approach of Pfannenstiel was preferred to the ilio-inguinal of Judet. Non-surgical treatment by trans-osseous traction and the wearing of a pelvic belt was recommended for cases of stable disjunction or severe associated lesions. The average length of stay is 17.27 days with extremes of 5 and 34 days. The functional assessment according to Majeed allowed us to classify 73.3% of patients’ cases as excellent, showing a good socio-professional reintegration. Symphyseal disjunction is a rare pathology but of serious functional consequences, regardless of the therapeutic method good management allows to minimize these functional sequelae.

有限维向量空间上d-disjunct矩阵的构作

Non-adaptive group testing(NGT)算法在许多领域中都有着非常广泛地应用,而d-disjunct矩阵是NGT算法的一个重要的数学模型.在有限维向量空间上构造了一类新的d-disjunct矩阵.

n阶射影平面上d-disjunct矩阵的构作

Non-adaptive group testing(NGT)算法在许多领域中都有着非常广泛的应用,而d-disjunct矩阵是NGT算法的一个重要的数学模型.研究了n阶射影平面上d-disjunct矩阵的构作并分析了它的检纠错能力.

Evolution of the Madrean-Tethyan disjunctions and the North and South American amphitropical disjunctions in plants

The present paper reviews advances in the study of two major intercontinental disjunct biogeographic patterns： （i） between Eurasian and western North American deserts with the Mediterranean climate （the Madrean- Tethyan disjunctions）; and （ii） between the temperate regions of North and South America （the amphitropical disjunctions）. Both disjunct patterns have multiple times of origin. The amphitropical disjunctions have largely resulted from long-distance dispersal, primarily from the Miocene to the Holocene, with available data indicating that most lineages dispersed from North to South America. Results of recent studies on the Mediterranean disjuncts between the deserts of Eurasia and western North America support the multiple modes of origin and are mostly consistent with hypotheses of long-distance dispersal and the North Atlantic migration. Axelrod＇s Madrean-Tethyan hypothesis, which implies vicariance between the two regions in the early Tertiary, has been favored by a few studies. The Beringian migration corridor for semiarid taxa is also supported in some cases.

Phylogenetic analysis of Toxicodendron(Anacardiaceae) and its biogeographic implications on the evolution of north temperate and tropical intercontinental disjunctions

Toxicodendron is a genus in the Rhus complex of Anacardiaceae with a disjunct distribution between eastern Asia and North America, extending to southeastern Asia and the neotropics. Nuclear （internal transcribed spacer, external transcribed spacer, and NIA-i3） and chloroplast （ndhF and trnL-F） sequences were used to construct phylogenetic relationships of Toxicodendron. Phylogenetic analysis of these data strongly support Toxieodendron as a monophyletic group distinct from other genera of the Rhus complex, and the phylogeny does not fully corroborate classification at the sectional level. Two temperate disjunct lineages were detected, one from section Toxicodendron and the other between the eastern North American Toxicodendron vernix and the eastern Asian Toxicodendron vernieifluum. Their divergence times were estimated to be 13.46 （7.95-19.42） and 7.53 （2.76-12.86） mya, respectively. The disjunction between section Griffithii （taxa from warm temperate to tropical Asia） and Toxieodendron striatum （from the neotropics） was supported and their divergence time was estimated to be 20.84 （11.1 6-30.52） mya in the early Miocene. Our biogeographic results and the paleontological data support the Bering land bridge as the most likely route to explain the temperate disjunctions, yet the tropical disjunction in Toxicodendron seems to be best explained by the North Atlantic land bridge hypothesis.

Evolution of biogeographic disjunction between eastern Asia and North America in Chamaecyparis:Insights from ecological niche models

The disjunct distribution of plants between eastern Asia(EA) and North America(NA) is one of the most well-known biogeographic patterns. However, the formation and historical process of this pattern have been long debated. Chamaecyparis is a good model to test previous hypotheses about the formation of this disjunct pattern as it contains six species disjunctly distributed in EA, western North America(WNA)and eastern North America(ENA). In this study, we applied ecological niche models to test the formation of the disjunct pattern of Chamaecyparis. The model calibrated with the EA species was able to predict the distribution of eastern NA species well, but not the western NA species. Furthermore, the eastern Asian species were shown to have higher niche overlap with the eastern North American species. The EA species were also shown to share more similar habitats with ENA species than with WNA species in the genus. Chamaecyparis species in WNA experienced a significant niche shift compared with congeneric species. Chamaecyparis had a low number of suitable regions in Europe and the middle and western NA during the Last Glacial Maximum(LGM) period, and became extinct in the former region whereas it retains residual distribution in the latter. The extirpations in western NA and Europe in response to the late Neogene and Quaternary climatic cooling and the more similar habitats between ENA and EA ultimately shaped the current intercontinental disjunct distribution of Chamaecyparis. Both current hypotheses may be also jointly applied to explain more eastern Asian and eastern North American disjunctions observed today.