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Yang Zhao,Weiwen Deng,Jian Wu,Rui He 한국자동차공학회 2017 International journal of automotive technology Vol.18 No.4
This paper proposes a constrained optimization-based torque control allocation method aimed to improve energy efficiency, and thus, driving range for electric vehicles. In the proposed method, the cost function is defined not only to achieve desired yaw moment for vehicle handling and stability, but also to minimize power losses for energy efficiency. The particular attention is paid to the power losses due to tire slips both longitudinally and laterally. The constraints are also set based on thorough investigation on various causes of power disppation such that the torque is allocated with restraint to use regenerative braking in its maximum capacity. The proposed control allocation method has been tested and verified to be effective on energy efficiency improvement through both simulation and experiment under various driving maneuvers.
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He, Zhen,Dong, Tingting,Wu, Weiwen,Chen, Wen,Liu, Xian,Li, Liangjun The Korean Society of Plant Pathology 2019 Plant Pathology Journal Vol.35 No.5
Odontoglossum ringspot virus (ORSV) is a member of the genus Tobamovirus. It is one of the most prevalent viruses infecting orchids worldwide. Earlier studies reported the genetic variability of ORSV isolates from Korea and China. However, the evolutionary rate, timescale, and phylogeographical analyses of ORSV were unclear. Twenty-one coat protein (CP) gene sequences of ORSV were determined in this study, and used them together with 145 CP sequences obtained from GenBank to infer the genetic diversities, evolutionary rate, timescale and migration of ORSV populations. Evolutionary rate of ORSV populations was $1.25{\times}10^{-3}nucleotides/site/y$. The most recent common ancestors came from 30 year ago (95% confidence intervals, 26-40). Based on CP gene, ORSV migrated from mainland China and South Korea to Taiwan island, Germany, Australia, Singapore, and Indonesia, and it also circulated within east Asia. Our study is the first attempt to evaluate the evolutionary rates, timescales and migration dynamics of ORSV.
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Zhen He,Tingting Dong,Weiwen Wu,Wen Chen,Xian Liu,Liangjun Li 한국식물병리학회 2019 Plant Pathology Journal Vol.35 No.5
Odontoglossum ringspot virus (ORSV) is a member of the genus Tobamovirus. It is one of the most prevalent viruses infecting orchids worldwide. Earlier studies reported the genetic variability of ORSV isolates from Korea and China. However, the evolutionary rate, timescale, and phylogeographical analyses of ORSV were unclear. Twenty-one coat protein (CP) gene sequences of ORSV were determined in this study, and used them together with 145 CP sequences obtained from GenBank to infer the genetic diversities, evolutionary rate, timescale and migration of ORSV populations. Evolutionary rate of ORSV populations was 1.25 × 10−3 nucleotides/site/y. The most recent common ancestors came from 30 year ago (95% confidence intervals, 26- 40). Based on CP gene, ORSV migrated from mainland China and South Korea to Taiwan island, Germany, Australia, Singapore, and Indonesia, and it also circulated within east Asia. Our study is the first attempt to evaluate the evolutionary rates, timescales and migration dynamics of ORSV.
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Sun Bohua,Deng Weiwen,Wu Jian,Li Yaxin,Wang Jinsong 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.6
Autonomous vehicles are aiming at improving driving safety and comfort. They need to perform socially accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. What’s more, understanding human drivers’ driving styles that make the systems more human-like or personalized is the key to improve the system performance, in particular, the acceptance and adaption of autonomous vehicles to human passengers. In this study, a personalized intention-aware autonomous driving strategy is proposed. An online driving style identification is proposed based on double-level Multi-dimension Gaussian Hidden Markov Process (MGHMP) with arbitration mechanism and evaluated in field test. A Mixed Observable Markov Decision Process (MOMDP) is built to model the general personalized intention-aware framework. A human-like policy generation mechanism is used to generate the possible candidates to overcome the difficulty in solving MOMDP. The index of surrounding vehicles’ intention of the upper-level MGHMP is updated during each prediction time step. The weighting factors of the reward function are configured with the identification result of lower-level MGHMP. The personalized intention-aware autonomous driving strategy is evaluated on a Real-Time Intelligent Simulation Platform. Results show that the proposed strategy can achieve the online identification accuracy above 95 % and for personalized autonomous driving in scenarios mixed with human-driven vehicles with uncertain intentions.
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