المؤلفون: Jing, Zhengjun, Xu, Xiaolong, Gu, Chunseng, Zhang, Yan, Shu, Qiang

المساهمون: Ministry of Education Engineering Research Center Innovation Fund Project

المصدر: Applied Artificial Intelligence ; volume 38, issue 1 ; ISSN 0883-9514 1087-6545

الإتاحة: https://doi.org/10.1080/08839514.2024.2340395Test

المؤلفون: Wu, Xu, Jing, Zhengjun, Wang, Xinwei

المساهمون: Natural Science Research of Jiangsu Higher Education Institutions of China

المصدر: Heliyon ; volume 10, issue 5, page e27104 ; ISSN 2405-8440

الإتاحة: https://doi.org/10.1016/j.heliyon.2024.e27104Test
https://api.elsevier.com/content/article/PII:S2405844024031359?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2405844024031359?httpAccept=text/plainTest

المؤلفون: Miao, Miao, Jing, Zhengjun, Xu, Xiaolong, Xue, Meiqing

المصدر: Heliyon ; volume 10, issue 5, page e26402 ; ISSN 2405-8440

الإتاحة: https://doi.org/10.1016/j.heliyon.2024.e26402Test
https://api.elsevier.com/content/article/PII:S2405844024024332?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2405844024024332?httpAccept=text/plainTest

المؤلفون: Han, Hongzhang, Tang, Jun, Jing, Zhengjun

المساهمون: National Natural Science Foundation of China

المصدر: Heliyon ; volume 10, issue 1, page e23577 ; ISSN 2405-8440

مصطلحات موضوعية: Multidisciplinary

الإتاحة: https://doi.org/10.1016/j.heliyon.2023.e23577Test
https://api.elsevier.com/content/article/PII:S2405844023107857?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2405844023107857?httpAccept=text/plainTest

المؤلفون: JING Zhengjun, ZHANG Yaqi, BIE Shuang, FAN Qianyu, CHEN Jian

المصدر: Zhongliu Fangzhi Yanjiu, Vol 49, Iss 6, Pp 628-633 (2022)

مصطلحات موضوعية: papillary thyroid cancer, relapse, 131i, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

الوصف: The papillary thyroid cancer (PTC) patients have general good prognosis, but the relapse occurred in 20%-30% patients after initial treatment, which resulted in poor prognosis and treatment difficulty. 131I can act as a part of postoperative adjuvant therapy for the initial standard treatment of middle- or high-risk PTC patients, and it still could play important roles in the diagnosis and treatment of recurrent PTC. 131I can contribute to early detection, accurate location and iodine uptake evaluation of recurrence foci and is helpful for the subsequent treatment scheme. 131I can serve as a part of radical treatment for small recurrent foci, postoperative adjuvant treatment for resectable lesions and palliative treatment for unresectable ones. This paper reviews the value of 131I treatment as the adjuvant therapy followed by reoperation in recurrent PTC.

وصف الملف: electronic resource

العلاقة: http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2022.21.1214Test; https://doaj.org/toc/1000-8578Test

الوصول الحر: https://doaj.org/article/722bd1f71c914681bbaeba7a8c3810e5Test

المؤلفون: Han, Hongzhang, Jing, Zhengjun

المصدر: Tehnički vjesnik ; ISSN 1330-3651 (Print) ; ISSN 1848-6339 (Online) ; Volume 30 ; Issue 4

مصطلحات موضوعية: hurst index, network anomaly, node positioning, self similarity, wireless sensor

الوصف: Aiming at the problems of poor detection effect, high rate of missed detection and high rate of false detection in traditional methods, an anomaly detection method for wireless sensor networks based on improved GM model is proposed. The multilateral measurement method is used to locate the nodes in the wireless sensor network, and the state tracking of the running track of the located nodes is carried out. According to the tracking results, the self similarity between nodes is measured by Hurst index. Based on the measurement results, the improved GM model is used to predict the abnormal nodes. The abnormal values of the wireless sensor network are calculated by the distance between adjacent points, and the state of the current node is judged, this completes the anomaly detection of wireless sensor networks. The experimental results show that the proposed method is effective in anomaly detection of wireless sensor networks, and the rate of missed detection and false detection is low.

وصف الملف: application/pdf

العلاقة: https://hrcak.srce.hr/305492Test

الإتاحة: https://doi.org/10.17559/TV-20230228000387Test
https://hrcak.srce.hr/305492Test
https://hrcak.srce.hr/file/440477Test

المؤلفون: Cao, Xiangyu, Jing, Zhengjun, Zhao, Xiaorong, Xu, Xiaolong

المصدر: International Journal of Network Management ; ISSN 1055-7148 1099-1190

الوصف: Summary With the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer‐to‐order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security‐enhanced predictive maintenance scheme specifically designed for ETO‐type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications.

الإتاحة: https://doi.org/10.1002/nem.2263Test

المؤلفون: Zhang, Lijun, Zhang, Xu, Wang, Kaifei, Gan, Zhenwei, Liu, Shibo, Liu, Xiao, Jing, Zhengjun, Cui, Xudong, Lu, Jiahui, Liu, Jing

المساهمون: Natural Science Foundation of Shandong Province

المصدر: Journal of Bionic Engineering ; ISSN 1672-6529 2543-2141

مصطلحات موضوعية: Bioengineering, Biophysics, Biotechnology

الإتاحة: https://doi.org/10.1007/s42235-023-00465-4Test

المؤلفون: Nie, Pengli, Xu, Guangquan, Jiao, Litao, Liu, Shaoying, Liu, Jian, Meng, Weizhi, Wu, Hongyue, Feng, Meiqi, Wang, Weizhe, Jing, Zhengjun, Zheng, Xi

المصدر: Nie , P , Xu , G , Jiao , L , Liu , S , Liu , J , Meng , W , Wu , H , Feng , M , Wang , W , Jing , Z & Zheng , X 2021 , ' Sparse Trust Data Mining ' , IEEE Transactions on Information Forensics and Security , vol. 16 , no. 99 , pp. 4559-4573 . https://doi.org/10.1109/TIFS.2021.3109412Test

مصطلحات موضوعية: Anti-sparsification, Recommendation system, Sparse trust, Trust model

الوصف: As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

وصف الملف: application/pdf

العلاقة: https://orbit.dtu.dk/en/publications/d5416ced-1fb2-4571-a6a5-747ca9615325Test

الإتاحة: https://doi.org/10.1109/TIFS.2021.3109412Test
https://orbit.dtu.dk/en/publications/d5416ced-1fb2-4571-a6a5-747ca9615325Test
https://backend.orbit.dtu.dk/ws/files/259137906/HKKR_Sparse_Trust_Data_Mining.pdfTest

المؤلفون: Xu, Xiaolong, Jing, Zhengjun, Liu, Xingyu, Shao, Guandi, Cai, Qiuru

المصدر: 2023 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)

الإتاحة: https://doi.org/10.1109/ispce-asia60405.2023.10366028Test
http://xplorestaging.ieee.org/ielx7/10365710/10365749/10366028.pdf?arnumber=10366028Test