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arXiv 2024年

作者： Marchesin, Federico Hejda, Matěj Carmona, Tzamn Melendez Di Carlo, Stefano Savino, Alessandro Pavanello, Fabio Van Vaerenbergh, Thomas Bienstman, Peter Photonics Research Group Department of Information Technology Ghent University-imec Ghent Belgium Hewlett Packard Labs Diegem Belgium Department of Control and Computer Engineering Politecnico di Torino Turin Italy Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS Grenoble INP CROMA Grenoble France

Matrix-vector multiplications (MVMs) are essential for a wide range of applications, particularly in modern machine learning and quantum computing. In photonics, there is growing interest in developing architectures capable of performing linear operations with high speed, low latency, and minimal loss. Traditional interferometric photonic architectures, such as the Clements design, have been extensively used for MVM operations. However, as these architectures scale, improving stability and robustness becomes critical. In this paper, we introduce a novel photonic braid interferometer architecture that outperforms both the Clements and Fldzhyan designs in these aspects. Using numerical simulations, we evaluate the performance of these architectures under ideal conditions and systematically introduce non-idealities such as insertion losses, beam splitter imbalances, and crosstalk. The results demonstrate that the braid architecture offers superior robustness due to its symmetrical design and reduced layer count. Further analysis shows that the braid architecture is particularly advantageous in large-scale implementations, delivering better performance as the size of the interferometer increases. We also assess the footprint and total insertion losses of each architecture. Although waveguide crossings in the braid architecture slightly increase the footprint and insertion loss, recent advances in crossing technology significantly minimize these effects. Our study suggests that the braid architecture is a robust solution for photonic neuromorphic computing, maintaining high fidelity in realistic conditions where imperfections are inevitable. © 2024, CC BY-NC-ND.

关键词： Insertion losses

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Synergy Analysis of Industrial, research and University Training Centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as Automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC Automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing Center (SMC) at Automation Robotics and Intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory Automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Szilá rd Hunor Tó th Zsolt Já nos Viharos Á dá m Bá rdos Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering &#x0026 Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Budapest Hungary Faculty of Economics John von Neumann University Kecskem&#x00E9 t Hungary

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions.

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Volume-based Transition Zone Assessment of Hybrid Unmanned Aerial-Underwater Vehicles

Volume-based Transition Zone Assessment of Hybrid Unmanned A...

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IEEE Latin American Robotics Symposium, LARS

作者： Vivian Misaki Aoki Paulo Jefferson Dias De Oliveira Evald Pedro Miranda Pinheiro César Bastos Da Silva Dayana Dos Santos Cardoso Mauro André Barbosa Cunha Paulo Lilles Jorge Drews Intelligent Robotics and Automation Group Federal University of Rio Grande Rio Grande Brazil Department of Computer Engineering and Industrial Automation State University of Campinas Campinas Brazil Automation and Control Research Group Sul-rio-grandense Federal Institute Pelotas Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Quadrotors have been used in diverse areas, such as agriculture, civil construction, fire monitoring, and so on. However, these vehicles cannot perform missions that require navigation in aquatic environments, such as inspection of underwater structures and water sample collection, due to their hardware limitations. Therefore, hybrid unmanned aerial-underwater vehicles emerge as a feasible solution to accomplish these missions. Although quadrotors have well-accepted mathematical models, hybrid vehicles have a small quantity of works dealing with this topic. Moreover, we identified a gap in transition zone representation. Therefore, this paper presents a method for transition zone modeling based on vehicle geometry. Simulation results support the better representation of the proposed transition model in comparison to a sigmoid model.

关键词： Underwater structures Navigation Simulation Force Inspection Mathematical models Hardware

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Recovering Cloud Services Using Hybrid Clouds Under Power Outage 15th

Recovering Cloud Services Using Hybrid Clouds Under Power Ou...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Xia, Yu Xu, Xueyong Wang, Wanyuan He, Xiujun Wu, Weiwei Fang, Xiaolin School of Computer Science and Engineering Southeast University Nanjing China North Information Control Research Academy Group Co. Ltd Nanjing China

ISBN: (纸本)9783030642426

The rapid development of the Internet and cloud computing are giving people high quality online services, which are usually deployed in cloud data centers. While the clouds are not always failure-free, in case of a power failure in a cloud data center, it is important to reduce the cost of disaster recovery meanwhile ensuring cloud service performance. To solve the service recovery problem of cloud data center under a power outage, in this paper, we propose an online solution based on hybrid cloud framework, which resolves the service workload management optimization between private cloud and public one. By using Lyapunov optimization technology, the long-term online optimization problem can be transformed into a general one. Besides, we present a power-aware on-line control algorithm (POCA) which achieves a trade-off between power consumption and cost, and the simulation results show that the proposed POCA algorithm can give a better and efficient performance. © 2020, Springer Nature Switzerland AG.

关键词： Outages

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A Model Predictive Current control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Maryam Sarebanzadeh Mohammad Ali Hosseinzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Academica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment.

关键词： Current control Reactive power Total harmonic distortion Simulation Switches Cost function Transformers Matrix converters Voltage control Predictive control

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Video-Based Container Tracking System Using Deep Learning

Video-Based Container Tracking System Using Deep Learning

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International Conference on Cyber Physical, computer and Automation Systems, CPCAS 2019

作者： Rahmat, Basuki Via, Yisti Vita Wasian, Abdullah Purbasari, Intan Yuniar Sari, Ni Ketut Wurjani, Widi Bandong, Steven Joelianto, Endra Siregar, Persaulian Faculty of Computer Science Universitas Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9789813340619

The process of loading and unloading containers at the port is very important to be automated in order to increase productivity, revenues, efficiency and safety in the logistics transportation process especially in a maritime country like Indonesia. To achieve this, identification and tracking of container positions need to be done accurately so that container transfers can occur precisely and smoothly. In this study, YOLO deep learning is used to detect moving containers. The model is trained using container images, and then the validation and testing process are carried out on the model using other container images. The results of training are stored in several checkpoints which will be compared to get the most accurate model. Obtained YOLO gave good results for tracking containers with MAP value of 68.63% and LAMR of 0.31. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields

arXiv

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arXiv 2023年

作者： van der Ploeg, Chris Nyberg, Truls Sánchez, José Manuel Gaspar Silvas, Emilia van de Wouw, Nathan Netherlands Organisation for Applied Scientific Research Integrated Vehicle Safety Group Helmond5700 AT Netherlands Eindhoven University of Technology Dynamics and Control Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands Eindhoven University of Technology Control Systems Technology Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands KTH Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Scania CV AB Södertälje151 87 Sweden

As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Modulated Model Predictive control for a Four-Leg Indirect Matrix Converter

Modulated Model Predictive Control for a Four-Leg Indirect M...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Mohammad Ali Hosseinzadeh Maryam Sarebanzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Académica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Model Predictive control (MPC) has been increasingly adopted by various converters due to its numerous advantages, including fast dynamic response, multi-objective control, and ease of implementation. However, MPC’s variable switching frequency can degrade waveform quality compared to conventional control techniques that utilize proper modulation methods. To address this issue, combining MPC with a PWM technique allows the converter to operate at a fixed switching frequency, resulting in what is known as modulated model predictive control (M2PC). This study proposes an M2PC for a four-leg converter that is coupled with an indirect matrix converter at the input. The proposed M2PC enhances the output waveform quality using a PWM technique for the four-leg converter while maintaining a low fixed switching frequency. The performance of this approach is demonstrated through the implementation of the proposed M2PC technique in the MATLAB/Simulink environment, showing improvements in waveform quality.

关键词： Switching frequency Switches Pulse width modulation control systems Cost function Prediction algorithms Vectors Matrix converters MATLAB Predictive control

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RGB Arabic Alphabets Sign Language Dataset

arXiv

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arXiv 2023年

作者： Al-Barham, Muhammad Alsharkawi, Adham Al-Yaman, Musa Al-Fetyani, Mohammad Elnagar, Ashraf Sa’aleek, Ahmad Abu Al-Odat, Mohammad MLALP Research Group University of Sharjah United Arab Emirates Mechatronics Engineering Department The University of Jordan Jordan AppsWave for Information Technology Jordan Department of Computer Science University of Sharjah United Arab Emirates Al-Wefaq Control Systems Doha Qatar Student Guidance Department The University of Jordan Jordan

This paper introduces the RGB Arabic Alphabet Sign Language (AASL) dataset. AASL comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset. AASL is made available to the public on Kaggle.1 © 2023, CC BY.

关键词： Classification (of information)

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