2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第4页

Spatio-Temporal Traffic Data Recovery Based on Latent Feature Analysis 基于潜在特征分析的时空交通数据恢复

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004181

Yuting Ding, Di Wu

{"title":"Spatio-Temporal Traffic Data Recovery Based on Latent Feature Analysis","authors":"Yuting Ding, Di Wu","doi":"10.1109/ICNSC55942.2022.10004181","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004181","url":null,"abstract":"Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done much research on the recovery of missing traffic data, however, how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open problem. Aiming at the spatio-temporal characteristics of traffic speed data, this paper regards the recovery of missing data as a matrix completion problem and proposes a spatio-temporal traffic data completion method based on hidden feature analysis, which discovers spatio-temporal patterns and underlying structures from incomplete data to complete the recovery task. Therefore, we introduce spatial and temporal correlation to capture the main underlying features of each dimension. Finally, these latent features are applied to recover traffic data through latent feature analysis. The experimental and evaluation results show that the evaluation criterion value of the model is small, which indicates that the model has better performance. The results show that the model can accurately estimate the continuous missing data.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"94 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132708176","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

Energy Efficiency Forecasting for Central Air-conditioning Refrigeration Systems Based on Deep Neural Network 基于深度神经网络的中央空调制冷系统能效预测

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004057

Haitao Song, Yijun Chen, Jiajia Li, Tianyi Wang, Hao Shen, Cheng He

{"title":"Energy Efficiency Forecasting for Central Air-conditioning Refrigeration Systems Based on Deep Neural Network","authors":"Haitao Song, Yijun Chen, Jiajia Li, Tianyi Wang, Hao Shen, Cheng He","doi":"10.1109/ICNSC55942.2022.10004057","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004057","url":null,"abstract":"Central air-conditioning is a complex system project, and the assessment and forecasting of its performance often involves a very large number of factors. Accurate assessment of a system's energy efficiency is crucial for system energy demand management and performance improvement. Therefore, researchers have done a lot of related work focusing on energy efficiency forecasting by constructing thermodynamic and mech-anistic models of central air conditioning, and recently there have been attempts to combine these models with methods based on data mining and machine learning as well. The problem of predicting energy efficiency in refrigeration systems can be, generally, viewed as a multivariate time-series(MTS) problem following a hidden Markov process. With the development of artificial intelligence techniques, deep neural network-based tem-poral prediction models have made important advances in many application areas. This study aims to achieve higher accuracy for energy efficiency forecasting tasks, using an improved model based on LSTNet. We conducted performance prediction on a dataset collected from a real-world central air-conditioning refrigeration circulation system. As a result, we found significant correlation between the prediction result and ground truth. Our method is compared with several common baseline methods for assessing and predicting the performance of refrigeration systems. Experimental results show that our method generally achieves better performance than those baseline methods.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133332192","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

Anti-sideslip Line of Sight Method-based Path Tracking Control for a Multi-joint Snake Robot 基于防侧滑视线法的多关节蛇形机器人路径跟踪控制

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004143

Yang Xiu, Dongfang Li, Miaomiao Zhang, Rob Law, E. Wu

引用次数: 0

Modeling and Analysis of Microgrid Energy Scheduling Based on Colored Petri Net 基于有色Petri网的微电网能量调度建模与分析

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004111

Xin Liang, Yifan Hou, Mi Zhao

引用次数: 0

Scheduling of single-arm cluster tools mixedly processing two kinds of wafers 单臂集群工具混合加工两种晶圆的调度

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004192

Tingting Leng, Jufeng Wang, Chunfeng Liu

引用次数: 0

Research On Feature Extraction of Point Cloud Data Based on Contrastive Learning 基于对比学习的点云数据特征提取研究

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004142

Chaoqian Wang, Lixin Zheng, Shuwan Pan

引用次数: 0

Deep Deterministic Policy Gradient-based Virtual Coupling Control For High-Speed Train Convoys 基于深度确定性策略梯度的高速列车虚拟耦合控制

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004067

Giacomo Basile, Dario Giuseppe Lui, A. Petrillo, S. Santini

引用次数: 2

Suction Grasping Detection for Items Sorting in Warehouse Logistics using Deep Convolutional Neural Networks 基于深度卷积神经网络的仓储物流分拣抓取检测

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004168

Chen Zhang, Lixin Zheng, Shuwan Pan

{"title":"Suction Grasping Detection for Items Sorting in Warehouse Logistics using Deep Convolutional Neural Networks","authors":"Chen Zhang, Lixin Zheng, Shuwan Pan","doi":"10.1109/ICNSC55942.2022.10004168","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004168","url":null,"abstract":"Items sorting in warehouse logistics is a labor-intensive and time-consuming work. Combined with computer vision and real-time motion planning technologies, industrial robots have been ideal substitutes for human beings in that cases. But picking and placing a large quantity of object categories including known and novel objects in heavily cluttered environments is really a challenging task. This paper proposes a pipeline to address suction grasping detection for isolated objects. Firstly, a two-dimensional suction configuring is proposed. Secondly, we establish a dataset including depth images, color images and suction labels for logistics warehouse scenario. Thirdly, a lightweight network named Generative Grasp Convolutional Neural Network (GG-CNN) intended for planar antipodal grasp is adapted for predicting spatial suction affordance in pixel. Finally, we get a accuracy of 91.45% on test data sets. Primary contributions of our work are: (1) a practical annotation method and dataset collecting from retail industry, (2) an innovative application of GG-CNN.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130188869","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

NLOS Identification and Ranging Error Mitigation for UWB Signal 超宽带信号NLOS识别与测距误差缓解

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004171

Jinglong Zhou, Wen-Feng Li, Shaoyong Jiang

{"title":"NLOS Identification and Ranging Error Mitigation for UWB Signal","authors":"Jinglong Zhou, Wen-Feng Li, Shaoyong Jiang","doi":"10.1109/ICNSC55942.2022.10004171","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004171","url":null,"abstract":"Ultra-wideband(UWB) has achieved excellent application performance in many scenarios such as indoor positioning due to its strong penetration capability, multipath resistance and high positioning accuracy. For the problems such as large ranging errors of UWB in Non-Line-of-Sight(NLOS) environment, this paper firstly performs NLOS identification of UWB based on the position difference between first path(FP) and strongest path, the difference between received signal strength(RSS) and FP signal strength, and the distance residuals. Further, an NLOS error mitigation method with RSS and time of arrival fusion is proposed based on biased Kalman filtering(KF) and maximum likelihood estimation algorithm. Finally, experiments in dynamic and static scenarios are carried out to validate the proposed algorithm. The experimental results show that the identification accuracy of our method for NLOS is 95.42%. Under the static ranging scenario, our method improves 74.82% and 71.73% on average in the ranging accuracy compared with the original data and KF algorithm, respectively. In the dynamic positioning scenario, the average distance error of our method is 0.09 m, and it improves 62.5% in positioning accuracy compared to the original data and KF.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125510202","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: 3

Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data Cox-ResNet:一个基于残差神经网络的基因表达数据生存分析模型

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004157

Qingyan Yin, Wangwang Chen, Ruiping Wu, Zhi Wei

{"title":"Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data","authors":"Qingyan Yin, Wangwang Chen, Ruiping Wu, Zhi Wei","doi":"10.1109/ICNSC55942.2022.10004157","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004157","url":null,"abstract":"Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127516334","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