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 : Zhu Changsheng (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Changsheng Zhu,Mingfang Zhu,Jieqiong Liu,Li Feng 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.10
By coupling the phase field model of dendrite growth with the Lattice Boltzmann method (LBM), a Phase field LBM model (PFLBM) that can simulate 3D dendrite non-isothermal growing from the under-cooled melt of a pure substance in forced flow has been established. Taking high-purity succinonitrile (SCN) as an example, the effects of flow on 3D dendritic growth were studied. The calculation efficiency of LBM and SOLA algorithm was compared under the same condition. The results show that, with the PF-LBM model, the effects of forced flow on 3D dendrite growth morphology and the dendrite asymmetric growth can be quantitatively modeled in large scale, and the dendrite growth is consistent with the crystallization theory. The LBM algorithm overcomes the weakness of a large amount of calculation and slow computation speed that exists in the traditional algorithm, and it achieves a speedup of 11.7 when the calculation region size is 200 3 .
-
Roughness and micro pit defects on surface of SUS 430 stainless steel strip in cold rolling process
Li, Changsheng,Zhu, Tao,Fu, Bo,Li, Youyuan Techno-Press 2015 Advances in materials research Vol.4 No.4
Experiment on roughness and micro pit defects of SUS 430 ferrite stainless steel was investigated in laboratory. The relation between roughness and glossiness with reduction in height, roll surface roughness, emulsion parameters was analyzed. The surface morphology of micro pit defects was observed by SEM, and the effects of micro pit defects on rolling reduction, roll surface roughness, emulsion parameters, lubrication oil in deformation zone and work roll diameter were discussed. With the increasing of reduction ratio strip surface roughness Ra(s), Rp(s) and Rv(s) were decreasing along rolling and width direction, the drop value in rolling direction was faster than that in width direction. The roughness and glossiness were obtained under emulsion concentration 3% and 6%, temperature $55^{\circ}C$ and $63^{\circ}C$, roll surface roughness $Ra(r)=0.5{\mu}m$, $Ra(r)=0.7{\mu}m$ and $Ra(r)=1.0{\mu}m$. The glossiness was declined rapidly when the micro defects ratio was above 23%. With the pass number increasing, the micro pit defects were reduced, uneven peak was decreased and gently along rolling direction. The micro pit defects were increased with the roll surface roughness increase. The defects ratio was declined with larger gradient at pass number 1 to 3, but gentle slope at pass number 4 to 5. When work roll diameter was small, bite angle was increasing, lubrication oil in micro pit of deformation zone was decreased, micro defects were decreased, and glossiness value on the surface of strip was increased.
-
A New CIGWO-Elman Hybrid Model for Power Load Forecasting
Hao Jie,Zhu Changsheng,Guo Xiuting 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
Time series forecasting is a common task that needs to be implemented in many engineering applications. In this paper, for the power load forecasting problem, we explore the advantages of the grey wolf optimization (GWO) algorithm for Elman network optimization. To avoid model complexity, the structure of the Elman network is simplifi ed to improve its training effi ciency. Then, a chaotic sequence and random cosine function are introduced into the GWO algorithm. In addition, the updating methods of individual positions in the particle swarm optimization (PSO) algorithm and diff erential evolution (DE) algorithm are used as references for improving the GWO algorithm. The new chaotic cosine inertial weights GWO (CIGWO) algorithm is used to optimize the parameters of the Elman network, and the CIGWO-Elman network model is formed. Finally, CIGWO-Elman is applied to the actual load data of a city in eastern China to realize short-term power load prediction. The results show that the proposed model has better predictive accuracy and real-time performance than those of other methods
-
FUEL-SAVING CONTROL STRATEGY FOR FUEL VEHICLES WITH DEEP REINFORCEMENT LEARNING AND COMPUTER VISION
Han Ling,Liu Guopeng,Zhang Hui,Fang Ruoyu,Zhu Changsheng 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.3
This study uses deep reinforcement learning (DRL) combined with computer vision technology to investigate vehicle fuel economy. In a driving cycle with car-following and traffic light scenarios, the vehicle fuel-saving control strategy based on DRL can realize the cooperative control of the engine and continuously variable transmission. The visual processing method of the convolutional neural network is used to extract available visual information from an on-board camera, and other types of information are obtained through the vehicle’s inherent sensor. The various detected types of information are further used as the state of DRL, and the fuel-saving control strategy is built. A Carla–Simulink co-simulation model is established to evaluate the proposed strategy. An urban road driving cycle and highway road driving cycle model with visual information is built in Carla, and the vehicle power system is constructed in Simulink. Results show that the fuel-saving control strategy based on DRL and computer vision achieves improved fuel economy. In addition, in the Carla–Simulink co-simulation model, the fuel-saving control strategy based on DRL and computer vision consumes an average time of 17.55 ms to output control actions, indicating its potential for use in real-time applications.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
