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RISS 인기검색어
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- 저자 : Lijin Han (검색결과 5 건)
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Huaiqian Bao,Lijin Song,Zongzhen Zhang,Baokun Han,Jinrui Wang,Junqing Ma,Xingwang Jiang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9
This study proposes a framework for bearing remaining useful life (RUL) prediction that uses multidomain features and a dual-attention mechanism (DAM). First, sparsity measures are introduced as new feature parameters to comprehensively and accurately extract the degradation features of bearings. Second, a long short-term memory network integrated with DAM is applied for RUL prediction. DAM simultaneously applies the attention mechanism to the time steps and feature dimension to increase the attention to important information and enhance the prediction performance of the network. Third, a pseudo-normalization method is proposed to solve the problem of unknown bearing test data in actual working conditions under the premise of retaining the original data characteristics and RUL prediction accuracy as much as possible. Lastly, the proposed framework is experimentally proven on public datasets and compared with other methods to prove its feasibility and effectiveness.
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Cong Liu,Hui Liu,Lijin Han,Changle Xiang 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.1
This paper proposes a novel integrated multi-algorithm fusion localization and trajectory tracking framework for autonomous vehicles under extreme conditions with non-Gaussian noises. Firstly, in order to solve the problem that GPS signals are interfered with non-Gaussian noises or lost, a localization method based on Particle Filter (PF) is designed, which takes full advantage of the reference objects position information and vehicle driving state information, thus realizing the self-localization for high-speed autonomous vehicles. Besides, considering the accumulated errors of the model-driven Inertial Measurement Unit (IMU) in the long-horizon positioning prediction, an online future driving state prediction algorithm based on multi-order variable-step Markov model (MM) is proposed to calculate the future vehicle position in scenarios without reference. The fusion of these two methods can give full play to their respective advantages, thus improving the accuracy and robustness of the whole localization algorithm in scenes with non-Gaussian noises. Then, the location information and the future driving state are applied to the trajectory tracking controller based on adaptive model predictive control (AMPC). Finally, the CarSim-Matlab/Simulink cGAOo-simulations results show the effectiveness of the proposed framework when GPS signal is interfered with non-Gaussian noises, which further improve the positioning accuracy and autonomous tracking stability.
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Control Strategy for an Electromechanical Transmission Vehicle Based on a Double Markov Process
Liu Hui,Xu Riming,Han Lijin,Xiong Shanshan 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.3
Based on an analysis of the driving demand and system dynamics of heavy-duty vehicles equipped with electromechanical transmission (EMT), a double Markov model is put forward to represent drivers’ power demand for driving and electricity. Transfer probability matrices are calculated by utilizing the maximum likelihood estimation method. A power distribution control strategy based on stochastic dynamic programming (SDP) is proposed. With economy being the optimization goal, the model for power allocation control based on SDP is established while regarding the engine torque, motor speeds, vehicle speed and state of charge (SOC) as state variables’ engine speed and motor torques as control variables’ and power demands as interference variables. The SDP problem is solved by an improved policy iteration algorithm based on value iteration and policy iteration algorithms.
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Optimal Resource Allocation for Packet Delay Minimization in Multi-Layer UAV Networks
Li, Jin,Han, Youngnam IEEE 2017 IEEE Communications Letters Vol.21 No.3
<P>Research and development of heterogeneous small cell in cellular network accommodates the proliferation of data-hungry devices and applications. Meanwhile, for challenges in providing high-data-rate transmission in poor coverage area, utilizing unmanned aerial vehicles (UAVs) provides a promising solution attracting tremendous attention. However, tight integration of UAVs creates an obstruction in existing network to acquire high efficient resource utilization, which needs investigation in 3D network architecture. In this letter, we propose a resource allocation optimization mechanism to minimize mean packet transmission delay in 3D cellular network with multi-layer UAVs. Numerical results demonstrate effectiveness of the proposed algorithm, where optimal spectrum and power allocation can provide minimum packet transmission delay.</P>
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A Traffic Service Scheme for Delay Minimization in Multi-Layer UAV Networks
Li, Jin,Han, Youngnam IEEE 2018 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY Vol.67 No.6
<P>Research and development of unmanned aerial vehicles (UAVs) have accommodated the challenges in providing satisfactory coverage and rate performance to data-hungry devices and applications, especially in urgent situations and remote environments. Furthermore, a multi-layer UAV network divides the total distance between a user equipment and macrocell base station into multiple hops, which enables data to transmit at a higher bit rate over each hop with a lower total power consumption. However, multi-hopping through a multi-layer UAV network causes delay penalty to packets. In this correspondence paper, we classify data packets in UAVs at each layer of a multi-layer UAV network into incumbent packets and relayed packets, and propose a traffic service scheme for variable classes of packets. Additionally, packet delay for each class of packets at each layer of the multi-layer UAV network is investigated and minimum total packet delay is achieved by optimally allocating spectrum and power resources among layers of the UAV network. Numerical results demonstrate the effectiveness of the proposed algorithm.</P>
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