Pub Date : 2022-12-15DOI: 10.1109/ICNSC55942.2022.10004069
Kefan Wang, Jing An, Qiaoyan Kang
Classification is a common task and can be achieved by learning a predictive model from a labeled training dataset. However, the imbalanced data distribution makes the model tend to favor the majority class, which reduces the classification performance. Unlike traditional classification models, graph convolutional networks (GCNs) can extract useful feature information from unlabeled data. In this paper, a novel framework for imbalanced classification named effective-aggregation graph convolutional network (EGCN) is proposed. First, a graph generator constructs graph-structured data using both labeled and unlabeled data. Then, an aggregation control unit (ACU) is performed to improve the effectiveness of aggregation. ACU uses local estimation density to limit the aggregation of inter-class edges from a local perspective, and it enhances the aggregation of the minority class from a global perspective based on the imbalance ratio. Finally, the prediction results are obtained by a graph convolutional network. Experimental results on several real-world datasets show that EGCN has promising performance.
分类是一项常见的任务，可以通过从标记的训练数据集中学习预测模型来实现。然而，数据分布的不平衡使得模型倾向于大多数类，从而降低了分类性能。与传统的分类模型不同，图卷积网络(GCNs)可以从未标记的数据中提取有用的特征信息。本文提出了一种新的非平衡分类框架——有效聚合图卷积网络(EGCN)。首先，图生成器使用标记和未标记的数据构建图结构数据。然后，通过ACU (aggregation control unit)来提高聚合的有效性。ACU从局部角度利用局部估计密度限制类间边缘的聚集，从全局角度基于失衡比增强少数类的聚集。最后，利用图卷积网络得到预测结果。在多个真实数据集上的实验结果表明，EGCN具有良好的性能。
{"title":"Effective-aggregation Graph Convolutional Network for Imbalanced Classification","authors":"Kefan Wang, Jing An, Qiaoyan Kang","doi":"10.1109/ICNSC55942.2022.10004069","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004069","url":null,"abstract":"Classification is a common task and can be achieved by learning a predictive model from a labeled training dataset. However, the imbalanced data distribution makes the model tend to favor the majority class, which reduces the classification performance. Unlike traditional classification models, graph convolutional networks (GCNs) can extract useful feature information from unlabeled data. In this paper, a novel framework for imbalanced classification named effective-aggregation graph convolutional network (EGCN) is proposed. First, a graph generator constructs graph-structured data using both labeled and unlabeled data. Then, an aggregation control unit (ACU) is performed to improve the effectiveness of aggregation. ACU uses local estimation density to limit the aggregation of inter-class edges from a local perspective, and it enhances the aggregation of the minority class from a global perspective based on the imbalance ratio. Finally, the prediction results are obtained by a graph convolutional network. Experimental results on several real-world datasets show that EGCN has promising performance.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"278 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":"134308615","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/ICNSC55942.2022.10004104
Liuya Xu, Zhengchao Liu, Chunrong Pan
With the development of green production and industrial upgrading, the traditional production method of heavy manufacturing industry is in urgent need to change. Against the background that the energy structure cannot be changed in a short time, reasonable scheduling optimization is an effective solution to improve the production efficiency and energy utilization efficiency of enterprises. In the actual processing environment of the surveyed enterprises, the machines can have many different states during operation. These different states greatly increase the flexibility and complexity of the manufacturing shop, and the previous optimization methods are not suitable for this kind of manufacturing environment. For this reason, a multi-objective optimization model of flexible job shop scheduling considering multiple states of machines is proposed. Then, a two-stage optimization method is proposed for optimization. In the first stage, an improved genetic algorithm is proposed to solve the model. In the second stage, the green scheduling heuristic strategy is adopted to optimize the machine states. Finally, the feasibility of the model and the effectiveness of the solution method of this paper are verified by the optimization of practical cases.
{"title":"Green scheduling optimization for flexible job shops considering multiple states of machines","authors":"Liuya Xu, Zhengchao Liu, Chunrong Pan","doi":"10.1109/ICNSC55942.2022.10004104","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004104","url":null,"abstract":"With the development of green production and industrial upgrading, the traditional production method of heavy manufacturing industry is in urgent need to change. Against the background that the energy structure cannot be changed in a short time, reasonable scheduling optimization is an effective solution to improve the production efficiency and energy utilization efficiency of enterprises. In the actual processing environment of the surveyed enterprises, the machines can have many different states during operation. These different states greatly increase the flexibility and complexity of the manufacturing shop, and the previous optimization methods are not suitable for this kind of manufacturing environment. For this reason, a multi-objective optimization model of flexible job shop scheduling considering multiple states of machines is proposed. Then, a two-stage optimization method is proposed for optimization. In the first stage, an improved genetic algorithm is proposed to solve the model. In the second stage, the green scheduling heuristic strategy is adopted to optimize the machine states. Finally, the feasibility of the model and the effectiveness of the solution method of this paper are verified by the optimization of practical cases.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"35 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":"123674779","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/ICNSC55942.2022.10004102
Jialiang Wang, Yurong Zhong, Weiling Li
Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Building an LF model is a large-scale non-convex problem. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a distributed adaptive SLF (DASLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that DASLF model has a competitive advantage over state-of-the-art models in data representation ability.
{"title":"Distributed-Particle-Swarm-Optimization-Incorporated Second-order Latent Factor Model","authors":"Jialiang Wang, Yurong Zhong, Weiling Li","doi":"10.1109/ICNSC55942.2022.10004102","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004102","url":null,"abstract":"Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Building an LF model is a large-scale non-convex problem. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a distributed adaptive SLF (DASLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that DASLF model has a competitive advantage over state-of-the-art models in data representation ability.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 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":"130262148","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/ICNSC55942.2022.10004094
Kaiguang Yang, Ye Wang, Qianhao Luo, Xin Liu, Weiling Li
Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which are proposed under the assumption that data follow a balanced distribution and causes inaccurate staging results. To achieve accurate OP staging, we proposed an OP staging model who is able to handle imbalance data in this work. The proposed model adopts gray level co-occurrence matrix (GLCM) to extract texture feature of chest X-ray and implements classification with a weighted broad learning system (WBLS). Empirical studies on six data cases provided by a hospital indicate that proposed model can perform better OP staging than state-of-the-art classifiers with imbalanced data.
{"title":"Accurate Occupational Pneumoconiosis Staging with Imbalanced Data","authors":"Kaiguang Yang, Ye Wang, Qianhao Luo, Xin Liu, Weiling Li","doi":"10.1109/ICNSC55942.2022.10004094","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004094","url":null,"abstract":"Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which are proposed under the assumption that data follow a balanced distribution and causes inaccurate staging results. To achieve accurate OP staging, we proposed an OP staging model who is able to handle imbalance data in this work. The proposed model adopts gray level co-occurrence matrix (GLCM) to extract texture feature of chest X-ray and implements classification with a weighted broad learning system (WBLS). Empirical studies on six data cases provided by a hospital indicate that proposed model can perform better OP staging than state-of-the-art classifiers with imbalanced data.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"75 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":"127102509","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/ICNSC55942.2022.10004063
Yingzhu Han, Chuyi Dai, Ding Liu
In 2020, COVID-19 swept the world. To prevent the spread of the outbreak, it is crucial to ensure that everyone wears a mask during daily travel and in public places. However, relying on human inspection alone is inevitably negligent and there is a potential risk of cross-contamination between people. Automated detection by means of cameras and artificial intelligence becomes a technical solution. By training convolutional neural networks, image recognition can be implemented and image classification can be performed as a solution to the target mask-wearing detection problem. To this end, in this thesis, three typical convolutional neural network architectures, VGG-16, Inception V3, and DenseNet-121, are used as models based on deep learning to investigate the mask-wearing detection problem by using transfer learning ideas. By building six different models and comparing the performance of different typical network architectures on the same dataset using two transfer learning methods, feature extraction and fine-tuning, we can conclude that DenseNet-121 is the typical architecture with the best performance among the three networks, and fine-tuning has better transfer ability than feature extraction in solving the target mask wearing detection problem.
{"title":"Detection of Face Mask Wearing for COVID-19 Protection based on Transfer Learning and Classic CNN Model","authors":"Yingzhu Han, Chuyi Dai, Ding Liu","doi":"10.1109/ICNSC55942.2022.10004063","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004063","url":null,"abstract":"In 2020, COVID-19 swept the world. To prevent the spread of the outbreak, it is crucial to ensure that everyone wears a mask during daily travel and in public places. However, relying on human inspection alone is inevitably negligent and there is a potential risk of cross-contamination between people. Automated detection by means of cameras and artificial intelligence becomes a technical solution. By training convolutional neural networks, image recognition can be implemented and image classification can be performed as a solution to the target mask-wearing detection problem. To this end, in this thesis, three typical convolutional neural network architectures, VGG-16, Inception V3, and DenseNet-121, are used as models based on deep learning to investigate the mask-wearing detection problem by using transfer learning ideas. By building six different models and comparing the performance of different typical network architectures on the same dataset using two transfer learning methods, feature extraction and fine-tuning, we can conclude that DenseNet-121 is the typical architecture with the best performance among the three networks, and fine-tuning has better transfer ability than feature extraction in solving the target mask wearing detection problem.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"93 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":"127136892","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/ICNSC55942.2022.10004065
Chengyu Peng, Haibin Zhu, Linyuan Liu, R. Grewal
With the rise in popularity of cloud computing, there is a growing trend toward the storage of data in a cloud environment. However, there is a significant increase in the risk of privacy information leakage, and users could face serious challenges as a result of data leakage. In this paper, we propose an allocation scheme for the storage of data in a collaborative edge-cloud environment, with a focus on enhanced data privacy. Specifically, we first divide the datasets by fields to eliminate as much as possible the correlation between the leaked data. We then evaluate the sensitivity of the data and server trust to calculate the degree of fitting between them and combine the results with the performance score to obtain a server qualification value for the stored fields. Several constraints are also specified, and the E-CARGO model is used to formalize the problem. Based on the qualification value, we can find the optimal allocation using the IBM ILOG CPLEX Optimization (CPLEX) Package. In our experiments, our proposed solution allows the data to be stored in servers that better suit their requirements, while reducing the user overhead.
{"title":"Optimal Data Allocation in the Environment of Edge and Cloud Servers","authors":"Chengyu Peng, Haibin Zhu, Linyuan Liu, R. Grewal","doi":"10.1109/ICNSC55942.2022.10004065","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004065","url":null,"abstract":"With the rise in popularity of cloud computing, there is a growing trend toward the storage of data in a cloud environment. However, there is a significant increase in the risk of privacy information leakage, and users could face serious challenges as a result of data leakage. In this paper, we propose an allocation scheme for the storage of data in a collaborative edge-cloud environment, with a focus on enhanced data privacy. Specifically, we first divide the datasets by fields to eliminate as much as possible the correlation between the leaked data. We then evaluate the sensitivity of the data and server trust to calculate the degree of fitting between them and combine the results with the performance score to obtain a server qualification value for the stored fields. Several constraints are also specified, and the E-CARGO model is used to formalize the problem. Based on the qualification value, we can find the optimal allocation using the IBM ILOG CPLEX Optimization (CPLEX) Package. In our experiments, our proposed solution allows the data to be stored in servers that better suit their requirements, while reducing the user overhead.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"71 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":"127373371","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/ICNSC55942.2022.10004164
Lu Liu, Ruizhuo Song, Qinglai Wei
Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste.
{"title":"Event-Triggered Energy Optimization of Wireless Sensor Networks","authors":"Lu Liu, Ruizhuo Song, Qinglai Wei","doi":"10.1109/ICNSC55942.2022.10004164","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004164","url":null,"abstract":"Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"4 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":"128820892","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/ICNSC55942.2022.10004158
Xingyang Li, J. Fu, Zixi Jia, Ziyan Zhao, Siyi Li, Shixin Liu
Blocking job shop scheduling problems are common in industrial environments. Various existing studies tackle them to enhance the production efficiency of job shops with machine blocking properties. In the environment of intelligent manufacturing, robots are commonly used to transfer the jobs to be processed among different processes. However, no previous work considers the integrated optimization of blocking job shop scheduling and transfer robot assignment. Facing the new and key demand of production scheduling, this work considers a novel blocking job shop scheduling problem with transfer robots whose speed varies with or without cargo load. It is first formulated by using constraint programming as a baseline model. By analyzing the characteristics of both the considered problem and baseline model this work proposes an improved constraint programming model. Numerous experiments on an adapted benchmark dataset show that the improved constraint programming model can well solve the concerned problem. Comparing with a baseline model, it can greatly enhance the solution efficiency and accuracy. Its great performance shows its high potential to be used in practical industrial scenarios.
{"title":"Constraint Programming for a Novel Integrated Optimization of Blocking Job Shop Scheduling and Variable-Speed Transfer Robot Assignment","authors":"Xingyang Li, J. Fu, Zixi Jia, Ziyan Zhao, Siyi Li, Shixin Liu","doi":"10.1109/ICNSC55942.2022.10004158","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004158","url":null,"abstract":"Blocking job shop scheduling problems are common in industrial environments. Various existing studies tackle them to enhance the production efficiency of job shops with machine blocking properties. In the environment of intelligent manufacturing, robots are commonly used to transfer the jobs to be processed among different processes. However, no previous work considers the integrated optimization of blocking job shop scheduling and transfer robot assignment. Facing the new and key demand of production scheduling, this work considers a novel blocking job shop scheduling problem with transfer robots whose speed varies with or without cargo load. It is first formulated by using constraint programming as a baseline model. By analyzing the characteristics of both the considered problem and baseline model this work proposes an improved constraint programming model. Numerous experiments on an adapted benchmark dataset show that the improved constraint programming model can well solve the concerned problem. Comparing with a baseline model, it can greatly enhance the solution efficiency and accuracy. Its great performance shows its high potential to be used in practical industrial scenarios.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"34 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":"129671316","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/ICNSC55942.2022.10004053
Haitao Song, Hongyu Ji, Ye Yu, Bing Xie
Observability is an important property of complex system. Achieving good system observability can help reveal information about the system's state and structure, providing great convenience to the operation and maintenance of the system. Moreover, observability makes the system more efficient in performance management, troubleshooting and updating. Observability means better user experience, less burden of operation and maintenance, and it helps save cost of dealing with system failures. The implementation and optimization of system observability in different scenarios have been heated topics since the concept was introduced. Importantly, hospital information system(HIS) is an infrastructure in the operation of modern public health institutions. HIS has many functions such as hospital management, patient medical information management and decision-making. Therefore, maintaining the normal and stable operation of HIS has far-reaching social and livelihood significance. In this paper, we take HIS as a scenario and discuss the issues related to the implementation of observability in HIS based on Artificial Intelligence for IT Operations(AIOps) and microservices architecture(MSA) by searching and summarizing different dimensions of the literature. In addition, focusing on some specific applications and service scenarios we analyze the potential observability requirements and provide possible solution ideas.
{"title":"A review of observability issues in hospital information system","authors":"Haitao Song, Hongyu Ji, Ye Yu, Bing Xie","doi":"10.1109/ICNSC55942.2022.10004053","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004053","url":null,"abstract":"Observability is an important property of complex system. Achieving good system observability can help reveal information about the system's state and structure, providing great convenience to the operation and maintenance of the system. Moreover, observability makes the system more efficient in performance management, troubleshooting and updating. Observability means better user experience, less burden of operation and maintenance, and it helps save cost of dealing with system failures. The implementation and optimization of system observability in different scenarios have been heated topics since the concept was introduced. Importantly, hospital information system(HIS) is an infrastructure in the operation of modern public health institutions. HIS has many functions such as hospital management, patient medical information management and decision-making. Therefore, maintaining the normal and stable operation of HIS has far-reaching social and livelihood significance. In this paper, we take HIS as a scenario and discuss the issues related to the implementation of observability in HIS based on Artificial Intelligence for IT Operations(AIOps) and microservices architecture(MSA) by searching and summarizing different dimensions of the literature. In addition, focusing on some specific applications and service scenarios we analyze the potential observability requirements and provide possible solution ideas.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"20 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":"129684302","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/ICNSC55942.2022.10004127
Junheng Cheng, Yanhong Lin, Xiaoyu He
Small and medium-sized enterprises (SMEs) are the backbone of most countries' economy. However, they often face financing difficulties and high financing costs. In recent years, supply chain finance develops rapidly and provides a good way for SMEs to alleviate their financing problems. Inventory pledge financing is one of the most widely used supply chain financing modes. This paper studies an assets optimization problem for the companies that adopt inventory pledge financing, which involves collaterals selection, purchasing and selling decisions for multiple periods during the entire pledge horizon. For the problem, we construct a mixed-integer linear programming model and verify its effectiveness by CPLEX with a practice-based case and randomly generated instances.
{"title":"Mixed-integer linear programming for enterprise's inventory pledge financing decision","authors":"Junheng Cheng, Yanhong Lin, Xiaoyu He","doi":"10.1109/ICNSC55942.2022.10004127","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004127","url":null,"abstract":"Small and medium-sized enterprises (SMEs) are the backbone of most countries' economy. However, they often face financing difficulties and high financing costs. In recent years, supply chain finance develops rapidly and provides a good way for SMEs to alleviate their financing problems. Inventory pledge financing is one of the most widely used supply chain financing modes. This paper studies an assets optimization problem for the companies that adopt inventory pledge financing, which involves collaterals selection, purchasing and selling decisions for multiple periods during the entire pledge horizon. For the problem, we construct a mixed-integer linear programming model and verify its effectiveness by CPLEX with a practice-based case and randomly generated instances.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"2 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":"129782013","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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