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 a...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.展开更多
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 fault...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.展开更多
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 ...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.展开更多
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 trai...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.展开更多
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 ...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.展开更多
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 t...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.展开更多
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 app...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.展开更多
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 estimat...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.展开更多
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 o...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.展开更多
Self-powered photovoltaic windows,which integrate photovoltaic with electrochromic devices,have attracted widespread attention of scholars since they can generate electricity in situ and reduce building energy consump...Self-powered photovoltaic windows,which integrate photovoltaic with electrochromic devices,have attracted widespread attention of scholars since they can generate electricity in situ and reduce building energy consumption by modulating the transmitted solar radiation.However,previous studies mainly focused on the material development and performance characterization,lack of comfort assessment and energy saving potential of its application to buildings.To address this issue,an adjustable semi-transparent photovoltaic(ATPV)window which integrates CdTe-based photovoltaic and WO3-based electrochromic,was taken as the research object,and a novel rule-based control strategy taking the beam solar radiation luminous efficacy(CtrlEff)as decision variable was proposed for the first time.The ATPV window model was established in WINDOW software based on the measured data,and then it was exported to integrated with a medium office building model in EnergyPlus for performance evaluation including the visual comfort,thermal comfort,net energy consumption,and net-zero energy ratio.The results of a case study in Changsha(E 112°,N 28°)indicated that the ATPV window under the CtrlEff strategy can effectively reduce the southward and westward intolerable glare by 86.9%and 94.9%,respectively,and increase the thermal comfort hours by 5%and 2%,compared to the Low-E window.Furthermore,the net-zero energy consumption can be decreased by 58.7%,65.7%,64.1%,and 53.8%for south,west,east,and north orientations,and the corresponding net-zero energy ratios are 65.1%,54.6%,62.7%,and 61.6%,respectively.The findings of this study provide new strategies for the control and optimization of the adjustable window.展开更多
The rapid increase in the number of unmanned aerial vehicles(UAVs) poses a threat to the safety of personnel, ground facilities,and aircraft. Therefore, as the basis of UAV safety protection, requirements on collision...The rapid increase in the number of unmanned aerial vehicles(UAVs) poses a threat to the safety of personnel, ground facilities,and aircraft. Therefore, as the basis of UAV safety protection, requirements on collision avoidance technology are getting higher in the mean time. Although plenty of research on UAV collision avoidance systems and methods exists, there is still a lack of integrated view of the UAV safety in the current civil airspace. The reason behind this is that the collision avoidance is always treated as a simple and basic behavior of autonomous robots, wherein a UAV is considered not so much different from other unmanned moving vehicles except its dynamic model. Regulations and rules that are specific to the UAVs under the background of airspace safety are not emphasized and put into consideration in the system and method design. This review paper serves as a guide for developing the UAV collision avoidance technology by summarizing the existing regulations and rules related to UAV collision avoidance, discussing the mathematical formulation of the rule-based collision avoidance methods, and testing and benchmarking those methods.展开更多
At local or regional scales,where Landsat has been extensively applied to monitor burned areas,semi-or fully automated methods are not very common.Koutsias et al.([2013].“A Rule-Based Semi-Automatic Method to map Bur...At local or regional scales,where Landsat has been extensively applied to monitor burned areas,semi-or fully automated methods are not very common.Koutsias et al.([2013].“A Rule-Based Semi-Automatic Method to map Burned Areas:Exploring the USGS Historical Landsat Archives to Reconstruct Recent Fire History.”International Journal of Remote Sensing 34(20):7049–7068)developed a semi-automatic method to map burned areas consisted of a set of rules that are valid especially when the post-fire image has been captured shortly after the fire.However,the rule-based approach is not free of errors that eventually create limitations to adopt this method for reconstructing the fire history in a fully automated mode.In this work,we improved the method by revisited and improving the rules that have been developed in the first paper,especially for reducing the omission errors.Minor changes have been made to almost all rules;however,one of the rules has been revised and changed the most as compared to the others by also considering the short-wave infrared channel TM5(in Landsat-5 or Landsat-7).In this case,the algorithm could successfully capture the fire scar in dry vegetated areas as well.Due to the lack of reference data collected in the field,we used reference data collected by photo interpretation,and the accuracy assessment showed that these had a high agreement with the burned land mapping results.展开更多
A series of Petri net-based definitions were formulated for describing four types of structural errors in a rule-based system(RBS),including inconsistency,incompleteness,redundancy and circularity.A marked v-Petri net...A series of Petri net-based definitions were formulated for describing four types of structural errors in a rule-based system(RBS),including inconsistency,incompleteness,redundancy and circularity.A marked v-Petri net model of acyclic RBS was constructed.Then,its reachability tree was generated to record all reachable relations between propositions in RBS.Moreover,a backward reasoning forest of a reachable marking was generated for explicitly representing reachable paths in RBS.Finally,a set of theorems and algorithms were provided to analyze and check structural errors.The usability of the research results presented in this paper was illustrated by an example.展开更多
In recent years,the complex and changeable world situation has become particularly prominent,and the trend of international relations,especially the major country relations is uncertain,which is of great concern.The e...In recent years,the complex and changeable world situation has become particularly prominent,and the trend of international relations,especially the major country relations is uncertain,which is of great concern.The establishment of a new international order based on new rules is becoming the focus and gravity of study of international relations.展开更多
To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a sys...To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.展开更多
A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations an...A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.展开更多
Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates...Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.展开更多
Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring...Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.展开更多
An investigation on the impact and significance of the Alpha Go vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the Alpha Go Thesis and its extension in accordance with the Church-Turing Thesis...An investigation on the impact and significance of the Alpha Go vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the Alpha Go Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the Alpha Go program provide an engineering solution for tackling issues in complexity and intelligence. Specifically,the Alpha Go Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with Alpha Go-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of Alpha Go and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.展开更多
The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiti...The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.展开更多
文摘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.
基金supported by National Natural Science Foundation of China(Grant No. 51275264)National Hi-tech Research and Development Program of China(863 Program, Grant No. 2011AA11A269)
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
基金the National Natural Science Foundation of China(No.51978252)the High-tech Industry Technology Innovation Leading Plan of Hunan Province(2020GK2076)+2 种基金the Science and Technology Innovation Program of Hunan Province(2020RC5003)the Hunan Province Innovation Development Program(2020RC4045)the Hunan Province Key R&D Program(2021SK2045).
文摘Self-powered photovoltaic windows,which integrate photovoltaic with electrochromic devices,have attracted widespread attention of scholars since they can generate electricity in situ and reduce building energy consumption by modulating the transmitted solar radiation.However,previous studies mainly focused on the material development and performance characterization,lack of comfort assessment and energy saving potential of its application to buildings.To address this issue,an adjustable semi-transparent photovoltaic(ATPV)window which integrates CdTe-based photovoltaic and WO3-based electrochromic,was taken as the research object,and a novel rule-based control strategy taking the beam solar radiation luminous efficacy(CtrlEff)as decision variable was proposed for the first time.The ATPV window model was established in WINDOW software based on the measured data,and then it was exported to integrated with a medium office building model in EnergyPlus for performance evaluation including the visual comfort,thermal comfort,net energy consumption,and net-zero energy ratio.The results of a case study in Changsha(E 112°,N 28°)indicated that the ATPV window under the CtrlEff strategy can effectively reduce the southward and westward intolerable glare by 86.9%and 94.9%,respectively,and increase the thermal comfort hours by 5%and 2%,compared to the Low-E window.Furthermore,the net-zero energy consumption can be decreased by 58.7%,65.7%,64.1%,and 53.8%for south,west,east,and north orientations,and the corresponding net-zero energy ratios are 65.1%,54.6%,62.7%,and 61.6%,respectively.The findings of this study provide new strategies for the control and optimization of the adjustable window.
基金supported by the National Natural Science Foundation of China(Grant No.61803309)the New Concept Air Combat Weapon Technology Innovation Workstation(Grant No.20-163-00-GZ-016-001-01)+1 种基金the Aeronautical Science Foundation of China(Grant Nos.019ZA053008 and 20185553034)the CETC Key Laboratory of Data Link Technology Open Project Fund(Grant No.CLDL-20202101 2)。
文摘The rapid increase in the number of unmanned aerial vehicles(UAVs) poses a threat to the safety of personnel, ground facilities,and aircraft. Therefore, as the basis of UAV safety protection, requirements on collision avoidance technology are getting higher in the mean time. Although plenty of research on UAV collision avoidance systems and methods exists, there is still a lack of integrated view of the UAV safety in the current civil airspace. The reason behind this is that the collision avoidance is always treated as a simple and basic behavior of autonomous robots, wherein a UAV is considered not so much different from other unmanned moving vehicles except its dynamic model. Regulations and rules that are specific to the UAVs under the background of airspace safety are not emphasized and put into consideration in the system and method design. This review paper serves as a guide for developing the UAV collision avoidance technology by summarizing the existing regulations and rules related to UAV collision avoidance, discussing the mathematical formulation of the rule-based collision avoidance methods, and testing and benchmarking those methods.
基金funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no.633464(https://cordis.europa.eu/project/id/633464).
文摘At local or regional scales,where Landsat has been extensively applied to monitor burned areas,semi-or fully automated methods are not very common.Koutsias et al.([2013].“A Rule-Based Semi-Automatic Method to map Burned Areas:Exploring the USGS Historical Landsat Archives to Reconstruct Recent Fire History.”International Journal of Remote Sensing 34(20):7049–7068)developed a semi-automatic method to map burned areas consisted of a set of rules that are valid especially when the post-fire image has been captured shortly after the fire.However,the rule-based approach is not free of errors that eventually create limitations to adopt this method for reconstructing the fire history in a fully automated mode.In this work,we improved the method by revisited and improving the rules that have been developed in the first paper,especially for reducing the omission errors.Minor changes have been made to almost all rules;however,one of the rules has been revised and changed the most as compared to the others by also considering the short-wave infrared channel TM5(in Landsat-5 or Landsat-7).In this case,the algorithm could successfully capture the fire scar in dry vegetated areas as well.Due to the lack of reference data collected in the field,we used reference data collected by photo interpretation,and the accuracy assessment showed that these had a high agreement with the burned land mapping results.
基金supported by the National Basic Research Program of China (Nos.2003CB317002 and 2007CB316502)the National Natural Science Foundation of China (Grant Nos.60534060 and 90718012).
文摘A series of Petri net-based definitions were formulated for describing four types of structural errors in a rule-based system(RBS),including inconsistency,incompleteness,redundancy and circularity.A marked v-Petri net model of acyclic RBS was constructed.Then,its reachability tree was generated to record all reachable relations between propositions in RBS.Moreover,a backward reasoning forest of a reachable marking was generated for explicitly representing reachable paths in RBS.Finally,a set of theorems and algorithms were provided to analyze and check structural errors.The usability of the research results presented in this paper was illustrated by an example.
文摘In recent years,the complex and changeable world situation has become particularly prominent,and the trend of international relations,especially the major country relations is uncertain,which is of great concern.The establishment of a new international order based on new rules is becoming the focus and gravity of study of international relations.
基金supported by the National Natural Science Foundation of China(Grant No.51677058)。
文摘To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.
基金supporting this work under Contracts No.MOST 110-2410-H-034-011 and MOST 110-2410-H-034-009,and 13th five-year plan of philosophy and social sciences of Guangdong Province,under Grants No.GD18CLJ02 and Department of education of Guangdong Province,China,No.2020WTSCX139.
文摘A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.
基金supported by the China Fundamental Research Funds for the Central Universities(2022JBQY006)。
文摘Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.
文摘Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.
基金supported by National Natural Science Foundation of China(61533019,71232006,61233001,71402178,71472174)
文摘An investigation on the impact and significance of the Alpha Go vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the Alpha Go Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the Alpha Go program provide an engineering solution for tackling issues in complexity and intelligence. Specifically,the Alpha Go Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with Alpha Go-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of Alpha Go and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.
基金supported in part by the National Natural Science Foundation of China(61473053)the Science and Technology Innovation Foundation of Dalian,China(2020JJ26GX033)。
文摘The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.
