2022 12th International Conference on Information Science and Technology (ICIST)最新文献_第3页

A Multimodal Dataset for Gait Recognition in Different Terrains using Wearable Sensors 基于可穿戴传感器的不同地形步态识别多模态数据集

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926866

Mengxue Yan, Yan Zhao, Ming Guo, Haoyu Sun, Jianlong Qiu, Feng Zhao

{"title":"A Multimodal Dataset for Gait Recognition in Different Terrains using Wearable Sensors","authors":"Mengxue Yan, Yan Zhao, Ming Guo, Haoyu Sun, Jianlong Qiu, Feng Zhao","doi":"10.1109/ICIST55546.2022.9926866","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926866","url":null,"abstract":"Gait has been shown to be a profound movement in human activities, and gait recognition is a commonly used biometric recognition in recent years. Gait recognition based on wearable sensors has been involved in various application areas. Especially in the area of medical, gait research is an essential issue. The purpose of this paper is to provide a multimodal public dataset for use with gait recognition. The dataset is derived of data from wearable inertial sensors and ECG sensor. Both sensors provide easy-to-operate and low-cost data recording for gait recognition. The gait dataset is based on the data from 15 healthy adults whose lower limbs have neither been injured nor operated on in the past year. Unlike other well-known datasets in the literature, this dataset contains inertial data (built-in gyroscope, accelerometer, geomagnetic field sensor) recorded from the ankle, as well as ECG data from a cardiac sensor. In this paper, the 15 volunteers were asked to walk at their most comfortable pace in four different terrains and complete the test. These four kinds of terrains are: flat land, sand, grassland and blind road. In addition, in order to verify the effectiveness of this multimodal dataset, this paper uses deep learning to identify the gait patterns of four terrains, and the recognition rate reaches 82%.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132300587","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

ER-MRL: Emotion Recognition based on Multimodal Representation Learning ER-MRL:基于多模态表示学习的情绪识别

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926848

Xiaoding Guo, Yadi Wang, Zhijun Miao, Xiaojin Yang, Jinkai Guo, Xianhong Hou, Feifei Zao

引用次数: 2

Theoretical Analysis of Value-Iteration-Based Q-Learning with Approximation Errors 具有近似误差的基于值迭代的q学习理论分析

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926794

Zhantao Liang, Mingming Ha, Derong Liu

引用次数: 1

An Adaptive K-Nearest-Neighbor Approach for Predicting Chemical Composition Content in Soil 自适应k -最近邻法预测土壤化学成分含量

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926778

Yijun Zhao, Shaozhi Li, Mian Wang, Xiang Wan, Kun Xia

引用次数: 0

Deep Temporal Sequence Prediction Neural Network for MIMO Detection 用于MIMO检测的深度时序预测神经网络

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926790

Yiqing Zhang, Wei Zheng, J. Xue, Jianyong Sun

引用次数: 0

Robust Self-Attention ConvLSTM-based Traffic Flow Prediction Model 基于鲁棒自注意卷积stm的交通流预测模型

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926853

Xueli Zhang, Wing W. Y. Ng, Ting Wang

{"title":"Robust Self-Attention ConvLSTM-based Traffic Flow Prediction Model","authors":"Xueli Zhang, Wing W. Y. Ng, Ting Wang","doi":"10.1109/ICIST55546.2022.9926853","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926853","url":null,"abstract":"Traffic flow forecasting has been receiving a lot of attention because of its important role in traffic control and management. Accurate traffic forecasting is critical to improving the performance of intelligent transportation systems. However, accurate traffic forecasting still faces the following challenges, including modeling the dynamics of traffic data along the temporal and spatial dimensions, significant differences in peak hour/peak hour traffic, and traffic flow data affected by partial noise. In this paper, we propose a hybrid and robust model with Self-Attention ConvLSTM networks and localized stochastic sensitive (LSS) for traffic flow prediction. The proposed model extracts features with long-range spatiotemporal dependencies with Self-Attention ConvLSTM. To further explore the long-term temporal features, we utilize LSTM module to extract daily and weekly periodic features as assistive features. The LSS reduces sensitivity to unseen samples around training samples and avoids large output fluctuations due to the noise or change of the data. Experiments on real traffic flow datasets show that the proposed method yields better prediction performance compared to other contrast methods.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122790059","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

Classification Algorithm of Logistics Packaging Based on Multi-scale Convolutional Neural Network 基于多尺度卷积神经网络的物流包装分类算法

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926919

Mengxiang Geng, Ming Guo, Jianlong Qiu, Yingchan Cao, Xiangyong Chen

{"title":"Classification Algorithm of Logistics Packaging Based on Multi-scale Convolutional Neural Network","authors":"Mengxiang Geng, Ming Guo, Jianlong Qiu, Yingchan Cao, Xiangyong Chen","doi":"10.1109/ICIST55546.2022.9926919","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926919","url":null,"abstract":"The rapid development of economy and society has led to the rapid increase of the output of logistics outer packaging garbage. How to realize the classification and recycling of logistics packaging garbage by intelligent methods has become a key factor for human beings to achieve sustainable development. To solve this problem, this paper proposes an image recognition model of logistics packaging, which is a convolution neural network model based on multi-scale, and adds channel and spatial attention mechanism. The model uses multi-scale convolution to extract richer image features. The attention mechanism is used to adaptively adjust the parts that need to be focused on, and the feature extraction ability of the model is enhanced. Compared with the traditional manual sorting method, this paper uses the deep learning technology to intelligently and automatically classify the logistics outer packaging. The experimental results show that the classification accuracy of data sets can reach 96% by using the method of deep learning, which is very helpful to improve the classification efficiency of logistics outer packaging.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125403094","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

An Event-Triggered Predictive Control for Weight Control System 体重控制系统的事件触发预测控制

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926926

Xuecheng Zhang, Xiaojie Qiu, Wenchao Meng, Yuliang Li, Lihong Zhang

{"title":"An Event-Triggered Predictive Control for Weight Control System","authors":"Xuecheng Zhang, Xiaojie Qiu, Wenchao Meng, Yuliang Li, Lihong Zhang","doi":"10.1109/ICIST55546.2022.9926926","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926926","url":null,"abstract":"In this paper, we deal with the weight control of cigarette production process, and we establish a system model with uncertainties and give an event-triggered predictive control algorithm. First, according to the actual situation in the production process, the corresponding relationship between the weight of the cigarette and the height of the leveling disc is established. The uneven distribution and uncertainty of the cut tobacco are taken into account in the model simultaneously. Secondly, a model predictive controller is designed, which can effectively reduce the weight error caused by detection lag, uneven distribution, and random density of cut tobacco. A prediction model and cost function for predicting the future weight of cigarettes are established, and the optimal control sequence is solved when the cost function is the smallest. Finally, considering the limited computing and communication resources of the controller, an event-triggered mechanism is introduced to effectively reduce the computing cost of the controller. Simulation results demonstrate the effectiveness of the proposed event-triggered predictive control method in the cigarette weight control system.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126239883","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

A Novel Fuzzy Rule Based Neuro-system with Sparse Rule Extraction for Classification Problems 基于稀疏规则提取的模糊神经系统分类问题研究

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926893

Qilin Ren, Guang-Fu Xue, Xiaoling Gong, Jian Wang

引用次数: 0

Attractivity Analysis for Recurrent Neural Networks with State-dependent External Input 具有状态依赖外部输入的递归神经网络的吸引性分析

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926830

Gang Baol, Kang Li, Zhenyan Song

引用次数: 0