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RISS 인기검색어
- 검색키워드
- 저자 : Yi‑Pan Zeng (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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A Comprehensive Review of Emerging Computational Methods for Gene Identification
( Ning Yu ),( Zeng Yu ),( Bing Li ),( Feng Gu ),( Yi Pan ) 한국정보처리학회 2016 Journal of information processing systems Vol.12 No.1
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
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Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches
( Ning Yu ),( Zeng Yu ),( Feng Gu ),( Tianrui Li ),( Xinmin Tian ),( Yi Pan ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.2
Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.
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A Comprehensive Review of Emerging Computational Methods for Gene Identification
Yu, Ning,Yu, Zeng,Li, Bing,Gu, Feng,Pan, Yi Korea Information Processing Society 2016 Journal of information processing systems Vol.12 No.1
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
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Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches
Yu, Ning,Yu, Zeng,Gu, Feng,Li, Tianrui,Tian, Xinmin,Pan, Yi Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.2
Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.
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An intelligent method to design die profile for rubber forming of complex curved flange part
Ling‑Yun Zhang,Shuai Zhou,Tian‑Zhang Zhao,Yi‑Pan Zeng 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.1
Rubber forming is an important forming process for the manufacture of aircraft sheet metal parts. The springback is one of the main defects in rubber forming. Classical springback compensation by displacement adjustment method using finite simulation is not satisfactory. In this research, the algorithms of compensating the arc and flange surface of complex curved flange with correction formula are proposed by experiment. The correction formula was developed based on the CATIA V5 R19 using Component Application Architecture. Compensate profile is presented including surface pick up, line pick up, division, compensation, extending, and trimming. The die profile of part with complex curved flanges in aircraft could be designed rapidly. It was found that the forming pressure has a little effect on the springback. This is within the tolerance limits of the part. The results reveal the method can achieve the industrial part precisely. The method is demonstrated on an aircraft wing rib part.
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