Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970809
Ke Li, Shunrui Xiong, Qiang Yang
The proper design of mobility-aware content caching scheme in vehicular networks is the critical expeditor for an efficient Intelligent Transportation System, which enables diverse applications such as content dissemination and the entertainment for commuting passengers. Due to the dynamics characteristic caused by the mobility of vehicles, it is relatively hard to implement accurate caching prediction and collect useful data samples with the traditional method. Using the recent advances in training deep neural networks, we present a deep reinforcement learning framework, namely RL-ResNet-v1, that learns content chunk allocation and makes online chunk compensation policy from high-dimensional inputs corresponding to the characteristics and requirements of users passing by multiple Road Side Units (RSUs) in a Vehicle-to-Infrastructure scenario. The realized online content caching scheme serves to reduce data redundancy in each RSU with finite-capacity while promoting cache hit ratio that should meet chunk sequentially downloaded requirement. Simulation results show our content caching scheme not only achieves more than 20% improvement of the cache hit ratio, and effective cache ratio compared to baseline schemes, but also adapt to the temporal variation of vehicle speed and network bandwidth.
{"title":"Mobility-Aware Online Content Caching for Vehicular Networks based on Deep Reinforcement Learning","authors":"Ke Li, Shunrui Xiong, Qiang Yang","doi":"10.1109/ISPCE-ASIA57917.2022.9970809","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970809","url":null,"abstract":"The proper design of mobility-aware content caching scheme in vehicular networks is the critical expeditor for an efficient Intelligent Transportation System, which enables diverse applications such as content dissemination and the entertainment for commuting passengers. Due to the dynamics characteristic caused by the mobility of vehicles, it is relatively hard to implement accurate caching prediction and collect useful data samples with the traditional method. Using the recent advances in training deep neural networks, we present a deep reinforcement learning framework, namely RL-ResNet-v1, that learns content chunk allocation and makes online chunk compensation policy from high-dimensional inputs corresponding to the characteristics and requirements of users passing by multiple Road Side Units (RSUs) in a Vehicle-to-Infrastructure scenario. The realized online content caching scheme serves to reduce data redundancy in each RSU with finite-capacity while promoting cache hit ratio that should meet chunk sequentially downloaded requirement. Simulation results show our content caching scheme not only achieves more than 20% improvement of the cache hit ratio, and effective cache ratio compared to baseline schemes, but also adapt to the temporal variation of vehicle speed and network bandwidth.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133908725","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970899
S. Mak, S. Au, W. F. Tang, C.H. Li, C.C. Lee, W. H. Chiu
Dog is the most common pets around the world. Many families spent much to feed the dogs and treat as a crtical family members. As the hearing capability of dogs is wider than human, no safety standard is availavble to regulate the dogh's products, such as whistle. This paper is to study the range of dog's hearing capability and itds measurement method.
{"title":"A Study on Hearing Hazards and sound measurement for Dogs","authors":"S. Mak, S. Au, W. F. Tang, C.H. Li, C.C. Lee, W. H. Chiu","doi":"10.1109/ISPCE-ASIA57917.2022.9970899","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970899","url":null,"abstract":"Dog is the most common pets around the world. Many families spent much to feed the dogs and treat as a crtical family members. As the hearing capability of dogs is wider than human, no safety standard is availavble to regulate the dogh's products, such as whistle. This paper is to study the range of dog's hearing capability and itds measurement method.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123409863","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9971052
Hua Chang, Pengfei Yi, R. Liu, Jing Dong, Yaqing Hou, D. Zhou
Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach.
{"title":"Humanoid Robot Collaborative Lifting Integrating Executable Judgment","authors":"Hua Chang, Pengfei Yi, R. Liu, Jing Dong, Yaqing Hou, D. Zhou","doi":"10.1109/ISPCE-ASIA57917.2022.9971052","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971052","url":null,"abstract":"Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399175","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970977
Kwaku Ayepah, Mei Sun, Q. Jia
This research examines the bipartite synchronization of dynamical network with switching signed topologies, where the nodes are governed by certain nonlinear dynamics, and an event-triggered control strategy is applied by under periodic sampling communications. It is shown that when the topologies switch within a finite set of signed graph, all nodes are guaranteed to achieve bipartite synchronization if the time average of the algebraic connectivity of the corresponding unsigned graph over certain length of time is large enough, and the main theorem details the impacts of nodal dynamics, network structure on synchronization, and gives a criterion for selecting the involved control parameters. Finally, some numerical simulations are presented to show the validity of our theoretical results and the efficiency of the proposed controller.
{"title":"Event-based Bipartite Synchronization of Nonlinear Dynamical Networks With Sampled Data","authors":"Kwaku Ayepah, Mei Sun, Q. Jia","doi":"10.1109/ISPCE-ASIA57917.2022.9970977","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970977","url":null,"abstract":"This research examines the bipartite synchronization of dynamical network with switching signed topologies, where the nodes are governed by certain nonlinear dynamics, and an event-triggered control strategy is applied by under periodic sampling communications. It is shown that when the topologies switch within a finite set of signed graph, all nodes are guaranteed to achieve bipartite synchronization if the time average of the algebraic connectivity of the corresponding unsigned graph over certain length of time is large enough, and the main theorem details the impacts of nodal dynamics, network structure on synchronization, and gives a criterion for selecting the involved control parameters. Finally, some numerical simulations are presented to show the validity of our theoretical results and the efficiency of the proposed controller.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682134","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9971098
Rongying Li, Wenxiu Xie, Jiaying Song, Leung-Pun Wong, Fu Lee Wang, Tianyong Hao
Lexical simplification aims to convert complex words in a sentence into semantic equivalent but simple words. Most existing methods ignore sentence contextual information, which inevitably produces a large number of spurious substitute candidates. To that end, this paper proposes a new context-driven Merge-sort model which leverages contextual information in each step of lexical simplification, and a new merging method to combine ranking results produced by the proposed model. Based on standard datasets, our model outperforms a list of baselines including the state-of-the-art LSBert model, indicating its effectiveness in community-oriented lexical simplification.
{"title":"A Context-Driven Merge-Sort Model for Community-Oriented Lexical Simplification","authors":"Rongying Li, Wenxiu Xie, Jiaying Song, Leung-Pun Wong, Fu Lee Wang, Tianyong Hao","doi":"10.1109/ISPCE-ASIA57917.2022.9971098","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971098","url":null,"abstract":"Lexical simplification aims to convert complex words in a sentence into semantic equivalent but simple words. Most existing methods ignore sentence contextual information, which inevitably produces a large number of spurious substitute candidates. To that end, this paper proposes a new context-driven Merge-sort model which leverages contextual information in each step of lexical simplification, and a new merging method to combine ranking results produced by the proposed model. Based on standard datasets, our model outperforms a list of baselines including the state-of-the-art LSBert model, indicating its effectiveness in community-oriented lexical simplification.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116255114","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9971016
Hu Min, Angbo Xie, Xuejiao Peng, Shun Lu, Xinying Xie, Xinru Lin, Qijie Chen, Xinyan Mo, Xuan Li, Guo Luo
In this paper, the combination of wavelet neural networks (WNN) and sliding mode control (SMC) is proposed and simulated to solve the problem of trajectory-tracking control of a two-link robot manipulator with periodic interference. The difficulties of designing control algorithm are mainly focused on achieving accurate trajectory tracking and good control performance with the guarantee of stability and robustness under uncertain cyclical interference. In order to deal with these issues, WNN is used to approximate the functions of control object and unknown periodic disturbance. In this three-layer neural networks design, a widely used Mexican hat wavelet as an activation function has been applied for hidden-layer neurons. Combined with the SMC theory, the adaptive learning laws of networks parameters are derived in the sense of Lyapunov stability analysis so that the tracking error and convergence of the weight can be guaranteed in this control system. The better effectiveness of proposed SMC and WNN control algorithm is demonstrated by numerical simulation on a two-link robot manipulator, as comparing with that of Gauss Radial Basis Function (GRBF) neural networks.
{"title":"Trajectory Tracking of Two-Joint Space Robot using Wavelet Neural Networks and Sliding Mode Control","authors":"Hu Min, Angbo Xie, Xuejiao Peng, Shun Lu, Xinying Xie, Xinru Lin, Qijie Chen, Xinyan Mo, Xuan Li, Guo Luo","doi":"10.1109/ISPCE-ASIA57917.2022.9971016","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971016","url":null,"abstract":"In this paper, the combination of wavelet neural networks (WNN) and sliding mode control (SMC) is proposed and simulated to solve the problem of trajectory-tracking control of a two-link robot manipulator with periodic interference. The difficulties of designing control algorithm are mainly focused on achieving accurate trajectory tracking and good control performance with the guarantee of stability and robustness under uncertain cyclical interference. In order to deal with these issues, WNN is used to approximate the functions of control object and unknown periodic disturbance. In this three-layer neural networks design, a widely used Mexican hat wavelet as an activation function has been applied for hidden-layer neurons. Combined with the SMC theory, the adaptive learning laws of networks parameters are derived in the sense of Lyapunov stability analysis so that the tracking error and convergence of the weight can be guaranteed in this control system. The better effectiveness of proposed SMC and WNN control algorithm is demonstrated by numerical simulation on a two-link robot manipulator, as comparing with that of Gauss Radial Basis Function (GRBF) neural networks.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122089978","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}
Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9971054
Miaoyu Liao, B. Ling
This paper employs the electroencephalogrms (EEGs) to analyze the effect of eating the chocolate on the happiness of the subjects. In particular, the EEGs are acquired by a single channel head band as well as the questionnaires on the happiness are conducted before and after eating the chocolate. Here, each EEG is acquired for 10 minutes. Then, the EEGs are transmitted to the cloud system via a bluetooth module. In the cloud system, the EEGs are first denoised using an ideal lowpass filtering via the discrete Fourier transform approach. Next, different features are extracted from different brain waves localized in different frequency bands. By performing the classification of the EEGs between before eating the chocolate and after eating the chocolate for all the EEGs in the test set, the classification accuracy is employed as the score of the happiness. It is found that our obtained score of the happiness is very close to the score obtained in the questionnaires. This implies that the chocolate can improve the happiness of the subjects and the happiness of the subjects can be reflected by the EEGs.
{"title":"Effect of Eating Chocolate on Happiness via Electroencephalogram Analysis","authors":"Miaoyu Liao, B. Ling","doi":"10.1109/ISPCE-ASIA57917.2022.9971054","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971054","url":null,"abstract":"This paper employs the electroencephalogrms (EEGs) to analyze the effect of eating the chocolate on the happiness of the subjects. In particular, the EEGs are acquired by a single channel head band as well as the questionnaires on the happiness are conducted before and after eating the chocolate. Here, each EEG is acquired for 10 minutes. Then, the EEGs are transmitted to the cloud system via a bluetooth module. In the cloud system, the EEGs are first denoised using an ideal lowpass filtering via the discrete Fourier transform approach. Next, different features are extracted from different brain waves localized in different frequency bands. By performing the classification of the EEGs between before eating the chocolate and after eating the chocolate for all the EEGs in the test set, the classification accuracy is employed as the score of the happiness. It is found that our obtained score of the happiness is very close to the score obtained in the questionnaires. This implies that the chocolate can improve the happiness of the subjects and the happiness of the subjects can be reflected by the EEGs.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131233869","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}
Change point detection, as an important technique in artificial intelligence, aims to identify abrupt changes in complex systems. In this paper, we propose a novel gradient-sampling-based approach for change point detection in piecewise linear model. The convergence to a point satisfying the first-order optimality condition is guaranteed. Through extensive numerical experiments, we compare the proposed algorithm with the well known method of Muggeo's segmentation by dynamic programming. By computing the change points on the dataset concerning the relationship between the residential electricity consumption and temperature in Fujian Province, we demonstrate that the proposed algorithm outperforms Muggeo's method. Moreover, when using the change points for power load forecasting, the change points from the proposed algorithm can significantly improve the predictive performance of the Long Short-Term Memory (LSTM) model.
{"title":"A Gradient-Sampling-based Algorithm for Change Point Detection in Piecewise Linear Model","authors":"Kai Xiao, Yimin Shen, Xiaorui Qian, Xiangpeng Zhan, Yuanyuan Guo, Wen Huang","doi":"10.1109/ISPCE-ASIA57917.2022.9971107","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971107","url":null,"abstract":"Change point detection, as an important technique in artificial intelligence, aims to identify abrupt changes in complex systems. In this paper, we propose a novel gradient-sampling-based approach for change point detection in piecewise linear model. The convergence to a point satisfying the first-order optimality condition is guaranteed. Through extensive numerical experiments, we compare the proposed algorithm with the well known method of Muggeo's segmentation by dynamic programming. By computing the change points on the dataset concerning the relationship between the residential electricity consumption and temperature in Fujian Province, we demonstrate that the proposed algorithm outperforms Muggeo's method. Moreover, when using the change points for power load forecasting, the change points from the proposed algorithm can significantly improve the predictive performance of the Long Short-Term Memory (LSTM) model.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125995058","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：