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Data-Driven Model Predictive control for Virtually Coupled Train Set Using Behavioral Systems Theory

Data-Driven Model Predictive Control for Virtually Coupled T...

引用

International Conference on Intelligent Transportation

作者： Xiaolin Luo Dongming Wang Tao Tang Yong Zhang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of California Riverside CA USA School of Traffic and Transportation Beijing Jiaotong University Beijing China State Key Laboratory of Advanced Rail Autonomous Operation School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Virtual coupling is effective to improve the flexibility and efficiency of railway services. It forms multiple train units as a virtually coupled train set (VCTS). To separate units safely by a minimal distance, relative-braking distance (RBD) is employed. However, it is numerically calculated without explicit models in practice, which makes the design of VCTS control approaches challenging. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach that deploys behavioral systems theory and does not need an explicit model of RBD. Specifically, Hankel matrices are constructed to formulate the unknown dynamics of VCTS, based on the prior-measured trajectories of VCTS operation. Then, using the past data and information received from the preceding unit, the controller of each unit is yielded by solving a computationally-efficient optimal control problem. Finally, experiments are conducted, and results show that our approach can stably operate VCTS, and saves by over 99% computation time than the nonlinear model predictive control approach.

关键词： computational modeling Predictive models Stability analysis Rail transportation Numerical models Trajectory Quadratic programming Oscillators Predictive control Sorting

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Distance and Angle Formation Scheme for Collision Avoidance of Differential-drive Mobile Robots

Distance and Angle Formation Scheme for Collision Avoidance ...

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ieee Colombian Conference on Automatic control (CCAC)

作者： Mario Ramírez-Neria Jaime González-Sierra Rodrigo Ramírez-Juárez Juan C. Tejada Alejandro Toro-Ossaba Adriana Noguera Santiago Rúa Institute of Applied Research and Technology Universidad Iberoamericana Ciudad de México Ciudad de Mexico México Unidad Profesional Interdisciplinaria de Ingeniería Campus Hidalgo Instituto Politécnico Nacional San Agustín Tlaxiaca Hidalgo México Computational Intelligence and Automation Research Group (GIICA) Universidad EIA Envigado Colombia ECBTI Universidad Nacional Abierta y a Distancia Yopal Colombia Electronics and Telecommunications Engineering Department Universidad de Medellín Medellín Colombia

This paper proposes combining displacement-based and distance-based approaches to design a strategy for regulation control with collision avoidance for two differential-drive mobile robots. For the attractive control law, the displacement-based method is used. By employing a coordinates change, the front point of the robot is considered to avoid singularities in the control strategy. For the repulsive control law, the displacement-based technique is considered. In this sense, the distance and angle formation dynamics between robots are determined by applying the same coordinates change. Based on those mentioned above, the control strategy consists of two parts: the former, with negative feedback, which is capable to drive the robots to their desired position, while the latter, with positive feedback, which allows to create an unstable focus that produces a counterclockwise angular movement and makes that the robots move away from each other, avoiding collisions between them. Experimental results show the effectiveness of the proposed control law.

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Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning

Real-Time Trajectory Adaptation for Quadrupedal Locomotion u...

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ieee International Conference on Robotics and automation (ICRA)

作者： Gangapurwala, Siddhant Geisert, Mathieu Orsolino, Romeo Fallon, Maurice Havoutis, Ioannis Univ Oxford Oxford Robot Inst Dynam Robots Syst DRS Grp Oxford England

ISBN: (纸本)9781728190778

We present a control architecture for real-time adaptation and tracking of trajectories generated using a terrain-aware trajectory optimization solver. This approach enables us to circumvent the computationally exhaustive task of online trajectory optimization, and further introduces a control solution robust to systems modeled with approximated dynamics. We train a policy using deep reinforcement learning (RL) to introduce additive deviations to a reference trajectory in order to generate a feedback-based trajectory tracking system for a quadrupedal robot. We train this policy across a multitude of simulated terrains and ensure its generality by introducing training methods that avoid overfilling and convergence towards local optima. Additionally, in order to capture terrain information, we include a latent representation of the height maps in the observation space of the RL environment as a form of exteroceptive feedback. We test the performance of our trained policy by tracking the corrected set points using a model-based whole-body controller and compare it with the tracking behavior obtained without the corrective feedback in several simulation environments, and show that introducing the corrective feedback results in increase of the success rate from 72.7% to 92.4% for tracking precomputed dynamic long horizon trajectories on flat terrain and from 47.5% to 803% on a complex modular uneven terrain. We also show successful transfer of our training approach to the real physical system and further present cogent arguments in support of our framework.

关键词： Training Trajectory tracking Conferences computational modeling Reinforcement learning Computer architecture Real-time systems

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Optimal Elevators control for Buildings Based on Associative Memory

Optimal Elevators Control for Buildings Based on Associative...

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Innovative Approaches in Smart Technologies (ISAS), International symposium on

作者： Saadaldeen Rashid Ahmed Abdulqadir Ismail Abdullah Sabah Jebur AlJanabi Zaki Saeed Tawfeek Ahmed Dheyaa Radhi Rafad Imad Kadhim Sameer Algburi Wassan Sdnsn Hashim Jungpil Shin Computer Science Department Bayan University Erbil Kurdistan Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Kurdistan Region Iraq Electrical Power Techniques Al-Ma’moon University College Al-Washash Baghdad Iraq Engineering Computer Technology department AL Hikma University College Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala Iraq Medical Devices Technology Engineering University of hilla Babylon Iraq Al-Kitab University Kirkuk Iraq School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Conventional elevators encounter the issues of energy efficiency, waiting periods, and lack of adaptation under dynamic conditions of the buildings. To this purpose, this paper provides an elevator control optimization model based on associative memory in order to tackle these challenges. To minimize energy usage and waiting time and increase the responsiveness of the entire system to real-time passenger demand. This technology leverages associative memory—specifically Hopfield networks—to effectively learn and reallocate elevator assignments adjusted to past traffic flows. The observed results represent up to 31% energy consumption reductions and 33% waiting time savings that show the function of the proposed system in optimizing traffic and energy consumption patterns at different building sizes and traffic scenarios. This research introduces a new elevator control system employing associative memory, which demonstrates considerable improvement in energy efficiency and lower waiting times for passengers. These findings will lead to additional exploration, notably for real-time application, interaction with IoT and building management systems, and scaling to bigger structures.

关键词： Associative memory Energy consumption automation computational modeling control systems Elevators Real-time systems Energy efficiency Internet of Things Building management systems

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On the Functional Test of Special Function Units in GPUs 24

On the Functional Test of Special Function Units in GPUs

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24th International symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Guerrero-Balaguera, Juan-David Condia, Josie E. Rodriguez Reorda, Matteo Sonza Politecn Torino Dept Control & Comp Engn Turin Italy

ISBN: (纸本)9781665435956

The Graphics Processing Units (GPUs) usage has extended from graphic applications to others where their high computational power is exploited (e.g., to implement Artificial intelligence algorithms). These complex applications usually need highly intensive computations based on floating-point transcendental functions. GPUs may efficiently compute these functions in hardware using ad hoc Special Function Units (SFUs). However, a permanent fault in such units could be very critical (e.g., in safety-critical automotive applications). Thus, test methodologies for SFUs are strictly required to achieve the target reliability and safety levels. In this work, we present a functional test method based on a Software-Based Self-Test (SBST) approach targeting the SFUs in GPUs. This method exploits different approaches to build a test program and applies several optimization strategies to exploit the GPU parallelism to speed up the test procedure and reduce the required memory. The effectiveness of this methodology was proven by resorting to an open-source GPU model (FlexGripPlus) compatible with NVIDIA GPUs. The experimental results show that the proposed technique achieves 90.75% of fault coverage and up to 94.26% of Testable Fault Coverage, reducing the required memory and test duration with respect to pseudorandom strategies proposed by other authors.

关键词： Functional Testing Graphics Processing Units Infield lest Software-based self-test Special Function Units

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3D Deep Adaptively Efficient Attention Model to segment Automated Breast Ultrasound

3D Deep Adaptively Efficient Attention Model to segment Auto...

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ieee International symposium on Applications of Ferroelectrics (ISAF)

作者： Hyunsu Jeong Chiho Yoon Hyunseok Lim Jongjun Won Gongning Luo Mingwang Xu Kiduk Kim Namkug Kim Chulhong Kim Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang South Korea Department of Electrical Engineering Pohang University of Science and Technology Pohang South Korea Department of Medical Science Asan Medical Institute of Convergence Science and Technology Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Faculty of Computing Harbin Institute of Technology Harbin China Department of Convergence Medicine Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Department of Biomedical Engineering Asan Medical Institute of Convergence Science and Technology Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Department of Electrical Engineering Graduate School of Artificial Intelligence Convergence IT Engineering Mechanical Engineering Medical Science and Engineering and Medical Device Innovation Center Pohang University of Science and Technology Pohang South Korea

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

3D automated breast ultrasound (ABUS) data includes a huge number of slices with lesion-related artifacts and size variants, causing false positive cases. In addition, applying transformer-based models with self-attention considering long-range dependency to 3D ABUS data is challenging because of the small number of data. We propose 3D deep adaptively efficient attention model with a multi-task learning to segment the small number of 3D ABUS datasets. Our network consists of adaptive hybrid-transformer blocks for computationally efficient self-attention and multi-task learning to minimize false positive cases. Additionally, a novel attention gate is also implanted to considering long-range dependency between channel-attentive features. Our model performed 58.27%, 23.59 mm, and 0.054% on Dice, HD95, and FPR, respectively.

关键词： Adaptation models Solid modeling Three-dimensional displays Ultrasonic imaging computational modeling Biological system modeling Breast Logic gates Transformers Multitasking

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

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Sample Efficient Reinforcement Learning with Double Importance Sampling Weight Clipping

Sample Efficient Reinforcement Learning with Double Importan...

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ieee symposium on computational intelligence and Games, CIG

作者： Jiale Han Mingxiao Feng Wengang Zhou Houqiang Li University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive Nation Science Center University of Science and Technology of China Hefei China

Proximal Policy Optimization (PPO) is a stable on-policy policy gradient (PG) method thanks to its clipped importance sampling (IS) weight objective of policy improvement. However, on-policy PG methods usually suffer from poor sample efficiency. In contrast, off-policy methods have demonstrated better sample efficiency by making more effective use of all collected samples during training. In this work, we aim to develop methods that inherit both the stability of on-policy PG methods and the data efficiency of off-policy methods. To this end, we present GeDISC, an off-policy algorithm that improves sample efficiency by reusing off-policy samples drawn from prior policies. Besides, we propose double IS weight clipping to control the high instability caused by off-policy data. We take the recently proposed generalized clipping mechanism for off-policy data as the first clipping to bound the policy update from the current policy and meanwhile we extend the standard clipping mechanism in PPO as the second clipping to prevent high variance and bias brought by extremely old samples. Extensive experiments on continuous and discrete control tasks show that the proposed new algorithm outperforms PPO and other SOTA PPO-based off-policy algorithms.

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Recognition and Classification of Ship Noise in Underwater Soundscape Based on Yet Another Mobile Network and Unmanned Surface Vehicle

Recognition and Classification of Ship Noise in Underwater S...

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International symposium on Underwater Technology

作者： Yuan-Sheng Tsai Yin-Ying Fang Chi-Fang Chen Meng-Fan Tsai Shih-Hsien Weng Ting-Jung Kuo Department of Applied Artificial Intelligence Ming Chuan University Taoyuan Taiwan Department of Engineering Science and Ocean Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

This paper proposes using Yet another mobile network (YAMNet) to detect engine noise from ships within the underwater soundscape. This novel approach combines deep learning with sound classification techniques to accurately identify and categorize engine noises from various vessels in the marine environment. It holds significant potential for marine ecological protection, ship management, and noise pollution control applications. YAMNet is a deep learning model based on the MobileNet architecture, designed for audio event detection and classification. MobileNet is a lightweight convolutional neural network (CNN) known for its high computational efficiency and compactness, making it suitable for execution on resource-constrained Internet of Things (IoT) devices through edge computing. YAMNet was originally designed to effectively process audio data from various environments by converting sound signals into spectrograms, which are then processed through the deep learning network for feature extraction and classification. This enables YAMNet to quickly and accurately recognize various audio events, such as natural sounds, mechanical noises, and human activities.

关键词： Deep learning Biological system modeling Oceans Noise Transfer learning Pollution control Marine vehicles Protection Engines Monitoring

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Application of Deep Learning in Automated Meal Recognition

Application of Deep Learning in Automated Meal Recognition

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Mao, Jiaxiang Tran, Dat Ma, Wanli Naumovski, Nenad Kellett, Jane Martinez-marroquin, Elisa Slattery, Andrew Univ Canberra Canberra ACT Australia Canberra Hosp Canberra ACT Australia

ISBN: (纸本)9781728125473

Deep learning is a widely used data analysis tool and has its proven value in solving problems and challenges in data science. In the nutrition domain, automated recognition of meals is an essential task within the food quality control and diet management. Adequate food supply and precise distribution of nutrients are extremely important. The availability of deep learning to facilitate these tasks would improve a critical step of the meal service process. Therefore, the aim of this research is to study deep learning applications as automated meal recognition for patients at the Canberra Hospital. specifically using convolutional neural networks (CNN). The application of applying deep learning to food quality control are important in reducing human mistakes that may result to providing wrong foods to patients in the current food service at Canberra hospital.

关键词： Food science Food quality control CNN Deep Learning

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