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Optimal control of electric automated buses in intercity lines: scenario-based analysis of the Pareto-optimal solutions

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IFAC-PapersOnLine 2024年第10期58卷 37-42页

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with the optimal control of electric and automated buses that have to follow an intercity line, where some stops are equipped with a flash-charging infrastructure to charge the batteries, while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the roads of the bus line, and to minimize two objectives related to the deviations from the bus timetable and the lack of energy, at the end of the bus trip, with respect to a desired final energy level. The resulting optimal control problem is a multi-objective linear quadratic problem with both continuous and integer variables. In this paper, we propose and apply two methods based on lexicographic ordering aimed at determining the Pareto-optimal solutions of the control problem in twelve scenarios inspired by a real case.

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & Sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Continuous query-based data trading 24

Continuous query-based data trading

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2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of computer Systems, SIGMETRICS/PERFORMANCE 2024

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400706240

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Copyright held by the owner/author(s).

关键词： Integer programming

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Feature Concatenation-Based Deep Learning Method for Multiclass Wound Classification

Feature Concatenation-Based Deep Learning Method for Multicl...

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2024 International Conference on electrical and computer engineering Researches, ICECER 2024

作者： Aljasem, Muteb Alhababi, Mustafa Javed, Ali Aldoulah, Zaid Abouheaf, Mohammed Mayyas, Mohammad Al-Rousan, Wasseem Bowling Green State University Robotics Engineering Bowling GreenOH United States Wayne State University Electrical and Computer Engineering DetroitMI United States University of Engineering and Technology Department of Software Engineering Taxila Pakistan Alasala colleges Electrical and Electronics Engineering Dammam Saudi Arabia Hamilton Miami University Department of Engineering Technology OH United States Philadelphia University Department of Electrical Engineering Amman Jordan

ISBN: (纸本)9798331539733

Chronic wounds represent a significant global healthcare challenge, affecting millions of individuals and leading to substantial financial burdens. Accurate classification of chronic wounds is crucial for guiding effective treatment strategies, improving patient outcomes, and reducing healthcare costs. However, existing methods in chronic wound classification predominantly focus on binary classification, with limited success in multi-class scenarios. Consequently, there is a need for diagnostic solutions that can reliably classify multiple wound types. To overcome these challenges, we present a novel feature concatenation-based deep learning method for reliable multiclass classification of chronic wounds. Specifically, we employed the DenseNet121 and XceptionNet models for generating effective and compact feature maps that are then passed to a Multilayer perceptron Classifier to accurately identify different types of wounds. The ability of DenseNet121 to truncate redundant features and XceptionNet to handle complex features with fewer computational resources allows us to generate compact and efficient feature maps. We evaluated the performance of our method using the publicly available AZH and Medetec datasets, as well as their augmented versions. Multiple augmentation techniques were employed to increase sample size and diversity, addressing overfitting and class imbalance issues. A rigorous experimental study performed on the AZH, Medetec, and augmented collections demonstrates the effectiveness of our method for multiclass wound classification. © 2024 IEEE.

关键词： Deep learning

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation 37

Risk-Aware Decentralized Safe Control via Dynamic Responsibi...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Lyu, Yiwei Luo, Wenhao Dolan, John M. Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Computer Science University of North Carolina at Charlotte United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781577358800

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual agent faces in a multi-agent interaction environment. With our proposed CBF-inspired risk evaluation framework, the responsibility portions between pairwise agents are dynamically updated based on the relative risk they face. Our method allows agents with lower risk to enjoy a higher level of freedom in terms of a wider action space, and the agents exposed to higher risk are constrained more tightly on action spaces, and are therefore forced to proceed with caution. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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INTEGRATION OF IOT NETWORK AND SENSORS WITH A FULL-SIZED QUADRUPED IN A SIMULATED HUMAN-CENTERED ENVIRONMENT

INTEGRATION OF IOT NETWORK AND SENSORS WITH A FULL-SIZED QUA...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Ho, Jonathan Cajuste, Jefferson Schneidler, Alexander Tatoglu, Akin Sohn, Kiwon Department of Electrical and Computer Engineering University of Hartford West HartfordCT United States Robotics Engineering University of Hartford West HartfordCT United States Department of Mechanical Aerospace and Acoustical Engineering University of Hartford West HartfordCT United States

ISBN: (纸本)9780791888636

This paper presents the updated mechanical design, integrated IoT network, and developed inverse kinematics and gait models of HARQ (Human Assistive and Robust Quadruped). Based on the mechanical limitations of its predecessor, a new connector, foot, planetary gearbox, leg, and body design are presented. Due to additions and improvements to the mechanical design, new inverse kinematics and gait modes are developed to permit HARQ to navigate a physical workspace. To make use of these improvements, an IoT network is integrated with HARQ to allow the quadruped to wirelessly gather and respond to information in real-time. To evaluate HARQ, an indoor experimental space was designed to accommodate an isolated IoT network, sufficient workspace, and simulate a typical human environment. HARQ was able to respond to wireless motion sensor data proctored over an MQTT network. The results are promising steps towards IoT integration into robots. Copyright © 2024 by ASME.

关键词： Inverse kinematics

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Robust STL Control Synthesis under Maximal Disturbance Sets 63

Robust STL Control Synthesis under Maximal Disturbance Sets

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana Kth Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm Sweden University of Southern California Thomas Lord Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350316339

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV). © 2024 IEEE.

关键词： Control system synthesis

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Squid-inspired and wirelessly controllable display for active camouflage in aquatic-environment

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npj Flexible Electronics 2024年第1期8卷 900-909页

作者： Doyoung Kim Seung Won Seon Minkyung Shin Jihwan Kim Bogeun Kim Janghoon Joo Sang Uk Park Wooseok Kim Hee Kyu Lee Byeong Woon Lee Se Gi Lee Su Eon Lee Ji-Hun Seo Seung Ho Han Bong Hoon Kim Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon 16419Republic of Korea Department of Smart Wearable Engineering Soongsil UniversitySeoul 06978Republic of Korea Electronic Convergence Materials and Devices Research Center Korea Electronics Technology Institute(KETI)Seongnam 13509Republic of Korea Department of Materials Science and Engineering Korea UniversitySeoul 02841Republic of Korea Department of Robotics and Mechatronics Engineering DGISTDaegu 42988Republic of Korea

Achieving optimal camouflage in an aquatic environment necessitates the ability to modulate transmittance in response to the surrounding obscurity and potential *** adaptation involves a dynamic transition from transparency to a deep-blue color,especially in low-light or dark *** a strategy promotes a seamless assimilation with the surroundings,enabling the absorption of searchlights and,subsequently,diminishing the risk of detection by ***,the presence of sophisticated mechanisms that facilitates stable and efficient control of transmittance is imperative,enabling smooth transition between transparent and deep-blue hues within the aquatic *** study presents nature-inspired programmable camouflage system that integrates an electrochromic display as the primary transmittance change element and a wireless base module for power and data *** technology offers a robust and flexible construction,ensuring stable operation as demonstrated through mechanical-fatigue experiments and quantitative simulation.A custom circuit and a power-control software package enable active control of multiple electrochromic displays while submerged in water.

关键词： transparency submerged lights

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Design and Development of Semi-Steering and IoT Systems for Electric Mobility Systems 6

Design and Development of Semi-Steering and IoT Systems for ...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Abdelmalek, Shady R. Retig, Ma Althea F. B. Ansari, Fakeha S. Gomaa, Mennatallah A. Alaaeldin, Amr Kaddah, Sahar S. Badr, Basem M. University of Genoa Faculty of Engineering Robotics Department Italy The British University in Dubai Faculty of Engineering and It Computer Science Department United Arab Emirates Mansoura University Renewable and Sustainable Energy Department Mansoura Egypt Mansoura University Faculty of Engineering Electrical Power Department Mansoura Egypt Mansoura University Faculty of Engineering Electrical Engineering Department Mansoura Egypt Lotus Power VancouverBC Canada

ISBN: (纸本)9798350378511

Connecting electric mobility (E-Mobility) systems with Internet of Things (IoT) technology is a crucial strategy for modern transportation and driving assistance systems. IoT and Advanced Driver Assistance Systems (ADAS) applications in the field are examined in this research. Different techniques are designed and developed for ADAS functionality, such as lane and object detection via image and video segmentations. Multiple lane detection models are developed and tested, each of which is then deployed on AWS (Amazon Web Services) cloud and optimized using OpenVINO to speed up inference. Furthermore, telemetry data from various embedded devices are transmitted to AWS IoT core and presented on an easy-to-use web application. This paper shows how deploying systems on cloud platforms like AWS improves scalability and operational efficiency. It also highlights how IoT and cloud technologies may improve the performance of E-Mobility systems and offers a strong foundation for further advancements in the ADAS area. © 2024 IEEE.

关键词： Web services

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