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10th E-Health and Bioengineering Conference, EHB 2022

作者： Brezulianu, Adrian Popa, Iolanda Geman, Oana Chiuchisan, Iuliana Nastase, Carmen GREENSOFT S.R.L. Iasi Romania Computer Science Electronics and Automation Department Faculty of Electrical Engineering and Computer Science "Stefan cel Mare" University of Suceava Suceava Romania Faculty of Economics Administration and Business "Stefan cel Mare" University of Suceava Romania

ISBN: (纸本)9781665485579

Technologies based on distributed chains (blockchain) are compared, by some authors, as being as innovative as the transition from analog to digital, with revolutionary consequences in the medium and long term regarding the strategies for generating, transmitting, and storing data/information and the relationships between them. This paper presents a proposal for a management platform solution based on Solana blockchain and Agile software methodologies, for academic areas (for example, for the dissemination of doctoral theses) and also, for the commercial area (various types of economic contracts, etc.). © 2022 IEEE.

关键词： Blockchain

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Dynamic Legged Ball Manipulation on Rugged Terrains with Hierarchical Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zhu, Dongjie Yang, Zhuo Wu, Tianhang Ge, Luzhou Li, Xuesong Liu, Qi Li, Xiang Department of Automation Tsinghua University China School of Computer Science Beijing Institute of Technology China Faculty of Robot Science and Engineering Northeastern University United States

Advancing the dynamic loco-manipulation capabilities of quadruped robots in complex terrains is crucial for performing diverse tasks. Specifically, dynamic ball manipulation in rugged environments presents two key challenges. The first is coordinating distinct motion modalities to integrate terrain traversal and ball control seamlessly. The second is overcoming sparse rewards in end-to-end deep reinforcement learning, which impedes efficient policy convergence. To address these challenges, we propose a hierarchical reinforcement learning framework. A high-level policy, informed by proprioceptive data and ball position, adaptively switches between pretrained low-level skills such as ball dribbling and rough terrain navigation. We further propose Dynamic Skill-Focused Policy Optimization to suppress gradients from inactive skills and enhance critical skill learning. Both simulation and real-world experiments validate that our methods outperform baseline approaches in dynamic ball manipulation across rugged terrains, highlighting its effectiveness in challenging environments. Videos are on our website: ***. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Technological Machines Diagnostics of Grain Processing Enterprises as the Object of automation

Technological Machines Diagnostics of Grain Processing Enter...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Hayitov, Aziz Sharifov, Husen 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria Bukhara Institute of Natural Resource Management of the National Research University 'Tashkent Institute of Irrigation and Agricultural Mechanization Institute' Tashkent Uzbekistan

ISBN: (纸本)9798350312911

The article gives the basic concepts of reliability, performance, durability, considers the issues of changing the technical state of the machine during operation. The models under consideration have the ability to introduce various perturbations in order to simulate physical defects, and numerical simulation on a computer made it possible to obtain solutions in the form of temporary realizations of oscillations for different states of the simulation object. The cooperative use of the full-scale method and mathematical modeling made it possible to reduce the volume of full-scale experiments and apply statistical methods of analysis in the problem of classifying the technical condition by constructing mathematical models of the probability of failure and uptime during their operating time. The content of the main types of machine states is revealed, considerable attention is paid to the issues of identifying and influencing failures and the causes of failures in the machine, the reasons for the loss of performance during operation. © 2023 IEEE.

关键词： Deterioration

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Improving Transparency in Romanian Public Procurement: Machine Learning to Classify Bidders and Validate Decisions

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Procedia computer science 2024年 246卷 1210-1219页

作者： Iasmina Oana Silaschi Ioan Daniel Pop Adriana Mihaela Coroiu Automation and Computing Faculty Politehnica University Timişoara Timiş 300223 Romania Computer Science Department Babeş-Bolyai University Cluj-Napoca Cluj 40084 Romania

The primary objective of this research paper is to conduct a real-life case study, centred around a substantial dataset procured from numerous Romanian public procurement tenders. The study aims to classify the bidders according to their compatibility and suitability, an assessment determined by the accuracy of different machine learning models. The dataset used, is a vast body of information. More precisely, it includes data from a total of 289,472 enterprises, 47,974 contracting authorities and 42,474 tenders. The use of this extensive data set ensures a robust and comprehensive analysis, thus enabling the extraction of meaningful insights and facilitating the drawing of reliable conclusions. In the quest to rank bidders effectively, the performance of nine intelligent algorithms is evaluated and compared. The top-performing algorithms in this context appear to be Decision Trees, along with ensemble methods derived from them, namely the Random Forest Classifier and the Extra Trees Classifier. The outcomes of this study are satisfying, outperforming the metrics of a similar study conducted in the same domain. Given the potential concerns about Romania’s integrity in dealing with contracting companies for public procurement auctions, this study holds significant value in its ability to validate the decision-making process employed in previous auctions. By casting light on the effectiveness of past procurement decisions and offering strategic guidance for future acquisitions, this research holds substantial implications for improving transparency and streamlining the bidder selection process within the public procurement area. Therefore, with potential for future work upon it, it promises to become a valuable addition in enhancing the integrity and efficiency of public procurement in Romania.

关键词： precision labelled data random forest classifier extra trees classifier public procurement

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Battery-friendly charging process scheduling of electric vehicle fleets at company sites

Battery-friendly charging process scheduling of electric veh...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Nicki Bodenschatz Markus Eider Daniel Kratschmer Andreas Berl Armin Zimmermann Faculty Computer Science Technische Hochschule Deggendorf Deggendorf Germany Department of Computer Science and Automation Technische Universität Illmenau Ilmenau Germany

Companies adopt electric cars for their vehicle fleets to be more environmental friendly and sustainable. However, there is a battery degradation over the course of time. Wrong charging behavior can accelerate this process. To improve the battery-lifetime, an intelligent scheduling of the charging processes is necessary. The schedule also needs to take other aspects, like the power grid or planned tours of the cars in consideration, to guarantee their full operability. The paper formulates this scenario as an integer linear problem to compute optimal battery-friendly charging schedules that take into account the various requirements and restrictions of electric vehicle fleets in companies. Results for examples from an application project validate the research.

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Portable Baby Incubator Monitoring using Smartphone 7

Portable Baby Incubator Monitoring using Smartphone

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7th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2023

作者： Salahuddin, Nur Sultan Sari, Sri Poernomo Tumewa, Nora Trisna Musyaffa, Aqilla Rahman Wijaya, Arif Herawati, Ema Yudhanto, Satrio Dwi Ambariani Pujiati Gunadarma University Faculty of Computer Science and Information Technology Jakarta Indonesia Gunadarma University Faculty of Industrial Technology Jakarta Indonesia Gunadarma University Multimedia and Robotics Laboratory Depok Indonesia Gunadarma University Industrial Automation Laboratory Depok Indonesia Gunadarma University Faculty of Health Sciences and Pharmacy Depok Indonesia

ISBN: (纸本)9798350385526

Most neonatal deaths occur at the age of 0-6 days (78.5%) and prematurity is one of the main causes of infant death in Indonesia, ranking fifth in the number of premature births. A baby incubator is very necessary to help premature babies adapt to their new environment. An incubator is a health care tool for critical/premature neonates that provides warmth and humidity according to the needs of newborns under controlled conditions. A portable baby incubator monitoring system using wifi as a communication medium to monitor babies, equipped with a DS18B20 sensor to measure the baby's body temperature, an AD8232 sensor to measure the baby's heartbeat and an ESP32 Cam to capture the baby's face has been created. The test results showed that this incubator was able to detect the baby's body temperature, the baby's heartbeat and take the baby's face remotely using an Android-based smartphone, weighs around 5.8 kg, has a simple design, can be carried around making it easier for medical personnel to carry out supervision premature baby. © 2023 IEEE.

关键词： Smartphones

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

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