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Multiple-period Optimal Nurse Scheduling under Learning Effect 学习效应下的多期护理优化调度
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736243
Bowen Pang, Xiaolei Xie
{"title":"Multiple-period Optimal Nurse Scheduling under Learning Effect","authors":"Bowen Pang, Xiaolei Xie","doi":"10.1109/ICCSI53130.2021.9736243","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736243","url":null,"abstract":"The learning effect is the phenomenon that the job processing time declines when the number of jobs finished increases for workers in the manufacturing system. Although learning and gaining experience is important in the nursing practice in hospitals, this effect has not been included in the current researches on nurse scheduling problems. In this work, we summarize the properties of the learning effect in the nursing practice and formulate a dynamic programming model to illustrate the scheduling problem considering this effect. A priority rule is raised and proved to guide the training of nurses and two decision policies are put forward. In numerical experiments, the comparison between the two decision policies are given based on different starting states of the system; the illustration of the priority rule is also presented.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651782","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
Distributed square-root cubature Kalman filter for underwater cyber-physical systems with multiple fading measurements 具有多重衰落测量的水下信息物理系统的分布平方根立方卡尔曼滤波
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736166
Ye Chen, P. Ye, Yuan Liang, Lin Meng
{"title":"Distributed square-root cubature Kalman filter for underwater cyber-physical systems with multiple fading measurements","authors":"Ye Chen, P. Ye, Yuan Liang, Lin Meng","doi":"10.1109/ICCSI53130.2021.9736166","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736166","url":null,"abstract":"In this article, we focus on the problem of the distributed nonlinear state estimation for a class of underwater cyber-physical systems with fading measurements. In this article, we propose a modified square-root cubature Kalman filter to tackle with the nonlinear system model. And the diffusion strategy is introduced in this paper to improve the system's estimation performance. To ease the internode communication cost, we propose an event-triggered mechanism to avoid the unnecessary information transmission. The corresponding esti-mation algorithm is also derived in this article. The simulation results show that the proposed algorithm can estimate the target's state accurately with less communication resources.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321963","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
A CUDA-Parallelized Fast AutoEncoder for Highly Efficient Latent Factor Analysis on High-Dimensional and Sparse Matrices from Recommender Systems 基于cuda并行快速自编码器的高维稀疏矩阵潜在因子分析
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736205
Fei Luo, Zhigang Liu
{"title":"A CUDA-Parallelized Fast AutoEncoder for Highly Efficient Latent Factor Analysis on High-Dimensional and Sparse Matrices from Recommender Systems","authors":"Fei Luo, Zhigang Liu","doi":"10.1109/ICCSI53130.2021.9736205","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736205","url":null,"abstract":"An AutoEncoder (AE)-based latent factor analysis model can precisely extract non-linear latent features from a High-dimensional and Sparse (HiDS) matrix from a recommender system. However, it requires prefilling an HiDS matrix's unknown data to achieve its compatibility with a GPU platform, which leads to tremendous consumption of computation and storage. To address this issue, this paper presents a CUDA-Parallelized Fast AutoEncoder (CPFAE) for highly efficient latent factor analysis on a high-dimensional and sparse matrix from a recommender system. Its main idea is two-fold: a) implementing mini-batch-based weight update in the form of efficient sparse matrix multiplication to train the neural network, and b) implementing an efficient computation model for a compressed sparse matrix to make full use of a GPU platform's computation power. Experimental results on two HiDS matrices from real applications demonstrate that compared with a state-of-the-art AE-based model, CPFAE achieves significant gain in computation and storage efficiency.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993187","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
Design of a Joint Microservices-based Smart Epidemic Prevention Platform 基于联合微服务的智能防疫平台设计
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736199
Liang Zhang
{"title":"Design of a Joint Microservices-based Smart Epidemic Prevention Platform","authors":"Liang Zhang","doi":"10.1109/ICCSI53130.2021.9736199","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736199","url":null,"abstract":"Currently, as COVID-19 spreads around the world, epidemic prevention departments from different provinces and regions often need to carry out cross-regional cooperation. In the paper, some new office platforms based on some office needs of the joint prevention and control work are tried to build. In the paper, the platforms built is capable of visualizing the risk map of regional risks, covering a multi-segment integrated joint prevention and control collaborative business platform system of the CDC, customs, relevant medical institutions and health tracking management departments. For the staff in each business link, they can access the data of cases, close contacts and key health tracking objects through the platform. Based on the work, it's necessary to print relevant certificates online, and at the same time, they also supplement the business data of the link to the data chain, providing better information services for improving the efficiency of joint prevention and control. In the process of making an analysis of the business needs, it is found that when building the platform, it will face problems such as many functional modules, variable demand, and too large amount of concurrency at a certain time. In the paper, the implemented architecture techniques would be explained, the details of the key technology implementation adopted by micro-services in realizing the platform would be clarified, and the significance of the platform to the epidemic prevention work would be finally discussed.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122408485","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
Laboratory Behavior Detection Method Based on Improved Yolov5 Model 基于改进Yolov5模型的实验室行为检测方法
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736251
Zhaofeng Zhang, Daiqin Ao, Luoyu Zhou, Xiaolong Yuan, Mingzhang Luo
{"title":"Laboratory Behavior Detection Method Based on Improved Yolov5 Model","authors":"Zhaofeng Zhang, Daiqin Ao, Luoyu Zhou, Xiaolong Yuan, Mingzhang Luo","doi":"10.1109/ICCSI53130.2021.9736251","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736251","url":null,"abstract":"With the development of deep learning and big data, behavior detection has become a hot spot in computer vision. Laboratory is an important place for teaching or scientific research. As the subject of the laboratory, laboratory behavior of students determines the quality of experimental teaching. Therefore, this paper took the laboratory as the research scene and proposed a laboratory behavior detection method based on deep learning. Firstly, the common categories of laboratory behaviors were defined and a dataset of laboratory behaviors was established. Then, YOLOv5 model was improved and a laboratory behavior detection method was proposed based on the improved YOLOv5. Lastly, the proposed method was trained and tested based on the laboratory behavior dataset. The experimental results have shown that the improved YOLOv5 model can be well applied to laboratory behavior detection of students. Compared with the original YOLOv5 model, the improved model can better adapt to the data characteristics of the laboratory behavior. Its precision and recall are significantly improved, and mAP (mean average precision) is increased by 2.1%. The proposed laboratory behavior detection method can not only be used to analyze laboratory behavior of students and optimize the experimental teaching. Moreover, it can be extended to remote laboratory surveillance and improve the quality of remote laboratory.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131643642","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: 2
A deep learning algorithm for predicting protein-protein interactions with nonnegative latent factorization 基于非负潜因子分解的蛋白质相互作用预测的深度学习算法
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736228
Liwei Wang, Lun Hu
{"title":"A deep learning algorithm for predicting protein-protein interactions with nonnegative latent factorization","authors":"Liwei Wang, Lun Hu","doi":"10.1109/ICCSI53130.2021.9736228","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736228","url":null,"abstract":"Protein-protein interaction (PPI) networks play an essential role in the study of proteomics. Given the fact that known PPI data are extremely incomplete, high-throughput technologies have been developed to significantly increase the amount of PPI data, but they are prone to generate false positive PPIs and accordingly affect the performance of computational prediction algorithms. To overcome this problem, we propose a novel deep learning algorithm for predicting PPIs with symmetric nonnegative latent factorization (SNLF). In particular, we first improve the quality of PPI data by applying an established SNLF model. Quasi-Sequence-Order is then used to encode proteins based on the modality of their sequence information. Principal component analysis is utilized to generate the features of proteins in a more compact manner. After that, we adopt graph variational autoencoder to learn the embedding of each protein by considering protein features and network topology. Finally, the embeddings of proteins are concatenated in pairs as input to train a simple feedforward neural network for prediction. Experiments have been performed on five different PPI datasets by comparing the performance of our algorithm with the state-of-the-art prediction algorithms, and the results demonstrate that the proposed model is promising in predicting PPIs.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127586006","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
A Comprehensive Management Protocol for Campus Emergencies 校园突发事件综合管理规程
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736220
Zhang Han, Huang Yanyan, Geng Ze
{"title":"A Comprehensive Management Protocol for Campus Emergencies","authors":"Zhang Han, Huang Yanyan, Geng Ze","doi":"10.1109/ICCSI53130.2021.9736220","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736220","url":null,"abstract":"The emergency response system of a higher education institution is very important in protecting the safety, health and property of faculty, administrators, staff and students. The types and nature of emergencies that may occur on college campuses are summarized and analyzed in this paper. Effective and efficient emergency management mechanisms are studied. The systemic construction of emergency response plans and their evaluation are discussed. A case study on a public health crisis in an institution is presented. Some future research directions are also pointed out at the end of the paper.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351847","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
SS#11 A Data-Driven Approach to Evaluate Operating Cost of SCUC Problem SS#11一个数据驱动的方法来评估SCUC问题的运行成本
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736247
Jiahua Wang, Jiang Wu, Oiaozhu Zhai
{"title":"SS#11 A Data-Driven Approach to Evaluate Operating Cost of SCUC Problem","authors":"Jiahua Wang, Jiang Wu, Oiaozhu Zhai","doi":"10.1109/ICCSI53130.2021.9736247","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736247","url":null,"abstract":"This paper proposes an optimal estimation approach for the security-constrained unit commitment (SCUC) problem, which has the potential to be applied to some problems in the power system. A large number of numerical tests based on different systems show that the optimal operating cost of SCUC is not sensitive to the fluctuation of the injected power. The function between operating cost and the injected power can be obtained easily through using a simple data fitting method. Further tests show that the change in the power network topology will not have an excessive impact on the fitting effect, and the penetration rate of renewable energy is the main challenge. This conclusion can be used to quickly obtain the lower bound of the SCUC solution, and other actual scenarios that only focus on operating costs.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389292","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
Heave Motion Estimation Based on Cubature Kalman Filter 基于库伯卡尔曼滤波的升沉运动估计
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736261
Peng Guo, Jun Yu Li, Tianxiong Chen, Zhenxing Wu
{"title":"Heave Motion Estimation Based on Cubature Kalman Filter","authors":"Peng Guo, Jun Yu Li, Tianxiong Chen, Zhenxing Wu","doi":"10.1109/ICCSI53130.2021.9736261","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736261","url":null,"abstract":"To solve the high-dimensional nonlinear problem of the ship heave motion model, a cubature Kalman filter (CKF) is used to improve the estimation accuracy of the nonlinear filter. The mathematic model of ship heave motion is established based on the Longuet Higgins wave model and the accelerometer measurement model. The fast fourier transform (FFT) is used to analyze the acceleration information. Because of the non-linearity of the heave motion model and the measurement noise and zero bias existing in the inertial measurement unit (IMU), CKF is used to estimate the heave motion. The proposed method is evaluated with simulation and measurement results from an experimental setup. A six-degree-of-freedom motion platform is used for experimental verification. The experimental results show that the heave motion estimation based on CKF has a faster convergence speed and a more accurate estimation accuracy than the unscented Kalman filter algorithm (UKF). The mean square error of the heave motion estimation reaches 0.008m, it can obtain accurate and no-delay heave motion information.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129368151","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
Extracting Decision Tree from Trained Deep Reinforcement Learning in Traffic Signal Control 基于深度强化学习的交通信号控制决策树提取
2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736263
Yuanyang Zhu, Xiao Yin, Ruyu Li, Chunlin Chen
{"title":"Extracting Decision Tree from Trained Deep Reinforcement Learning in Traffic Signal Control","authors":"Yuanyang Zhu, Xiao Yin, Ruyu Li, Chunlin Chen","doi":"10.1109/ICCSI53130.2021.9736263","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736263","url":null,"abstract":"Deep reinforcement learning (DRL) has achieved promising results on traffic signal control systems. However, due to the complexity of the decisions of deep neural networks, it is a great challenge to explain and visualize the policy of reinforcement learning (RL) agents. The decision tree can provide useful information for experts responsible for making reliable decisions. In this paper, we employ decision trees to extract models with readable interpretations from expert policy achieved by DRL methods. We evaluate our methods via a single-intersection traffic signal control task on the simulation platform of Urban MObility (SUMO). The experimental results demonstrate that the extracted decision trees can be used to understand the learning process and the learned optimal policy of the DRL methods.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125535954","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
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