2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)最新文献_第3页

Herd Behavior Analysis of Online Game Users Based on Information Cascade 基于信息级联的网络游戏用户群体行为分析

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970682

Yunshen Shi, Peng Zhu

引用次数: 0

Research on Real-Time Truck Dispatching Model in Open-pit Mine Based on Improved Genetic Algorithm 基于改进遗传算法的露天矿卡车实时调度模型研究

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970589

Wennan Yuan, Dawei Li, Dawei Jiang, Yanxiang Jia, Zhengyu Liu, Weiwei Bian

{"title":"Research on Real-Time Truck Dispatching Model in Open-pit Mine Based on Improved Genetic Algorithm","authors":"Wennan Yuan, Dawei Li, Dawei Jiang, Yanxiang Jia, Zhengyu Liu, Weiwei Bian","doi":"10.1109/ICCSI55536.2022.9970589","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970589","url":null,"abstract":"Trucks are the primary transportation equipment of open-pit mine. The reasonable truck dispatching schedule is the effective way to improve the economic benefits of enterprises, save transportation costs, reduce energy and achieve efficient and intelligent production. Truck dispatching optimization algorithm is the kernel of truck scheduling. Based on the objective function of minimizing the total transportation cost, this paper establishes a real-time truck dispatching model, coupled with a series of corresponding constraints. Combined with the advantages of genetic algorithm, the principle and process of genetic algorithm to solve the optimization model are proposed. The adaptive probability functions of crossover and mutation are designed for improving the effectiveness. In addition, the penalty function and renovation procedure are implemented to ensure that the iterative evolution is always carried out within the feasible solution space. Finally, the application and flexibility of the proposed approach is verified by a mine case study.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019337","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}

引用次数: 0

Small Sample Traffic Flow Forecast Method Based on Beetle Antennae Search Algorithm and Support Vector Regression 基于甲虫天线搜索算法和支持向量回归的小样本交通流预测方法

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970609

Yun Zhu, Chengwenyuan Huang, Jianyu Wang, Yan Su, Tianjin Zhou

{"title":"Small Sample Traffic Flow Forecast Method Based on Beetle Antennae Search Algorithm and Support Vector Regression","authors":"Yun Zhu, Chengwenyuan Huang, Jianyu Wang, Yan Su, Tianjin Zhou","doi":"10.1109/ICCSI55536.2022.9970609","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970609","url":null,"abstract":"Short-term traffic status refers to the traffic status information with a time interval of no more than 15 minutes. The accurate short-term traffic state prediction information can help traffic managers better control and coordinate vehicles, and also provide key traffic information for drivers' intelligent driving. However, the commonly used prediction algorithms often need large data samples to support, but in some sections which could not provide a big sample of traffic data or lack big data, the prediction accuracy of these algorithms will be greatly reduced. Based on the small amount of data of short-term traffic flow in some sections, combined with the fast search speed of Beetle Antennae Search algorithm and the high accuracy of Support Vector Regression algorithm in the case of small samples, this paper proposed a fast and accurate small sample traffic flow prediction model. Finally, the measured traffic flow data collected by the PEMS system in California were selected. After reasonable pretreatment of this data, this paper used the BAS-SVR model to output forecast results, the final test results showed that BAS-SVR had an excellent prediction effect in a small sample of data.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133628888","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}

引用次数: 1

Population-coded Spiking Neural Network with Reinforcement Learning for Mapless Navigation 基于强化学习的种群编码脉冲神经网络无地图导航

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970598

Rui Xu, Yifei Wu, Xiaoling Qin, Peng Zhao

{"title":"Population-coded Spiking Neural Network with Reinforcement Learning for Mapless Navigation","authors":"Rui Xu, Yifei Wu, Xiaoling Qin, Peng Zhao","doi":"10.1109/ICCSI55536.2022.9970598","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970598","url":null,"abstract":"Most of the navigation methods currently applied to mobile robots cost too much in building and maintaining maps. Therefore, it is crucial to implement mapless navigation for mobile robots. Although the recent deep reinforcement learning (DRL) methods have been able to make full use of the on-board resources to explore unknown space, their high energy cost limits their application. The low energy consumption of the spiking neural network (SNN) can help the DRL to overcome this difficulty. In this paper, we combine the SNN with the deep deterministic policy gradient (DDPG) method. To address the problem of long intervals between two adjacent pulses in the integrate-and-fire (LIF) neuron model, we change the way the membrane voltage resets and the dynamics of the neuron during the refractory period. On this basis, the environmental information was encoded using neuron population coding method and the two networks were trained jointly using an extended spatial-temporal backpropagation (STBP) method. The simulation results show that the proposed method achieves a higher success rate in navigation compared to traditional deep learning algorithms.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357516","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}

引用次数: 1

Research on Ground-to-Air Missile Fitting Algorithm Based on Deep BP Neural Network 基于深度BP神经网络的地空导弹拟合算法研究

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970659

Wei Peng, Zhigang Lv, Chuchao He

引用次数: 1

Bolt Looseness Location Identification Using Band-limited Intrinsic Multiscale Entropy Analysis and Convolutional Neural Network 基于带限内禀多尺度熵分析和卷积神经网络的螺栓松动位置识别

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970704

Bohai Tan, Tao Wang, Rui Yuan, Shizhuang Zhang, Guangtao Lu

{"title":"Bolt Looseness Location Identification Using Band-limited Intrinsic Multiscale Entropy Analysis and Convolutional Neural Network","authors":"Bohai Tan, Tao Wang, Rui Yuan, Shizhuang Zhang, Guangtao Lu","doi":"10.1109/ICCSI55536.2022.9970704","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970704","url":null,"abstract":"The multi-bolt joints has been widely used in many industries. A few bolts looseness may gradually lead to structure failures and catastrophic consequences if undetected. In this paper, a bolt looseness location identification method using band-limited intrinsic multiscale entropy analysis and convolutional neural network (CNN) is proposed. First, different from the traditional excitation signals, the chaotic ultrasonic signals are used as excitation signals to obtain high-frequency nonlinear responses of bolt joint. Then, the response signal received by the sensing piezoelectric patch is decomposed by variational mode decomposition to obtain band-limited intrinsic modal functions (BLIMFs). Each BLIMF is an amplitude-modulated-frequency-modulated signals, which carries the protentional bolt looseness information. The multiscale sample entropy values of each BLIMF are calculated to construct a feature matrix containing the looseness feature of each signal component in the multiscale. Finally, the looseness feature matrixes are transferred to CNN for training a classifier to identify which bolt is loose. To verify the proposed method, an experiment with piezoelectric active sensing is designed. The bolt looseness in different positions is controlled by loosening one or more M1 bolts which are mounted on an aluminum alloy plate. The experimental result shows that all loosened bolts at different locations are effectively identified, which verify the validity of the proposed method in this paper.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327985","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}

引用次数: 0

Damage Detection of an Aluminum Plate by Using Nonlinear Ultrasound with a Frequency-swept Excitation 基于扫频激励的非线性超声检测铝板损伤

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970651

Ming Xiang, Guangtao Lu, Zhe Liu, Long Wu, Jiacheng Wang, Tao Wang

{"title":"Damage Detection of an Aluminum Plate by Using Nonlinear Ultrasound with a Frequency-swept Excitation","authors":"Ming Xiang, Guangtao Lu, Zhe Liu, Long Wu, Jiacheng Wang, Tao Wang","doi":"10.1109/ICCSI55536.2022.9970651","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970651","url":null,"abstract":"In the traditional nonlinear ultrasound methods, one or dual excitation pulses with fixed center frequencies are usually simultaneously sent to the structures to generate nonlinear interaction near the damage, and some nonlinear parameters are applied to identify the structural damages in early age. However, when the damage evolves, the nonlinear effect due to damages usually changes, and the best matching frequency also shifts. Therefore, a new method based on nonlinear ultrasound with a frequency-swept excitation is proposed. In this new method, a frequency-swept signal is proposed to take place of the pulse with a fixed frequency, the wavelet packet decomposition is introduced to process the nonlinear response signal, and a nonlinear damage index is used for damage size estimation. To validate the method, some experiments are conducted on an aluminum plate. The experimental results of three specimens show that the nonlinear damage index is influenced by both the frequency band of the excitation signal and the diameter of the simulated damage. Moreover, when the best frequency band (260 kHz~280 kHz) is selected, the proposed damage index is linearly increases as the size of the simulated damage changes from 0 mm to 0.7 mm, which indicates that this index can be applied to identify damages with a small size on plates. This study puts forward a new avenue to detect structural damages with a small size in an early age on plate-like structures.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115621043","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}

引用次数: 0

Personnel Positioning and Management System Based UWB in Shipyard 基于超宽带的船厂人员定位与管理系统

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970701

Xinyu Liu, Peng Xu, Yue Wang, Weixiang Xu, Tong Zhu, Xiaofei Yang

引用次数: 0

Model Parameter Adaptive Approach of Extended Object Tracking Using Random Matrix and Identification 基于随机矩阵和辨识的扩展目标跟踪模型参数自适应方法

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970662

Jin-Tao Tan, Guoqing Qi, Jun-Jie Qi, Yu-Jie Yang, Yinyi Li, A. Sheng

引用次数: 1

Instance Segmentation of Ship Objects in Remote Sensing Images Based on Attention Mechanism 基于注意机制的遥感图像船舶目标实例分割

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970703

Bosong Chai, Heyu Gao, Yuandan Feng, Chenming Cui

{"title":"Instance Segmentation of Ship Objects in Remote Sensing Images Based on Attention Mechanism","authors":"Bosong Chai, Heyu Gao, Yuandan Feng, Chenming Cui","doi":"10.1109/ICCSI55536.2022.9970703","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970703","url":null,"abstract":"Detection and segmentation of ship targets in remote sensing images is a research hotspot in the field of computer vision. However, due to the large coverage area of sea surface remote sensing images, the complex and changeable environment of the ship target, such as cloud interference, coastal buildings, navigation ripples, the ship causes low detection and segmentation effect. In this paper, we propose an attention module-based method for background noise processing in remote sensing images. To solve the problem of complex background features and noise interference in remote sensing images, this paper introduces an attention module to suppress noise and other interfering features in the complex background by using channel attention mechanism and spatial attention mechanism, which can enhance the network's ability to extract object features, and improve the detection and segmentation effect of the network on remote sensing images. Firstly, we introduce Group Convolution into the original Residual Network to enhance the feature representation capability of the model. Secondly, Swish activation function with better performance in deep network is introduced to replace ReLU activation function in original Residual Network to improve the accuracy of ship detection and segmentation. Finally, in view of the complex environment of ships in remote sensing images and the problem of noise interference, we introduce attention mechanism to suppress the interference area and highlight the characteristics of ship areas. The experimental results show that with the improved method, the average accuracy (AP) of ship detection and segmentation has increased from 70.7% and 62.0% to 76.8% and 66.4%, respectively.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124712174","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}

引用次数: 0