http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : He Jinghan (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids
Yiwen Le,Jinghan He 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.3
Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.
-
Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids
Le, Yiwen,He, Jinghan The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.3
Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.
-
Mirsaeidi Sohrab,Li Shangru,Devkota Subash,He Jinghan,Li Meng,Wang Xiaojun,Konstantinou Charalambos,Said Dalila Mat,Muttaqi Kashem M. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profi le, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from diff erent aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with diff erent weight values. Furthermore, the impact of enhancement in the number of DGs on diff erent aspects of power system performance is investigated. Finally, to increase the accuracy of the results, three diff erent nature-inspired optimization algorithms, i.e. Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are deployed, and their speed in approaching the global optimum is compared with each other. The simulation results on IEEE 14-bus system indicate that the proposed strategy not only can reinforce the overall network performance through reduction of active and reactive power losses, and voltage deviation but also lead to the improvement of network voltage profi le.
-
( Jing Han ),( Yong Sheng Tian ),( Jing Xu ),( Li Juan Wang ),( Bo Wang ),( Ri He Peng ),( Quan Hong Yao ) 한국미생물 · 생명공학회 2014 Journal of microbiology and biotechnology Vol.24 No.9
Glyphosate is the active component of the top-selling herbicide, the phytotoxicity of which is due to its inhibition of the shikimic acid pathway. 5-Enolpyruvylshikimate-3-phosphate synthase (EPSPS) is a key enzyme in the shikimic acid pathway. Glyphosate tolerance in plants can be achieved by the expression of a glyphosate-insensitive aroA gene (EPSPS). In this study, we used a PCR-based two-step DNA synthesis method to synthesize a new aroA gene (aroAR. leguminosarum) from Rhizobium leguminosarum. In vitro glyphosate sensitivity assays showed that aroAR. leguminosarum is glyphosate tolerant. The new gene was then expressed in E. coli and key kinetic values of the purified enzyme were determined. Furthermore, we transformed the aroA gene into Arabidopsis thaliana by the floral dip method. Transgenic Arabidopsis with the aroAR. leguminosarum gene was obtained to prove its potential use in developing glyphosate-resistant crops.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
