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2020 Forum on Integrated and Sustainable Transportation systems, FISTS 2020

作者： Cussigh, Maximilian Lochel, Chris Straub, Tobias Hamacher, Thomas Technical University of Munich Chair of Renewable and Sustainable Energy Systems Department of Electrical and Computer Engineering Lichtenbergstraße 4a Garching85748 Germany Technical University of Munich Department of Mathematics Boltzmannstraße 3 Garching85748 Germany Institute of Vehicle System Technology Karlsruhe Institute of Technology Rintheimer Querallee 2 Karlsruhe76131 Germany

ISBN: (数字)9781728195032

ISBN: (纸本)9781728195032

The degree of electrification of vehicle powertrains rose significantly in the last years. For fully electric vehicles particularly longer trips are a problem because of limited range, the necessity to charge and consequently higher travel time. Thus, accurate data on energy consumption, driving and charging options and corresponding routing are critical for long trips. This requires innovations that foster the popularity of battery electric vehicles (BEVs). A combined strategy including route, charging and velocity suggestions can enable seamless use of electric vehicles on long distance trips. Based on a mixed-integer nonlinear program (MINLP) a heuristic approach is shown that calculates route and charger options in heterogeneous networks followed by charging amount and prospective velocities in a time-optimal way. For the comparison of the obtained route-charging-velocity plan, a second dynamic programming (DP) approach is shown. A realistic driving scenario serves for method evaluations. The optimal strategy's aim is a minimum travel time, assuming a predefined final state of charge (SOC). The discussion evaluates both approaches concerning computing time and the obtained results, particularly velocity and charging time. The methods' real-time applicability is shown by calculating optimal strategies. © 2020 IEEE.

关键词： Velocity

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A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

arXiv

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

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Crack identification using extended IsoGeometric analysis and particle swarm optimization 7th

Crack identification using extended IsoGeometric analysis an...

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7th International Conference on Fracture Fatigue and Wear, FFW 2018

作者： Khatir, Samir Wahab, Magd Abdel Benaissa, Brahim Köppen, Mario Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Ghent Belgium Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9789811304101

The eXtended isogeometric analysis (X-IGA) combined with Particle swarm optimization (PSO) is used for crack identification in twodimensional linear elastic problems based on inverse problem. The application of fracture mechanics test under mode II loading is performed. The X-IGA possesses the advantages of the combination between eXtended Finite Element Method (X-FEM) and the Isogeometric Analysis (IGA). The objective function minimizes the gap between the calculated and measured displacements. Convergence studies at various positions of crack on the plate are calculated and the results shows that the proposed technique can detect damage with minimum accuracy 95% for the position and maximum accuracy 98%. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Inverse problem PSO and crack identification and plate 2D XIGA

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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PI-D controller design with finite number of frequency responces model via a ratio of fourier transformations of input and output signals

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2019年第4期139卷 402-408页

作者： Kami, Yasushi Tanaka, Ryosuke Mitani, Yuuichiroh Nobuyama, Eitaku Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3 Nishioka Uozumi Akashi Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600 Ooka Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan

This paper shows a PI-D controller design method with a finite number of frequency responses(FNFR) model so that it stabilizes a closed-loop system and matches a transfer function of the closed-loop system to that of ideal model. In this paper FNFR model is introduced with a ratio of the discrete Fourier transformation of input and output signals. The main contribution of this paper is to propose a method to reduce the control problem to a convex optimization problem. The efficiency of the method with FNFR model is shown through experimental results via a magnetic levitation system © 2019 The institute of Electrical Engineers of Japan.

关键词： Controllers

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A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

arXiv

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arXiv 2025年

作者： Ding, Kuiyuan Guo, Caili Yang, Yang Hu, Wuxia Eldar, Yonina C. Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user’s requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements, such as improved data transmission quality or reduced data transmission latency. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness of the prototype in personalizing and addressing user r

关键词： Structured Query Language

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Multi-granularity ensemble sample selection and label correction for classification with noisy labels

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Applied Soft Computing 2025年 180卷

作者： Kecan Cai Hongyun Zhang Witold Pedrycz Duoqian Miao Chaofan Chen Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkiye

Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.

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A Compact Low Power AI Module Mounted on Drone for Plant Monitor system 8

A Compact Low Power AI Module Mounted on Drone for Plant Mon...

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8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019

作者： Kobayashi, Tomoki Yokogawa, Tomoyuki Igawa, Nao Sato, Yoichiro Fujii, Satoshi Arimoto, Kazutami Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama Japan Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan Information and Communication Systems Engineering National Institute of Technology Okinawa College Nago Okinawa Japan

This paper describes the compact and low power AI DNN discriminator which is mounted on the small drone like a hobby type. By adopting this AI module, the effective plant monitoring system using the drone has been ach... 详细信息

ISBN: (纸本)9781728126272

关键词： Energy efficiency

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COMPARING PERFORMANCE OF CROP SPECIES MIXTURES AND PURE STANDS

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Frontiers of Agricultural Science and engineering 2021年第3期8卷 481-489页

作者： Wopke VAN DER WERF Lizhen ZHANG Chunjie LI Ping CHEN Chen FENG Zhan XU Chaochun ZHANG Chunfeng GU Lammert BASTIAANS David MAKOWSKI TjeerdJan STOMPH Wageningen University Centre for Crop Systems Analysis6700 AK Wageningenthe Netherlands College of Resources and Environmental Sciences China Agricultural UniversityBeijing 100193China College of Resources and Environmental Sciences National Academy of Agriculture Green DevelopmentChina Agricultural UniversityBeijing 100193China College of Agronomy/Sichuan Engineering Research Center for Crop Strip Intercropping System/Key Laboratory of Crop Ecophysiology and Farming System in Southwest Ministry of Agriculture and Rural Affairs of ChinaChengdu 611130China Tillage and Cultivation Research Institute Liaoning Academy of Agricultural SciencesShenyang 110161China INRAe AgroParisTechUniversitéParis-SaclayUnit Applied Mathematics and Computer Science(UMR MIA 518)16 rue Claude Bernard F-75231 ParisFrance

Intercropping is the planned cultivation of species mixtures on agricultural *** has many attributes that make it attractive for developing a more sustainable agriculture,such as high yield,high resource use efficiency,lower input requirements,natural suppression of pests,pathogens and weeds,and building a soil with more organic carbon and *** is needed which species combinations perform best under different circumstances and which management is suitable to bring out the best from intercropping in a given production *** literature is replete with case studies on intercropping from across the globe,but evidence synthesis is needed to make this information ***-analysis requires a careful choice of metric that is appropriate for answering the question at hand,and which lends itself for a robust *** paper reviews some metrics that may be used in the quantitative synthesis of literature data on intercropping.

关键词： intercropping species mixtures metaanalysis metrics indicators

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