For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a pro...For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a program to achieve viewing the results of examination anytime or anywhere with the help of cell phone applications software. The program proposed to solve the problem that user have to wait for the medical test results for a long time and cause the time waste, compared to the programs which using e-mail, SMS and mailing report card to inform the user the results, this one is to be more efficient, while also better meet user and medical institutions’ requirements. In addition, in order to test the practicality and feasibility of the program, with IOS devices as the material basis, this paper constructs a Web client system, based on the GPRS technology, we can realize the data communications between the Web server and a client which is the workbench IOS devices. Through testing, we can proof this Web client system has certain practical and development value, with the physical examination institutions become popular gradually, I believe that this design ideas of the system will be used extensively in the medical system, and also will bring a person more convenient.展开更多
This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the cli...This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.展开更多
In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand...In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor- rectly.展开更多
Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and...Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and topological application of this platform. It puts forward a solution to the real time problem in the industrial distributed supervisor system.展开更多
The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client ...The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client server computation mode in a power system enterprise. The structure and function are also introduced.展开更多
Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients m...Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.展开更多
Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical o...Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with federated learning applications.Blockchain has the characteristics of decentralization,distribution,and security.The blockchain-enabled federated learning further improve the security and performance of model training,while also expanding the application scope of federated learning.Blockchain has natural financial attributes that help establish a federated learning data market.However,the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices,which have different computing,communication,and storage resources,and the data quality of each device may also vary.Therefore,how to effectively select the clients with the data required for federated learning task is a research hotspot.In this paper,a two-stage client selection scheme for blockchain-enabled federated learning is proposed,which first selects clients that satisfy federated learning task through attribute-based encryption,protecting the attribute privacy of clients.Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm.Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor.展开更多
文摘For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a program to achieve viewing the results of examination anytime or anywhere with the help of cell phone applications software. The program proposed to solve the problem that user have to wait for the medical test results for a long time and cause the time waste, compared to the programs which using e-mail, SMS and mailing report card to inform the user the results, this one is to be more efficient, while also better meet user and medical institutions’ requirements. In addition, in order to test the practicality and feasibility of the program, with IOS devices as the material basis, this paper constructs a Web client system, based on the GPRS technology, we can realize the data communications between the Web server and a client which is the workbench IOS devices. Through testing, we can proof this Web client system has certain practical and development value, with the physical examination institutions become popular gradually, I believe that this design ideas of the system will be used extensively in the medical system, and also will bring a person more convenient.
文摘This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.
文摘In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor- rectly.
基金The Natural Science Foundation of Hunan Province!(No.96 10 1 3 0 )
文摘Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and topological application of this platform. It puts forward a solution to the real time problem in the industrial distributed supervisor system.
文摘The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client server computation mode in a power system enterprise. The structure and function are also introduced.
基金supported by Key Research and Development Program of China (No.2022YFC3005401)Key Research and Development Program of Yunnan Province,China (Nos.202203AA080009,202202AF080003)+1 种基金Science and Technology Achievement Transformation Program of Jiangsu Province,China (BA2021002)Fundamental Research Funds for the Central Universities (Nos.B220203006,B210203024).
文摘Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.
文摘Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with federated learning applications.Blockchain has the characteristics of decentralization,distribution,and security.The blockchain-enabled federated learning further improve the security and performance of model training,while also expanding the application scope of federated learning.Blockchain has natural financial attributes that help establish a federated learning data market.However,the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices,which have different computing,communication,and storage resources,and the data quality of each device may also vary.Therefore,how to effectively select the clients with the data required for federated learning task is a research hotspot.In this paper,a two-stage client selection scheme for blockchain-enabled federated learning is proposed,which first selects clients that satisfy federated learning task through attribute-based encryption,protecting the attribute privacy of clients.Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm.Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor.
