Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.
{"title":"Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets","authors":"Qiang Peng;Husheng Wu;Ruisong Xue","doi":"10.23919/CSMS.2021.0022","DOIUrl":"10.23919/CSMS.2021.0022","url":null,"abstract":"Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 3","pages":"163-175"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9600623/09600625.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48534144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimization is ubiquitous in the control of quantum dynamics in atomic, molecular, and optical systems. The ease or difficulty of finding control solutions, which is practically crucial for developing quantum technologies, is highly dependent on the geometry of the underlying optimization landscapes. In this review, we give an introduction to the basic concepts in the theory of quantum optimal control landscapes, and their trap-free critical topology under two fundamental assumptions. Furthermore, the effects of various factors on the search effort are discussed, including control constraints, singularities, saddles, noises, and non-topological features of the landscapes. Additionally, we review recent experimental advances in the control of molecular and spin systems. These results provide an overall understanding of the optimization complexity of quantum control dynamics, which may help to develop more efficient optimization algorithms for quantum control systems, and as a promising extension, the training processes in quantum machine learning.
{"title":"Optimization Landscape of Quantum Control Systems","authors":"Xiaozhen Ge;Rebing Wu;Herschel Rabitz","doi":"10.23919/CSMS.2021.0014","DOIUrl":"10.23919/CSMS.2021.0014","url":null,"abstract":"Optimization is ubiquitous in the control of quantum dynamics in atomic, molecular, and optical systems. The ease or difficulty of finding control solutions, which is practically crucial for developing quantum technologies, is highly dependent on the geometry of the underlying optimization landscapes. In this review, we give an introduction to the basic concepts in the theory of quantum optimal control landscapes, and their trap-free critical topology under two fundamental assumptions. Furthermore, the effects of various factors on the search effort are discussed, including control constraints, singularities, saddles, noises, and non-topological features of the landscapes. Additionally, we review recent experimental advances in the control of molecular and spin systems. These results provide an overall understanding of the optimization complexity of quantum control dynamics, which may help to develop more efficient optimization algorithms for quantum control systems, and as a promising extension, the training processes in quantum machine learning.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"77-90"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45464357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.
{"title":"A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems","authors":"Fuqing Zhao;Shilu Di;Jie Cao;Jianxin Tang;Jonrinaldi","doi":"10.23919/CSMS.2021.0010","DOIUrl":"10.23919/CSMS.2021.0010","url":null,"abstract":"A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"91-108"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43278531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Shuttle-Based Storage and Retrieval System (SBS/RS) has been widely studied because it is currently the most efficient automated warehousing system. Most of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design stage. Hence, the control of existing SBS/RSs has been rarely investigated. In existing SBS/RSs, some empirical rules, such as storing loads column by column, are used to control or schedule the storage process. The question is whether or not the control of the storage process in an existing system can be improved further by using a different approach. The storage process is controlled to minimize the makespan of storing a series of loads into racks. Empirical storage rules are easy to control, but they do not reach the minimum makespan. In this study, the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is evaluated. Specifically, a reinforcement learning algorithm called the actor-critic algorithm is used. This algorithm is made up of two neural networks and is effective in making decisions and updating itself. It can also reduce the makespan relative to the existing empirical rules used to improve system performance. Experiment results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads, the actor-critic algorithm can reduce the makespan by 6.67% relative to the column-by-column storage rule. The proposed algorithm also reduces the makespan by more than 30% when the number of loads being stored is in the range of 7-45, which is equal to 9.7%-62.5% of the systems' storage capacity.
{"title":"Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning","authors":"Lei Luo;Ning Zhao;Gabriel Lodewijks","doi":"10.23919/CSMS.2021.0013","DOIUrl":"10.23919/CSMS.2021.0013","url":null,"abstract":"The Shuttle-Based Storage and Retrieval System (SBS/RS) has been widely studied because it is currently the most efficient automated warehousing system. Most of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design stage. Hence, the control of existing SBS/RSs has been rarely investigated. In existing SBS/RSs, some empirical rules, such as storing loads column by column, are used to control or schedule the storage process. The question is whether or not the control of the storage process in an existing system can be improved further by using a different approach. The storage process is controlled to minimize the makespan of storing a series of loads into racks. Empirical storage rules are easy to control, but they do not reach the minimum makespan. In this study, the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is evaluated. Specifically, a reinforcement learning algorithm called the actor-critic algorithm is used. This algorithm is made up of two neural networks and is effective in making decisions and updating itself. It can also reduce the makespan relative to the existing empirical rules used to improve system performance. Experiment results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads, the actor-critic algorithm can reduce the makespan by 6.67% relative to the column-by-column storage rule. The proposed algorithm also reduces the makespan by more than 30% when the number of loads being stored is in the range of 7-45, which is equal to 9.7%-62.5% of the systems' storage capacity.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"131-144"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43439508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reasonable evacuation strategies are important in reducing casualties in the event of a fire. In this work, we conduct a simulation of a fire evacuation of a large public building based on the building information modeling technology to find the best evacuation strategy. We identify the tolerance limit of evacuees in case of a fire as the basis of the simulation using the fire dynamics simulator software. The following four evacuation strategies are proposed and simulated: stratified evacuation only by stairs, stratified evacuation mainly by stairs and supplemented by fire elevators, holistic evacuation only by stairs, and holistic evacuation mainly by stairs and supplemented by fire elevators. The case study of a college canteen shows that if 10% of evacuees (mainly elderly people who walk slowly and children who take up less space) are instructed to evacuate via fire elevators and the other 90% of evacuees (young men and women who move fast) use the stairs, the evacuation time can be reduced to a minimum. Some improvements in the design drawing result in the enhanced efficiency of the proposed strategy. The findings of this work are of great significance for the optimization of the structural design of large public buildings and provide some references for emergency evacuation.
{"title":"Simulation Research on Fire Evacuation of Large Public Buildings Based on Building Information Modeling","authors":"Fuyu Wang;Xiao Xu;Mengkai Chen;Juma Nzige;Fawen Chong","doi":"10.23919/CSMS.2021.0012","DOIUrl":"10.23919/CSMS.2021.0012","url":null,"abstract":"Reasonable evacuation strategies are important in reducing casualties in the event of a fire. In this work, we conduct a simulation of a fire evacuation of a large public building based on the building information modeling technology to find the best evacuation strategy. We identify the tolerance limit of evacuees in case of a fire as the basis of the simulation using the fire dynamics simulator software. The following four evacuation strategies are proposed and simulated: stratified evacuation only by stairs, stratified evacuation mainly by stairs and supplemented by fire elevators, holistic evacuation only by stairs, and holistic evacuation mainly by stairs and supplemented by fire elevators. The case study of a college canteen shows that if 10% of evacuees (mainly elderly people who walk slowly and children who take up less space) are instructed to evacuate via fire elevators and the other 90% of evacuees (young men and women who move fast) use the stairs, the evacuation time can be reduced to a minimum. Some improvements in the design drawing result in the enhanced efficiency of the proposed strategy. The findings of this work are of great significance for the optimization of the structural design of large public buildings and provide some references for emergency evacuation.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"122-130"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42081394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the fragmentation of public opinion dissemination and the lag of network users' cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIRR-I) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.
{"title":"Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory","authors":"Bin Wu;Ting Yuan;Yuqing Qi;Min Dong","doi":"10.23919/CSMS.2021.0008","DOIUrl":"10.23919/CSMS.2021.0008","url":null,"abstract":"Given the fragmentation of public opinion dissemination and the lag of network users' cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIRR-I) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"109-121"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43325947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a multi-staqe optimization (MSO) method to improve the accuracy of the global map. In the map acquisition stage, we windowed the map based on the position of the robot to obtain the local map. Furthermore, we adopted the extended Kalman filter (EKF) to improve the positioning accuracy, thereby enhancing the accuracy of the map acquisition by the single robot. In the map transmission stage, considering the robustness of the multi-robot system in the real environment, we designed a dynamic self-organized communication topology (DSCT) based on the master and slave sketch to ensure the efficiency and accuracy of map transferring. In the map merging stage, multi-layer information filtering (MLIF) was investigated to increase the accuracy of the global map. We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods. In addition, the practicability of this method has been verified on the Turtlebot3 burger robot. Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.
{"title":"Multi-Robot Indoor Environment Map Building Based on Multi-Stage Optimization Method","authors":"Hui Lu;Siyi Yang;Meng Zhao;Shi Cheng","doi":"10.23919/CSMS.2021.0011","DOIUrl":"10.23919/CSMS.2021.0011","url":null,"abstract":"For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a multi-staqe optimization (MSO) method to improve the accuracy of the global map. In the map acquisition stage, we windowed the map based on the position of the robot to obtain the local map. Furthermore, we adopted the extended Kalman filter (EKF) to improve the positioning accuracy, thereby enhancing the accuracy of the map acquisition by the single robot. In the map transmission stage, considering the robustness of the multi-robot system in the real environment, we designed a dynamic self-organized communication topology (DSCT) based on the master and slave sketch to ensure the efficiency and accuracy of map transferring. In the map merging stage, multi-layer information filtering (MLIF) was investigated to increase the accuracy of the global map. We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods. In addition, the practicability of this method has been verified on the Turtlebot3 burger robot. Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 2","pages":"145-161"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45148750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fault diagnosis plays the increasingly vital role to guarantee the machine reliability in the industrial enterprise. Among all the solutions, deep learning (DL) methods have achieved more popularity for their feature extraction ability from the raw historical data. However, the performance of DL relies on the huge amount of labeled data, as it is costly to obtain in the real world as the labeling process for data is usually tagged by hand. To obtain the good performance with limited labeled data, this research proposes a threshold-control generative adversarial network (TCGAN) method. Firstly, the 1D vibration signals are processed to be converted into 2D images, which are used as the input of TCGAN. Secondly, TCGAN would generate pseudo data which have the similar distribution with the limited labeled data. With pseudo data generation, the training dataset can be enlarged and the increase on the labeled data could further promote the performance of TCGAN on fault diagnosis. Thirdly, to mitigate the instability of the generated data, a threshold-control is presented to adjust the relationship between discriminator and generator dynamically and automatically. The proposed TCGAN is validated on the datasets from Case Western Reserve University and Self-Priming Centrifugal Pump. The prediction accuracies with limited labeled data have reached to 99.96% and 99.898%, which are even better than other methods tested under the whole labeled datasets.
{"title":"A Threshold-Control Generative Adversarial Network Method for Intelligent Fault Diagnosis","authors":"Xinyu Li;Sican Cao;Liang Gao;Long Wen","doi":"10.23919/CSMS.2021.0006","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0006","url":null,"abstract":"Fault diagnosis plays the increasingly vital role to guarantee the machine reliability in the industrial enterprise. Among all the solutions, deep learning (DL) methods have achieved more popularity for their feature extraction ability from the raw historical data. However, the performance of DL relies on the huge amount of labeled data, as it is costly to obtain in the real world as the labeling process for data is usually tagged by hand. To obtain the good performance with limited labeled data, this research proposes a threshold-control generative adversarial network (TCGAN) method. Firstly, the 1D vibration signals are processed to be converted into 2D images, which are used as the input of TCGAN. Secondly, TCGAN would generate pseudo data which have the similar distribution with the limited labeled data. With pseudo data generation, the training dataset can be enlarged and the increase on the labeled data could further promote the performance of TCGAN on fault diagnosis. Thirdly, to mitigate the instability of the generated data, a threshold-control is presented to adjust the relationship between discriminator and generator dynamically and automatically. The proposed TCGAN is validated on the datasets from Case Western Reserve University and Self-Priming Centrifugal Pump. The prediction accuracies with limited labeled data have reached to 99.96% and 99.898%, which are even better than other methods tested under the whole labeled datasets.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 1","pages":"55-64"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49910171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nonlinear Equations (NEs), which may usually have multiple roots, are ubiquitous in diverse fields. One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run, however, it is a difficult and challenging task in numerical computation. In recent years, Intelligent Optimization Algorithms (IOAs) have shown to be particularly effective in solving NEs. This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs. This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques. Then, solving NEs with IOAs is reviewed, followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms. Finally, this paper points out the challenges and some possible open issues for solving NEs.
{"title":"Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey","authors":"Wenyin Gong;Zuowen Liao;Xianyan Mi;Ling Wang;Yuanyuan Guo","doi":"10.23919/CSMS.2021.0002","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0002","url":null,"abstract":"Nonlinear Equations (NEs), which may usually have multiple roots, are ubiquitous in diverse fields. One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run, however, it is a difficult and challenging task in numerical computation. In recent years, Intelligent Optimization Algorithms (IOAs) have shown to be particularly effective in solving NEs. This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs. This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques. Then, solving NEs with IOAs is reviewed, followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms. Finally, this paper points out the challenges and some possible open issues for solving NEs.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 1","pages":"15-32"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49918167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world. However, conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive. In this study, we used an autonomous-driving simulator, and investigated the three-dimensional environmental perception problem of the simulated system. Using the open-source CARLA simulator, we generated a CarlaSim from unreal traffic scenarios, comprising 15000 camera-LiDAR (Light Detection and Ranging) samples with annotations and calibration files. Then, we developed Multi-Sensor Fusion Perception (MSFP) model for consuming two-modal data and detecting objects in the scenes. Furthermore, we conducted experiments on the KITTI and CarlaSim datasets; the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy, inference efficiency, and generalization performance. The results of this study will faciliate the future development of autonomous-driving simulated tests.
{"title":"3D Environmental Perception Modeling in the Simulated Autonomous-Driving Systems","authors":"Chunmian Lin;Daxin Tian;Xuting Duan;Jianshan Zhou","doi":"10.23919/CSMS.2021.0004","DOIUrl":"https://doi.org/10.23919/CSMS.2021.0004","url":null,"abstract":"Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world. However, conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive. In this study, we used an autonomous-driving simulator, and investigated the three-dimensional environmental perception problem of the simulated system. Using the open-source CARLA simulator, we generated a CarlaSim from unreal traffic scenarios, comprising 15000 camera-LiDAR (Light Detection and Ranging) samples with annotations and calibration files. Then, we developed Multi-Sensor Fusion Perception (MSFP) model for consuming two-modal data and detecting objects in the scenes. Furthermore, we conducted experiments on the KITTI and CarlaSim datasets; the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy, inference efficiency, and generalization performance. The results of this study will faciliate the future development of autonomous-driving simulated tests.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"1 1","pages":"45-54"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/CSMS.2021.0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49918170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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