Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model...Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.展开更多
A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming probl...A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.展开更多
Generally fuzzy control system (FCS) is worked in washing machine. For the fuzzy set theory, membership functions are the building blocks. In a fuzzy set, fuzziness is determined by its membership functions. The shape...Generally fuzzy control system (FCS) is worked in washing machine. For the fuzzy set theory, membership functions are the building blocks. In a fuzzy set, fuzziness is determined by its membership functions. The shapes of membership functions are important, because it has an effect on fuzzy inference system. The shapes of membership functions can be triangular, trapezoidal and gaussian. The most widely used triangular membership function is used in this paper, because it can capture the short time period. In washing machine, open loop control system is found. This paper applies a fuzzy synthetic evaluation method (FSEM) for washing cloth in washing machine as FSEM can handle the multiple criteria with the help of evaluation matrix generated from membership function and weight matrix generated by Analytical Hierarchy Process (AHP). The purpose of this research is to minimize the wash time. By applying FSEM, we get a wash time which is less than that wash time got from applying the Mamdani approach in FCS. An example is given for illustration. For more reduction of wash time, statistical averaging method is also used. To reduce the wash time, statistical averaging method can be used in Mamdani approach also.展开更多
Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute ...Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making.In the process,anomaly detection for wireless sensor data plays an important role.However,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency.First,the data stream is usually generated quickly,which means that the data stream is infinite and enormous.Hence,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space.Second,there exist correlations among different data streams,and traditional algorithms hardly consider these correlations.Third,the underlying data generation process or distribution may change over time.Thus,traditional anomaly detection algorithms with no model update will lose their effects.Considering these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection.Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach.Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.展开更多
Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to...Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to predict a sports match.Therefore,a method is proposed to predict the result of the next match by using teams'historical match data.We combined the Long Short-Term Memory(LSTM)model with the attention mechanism and put forward an ASLSTM model for predicting match results.Furthermore,to ensure the timeliness of the prediction,we add the time sliding window to make the prediction have better timeliness.Taking the football match as an example,we carried out a case study and proposed the feasibility of this method.展开更多
In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single...In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single objective function from the fuzzy multi-objective linear programming problems. At first, a numerical example of solving fuzzy multi-objective linear programming problem has been provided to validate the maximum risk reduction by the proposed method. The proposed method has been applied to assess the risk of damage due to natural calamities like flood, cyclone, sidor, and storms at the coastal areas in Bangladesh. The proposed method of solving the fuzzy multi-objective linear programming problems by the statistical method has been compared with the Chandra Sen’s method. The numerical results show that the proposed method maximizes the risk reduction capacity better than Chandra Sen’s method.展开更多
A search was made for possible half-metallic(HM)antiferromagnet(AFM)in all the(C_(2)^(92)=406)double perovskites structures of Sr2BB′O6 where BB′pairs are any combination of 3d,4d or 5d transition elements with the ...A search was made for possible half-metallic(HM)antiferromagnet(AFM)in all the(C_(2)^(92)=406)double perovskites structures of Sr2BB′O6 where BB′pairs are any combination of 3d,4d or 5d transition elements with the exception of La.Sr can also be replaced by Ca or Ba whenever HM-AFM was found and similar calculations were then performed in order to probe further possibilities.It was found that A_(2)MoOsO_(6),A_(2)TcReO_(6),A_(2)CrRuO_(6),where A=Ca,Sr,Ba,are all potential candidates for HM-AFM.The AFM of A2BB′O6 comes from both the superexchange mechanism and the generalized double exchange mechanism via the B(t2g)-O2pp-B′(t2g)coupling,With the latter also being the origin of their HM.Also considered were the effects of spin-orbit coupling(SOC)and correlation(+U)by introducing+SOC and+U corrections.It is found that the SOC effect has much less influence than the correlation effect on the HMproperty of the compounds.For A_(2)TcReO_(6)and A_(2)CrRuO_(6),after+U,they become nearlyMott-Insulators.In the future,it is hoped that therewill be further experimental confirmation for these possible HM-AFMcandidates.展开更多
In this paper,we present calculations based on density functional theory using generalized gradient approximation(GGA)in double perovskite structure La_(2)BB'O_(6)(B,B'=3d transition metal)out of 45(C_(2)^(10)...In this paper,we present calculations based on density functional theory using generalized gradient approximation(GGA)in double perovskite structure La_(2)BB'O_(6)(B,B'=3d transition metal)out of 45(C_(2)^(10))combinational possibilities.Considering 4 types of magnetic states,namely,ferromagnetic(FM),ferrimagnetic(FiM),antiferromagnetics(AF),and nonmagnetic(NM)with full structure optimization,13 possible surviving,stable FM/FiM-HM materials containing 6 FM-HM materials(La_(2)ScNiO_(6),La_(2)CrCoO_(6),La_(2)CrNiO_(6),La_(2)VScO_(6),La_(2)VZnO_(6),and La_(2)VNiO_(6))and 7 FiM-HM materials(La_(2)VFeO_(6),La_(2)ZnCoO_(6),La_(2)TiCoO_(6),La_(2)CrZnO_(6),La_(2)CrMnO_(6),La_(2)ScFeO_(6),and La_(2)TiMnO_(6))are found.Considering the correlation effect(GGA+U),there are 6 possible half-metallic stable,surviving(HM)materials containing 3 FMHM materials(La_(2)ScNiO_(6),La_(2)CrCoO_(6),and La_(2)CrNiO_(6))and 3 FiM-HM materials(La_(2)VFeO_(6),La_(2)ZnCoO_(6),and La_(2)TiCoO_(6)).展开更多
基金supported by the National Natural Science Foundation of China(No.61872219)the Natural Science Foundation of Shandong Province(ZR2019MF001).
文摘Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.
文摘A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.
文摘Generally fuzzy control system (FCS) is worked in washing machine. For the fuzzy set theory, membership functions are the building blocks. In a fuzzy set, fuzziness is determined by its membership functions. The shapes of membership functions are important, because it has an effect on fuzzy inference system. The shapes of membership functions can be triangular, trapezoidal and gaussian. The most widely used triangular membership function is used in this paper, because it can capture the short time period. In washing machine, open loop control system is found. This paper applies a fuzzy synthetic evaluation method (FSEM) for washing cloth in washing machine as FSEM can handle the multiple criteria with the help of evaluation matrix generated from membership function and weight matrix generated by Analytical Hierarchy Process (AHP). The purpose of this research is to minimize the wash time. By applying FSEM, we get a wash time which is less than that wash time got from applying the Mamdani approach in FCS. An example is given for illustration. For more reduction of wash time, statistical averaging method is also used. To reduce the wash time, statistical averaging method can be used in Mamdani approach also.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.30919011282.
文摘Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making.In the process,anomaly detection for wireless sensor data plays an important role.However,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency.First,the data stream is usually generated quickly,which means that the data stream is infinite and enormous.Hence,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space.Second,there exist correlations among different data streams,and traditional algorithms hardly consider these correlations.Third,the underlying data generation process or distribution may change over time.Thus,traditional anomaly detection algorithms with no model update will lose their effects.Considering these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection.Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach.Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.
文摘Sports matches are very popular all over the world.The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match.It's a challenging effort to predict a sports match.Therefore,a method is proposed to predict the result of the next match by using teams'historical match data.We combined the Long Short-Term Memory(LSTM)model with the attention mechanism and put forward an ASLSTM model for predicting match results.Furthermore,to ensure the timeliness of the prediction,we add the time sliding window to make the prediction have better timeliness.Taking the football match as an example,we carried out a case study and proposed the feasibility of this method.
文摘In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single objective function from the fuzzy multi-objective linear programming problems. At first, a numerical example of solving fuzzy multi-objective linear programming problem has been provided to validate the maximum risk reduction by the proposed method. The proposed method has been applied to assess the risk of damage due to natural calamities like flood, cyclone, sidor, and storms at the coastal areas in Bangladesh. The proposed method of solving the fuzzy multi-objective linear programming problems by the statistical method has been compared with the Chandra Sen’s method. The numerical results show that the proposed method maximizes the risk reduction capacity better than Chandra Sen’s method.
基金supports received from the National Science Council(99B0320)the National Center for Theoretical Sciences(NCTS),South Taiwan.
文摘A search was made for possible half-metallic(HM)antiferromagnet(AFM)in all the(C_(2)^(92)=406)double perovskites structures of Sr2BB′O6 where BB′pairs are any combination of 3d,4d or 5d transition elements with the exception of La.Sr can also be replaced by Ca or Ba whenever HM-AFM was found and similar calculations were then performed in order to probe further possibilities.It was found that A_(2)MoOsO_(6),A_(2)TcReO_(6),A_(2)CrRuO_(6),where A=Ca,Sr,Ba,are all potential candidates for HM-AFM.The AFM of A2BB′O6 comes from both the superexchange mechanism and the generalized double exchange mechanism via the B(t2g)-O2pp-B′(t2g)coupling,With the latter also being the origin of their HM.Also considered were the effects of spin-orbit coupling(SOC)and correlation(+U)by introducing+SOC and+U corrections.It is found that the SOC effect has much less influence than the correlation effect on the HMproperty of the compounds.For A_(2)TcReO_(6)and A_(2)CrRuO_(6),after+U,they become nearlyMott-Insulators.In the future,it is hoped that therewill be further experimental confirmation for these possible HM-AFMcandidates.
基金the resource support from the Computational Materials Research Focus Group(CMRFG)the financial supports from the National Science Council(99B0320)+1 种基金the Center for General Education of National Normal Universitythe National Center for High-Performance Computing for computer time and facilities.
文摘In this paper,we present calculations based on density functional theory using generalized gradient approximation(GGA)in double perovskite structure La_(2)BB'O_(6)(B,B'=3d transition metal)out of 45(C_(2)^(10))combinational possibilities.Considering 4 types of magnetic states,namely,ferromagnetic(FM),ferrimagnetic(FiM),antiferromagnetics(AF),and nonmagnetic(NM)with full structure optimization,13 possible surviving,stable FM/FiM-HM materials containing 6 FM-HM materials(La_(2)ScNiO_(6),La_(2)CrCoO_(6),La_(2)CrNiO_(6),La_(2)VScO_(6),La_(2)VZnO_(6),and La_(2)VNiO_(6))and 7 FiM-HM materials(La_(2)VFeO_(6),La_(2)ZnCoO_(6),La_(2)TiCoO_(6),La_(2)CrZnO_(6),La_(2)CrMnO_(6),La_(2)ScFeO_(6),and La_(2)TiMnO_(6))are found.Considering the correlation effect(GGA+U),there are 6 possible half-metallic stable,surviving(HM)materials containing 3 FMHM materials(La_(2)ScNiO_(6),La_(2)CrCoO_(6),and La_(2)CrNiO_(6))and 3 FiM-HM materials(La_(2)VFeO_(6),La_(2)ZnCoO_(6),and La_(2)TiCoO_(6)).
