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International Conference on Software engineering - Companion, ICSE, Companion

作者： Gustavo Rezende Silva Nadia Hammoudeh Garcia Darko Bozhinoski Harshavardhan Deshpande Mario Garzon Oviedo Andrzej Wasowski Mariano Ramírez Montero Carlos Hernandez Corbato Department of Cognitive Robotics Delft University of Technology Delft Netherlands Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany IRIDIA University Libre du Bruxelles Bruxelles Belgium Computer Science Department IT University Conpenhagen Denmark

Self-adaptation can be used in robotics to increase system robust- ness and reliability. This work describes the Metacontrol method for self-adaptation in robotics. Particularly, it details how the MROS (Metacontrol for ROS Systems) framework implements and pack- ages Metacontrol, and it demonstrate how MROS can be applied in a navigation scenario where a mobile robot navigates in a factory floor. Video: https://***/watchvISe9aMskJuE

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Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

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Journal of The Institution of Engineers (India): Series A 2020年第1期101卷 27-40页

作者： Sarkar, Amrita Sarkar, Satyaki Department of Computer Science and Engineering Sarala Birla University Ranchi835103 India Department of Architecture Birla Institute of Technology Mesra Ranchi835215 India

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident. © 2019, The Institution of Engineers (India).

关键词： Forecasting

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The Role of the Essential Manifold in Data Mining – An Introductory Approach 23rd

The Role of the Essential Manifold in Data Mining – An I...

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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： de Fátima Pina, Maria Andrade, Marina Alexandra ISCTE - IUL University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal ISLA - Santarém Superior Institute of Management and Administration of Santarém Rua Teixeira Guedes 31 Santarém2000-029 Portugal ISR - UC Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Rua Silvio Lima - Pólo II Coimbra3030-290 Portugal ISTAR - IUL Information Sciences and Technologies and Architecture Research Center University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal

ISBN: (纸本)9783031371288

Interpolating data and the application of data mining techniques in nonlinear manifolds plays a significant role in different areas of knowledge, ranging from computer vision and robotics, to industrial and medical requests, and these growing number of applications have sparked the research interest of the scientific community to these topics. The Generalized Essential manifold, briefly, Essential manifold, consisting of the product of the Grassmann manifold of all k-dimensional subspaces of Rn and the Lie group of rotations in Rn, for instance, plays an important role in the problem of recovering the structure and motion from a sequence of images, also known as stereo matching, which is a crucial problem in image processing and computer vision. A well-known recursive procedure to generate interpolating polynomial curves in Euclidean spaces is the classical De Casteljau algorithm, which is a simple and powerful tool widely used in the field of computer Aided Geometric Design, particularly because it is essentially geometrically based. This algorithm has been generalized to geodesically complete Riemannian manifolds. Thus, having this in mind, in this work we present all the ingredients for a detailed implementation of the generalized De Casteljau algorithm to generate geometric cubic polynomials in the Essential manifold preparing the ground to solve different real interpolation problems in this manifold. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Computational and experimental design of fast and versatile magnetic soft robotic low Re swimmers

arXiv

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arXiv 2024年

作者： Pramanik, R. Park, M. Ren, Z. Sitti, M. Verstappen, R.W.C.P. Onck, P.R. Computational & Numerical Mathematics Group Bernoulli Institute for Mathematics Computer Science & Artificial Intelligence University of Groningen Netherlands Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany School of Mechanical Engineering and Automation Beihang University Beijing China Institute for Biomedical Engineering ETH Zurich Zurich Switzerland School of Medicine College of Engineering Koc University Istanbul Turkey Micromechanics Group Zernike Institute for Advanced Materials University of Groningen Netherlands

Miniaturized magnetic soft robots have shown extraordinary capabilities of contactless manipulation, complex path maneuvering, precise localization, and quick actuation, which have equipped them to cater to challenging biomedical applications such as targeted drug delivery, internal wound healing, and laparoscopic surgery. However, despite their successful fabrication by several different research groups, a thorough design strategy encompassing the optimized kinematic performance of the three fundamental biomimetic swimming modes at miniaturized length scales has not been reported till now. Here, we resolve this by designing magnetic soft robotic swimmers (MSRSs) from the class of helical and undulatory low Reynolds number (Re) swimmers using a fully coupled, experimentally calibrated computational fluid dynamics model. We study (and compare) their swimming performance, and report their steady-state swimming speed for different non-dimensional numbers that capture the competition by magnetic loading, non-linear elastic deformation and viscous solid-fluid coupling. We investigate their stability for different initial spatial orientations to ensure robustness during real-life applications. Our results show that the helical’finger-shaped’ swimmer is, by far, the fastest low Re swimmer in terms of body lengths per cycle, but that the undulatory’carangiform’ swimmer proved to be the most versatile, bidirectional swimmer with maximum stability. © 2024, CC BY-NC-ND.

关键词： Reynolds number

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research institute - GIRI.

关键词： Malware

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Solutions for Creating Virtual Tracks for Autonomous Vehicles, Based on Vehicle-to-Infrastructure Technology

Solutions for Creating Virtual Tracks for Autonomous Vehicle...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Marzena Banach Piotr Bogacki Zofia Długosz Tomasz Talaska Rafał Długosz Institute of Architecture and Spatial Planning Poznan University of Technology Poznan Poland Aptiv Services Poland Kraków Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

In this work, we propose a solution in which vehicle-to-infrastructure (V2I) technology could be used to provide virtual regular tracks, which would allow the vehicle to be kept on a specific lane with a specific accuracy for a longer stretch of road or in specific points/areas, e.g. in cities, at intersections or roundabouts. The proposed methods may in the future support selected functions belonging to advanced driver assistance system (ADAS), the operation of which requires precise determination of the position of the vehicle on the road. In this work, we focus on solutions that can be cheap to produce, assemble the elements of the system, and then maintain it in operation. One of the key aspects is to achieve small sizes and low energy consumption of the wireless sensors which will form the basis of its operation. The solutions proposed in this work can be treated as theoretical considerations, as so far there is no global V2I system in which they could be fully verified.

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Detection of Unoccupied Areas of the Road in Images from Mono Camera with Use of Multilevel Image Decomposition

Detection of Unoccupied Areas of the Road in Images from Mon...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Piotr Bogacki Zofia Długosz Marzena Banach Tomasz Talaska Rafał Długosz Aptiv Services Poland Kraków Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland Institute of Architecture and Spatial Planning Poznan University of Technology Poznań Poland

The problem of detecting not occupied areas of the road is useful in many ADAS functions that include Lane Departure Warning (LDW), Lateral Control (LC), Adaptive Cruise Control (ACC). The problem may be in general defined as the ability to distinguish such areas from those that are occupied by other traffic members. The objective here is to provide data useful, for example, in planning vehicle's maneuvers. In this work, we propose a solution that supports the recognition of such areas. The proposed method is based on a procedure similar to the one used in Discrete Wavelet Transform (DWT) used in image decomposition. One of the introduced modifications relies on using simpler filters, whose structure facilitates their hardware implementation.

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Inventory Management System based on IoT and Microservices architecture Design

Inventory Management System based on IoT and Microservices A...

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International Electrical engineering Congress (iEECON)

作者： Paisan Sithiyopasakul Chavinkorn Piyatananugoon Vasutorn Chaowalittawin Woranidtha Krungseanmuang Posathip Sathaporn Isoon Kanjanasurat Boonchana Purahong Tunjai Archevapanich Attasit Lasakul Computer Engineering Department King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand College of Computing Khon Kean University KhonKean Thailand Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand

This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second.

关键词： Productivity Industries Protocols Microservice architectures QR codes Inventory management Manufacturing

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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification

Ensemble of CNN classifiers using Choquet Fuzzy Integral Tec...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Boonchana Purahong Woranidtha Krungseanmuang Kasi Tenghongsakul Tuanjai Archevapanich Parkpoom Khunthawiwone Nitjaree Satayarak Attasit Lasakul Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.

关键词： Measurement Accuracy Electronic equipment Printed circuits Predictive models Feature extraction Internet

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