Search

Your search keyword '"Chen, Yeh-Cheng"' showing total 44 results
Start Over Author "Chen, Yeh-Cheng" Search Limiters Full Text Publication Year Range Last 10 years
44 results on '"Chen, Yeh-Cheng"'

18. Blockchain-Enabled Privacy-Preserving Authentication Mechanism for Transportation CPS With Cloud-Edge Computing

Author

Mei, Qian, Xiong, Hu, Chen, Yeh-Cheng, and Chen, Chien-Ming

Abstract

In this article, we propose a secure and effective blockchain-enabled privacy-preserving authentication scheme for the transportation cyber-physical system (CPS) with the cloud-edge computing environment, which supports unconditional anonymity and data batch integrity verification while greatly simplifying key management issues. The proposed privacy-preserving authentication scheme employs an elliptic curve to construct a pairing-free ring signature scheme, which greatly reduces the resources overhead in the transportation CPS with cloud-edge computing. Moreover, the authentication process is performed on the blockchain to provide more reliable service information for vehicular communication. Furthermore, the security proof of the scheme is demonstrated based on the elliptic curve discrete logarithm problem under the random oracle model, and gives the corresponding security analysis. Finally, a simulation experiment demonstrates that the proposed scheme is feasible compared with the existing schemes.

Published
2024
Full Text
View/download PDF

23. Selective Ensemble Learning Algorithm for Imbalanced Dataset

Author

Du Hongle, Zhang Yan, Ke Gang, Zhang Lin, and Chen Yeh-Cheng

Abstract

Under the imbalanced dataset, the performance of the base-classifier, the computingmethod of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem toimprove the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on cluster under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class’s samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some base-classifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean, which is selected to evaluate the classification performance of base-classifier forimbalanced dataset, is selected to compute the weight of each base-classifier, and then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree.

Published
2022

44. Using RFID technology to develop an intelligent equipment lock management system

Author

Chen, Yeh-Cheng, Chen, Ruey-Shun, Sun, Hung-Min, and Wu, S. Felix

Abstract

The equipment lock has acted as an important tool for power companies to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfil the control and track the potential illegal behaviour, the human labour and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This research focused on the design of RFID technology applied to the traditional equipment lock, with the mobile and electronic technology, we are able to strengthen the management/operating convenience of the lock and provide the solutions for anti-counterfeiting and spoilage detection, so that the national energy can be properly protected and fairly distributed. The integration of RFID data interface and mobile sensing devices will enhance the control of electricity meters and other metering devices for the power company, it will also provide accurate and mobile support with interactive mode, real-time display by the system, and real-time information services. It will serve as the 'last-mile' management tool for the electrical equipment and improve development of the intelligent electric grids.

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results