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SSRN 2024年

作者： Xie, Guojin Qin, Hongmao Xie, Heping Bian, Yougang Ding, Rongjun State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Hunan Changsha410082 China School of Mechanical and Automotive Engineering Guangxi University of Science and Technology Guangxi Liuzhou545616 China Wuxi Intelligent Control Research Institute of Hunan University Wuxi214115 China Xuzhou XCMG Mining Machinery Co. Ltd. Xuzhou210009 China

Based on the basic principles of model predictive control, the energy management and optimal control problems of large-scale mining hybrid dump trucks are studied. This paper presents an artificial neural network model to predict the vehicle speed and road slope in the predictive horizon. Based on the predicted information and the vehicle dynamics model, the future power demand of the vehicle is calculated, and a fuel economy optimization objective function is established to minimize the set objective function value under the condition of multiple system constraints, which is solved through dynamic programming algorithm (DP) rolling optimization in the finite predictive horizon to realize optimal energy allocation between the engine generator and battery pack. The vehicle speed and road slope are predicted in 5 different horizons to determine the optimal prediction horizon under the conditions of flat and slope roads. A prediction model is constructed, and combined with the vehicle system and dynamic model, a DP-MPC energy management strategy for series hybrid electric vehicle (SHEV) is designed, and the advantages and disadvantages of the strategy are evaluated through offline simulation testing. Finally, the effectiveness and real-time performance of the energy management strategy are verified through HIL experiments under different operating conditions. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Cyanometallate Framework Templated Synthesis of Hierarchically Porous La(Oh)3 for High-Efficient and Stable Phosphorus Removal from Tailwater

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SSRN 2023年

作者： Jing, Xiaoxu Zhang, Jing Li, Yungui Li, Qingqing Shen, Yi Zhang, Suorong Fang, Qile Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Sichuan Provincial Sci-Tech Cooperation Base of Low-cost Wastewater Treatment Technology Department of Environmental Engineering Southwest University of Science and Technology Mianyang621010 China Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province College of Environment Zhejiang University of Technology Hangzhou310032 China Chongqing Feiyang Measurement and Control Technology Research Institute Co. Ltd Chongqing402760 China

Lanthanum (La)-based hydroxides have been recognized as promising candidates for dephosphorization due to their high affinity and selectivity for phosphate adsorption. However, the development of porous La(OH)3 materials with enriching active sites and high structural stability remains a challenge in tailwater treatment. Here we developed a novel self-templating method based on an ion-exchange mechanism to construct hierarchically porous La(OH)3, where the OH− of an alkali conversion reagent replaces the [Fe(CN)6]4− cluster within the LaFe cyanometalate (CM) precursor, simultaneously completing the hydroxide conversion and pore-creating processes. The obtained hierarchically porous La(OH)3 shows excellent structural stability with no ion leaching and can maintain its primary microsphere structure and secondary nanoneedle morphology after multiple cycles of application. Due to the abundant micropores/mesopores, there are fully exposed active sites inside and outside the porous La(OH)3 microsphere, giving it an excellent phosphate adsorption capacity (maximum capacity of 163.6 mg P/g). Its high selectivity and environmental adaptability make it a favorable adsorbent for aquaculture tailwater treatment that can rapidly reduce the dissolved inorganic phosphorus level to below 0.05 mg P/L to meet the mariculture tailwater discharge standard in a wide range of salinities and temperatures. The information gleaned in this study provides valuable guidance for the materials design of porous La-based hydroxides for phosphorus removal. © 2023, The Authors. All rights reserved.

关键词： Aquaculture

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Model Predictive control Assisted Energy Management Strategy for Hybrid Mining Dump Trucks Based on Speed and Slope Prediction

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SSRN 2024年

关键词： Model predictive control

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Research on Active Disturbance Rejection control of Hydraulic Servo System in Complex Initial States

Research on Active Disturbance Rejection Control of Hydrauli...

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第40届中国控制会议

作者： Fanliang Meng Hao Yan School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control TechnologyBeijing Jiaotong UniversityMinistry of Education

In this paper,an improved active disturbance rejection control（ADRC） with variable gain extended state observer（ESO） is designed to control the hydraulic servo system with complex initial states.A method arranging a smooth increase process for the gain of the extended state observer is *** method using Lyapunov function proves that by setting the appropriate gain coefficient of the extended state observer,the asymptotic stability of the extended state observer can be ensured,and the sensitivity of the extended state observer to the initial observation error is *** simulation results show that,compared with the fixed-gain expanded state observer,the variable-gain expanded state observer has a more stable control process and better convergence effect.

关键词： Hydraulic Servo System Complex Initial State ADRC ESO Variable Gain

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An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

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Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things

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Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

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Chinese Journal of Mechanical Engineering 2019年第1期32卷 79-89页

作者： Dezun Zhao Jianyong Li Weidong Cheng Zhiyang He Department of Mechanical Engineering Tsinghua University School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

关键词： Bearing fault diagnosis Generalized demodulation transform Nonstationary conditions Gear noise

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New Sensing Technologies for Monitoring Machinery,Structures,and manufacturing Processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao Qingbo He Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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Type-II topological metals

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Frontiers of physics 2020年第4期15卷 101-112页

作者： Si Li Zhi-Ming Yu Yugui Yao Shengyuan A.Yang Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innouation Center for Quantum Efects and ApplicationsHunan Norrmal UniversityChangsha 410081China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement(MOE) Beijing Key Lab of Nanophotonics&Utrafine Optoelectronic Systemsand School of PhysicsBeijing Institute of TechnologyBeijing 100081China

Topological metals(TMs)are a kind of special metallic materials,which feature nontrivial band Cross-ings near the Fermi energy,giving rise to peculiar quasiparticle *** can be classified based on the characteristics of these band *** example,according to the dimensionality of the crossing,TMs can be classifed into nodal-point,nodal-line,and nodal-surface *** important property is the type of *** to degree of the tilt of the local dispersion around the crossing,we have typeI and type-II *** leads to significant distinctions in the physical properties of the materials,owing to their contrasting Fermi surface *** this article,we briefly review the recent advances in this research direction,focusing on the concepts,the physical properties,and the material realizations of the type-Il nodal-point and nodal-line TMs.

关键词： type-II nodal point nodal line topological metals

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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Forming mechanisms based simulation and prediction of grinding surface roughness for abrasive belt rail grinding 5

Forming mechanisms based simulation and prediction of grindi...

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5th CIRP Conference on Surface Integrity, CSI 2020

作者： Zhao, Chaoyue Li, Jianyong Wang, Wenxi School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Mechanical Transmissions Chongqing University Chongqing400044 China Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education China

Abrasive belt rail grinding (ABRG) was recently applied to repair the contour of the rail and eliminate the surface damage of the rail. Besides, it is desirable to not only suppress the occurrence of new damage but also to improve driving comfort and reduce wheel-rail wear. The surface roughness of the rail directly affects the contact noise, vibration, friction, and wear of the wheel-rail. However, due to the complicated contact state in belt grinding, the surface roughness forming mechanisms are still unclear. Therefore, it is necessary to study the influence of ABRG on surface roughness and to obtain a prediction method for roughness. In this paper, the initial morphology of the abrasive belt was created by using 2D digital filtering technology. The discretization method was used to simplify the entire belt grinding process into several local surface grinding processes carrying different contact pressure. The grinding depth of the abrasive particles on the local abrasive belt at various times was calculated, and the envelope of the cutting edge was induced according to the grinding depth of the effective abrasive grits. The roughness simulation of ABRG based on the forming mechanism was realized, and the 2D roughness of the processed surface was investigated. The good consistency between simulation and experimental results successfully proved the validity of the proposed method, and they both show that the contact force has a significant positive correlation to both Ra and Rsm. © 2020 The Author(s).

关键词： Surface roughness

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