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- 저자 : Guiliang Feng (검색결과 3 건)
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A Novel Self-Learning Differential Evolution Algorithm in Two-State Dynamic Optimization
Feng Guiliang,Cao Ning,Zhang Xiao 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.12
In this paper we propose a novel differential evolution algorithm based on self-learning, in order to improve the environment adaptive ability of the population in dynamic optimization. The proposed algorithm can monitor the environment changes using re-evaluation of individuals. We direct the population evolution based on the current best individual and another two random individuals, so that the convergence speed is faster and the diversity of the population is maintained. In this way we may reduce the influence from the frequent environment changes. Testing on six dynamic functions, we study the influences caused by period and dimensions. We also compared the proposed algorithm with existing algorithms, the experimental results show that our algorithm has a better environment adaptive ability and achieves better optimization result.
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Researches on the Prototype Implementation of Visual Data Mining Techniques
Guiliang Feng,Zhonghua Li,LianChun Zhang 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6
This paper describes the core functional components and a visualization module implementation process of visual data mining technology prototype system. The first part introduces the implementation technology of visual data mining technology prototype system and then describes the overall design of the architecture and features of visual data mining technology prototype system; Then paper describes the common components in the visualization of data mining technology prototype system. Research achievements of this article provides a useful reference to optimize visual data mining techniques.
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Key Technology for Food-Safety Traceability Based on a Combined Two-Dimensional Code
Zhonghua Li,Xinghua Sun,Ting Yan,Dong Yang,Guiliang Feng 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.2
Current food-traceability platforms suffer from problems such as inconsistent traceability standards, a lack ofpublic credibility, and slow access to data. In this work, a combined code and identification method wasdesigned that can achieve more secure product traceability using the dual anti-counterfeiting technology of aQR code and a hidden code. When the QR code is blurry, the hidden code can still be used to effectively identifyfood information. Based on this combined code, a food-safety traceability platform was developed. Theplatform follows unified encoding standards and provides standardized interfaces. Based on this innovation,the platform not only can serve individual food-traceability systems development, but also connect existingtraceability systems. These will help to solve the problems such as non-standard traceability content,inconsistent processes, and incompatible system software. The experimental results show that the combinedcode has higher accuracy. The food-safety traceability platform based on the combined code improves thesafety of the traceability process and the integrity of the traceability information. The innovation of this paperis invoking the combined code united the QR code‘s rapidity and the hidden code‘s reliability, developing aplatform that uses a unified coding standard and provides a standardized interface to resolve the differencesbetween multi-food-traceability systems. Among similar systems, it is the only one that has been connected tothe national QR code identification platform. The project has made profits and has significant economic andsocial benefits.
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