Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004181
Yuting Ding, Di Wu
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.
{"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}
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.
{"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}
Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004143
Yang Xiu, Dongfang Li, Miaomiao Zhang, Rob Law, E. Wu
This paper reports an anti-sideslip line of sight (LOS) method-based path tracking control method for a multi joint snake robot. In order to effectively eliminate the sideslip influence on direction guidance, a finite-time convergent sideslip observer is designed to compensate the LOS guidance law and improve the steering accuracy of the robot. Additionally, considering the external disturbance and state constraints, a barrier Lyapunov function-based backstepping adaptive controller is proposed to ensure the environmental robustness of the robot. In this work, the sideslip and interference are observed accurately, avoiding the imprecise constraint conditions. Finally, the validity and feasibility of the proposed method are proved by theoretical proof and numerical simulation.
{"title":"Anti-sideslip Line of Sight Method-based Path Tracking Control for a Multi-joint Snake Robot","authors":"Yang Xiu, Dongfang Li, Miaomiao Zhang, Rob Law, E. Wu","doi":"10.1109/ICNSC55942.2022.10004143","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004143","url":null,"abstract":"This paper reports an anti-sideslip line of sight (LOS) method-based path tracking control method for a multi joint snake robot. In order to effectively eliminate the sideslip influence on direction guidance, a finite-time convergent sideslip observer is designed to compensate the LOS guidance law and improve the steering accuracy of the robot. Additionally, considering the external disturbance and state constraints, a barrier Lyapunov function-based backstepping adaptive controller is proposed to ensure the environmental robustness of the robot. In this work, the sideslip and interference are observed accurately, avoiding the imprecise constraint conditions. Finally, the validity and feasibility of the proposed method are proved by theoretical proof and numerical simulation.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 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":"134427274","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004111
Xin Liang, Yifan Hou, Mi Zhao
Colored Petri nets (CPN) can be used to model and study systems with discrete, asynchronous, and concurrent behaviors. Microgrid systems have these features, that can be modeled and studied by CPN. In this paper, the energy scheduling between various distributed power sources and users in a microgrid system is studied based on the analysis of working characteristics of wind turbines, photovoltaic arrays and flexible loads. A CPN model of a microgrid system including distributed power generations is established, which can realize the functions of scheduling energy generated by distributed power generators in the microgrid system and interacting with the external power grid. Owing to the modular and hierarchical modeling method, the proposed model can be conveniently expanded in the scale and function as required, which has universality and adaptability. Finally, the theoretical significance and practical values of the established model are demonstrated by the system simulation.
{"title":"Modeling and Analysis of Microgrid Energy Scheduling Based on Colored Petri Net","authors":"Xin Liang, Yifan Hou, Mi Zhao","doi":"10.1109/ICNSC55942.2022.10004111","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004111","url":null,"abstract":"Colored Petri nets (CPN) can be used to model and study systems with discrete, asynchronous, and concurrent behaviors. Microgrid systems have these features, that can be modeled and studied by CPN. In this paper, the energy scheduling between various distributed power sources and users in a microgrid system is studied based on the analysis of working characteristics of wind turbines, photovoltaic arrays and flexible loads. A CPN model of a microgrid system including distributed power generations is established, which can realize the functions of scheduling energy generated by distributed power generators in the microgrid system and interacting with the external power grid. Owing to the modular and hierarchical modeling method, the proposed model can be conveniently expanded in the scale and function as required, which has universality and adaptability. Finally, the theoretical significance and practical values of the established model are demonstrated by the system simulation.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"196 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":"122756925","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004192
Tingting Leng, Jufeng Wang, Chunfeng Liu
This paper studies the scheduling problem of single-arm cluster tools that mixedly process two different kinds of wafers without sharing and revisiting processing modules (PMs). We balance internal workloads by adjusting the number of PMs used to process wafers, and balance the external workloads by configuring virtual PMs. We derive the scheduling conditions for single-arm cluster tools, which are more relaxed than the existing ones. We can also use less PMs to get the same production cycle time as the existing literature using configuration of virtual PMs only. We give some examples to show the application and power of the theory.
{"title":"Scheduling of single-arm cluster tools mixedly processing two kinds of wafers","authors":"Tingting Leng, Jufeng Wang, Chunfeng Liu","doi":"10.1109/ICNSC55942.2022.10004192","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004192","url":null,"abstract":"This paper studies the scheduling problem of single-arm cluster tools that mixedly process two different kinds of wafers without sharing and revisiting processing modules (PMs). We balance internal workloads by adjusting the number of PMs used to process wafers, and balance the external workloads by configuring virtual PMs. We derive the scheduling conditions for single-arm cluster tools, which are more relaxed than the existing ones. We can also use less PMs to get the same production cycle time as the existing literature using configuration of virtual PMs only. We give some examples to show the application and power of the theory.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"102 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":"122931681","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004142
Chaoqian Wang, Lixin Zheng, Shuwan Pan
Due to the development of laser radar, depth camera and other technologies, point cloud data is used in more and more fields. However, compared with two-dimensional image data, the cost of manually labeling point cloud data is higher. This paper present a simple contrastive process to obtain the feature extraction encoder of point cloud data through self-supervised learning, which can provide better support for tasks such as classification and segmentation. We translate a mini batch of date into two crops, the corresponding point clouds data are treated as positive example, and the not corresponding data are treated as negative example. Using InfoNCE as target function to get the unique feature of each data. Comparing to other existing contrastive structure, it performs a higher accuracy in classification task based on ModelNet40. At the same time, we used rotation, randomly cutting and randomly dropout point to realize data augmentation based on ModelNet40 for improving the performance of feature extraction.
{"title":"Research On Feature Extraction of Point Cloud Data Based on Contrastive Learning","authors":"Chaoqian Wang, Lixin Zheng, Shuwan Pan","doi":"10.1109/ICNSC55942.2022.10004142","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004142","url":null,"abstract":"Due to the development of laser radar, depth camera and other technologies, point cloud data is used in more and more fields. However, compared with two-dimensional image data, the cost of manually labeling point cloud data is higher. This paper present a simple contrastive process to obtain the feature extraction encoder of point cloud data through self-supervised learning, which can provide better support for tasks such as classification and segmentation. We translate a mini batch of date into two crops, the corresponding point clouds data are treated as positive example, and the not corresponding data are treated as negative example. Using InfoNCE as target function to get the unique feature of each data. Comparing to other existing contrastive structure, it performs a higher accuracy in classification task based on ModelNet40. At the same time, we used rotation, randomly cutting and randomly dropout point to realize data augmentation based on ModelNet40 for improving the performance of feature extraction.","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":"126133246","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004067
Giacomo Basile, Dario Giuseppe Lui, A. Petrillo, S. Santini
This work addresses the problem of Virtual Coupling (VC) control for uncertain heterogeneous nonlinear autonomous trains convoys sharing information among each other with Radio Block Center (RBC) and via Train-2-Train (T2T) communication network. To solve the problem we propose a novel no-supervised actor-critic Deep Deterministic Policy Gradient-based (DDPG) controller which drives each train within the convoy to track the reference behaviour, as imposed by the RBC, while maintaining a desired inter-train distance w.r.t. the preceding train. The effectiveness of the proposed approach is evaluated via a numerical analysis which is carried out in Python environment. The first step of validation involves the efficiency of the training process and discloses how the agent has learned the correct behaviour to track the train ahead. Then, we numerically prove how the overall closed-loop trains convoy under the action of the DDPG controller reaches the VC formation despite the presence of external disturbances acting on the train dynamics.
{"title":"Deep Deterministic Policy Gradient-based Virtual Coupling Control For High-Speed Train Convoys","authors":"Giacomo Basile, Dario Giuseppe Lui, A. Petrillo, S. Santini","doi":"10.1109/ICNSC55942.2022.10004067","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004067","url":null,"abstract":"This work addresses the problem of Virtual Coupling (VC) control for uncertain heterogeneous nonlinear autonomous trains convoys sharing information among each other with Radio Block Center (RBC) and via Train-2-Train (T2T) communication network. To solve the problem we propose a novel no-supervised actor-critic Deep Deterministic Policy Gradient-based (DDPG) controller which drives each train within the convoy to track the reference behaviour, as imposed by the RBC, while maintaining a desired inter-train distance w.r.t. the preceding train. The effectiveness of the proposed approach is evaluated via a numerical analysis which is carried out in Python environment. The first step of validation involves the efficiency of the training process and discloses how the agent has learned the correct behaviour to track the train ahead. Then, we numerically prove how the overall closed-loop trains convoy under the action of the DDPG controller reaches the VC formation despite the presence of external disturbances acting on the train dynamics.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"91 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":"125012828","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 : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004168
Chen Zhang, Lixin Zheng, Shuwan Pan
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.
{"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}
Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004171
Jinglong Zhou, Wen-Feng Li, Shaoyong Jiang
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.
{"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}
Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004157
Qingyan Yin, Wangwang Chen, Ruiping Wu, Zhi Wei
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.
{"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}
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