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International Conference on Intelligent Transportation

作者： Ning Wang Xiao Chen Jonas Mårtensson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Longitudinal and lateral motion planning poses a significant challenge to achieving full autonomy in automated vehicles. This work focuses on studying the motion planning problem for automated vehicles specifically in a highwaymerging scenario. The problem is modeled as an infinite horizon optimal control problem, taking into account finite control sets for the ego agents and uncontrolled state components of surrounding traffic. For this type of control problem, obtaining a real-time solution that meets both high safety and efficiency requirements can be difficult. In this study, we employ the rollout approach, which involves online optimization following the simulation of a known baseline policy instead of relying solely on extensive offline training. We compare the performance of one and multistep lookahead rollout algorithms against several state-of-the-art benchmark policies in simulation. The simulation results indicate that the rollout algorithm significantly enhances safety while simultaneously maintaining a high average speed within the merging scenario. Furthermore, we conduct simulation studies to assess the rollout methods in adapting to varying behaviors of surrounding vehicles. Additionally, we investigate the impact of different horizon settings and the introduction of terminal cost approximation.

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Water quality prediction of copper-molybdenum miningbeneficiation wastewater based on the PSO-SVR model

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Frontiers of Environmental Science & engineering 2023年第8期17卷 81-94页

作者： Xiaohua Fu Qingxing Zheng Guomin Jiang Kallol Roy Lei Huang Chang Liu Kun Li Honglei Chen Xinyu Song Jianyu Chen Zhenxing Wang Ecological Environment Management and Assessment Center Central South University of Forestry and TechnologyChangsha 410004China State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control South China Institute of Environmental SciencesMinistry of Ecology and EnvironmentGuangzhou 510655China Chinese Non-Ferrous Industrial Engineering Center of Pollution Control Technology&Equipment Science Environment Protection Co.Ltd.Changsha 410036China Institute of Computer Science University of TartuTartu 51009Estonia School of Environmental Science and Engineering Guangzhou UniversityGuangzhou 510006China A.B Freeman School of Business Tulane UniversityNew OrleansLA 70118USA Guangzhou Huacai Environmental Protection Technology Co. Ltd.Guangzhou 511480China

The mining-beneficiation wastewater treatment is highly complex and *** factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater *** oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation *** COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant *** reduces environmental risk and significantly improves the discharge quality of *** paper presents a novel AI algorithm PSO-SVR,to predict water *** optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD *** generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is *** compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation *** is because of the less overfitting tendency of PSO-SVR compared with neural network *** proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater *** addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

关键词： Chemical oxygen demand Mining-beneficiation wastewater treatment Particle swarm optimization Support vector regression Artificial neural network

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Efficient Supernet Training Using Path Parallelism

Efficient Supernet Training Using Path Parallelism

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IEEE Symposium on High-Performance computer Architecture

作者： Ying Xu Long Cheng Xuyi Cai Xiaohan Ma Weiwei Chen Lei Zhang Ying Wang Chinese Academy of Sciences Institute of Computing Technology School of Control and Computer Engineering North China Electric Power University

Compared to conventional neural networks, training a supernet for Neural Architecture Search (NAS) is very time consuming. Although current works have demonstrated that parallel computing can significantly speed up the training process, almost all of their parallelism still follow the conventional data- and model-based paradigms, which actually face performance issues in both computation and inter-node communication of the supernet training. To further improve the performance of current methods, we discover the unique path-parallelism that exists in supernet training, and proposed a novel training approach designed specifically for supernet. In detail, we focus on analyzing path correlations between subnets in a supernet and exploiting effective path-merging methods to reduce redundant computations and communications raised by concurrent subnets. Moreover, we also try to combine the proposed path parallelism with traditional intra-subnet parallelism to perform multi-level parallelization to further optimize the parallel performance. We present the detailed design and implementation of our method, and our experimental results show that our proposed approach can achieve up to 3.2x end-to-end speedup over conventional parallel training solutions, and 1.46x–5.78x speedup compared to the state-of-art supernet training frameworks.

关键词： Training Correlation Computational modeling Neural networks Merging Graphics processing units computer architecture

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Unknown Input Observers Breaking Confidentiality of controller States

Unknown Input Observers Breaking Confidentiality of Controll...

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IEEE Conference on Decision and control

作者： Enno Breukelman Henrik Sandberg School of Electrical Engineering and Computer Science Division of Decision and Control Systems KTH Royal Institute of Technology Malvinas väg 10 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Driven by ubiquitous digitalization and cyberattacks on critical infrastructure, there is a high interest in research on the security of cyber-physical systems. If an attacker gains access to protected and sensitive information, such as the internal states of a control system, this is considered a breach of confidentiality. Access to sensitive information can be the first step in a larger cyber-attack scheme, such as a stealthy false data injection attack. Considering process and measurement noise in the plant, existing research investigated when an attacker equipped with a Kalman filter can perfectly estimate the internal controller states if the attacker has access to plant measurements and all model parameters. For this estimate to converge, the controller is required to have stable poles. In this paper, we show that if the attacker has access to the control inputs instead of the plant measurements, the controller needs to have stable zeros. Additionally, we demonstrate that an attacker equipped with an Unknown Input Observer, using tools from delayed system inversion, can get a delayed yet perfect estimate of the controller states from the control inputs without knowledge of the plant’s parameters and noise characteristics. Lastly, we present simulation results from a three-tank system to showcase the differences in controller state estimation.

关键词： Simulation Noise Process control Observers Cyber-physical systems Critical infrastructure Noise measurement Security Kalman filters Cyberattack

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Characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of an acoustic emission waveguide through CFD-DEM modelling and electrostatic sensing

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Measurement: Sensors 2024年 38卷

作者： Zeng, Xingxing Yan, Yong Qian, Xiangchen Lu, Yunlong School of Control and Computer Engineering North China Electric Power University Beijing China Hangzhou International Innovation Institute Beihang University Hangzhou China

Characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of the waveguide protruded into the flow is essential for measuring the mass flow rate and size distribution of particles using acoustic emission methods. Computational fluid dynamics-discrete element method (CFD-DEM) is employed in this paper to simulate the collisions between particles and waveguides with a varying protrusion depth in a vertical pipe. Then an instrumentation system incorporating electrostatic sensors is developed to measure the particle velocity and concentration in the upstream of the waveguide. Experimental tests were carried out on a particle flow test rig. The experimental and modelling results show that the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of approximately 30%–35 %, and an increase in particle concentration of approximately 3%–20 %. The experimental results show a close agreement with the modelling data. © 2024

关键词： Computational fluid dynamics

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Modular Redesign of Mechatronic Systems: Formulation of Module Specifications Guaranteeing System Dynamics Specifications

arXiv

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arXiv 2024年

作者： Janssen, Lars A.L. Fey, Rob H.B. Besselink, Bart van de Wouw, Nathan Dynamics & Control Department of Mechanical Engineering Eindhoven University of Technology Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (represented by frequency response functions (FRFs)) of the redesigned system are defined with respect to the original system model, which already satisfies system specifications. Second, these allowed changes in the overall system dynamics (or system redesign specifications) are automatically translated to dynamics (FRF) specifications on module level that, when satisfied, guarantee overall system dynamics (FRF) specifications. This modularity in specification management supports local analysis and verification of module design changes, enabling design teams to work in parallel without the need to iteratively rebuild the system model to check fulfilment of system FRF specifications. A modular redesign process results that shortens time-to-market and decreases redesign costs. The framework’s effectiveness is demonstrated through three examples of increasing complexity, highlighting its potential to enable modular mechatronic system (re)design. © 2024, CC BY-NC-ND.

关键词： Specifications

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Self-Tuning Tube-based Model Predictive control

Self-Tuning Tube-based Model Predictive Control

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American control Conference (ACC)

作者： Damianos Tranos Alessio Russo Alexandre Proutiere Division of Decision and Control Systems School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden

We present Self-Tuning Tube-based Model Predictive control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm’s asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.

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Invariant Safe Contingency Model Predictive control for Intersection Coordination of Mixed Traffic

Invariant Safe Contingency Model Predictive Control for Inte...

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International Conference on Intelligent Transportation

作者： Xiao Chen Jonas Mårtensson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper addresses the coordination challenge at intersections of mixed traffic involving both Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). To strike a balance between coordination performance and safety guarantees, we propose an invariant safe Contingency Model Predictive control (CMPC) framework. The CMPC framework incorporates two parallel horizons for the ego vehicle: a nominal horizon optimized for performance based on the most likely prediction of the opponent HDV, and a contingency horizon designed to maintain an invariant safe backup plan for emergencies. In the contingency horizon, we consider the worst-case behavior of the human driver and formulate safety constraints using the forward reachable sets of the HDV within the planning horizon. These safety constraints are complemented by maximal invariant safe sets as terminal constraints. The two horizons are tied together by enforcing equality of the feedback inputs at the beginning of the horizons. We provide theoretical evidence supporting the recursive feasibility and persistent performance improvement of the invariant safe CMPC compared to our previously proposed nominal invariant safe Model Predictive control (MPC). Through simulation studies, we evaluate the proposed method. The simulation results demonstrate that the CMPC approach achieves enhanced performance by reducing conservatism while simultaneously preserving the invariant safety property.

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Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline

arXiv

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arXiv 2023年

作者： Lang, Xiaolei Chen, Chao Tang, Kai Ma, Yukai Lv, Jiajun Liu, Yong Zuo, Xingxing The Institute of Cyber-Systems and Control Zhejiang University Hangzhou China The Department of Computer Engineering Technical University of Munich Germany

In this paper, we propose an efficient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers significant advantages in terms of achieving real-time efficiency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable efficient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and efficacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://***/APRIL-ZJU/Coco-LIC. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Testing and Development of a Multi-Object Tracker Based on Deep Learning Techniques 1st

Testing and Development of a Multi-Object Tracker Based on D...

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Rajput, Shaurya Verma, Sourabh Verma, Om Prakash Gupta, Himanshu Sharma, Tarun Ateya, Abdelhamied A. Centre for Artificial Intelligence Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Instrumentation and Control Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science-Internet of Things ABES Institute of Technology Ghaziabad201009 India University Meerut India Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (纸本)9789819981342

In today’s scenario, computer vision is one of the fundamental research areas of artificial intelligence including object detection and object tracking which are the upcoming trends. In the present work, the TransTrack model has been reimplemented which includes ResNet50 as a backbone network. Further, to improve the overall performance of the TransTrack model, some recently developed backbones such as ResNet152 and SwinT have been incorporated. Moreover, to test the optimum training of the existing model, it has been retrained for higher epochs (such as 160 epochs). However, the accuracy of the existing tracker has improved significantly as the number of epochs increases but, at the same time, it increases the computational complexity. On the contrary, when the existing tracker (yields 68.4% accuracy with ResNet50) is incorporated with ResNet152 and SwinT, the performance has significantly improved (71% for ResNet152 and 71.6% for SwinT) by 1.7% and 2.1%, respectively. The mentioned analysis is enough to prove that the incorporation of a more efficient backbone has the potential to improve the performance of trackers. In the future, the newly introduced backbone may open the window to improve the performance of multi-object tracking (MOT). © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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