Pub Date : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642204
Man-Fai Leung, Jun Wang, Hangjun Che
This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns.
{"title":"Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection","authors":"Man-Fai Leung, Jun Wang, Hangjun Che","doi":"10.1109/ICICIP53388.2021.9642204","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642204","url":null,"abstract":"This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964973","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642157
Shanwen Guan, Xiao-peng Luo
Due to the widespread use of robotics in recent years, accurate localization and tracking have become active research topic. As a low-power wireless communication and sensing technology, Ultra-wideband (UWB) has relatively accurate positioning and sensing capabilities, and has broad application prospects for precise positioning and other fields. But due to the complex environment and obstacles, the non-line-of-sight(NLOS) error generated by it will be severe. It seriously affects the position estimation of the system, resulting in low positioning accuracy and poor robustness. Improving the accuracy and robustness of the UWB positioning technology in a complex environment, a method based on the fusion of UWB and IMU data, which effectively combines global positioning and local positioning, positioning, using LSTM neural network algorithm processes the IMU data, and The EKF algorithm merge the IMU and UWB. Compared with the traditional UWB positioning method, this method can effectively suppress Control the influence of NLOS interference in positioning estimation and improve the accuracy and robustness of the positioning system.
{"title":"Fusing Ultra-wideband Range Measurements with IMU for Mobile Robot Localization","authors":"Shanwen Guan, Xiao-peng Luo","doi":"10.1109/ICICIP53388.2021.9642157","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642157","url":null,"abstract":"Due to the widespread use of robotics in recent years, accurate localization and tracking have become active research topic. As a low-power wireless communication and sensing technology, Ultra-wideband (UWB) has relatively accurate positioning and sensing capabilities, and has broad application prospects for precise positioning and other fields. But due to the complex environment and obstacles, the non-line-of-sight(NLOS) error generated by it will be severe. It seriously affects the position estimation of the system, resulting in low positioning accuracy and poor robustness. Improving the accuracy and robustness of the UWB positioning technology in a complex environment, a method based on the fusion of UWB and IMU data, which effectively combines global positioning and local positioning, positioning, using LSTM neural network algorithm processes the IMU data, and The EKF algorithm merge the IMU and UWB. Compared with the traditional UWB positioning method, this method can effectively suppress Control the influence of NLOS interference in positioning estimation and improve the accuracy and robustness of the positioning system.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622238","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642197
Bai-gang Mi, Yi Fan, Yu Sun
With the coming of the new information epoch, the quantity of aeronautical intelligence information increases exponentially. In the effort to improve the efficiency and security of the aeronautical industry, it has become a key factor to determine how to acquire, extract, and represent knowledge from a large set of aeronautical intelligence information and forming a knowledge base to guide the intelligent development of aeronautical intelligence information management work. In order to promote integration and sharing of information regarding aeronautical intelligence information domain and obtain the deeper information and knowledge, the construction and application of NOTAM (Notice to Air Men) ontology are developed based on text mining. The NOTAM text is collected and analyzed by web crawler technology. Combined with the professional term in specific domain, we successfully extract the key concepts of the ontology by TF-IDF (term frequency-inverse document frequency) text features. Furthermore, the hierarchical and non-hierarchical relations are automatically extracted by text cluster methods and specific domain knowledge system. Finally, the ontology editor—protégé helps us to visualize the key concepts and the relations in the ontology. Meanwhile, a NOTAM text is instanced to verify the efficiency and precision of the NOTAM ontology.
随着新信息时代的到来，航空情报信息量呈指数级增长。如何从海量的航空情报信息中获取、提取和表示知识，形成知识库，指导航空情报信息管理工作智能化发展，已成为提高航空工业效率和安全性的关键因素。为了促进航空情报信息领域信息的整合与共享，获取更深入的信息和知识，基于文本挖掘技术开发了NOTAM (Notice to Air Men)本体的构建与应用。利用网络爬虫技术对NOTAM文本进行采集和分析。结合特定领域的专业术语，利用TF-IDF (term frequency-inverse document frequency)文本特征成功提取出本体的关键概念。在此基础上，利用文本聚类方法和特定的领域知识系统，自动提取层次关系和非层次关系。最后，本体编辑器proprosamug帮助我们将本体中的关键概念和关系可视化。同时，实例化了一个NOTAM文本，验证了NOTAM本体的效率和精度。
{"title":"Ontology Intelligent Construction Technology for NOTAM","authors":"Bai-gang Mi, Yi Fan, Yu Sun","doi":"10.1109/ICICIP53388.2021.9642197","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642197","url":null,"abstract":"With the coming of the new information epoch, the quantity of aeronautical intelligence information increases exponentially. In the effort to improve the efficiency and security of the aeronautical industry, it has become a key factor to determine how to acquire, extract, and represent knowledge from a large set of aeronautical intelligence information and forming a knowledge base to guide the intelligent development of aeronautical intelligence information management work. In order to promote integration and sharing of information regarding aeronautical intelligence information domain and obtain the deeper information and knowledge, the construction and application of NOTAM (Notice to Air Men) ontology are developed based on text mining. The NOTAM text is collected and analyzed by web crawler technology. Combined with the professional term in specific domain, we successfully extract the key concepts of the ontology by TF-IDF (term frequency-inverse document frequency) text features. Furthermore, the hierarchical and non-hierarchical relations are automatically extracted by text cluster methods and specific domain knowledge system. Finally, the ontology editor—protégé helps us to visualize the key concepts and the relations in the ontology. Meanwhile, a NOTAM text is instanced to verify the efficiency and precision of the NOTAM ontology.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128632538","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642208
Wing W. Y. Ng, Zixin Zhou, Ting Wang
Faces contain abundant biological and sociological information. Inter-ethnicity identification using facial images has been intensively studied, while intra-ethnicity classification has received less attention. In this paper, we propose an Ensemble of Convolutional Autoencoders (E-CAE) model to attempt to distinguish Chinese, Japanese, and Korean faces and individuals from different regions of China. To accomplish this task, CJK and RoC datasets are built and E-CAE yields a classification accuracy of 80.69% on CJK dataset and 61.81% on RoC dataset. The experimental results demonstrate that our model outperforms existing methods for fine-grained ethnicity recognition in terms of accuracy and robustness. To our knowledge, this is the first work that performs fine-grained ethnicity recognition at the scale of provinces.
{"title":"Fine-Grained Facial Ethnicity Recognition Based on Dual Convolutional Autoencoders","authors":"Wing W. Y. Ng, Zixin Zhou, Ting Wang","doi":"10.1109/ICICIP53388.2021.9642208","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642208","url":null,"abstract":"Faces contain abundant biological and sociological information. Inter-ethnicity identification using facial images has been intensively studied, while intra-ethnicity classification has received less attention. In this paper, we propose an Ensemble of Convolutional Autoencoders (E-CAE) model to attempt to distinguish Chinese, Japanese, and Korean faces and individuals from different regions of China. To accomplish this task, CJK and RoC datasets are built and E-CAE yields a classification accuracy of 80.69% on CJK dataset and 61.81% on RoC dataset. The experimental results demonstrate that our model outperforms existing methods for fine-grained ethnicity recognition in terms of accuracy and robustness. To our knowledge, this is the first work that performs fine-grained ethnicity recognition at the scale of provinces.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129211397","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642166
Zanhao Liang, Xiaoqin Wang, Zhuo Chen, Xiaonan Luo
A method to predict how the human body temperature changes over time is presented in this paper. In this work, classic recurrent neural network and its two variants are used to predict body temperature, and their predictions are compared to evaluate performance. To collect the data used for training, videos which save the temperature value in the form of pixel are recorded with FLIR ONE PRO LT, a thermal sensor, and frames extracted from the video are converted into numbers with optical character recognition technology. To make our method more valuable, the temperatures at different condition, like motionless and walking, are predicted. Experiment results show that classic recurrent neural network outperforms its two variants, this may because GRU and LSTM have more parameters than classic RNN, when training data are not enough, GRU and LSTM are more likely to overfit than classic RNN.
本文提出了一种预测人体温度随时间变化的方法。在这项工作中，使用经典的递归神经网络及其两种变体来预测体温，并将其预测结果进行比较以评估性能。为了收集训练数据，使用热传感器FLIR ONE PRO LT记录以像素形式保存温度值的视频，并使用光学字符识别技术将视频中提取的帧转换为数字。为了使我们的方法更有价值，我们预测了不同状态下的温度，比如静止和行走。实验结果表明，经典递归神经网络优于其两种变体，这可能是因为GRU和LSTM比经典RNN具有更多的参数，当训练数据不足时，GRU和LSTM比经典RNN更容易过拟合。
{"title":"Body Temperature Prediction with Recurrent Neural Network and its Variants","authors":"Zanhao Liang, Xiaoqin Wang, Zhuo Chen, Xiaonan Luo","doi":"10.1109/ICICIP53388.2021.9642166","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642166","url":null,"abstract":"A method to predict how the human body temperature changes over time is presented in this paper. In this work, classic recurrent neural network and its two variants are used to predict body temperature, and their predictions are compared to evaluate performance. To collect the data used for training, videos which save the temperature value in the form of pixel are recorded with FLIR ONE PRO LT, a thermal sensor, and frames extracted from the video are converted into numbers with optical character recognition technology. To make our method more valuable, the temperatures at different condition, like motionless and walking, are predicted. Experiment results show that classic recurrent neural network outperforms its two variants, this may because GRU and LSTM have more parameters than classic RNN, when training data are not enough, GRU and LSTM are more likely to overfit than classic RNN.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"56 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128342208","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642186
Yulin He, Chuandong Li, Xingxing Ju
As an important application of emotion artificial intelligence, emotion classification provides the basis for the realization of affective brain-computer interface (aBCI). In this study, the NeuCube is used to learn and classify Electroencephalogram (EEG) data from the DEAP dataset. NeuCube is a type of spiking neural network (SNN) framework developed based on the real human brain. It is very suitable for analyzing and processing spatio-temporal data. Based on the 10-fold cross-validation method, we obtain a mean accuracy of 68.91 % in the emotional binary valence classification problem. Meanwhile, the EEG data recorded from F3 and F4 electrode channels provide more information compared with Fp1 and Fp2. The results prove that the spiking neural network can be applied to the task of emotion classification effectively.
{"title":"Emotion Classification Using EEG Data in a Brain-Inspired Spiking Neural Network","authors":"Yulin He, Chuandong Li, Xingxing Ju","doi":"10.1109/ICICIP53388.2021.9642186","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642186","url":null,"abstract":"As an important application of emotion artificial intelligence, emotion classification provides the basis for the realization of affective brain-computer interface (aBCI). In this study, the NeuCube is used to learn and classify Electroencephalogram (EEG) data from the DEAP dataset. NeuCube is a type of spiking neural network (SNN) framework developed based on the real human brain. It is very suitable for analyzing and processing spatio-temporal data. Based on the 10-fold cross-validation method, we obtain a mean accuracy of 68.91 % in the emotional binary valence classification problem. Meanwhile, the EEG data recorded from F3 and F4 electrode channels provide more information compared with Fp1 and Fp2. The results prove that the spiking neural network can be applied to the task of emotion classification effectively.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127292373","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}
Aiming at the nonlinear and uncertain problems of upper limb exoskeleton rehabilitation robot (ULERR) during passive training, a sliding model controller based on radial basis neural network is designed in this paper. Firstly, a four-degree-of-freedom ULERR is designed for stroke patients in soft paralysis and spasticity, and a kinetic model was established. Secondly, RBF neural network is used to approximate the uncertainty caused by spastic disturbance of patients in the system. The weight in the neural network is replaced by a single parameter, and the adaptive algorithm is easy to adjust and has strong real-time performance. The asymptotic stability of the controller is verified by Lyapunov theorem. Finally, the desired training trajectory of the upper limb is obtained by a three-dimensional motion capture system, and the simulation experiments are carried out with Matlab software to prove that the proposed control method solves the chattering problem of traditional sliding mode control, to meet the control requirements of real-time rehabilitation training.
{"title":"Sliding Mode Control Algorithm of Upper Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network","authors":"Bangcheng Zhang, Shuai Liu, Ye Li, Zaixiang Pang, Yan-ling Hao, Xiyu Zhang","doi":"10.1109/ICICIP53388.2021.9642219","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642219","url":null,"abstract":"Aiming at the nonlinear and uncertain problems of upper limb exoskeleton rehabilitation robot (ULERR) during passive training, a sliding model controller based on radial basis neural network is designed in this paper. Firstly, a four-degree-of-freedom ULERR is designed for stroke patients in soft paralysis and spasticity, and a kinetic model was established. Secondly, RBF neural network is used to approximate the uncertainty caused by spastic disturbance of patients in the system. The weight in the neural network is replaced by a single parameter, and the adaptive algorithm is easy to adjust and has strong real-time performance. The asymptotic stability of the controller is verified by Lyapunov theorem. Finally, the desired training trajectory of the upper limb is obtained by a three-dimensional motion capture system, and the simulation experiments are carried out with Matlab software to prove that the proposed control method solves the chattering problem of traditional sliding mode control, to meet the control requirements of real-time rehabilitation training.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130872711","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642167
Ye Xingxing, Cai Guoyong, Wang Shunjie
With the continuous progress of mobile Internet technology and GPS positioning technology of mobile devices, Social Network and Location Based Services (LBS) are gradually converging to form Location Based Social Network (LBSN). POI (Point of Interest) recommendation systems face the problems of variable user interests, very sparse user and POI check-in matrices, and nonlinear interaction modeling. To address the above problems, a Graph-enhanced Attention Graph Neural Network model is proposed for POI recommendation (POI-GAGN in short). POI-GAGN mines user/POI node representations on user-POI interaction graph, user-user social interaction graph, and POI-POI association interaction graph through interaction node feature extraction module, learns POI attribute information representations through text feature extraction module, and extracts short-term preference representations of users through short-term preference extraction module. A graph-enhanced attention mechanism is designed to correlates node representations, attribute information representations of POI, and short-term preferences of users with each other to achieve better information fusion. Finally, we conduct sufficient experiments on two real datasets to prove that the recommendation effect of POI-GAGN is better than other current advanced POI recommendation methods, and POI-GAGN can better overcome the problems of data sparsity and cold start in recommendations.
随着移动互联网技术和移动设备GPS定位技术的不断进步，Social Network和Location Based Services (LBS)逐渐融合，形成Location Based Social Network (LBSN)。兴趣点(POI)推荐系统面临着用户兴趣变化、用户和兴趣点签入矩阵非常稀疏以及非线性交互建模等问题。为了解决上述问题，提出了一种用于POI推荐的图增强注意图神经网络模型(简称POI- gagn)。POI- gagn通过交互节点特征提取模块挖掘用户-POI交互图、用户-用户社交交互图、POI-POI关联交互图上的用户/POI节点表示，通过文本特征提取模块学习POI属性信息表示，通过短期偏好提取模块提取用户的短期偏好表示。设计了一种图增强关注机制，将节点表示、POI属性信息表示和用户短期偏好相互关联，实现更好的信息融合。最后，我们在两个真实数据集上进行了充分的实验，证明了POI- gagn的推荐效果优于目前其他先进的POI推荐方法，并且POI- gagn可以更好地克服推荐中的数据稀疏性和冷启动问题。
{"title":"POI Recommendation Based on Graph Enhanced Attention GNN","authors":"Ye Xingxing, Cai Guoyong, Wang Shunjie","doi":"10.1109/ICICIP53388.2021.9642167","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642167","url":null,"abstract":"With the continuous progress of mobile Internet technology and GPS positioning technology of mobile devices, Social Network and Location Based Services (LBS) are gradually converging to form Location Based Social Network (LBSN). POI (Point of Interest) recommendation systems face the problems of variable user interests, very sparse user and POI check-in matrices, and nonlinear interaction modeling. To address the above problems, a Graph-enhanced Attention Graph Neural Network model is proposed for POI recommendation (POI-GAGN in short). POI-GAGN mines user/POI node representations on user-POI interaction graph, user-user social interaction graph, and POI-POI association interaction graph through interaction node feature extraction module, learns POI attribute information representations through text feature extraction module, and extracts short-term preference representations of users through short-term preference extraction module. A graph-enhanced attention mechanism is designed to correlates node representations, attribute information representations of POI, and short-term preferences of users with each other to achieve better information fusion. Finally, we conduct sufficient experiments on two real datasets to prove that the recommendation effect of POI-GAGN is better than other current advanced POI recommendation methods, and POI-GAGN can better overcome the problems of data sparsity and cold start in recommendations.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132497253","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642194
Md Sakib Ullah Sourav, Xiaoyun Zhang, Huidong Wang
With the emergence of the COVID-19 pandemic, tackling mental health issues has become challenging too. A tendency has been observed in people spending more time on social media (SM) than usual and it has become the alternative source of interaction and news sharing. Previous research shows that intensive use of SM increases stress directly or indirectly. The aim of this study is to analyze the role of self-efficacy on information support from SM and COVID-19 depressions. To achieve this objective, a quantitative analysis was performed through an online questionnaire-based survey among SM users. The findings of this study prevail that with the help of effective information support from SM and through certain behavioral modifications with users’ high self-efficacy, COVID-19 stress might be lessened accordingly.
{"title":"Social Media as Information Support in Reducing COVID – 19 Depressions: Self-Efficacy as Mediator for Behavioral Modeling","authors":"Md Sakib Ullah Sourav, Xiaoyun Zhang, Huidong Wang","doi":"10.1109/ICICIP53388.2021.9642194","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642194","url":null,"abstract":"With the emergence of the COVID-19 pandemic, tackling mental health issues has become challenging too. A tendency has been observed in people spending more time on social media (SM) than usual and it has become the alternative source of interaction and news sharing. Previous research shows that intensive use of SM increases stress directly or indirectly. The aim of this study is to analyze the role of self-efficacy on information support from SM and COVID-19 depressions. To achieve this objective, a quantitative analysis was performed through an online questionnaire-based survey among SM users. The findings of this study prevail that with the help of effective information support from SM and through certain behavioral modifications with users’ high self-efficacy, COVID-19 stress might be lessened accordingly.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126983717","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642220
Yunbo Yang, Na Liu, Sitian Qin
This paper studies the properties of a class of multi- agent systems. First of all, this article lists some symbols and lemmas to be used afterwards. The first main part of this article studies a class of continuous-time multi-agent systems with event-trigger mechanism and gives its consensus analysis by applying Lyapunov method. At the same time, this article also gives a modified trigger mechanism, consisting of both time intervals and event-trigger intervals for this kind of continuous system. And it is proved that under the proposed trigger mechanism, the state solutions of the given system can finally reach a consensus and the Zeno effect does not appear. Moreover, the problem of average consensus of differential privacy is also studied with an event-trigger mechanism in a discrete-time multi-agent system. The consensus and accuracy of this discrete system in the sense of mean square is studied. Through the research, it is concluded that the output states of the discrete system under the given event-trigger mechanism finally reach a consensus in the mean square sense, and the state solutions converge to the weighted average of the initial state. At the end of this paper, numerical simulations are made to illustrate the feasibility of the algorithm of this paper in practice.
{"title":"The Consensus Research on a Class of Event-Triggered Multi-Agent System","authors":"Yunbo Yang, Na Liu, Sitian Qin","doi":"10.1109/ICICIP53388.2021.9642220","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642220","url":null,"abstract":"This paper studies the properties of a class of multi- agent systems. First of all, this article lists some symbols and lemmas to be used afterwards. The first main part of this article studies a class of continuous-time multi-agent systems with event-trigger mechanism and gives its consensus analysis by applying Lyapunov method. At the same time, this article also gives a modified trigger mechanism, consisting of both time intervals and event-trigger intervals for this kind of continuous system. And it is proved that under the proposed trigger mechanism, the state solutions of the given system can finally reach a consensus and the Zeno effect does not appear. Moreover, the problem of average consensus of differential privacy is also studied with an event-trigger mechanism in a discrete-time multi-agent system. The consensus and accuracy of this discrete system in the sense of mean square is studied. Through the research, it is concluded that the output states of the discrete system under the given event-trigger mechanism finally reach a consensus in the mean square sense, and the state solutions converge to the weighted average of the initial state. At the end of this paper, numerical simulations are made to illustrate the feasibility of the algorithm of this paper in practice.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130503048","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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