We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th...We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.展开更多
The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor net...The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.展开更多
Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the patte...Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.展开更多
针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重...针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。展开更多
针对传统频谱感知算法在复杂信道环境下鲁棒性欠佳的问题,以及深度学习感知算法面临的模型训练复杂度高等局限,提出了一种融合多种群人工鱼群算法与模糊孪生支持向量机(fuzzy twin support vector machine,FTSVM)的频谱感知方法.首先,...针对传统频谱感知算法在复杂信道环境下鲁棒性欠佳的问题,以及深度学习感知算法面临的模型训练复杂度高等局限,提出了一种融合多种群人工鱼群算法与模糊孪生支持向量机(fuzzy twin support vector machine,FTSVM)的频谱感知方法.首先,通过计算接收信号协方差矩阵的迹及其对角线外元素的均值,构建一个二维特征向量,由FTSVM进行训练识别;然后,使用样本的模糊隶属度调整了FTSVM超平面,从而使训练得到的模型更倾向于识别出初级用户存在的信号;最后,设计了多种群机制的改进人工鱼群算法,对频谱感知模型参数进行优化.仿真实验结果表明,在面临小样本数据集和低信噪比环境时,所提方法相较于其它的特征提取和SVM方法,在模型感知性能上实现了有效提升,−20 dB信噪比下检测概率达0.7以上.同时,优化算法的多种群机制缩短了模型的训练时间,相较于改进人工鱼群算法,训练时间缩短了约81%.展开更多
【背景】现有共享泊位分配信息化平台对动态随机需求难以即时响应,鲜有针对用户需求差异化进行泊位分配,且平台收益具有一定优化空间。【目标】构建并求解考虑供需异质性的共享泊位滚动时域分配模型,优化停车资源配置及社会总效益。【...【背景】现有共享泊位分配信息化平台对动态随机需求难以即时响应,鲜有针对用户需求差异化进行泊位分配,且平台收益具有一定优化空间。【目标】构建并求解考虑供需异质性的共享泊位滚动时域分配模型,优化停车资源配置及社会总效益。【方法】基于经典共享泊位分配模型框架,综合考虑停车供需异质性及低碳效益,制定“按需租用”策略,设计人工鱼群-遗传算法(Artificial Fish Shoal-Genetic Algorithm,AFSA-GA),利用Matlab进行数值仿真实验。【数据】重庆市沙坪坝区龙湖好城时光居民小区停车场总泊位数、空闲泊位数及其时空间特征。【结论】模型可实现泊位周转率近80%,用户接受率、请求时段接受率约90%,低碳收益占比约6.56%;与传统模型相比,可节省一定的车位租用成本,具有更高的系统收益,且泊位周转率与传统泊位分配模型相近;通过灵敏度分析发现,随需求量增加,收益先增后减,泊位周转率从剧增转为平稳,用户接受率、请求时段接受率降低速度逐渐加剧。【应用】该研究结果可为共享平台进行停车分配与管理决策提供理论参考。展开更多
基金Project(51779052)supported by the National Natural Science Foundation of ChinaProject(QC2016062)supported by the Natural Science Foundation of Heilongjiang Province,China+2 种基金Project(614221503091701)supported by the Research Fund from Science and Technology on Underwater Vehicle Laboratory,ChinaProject(LBH-Q17046)supported by the Heilongjiang Postdoctoral Funds for Scientific Research Initiation,ChinaProject(HEUCFP201741)supported by the Fundamental Research Funds for the Central Universities,China
文摘We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.
基金financially supported by Natural Science Foundation of Heilongjiang Province of China[Grant No.LH2019F042].
文摘The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43).
文摘Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.
文摘针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。
文摘【背景】现有共享泊位分配信息化平台对动态随机需求难以即时响应,鲜有针对用户需求差异化进行泊位分配,且平台收益具有一定优化空间。【目标】构建并求解考虑供需异质性的共享泊位滚动时域分配模型,优化停车资源配置及社会总效益。【方法】基于经典共享泊位分配模型框架,综合考虑停车供需异质性及低碳效益,制定“按需租用”策略,设计人工鱼群-遗传算法(Artificial Fish Shoal-Genetic Algorithm,AFSA-GA),利用Matlab进行数值仿真实验。【数据】重庆市沙坪坝区龙湖好城时光居民小区停车场总泊位数、空闲泊位数及其时空间特征。【结论】模型可实现泊位周转率近80%,用户接受率、请求时段接受率约90%,低碳收益占比约6.56%;与传统模型相比,可节省一定的车位租用成本,具有更高的系统收益,且泊位周转率与传统泊位分配模型相近;通过灵敏度分析发现,随需求量增加,收益先增后减,泊位周转率从剧增转为平稳,用户接受率、请求时段接受率降低速度逐渐加剧。【应用】该研究结果可为共享平台进行停车分配与管理决策提供理论参考。
