This paper discusses an optimization of operating a p ermutation circulation-type vehicle routing system (PCVRS, for short), in w hich several stages are located along by a single loop, and a fleet of vehicles travels...This paper discusses an optimization of operating a p ermutation circulation-type vehicle routing system (PCVRS, for short), in w hich several stages are located along by a single loop, and a fleet of vehicles travels on the loop unidirectionally and repeatedly. Traveling on the loop, each vehicle receives an object from the loading stage and then carries it to a cert ain processing stage, or receives an object from a certain processing stage and then carries it to the unloading stage per a turnaround. No passing is allowed f or the vehicles on the loop (from which the system is called permutation, and th is restriction may cause interferences between vehicles). Material handling systems such as PCVRS are actually encountered in flexible man ufacturing systems and in automated storage/retrieval systems. In this paper, we propose a heuristic algorithm for operating the PCVRS, which i ncorporates a new scheduling method for the vehicles with the SPT (shortest proc essing time) numbering of jobs and a round-robin manner of allocating jobs to t he stages, aiming to reduce interferences between the vehicles. We also give num erical results with respect to system performances attained by the heuristic. Description of the system The PCVRS consists of a set of n v vehicles V={V 1,V 2,...,V n v}, a set of n s, processing stages S p={S 1,S 2,...,S n s}, a loading stage S 0 and an unloading stage S n s +1. We denote by S=S p∪{S 0,S n s+l} the set of all the stages. The vehicles travel on a single loop unidirectionany and repeated ly. The system layout is depicted in Fig.1. There is a set of n jobs J={J 1,J 2,...,J n} to be processed b y the vehicles. Each job consists of two tasks: That is, each vehicle receives a n object from S 0 and then carries it to S l with a certain l∈{1,2, ...,n s} (a throw-in job), or receives an object from S l with a certain l∈{1,2,...,n s} and then carries it to S n s+1 (a throw-out job ) per a turnaround. The loop consists of buffer zones BZ(l) and travel zones TZ(l) (see Fig. 1). Each buffer zone BZ(l) is placed in front of stage S l, l=0,1,..., n s, n s+1, in order to avoid a collision between vehicles (i.e., the syste m adopts the so-called zone control strategy). A heuristic algorithm We develop a heuristic algorithm to obtain a good performance for the PCVRS. An operation π={A/B/C} for the PCVRS consists of three decision factors: (A) Numbering jobs Jobs are loaded into S 0 according to an assending order of job numbers. In this paper, we use the following rules to number jobs: SPT: Order jobs in the shortest processing time rule, i.e., P 1≤P 2≤...≤P n for the set of jobs J={J 1,J 2,...,J n}, rather than the FCFS numbering (i.e., number jobs in first-come-first-served order). The SPT rule intends to reduce interferences between two adjacent vehicles at stages. (B) Allocating jobs to stages For the purpose of balancing loads of processing stages, we adopt the following to allocate jobs to the stages: ORDER: Allocate n jobs to n s, processing stages by an in-order manner , i.e., let l(i) be the index of processing stage allocated job J i by ORDER, it holds that l(i)=n s+1-(i-[(i-1)/n s]n s).(1) The ORDER rule intends to process jobs parallel at stages as many as possible. (C) Scheduling vehicles The following method for scheduling vehicles under ORDER rule is already known: Fig.1 The vehicle ro uting system, PCVRS Fig.2 Mean turnaroun d times by heuristics Unchange: Assign n jobs to n v vehicles such that let k(i) be the i ndex of vehicle processing job J i, then k(i)= i-[(i-1)/n v]n v.(2) In csse of n v≥n s, mod (n v,n s)=0 or n v<n s, mod (n s,n v)=0 (mod(x,y) is the remainder of x/y), the number of interferences between vehicles is minimized at stage S 1 under Unchange sche dules, while in the other cases it is not [Lu et al. (2001a)]. Therefore, in t his paper, we develop a new scheduling method of the vehicles, denoted by Ex change, to modify Unchange schedules. Note展开更多
The three common genetic models(or modes of inheritance)in association analysis are the dominant,additive,and recessive models.It is known that the Cochran-Armitage trend test(CATT)which correctly incorporates informa...The three common genetic models(or modes of inheritance)in association analysis are the dominant,additive,and recessive models.It is known that the Cochran-Armitage trend test(CATT)which correctly incorporates information from genetic models,is more powerful than the commonly used Pearson’s chi-square test.However,the true genetic model is usually unknown in practice,and the power of the CAT test could be substantially reduced with a wrongly specified genetic model.To achieve a power that is close to that of a correctly specified CAT test,it is natural to apply trend tests under different possible genetic models and to report the most significant test result.This results in a MAX-type testing procedure,and it was found that this test is usually more powerful than the Pearson’s chi-square test.Although the signi-ficance(i.e.,p value)of the MAX-type test can be accessed by either large sample approximation or permutation methods,requirements for sample size or simulation replicates are demanding with respect to accuracy and efficiency.This paper proposes an approach to calculate the exact p values of MAX-type tests based on the combinatorial counting method.The simulation results show that the exact method is more accurate than the large sample approximation methods and more computationally efficient than the permutation method,and our method can be readily applied to genome-wide association studies(GWASs).The proposed methodis built in an R package,MaXact,which is available at the https://github.com/Myuan 2019/MaXact/.展开更多
Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE int...Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.展开更多
Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of ...Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.展开更多
文摘This paper discusses an optimization of operating a p ermutation circulation-type vehicle routing system (PCVRS, for short), in w hich several stages are located along by a single loop, and a fleet of vehicles travels on the loop unidirectionally and repeatedly. Traveling on the loop, each vehicle receives an object from the loading stage and then carries it to a cert ain processing stage, or receives an object from a certain processing stage and then carries it to the unloading stage per a turnaround. No passing is allowed f or the vehicles on the loop (from which the system is called permutation, and th is restriction may cause interferences between vehicles). Material handling systems such as PCVRS are actually encountered in flexible man ufacturing systems and in automated storage/retrieval systems. In this paper, we propose a heuristic algorithm for operating the PCVRS, which i ncorporates a new scheduling method for the vehicles with the SPT (shortest proc essing time) numbering of jobs and a round-robin manner of allocating jobs to t he stages, aiming to reduce interferences between the vehicles. We also give num erical results with respect to system performances attained by the heuristic. Description of the system The PCVRS consists of a set of n v vehicles V={V 1,V 2,...,V n v}, a set of n s, processing stages S p={S 1,S 2,...,S n s}, a loading stage S 0 and an unloading stage S n s +1. We denote by S=S p∪{S 0,S n s+l} the set of all the stages. The vehicles travel on a single loop unidirectionany and repeated ly. The system layout is depicted in Fig.1. There is a set of n jobs J={J 1,J 2,...,J n} to be processed b y the vehicles. Each job consists of two tasks: That is, each vehicle receives a n object from S 0 and then carries it to S l with a certain l∈{1,2, ...,n s} (a throw-in job), or receives an object from S l with a certain l∈{1,2,...,n s} and then carries it to S n s+1 (a throw-out job ) per a turnaround. The loop consists of buffer zones BZ(l) and travel zones TZ(l) (see Fig. 1). Each buffer zone BZ(l) is placed in front of stage S l, l=0,1,..., n s, n s+1, in order to avoid a collision between vehicles (i.e., the syste m adopts the so-called zone control strategy). A heuristic algorithm We develop a heuristic algorithm to obtain a good performance for the PCVRS. An operation π={A/B/C} for the PCVRS consists of three decision factors: (A) Numbering jobs Jobs are loaded into S 0 according to an assending order of job numbers. In this paper, we use the following rules to number jobs: SPT: Order jobs in the shortest processing time rule, i.e., P 1≤P 2≤...≤P n for the set of jobs J={J 1,J 2,...,J n}, rather than the FCFS numbering (i.e., number jobs in first-come-first-served order). The SPT rule intends to reduce interferences between two adjacent vehicles at stages. (B) Allocating jobs to stages For the purpose of balancing loads of processing stages, we adopt the following to allocate jobs to the stages: ORDER: Allocate n jobs to n s, processing stages by an in-order manner , i.e., let l(i) be the index of processing stage allocated job J i by ORDER, it holds that l(i)=n s+1-(i-[(i-1)/n s]n s).(1) The ORDER rule intends to process jobs parallel at stages as many as possible. (C) Scheduling vehicles The following method for scheduling vehicles under ORDER rule is already known: Fig.1 The vehicle ro uting system, PCVRS Fig.2 Mean turnaroun d times by heuristics Unchange: Assign n jobs to n v vehicles such that let k(i) be the i ndex of vehicle processing job J i, then k(i)= i-[(i-1)/n v]n v.(2) In csse of n v≥n s, mod (n v,n s)=0 or n v<n s, mod (n s,n v)=0 (mod(x,y) is the remainder of x/y), the number of interferences between vehicles is minimized at stage S 1 under Unchange sche dules, while in the other cases it is not [Lu et al. (2001a)]. Therefore, in t his paper, we develop a new scheduling method of the vehicles, denoted by Ex change, to modify Unchange schedules. Note
基金This work was supported by the Natural Science Foundation of Anhui Province(2008085MA09)the National Natural Science Foundation of China(11671375).
文摘The three common genetic models(or modes of inheritance)in association analysis are the dominant,additive,and recessive models.It is known that the Cochran-Armitage trend test(CATT)which correctly incorporates information from genetic models,is more powerful than the commonly used Pearson’s chi-square test.However,the true genetic model is usually unknown in practice,and the power of the CAT test could be substantially reduced with a wrongly specified genetic model.To achieve a power that is close to that of a correctly specified CAT test,it is natural to apply trend tests under different possible genetic models and to report the most significant test result.This results in a MAX-type testing procedure,and it was found that this test is usually more powerful than the Pearson’s chi-square test.Although the signi-ficance(i.e.,p value)of the MAX-type test can be accessed by either large sample approximation or permutation methods,requirements for sample size or simulation replicates are demanding with respect to accuracy and efficiency.This paper proposes an approach to calculate the exact p values of MAX-type tests based on the combinatorial counting method.The simulation results show that the exact method is more accurate than the large sample approximation methods and more computationally efficient than the permutation method,and our method can be readily applied to genome-wide association studies(GWASs).The proposed methodis built in an R package,MaXact,which is available at the https://github.com/Myuan 2019/MaXact/.
基金supported by National Natural Science Foundation of China(No.61871318 and 61833013)Shaanxi Provincial Key Research and Development Project(No.2019GY-099).
文摘Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.
基金the supported by National Natural Science Foundation of China(No.61871318 and 11574250)Scientific Research Plan Projects of Shaanxi Education Department(No.19JK0568).
文摘Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.
