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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Abdaljabar, Zainab Hussam Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices' sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized. © 2021 IEEE.

关键词： Decision trees

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Speed Control of DC Motor based on H∞ Mixed-sensitivity Algorithm

Speed Control of DC Motor based on H∞ Mixed-sensitivity Alg...

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Electrical engineering and Mechatronics Technology (ICEEMT), IEEE International Conference on

作者： Zhen-Wei Lu Xin-Zui Wang Fu-Cheng Cao Yan-Li Yang Jihua Medical Institute Jihua Laboratory Foshan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China

In order to restrain the uncertainty of system parameters and the influence of external disturbances on the speed of DC motor, this paper adopts the $H_{\infty}$ mixed-sensitivity algorithm to design a speed controller based on the model of the controlled motor. First, the Pseudo Random Binary Sequence (PRBS) is input to the motor and the corresponding speed output sequence is measured. The Least Squares Method (LSM) is used to conduct system identification; Then, design a weighting function based on the established system performance indicators, and use MATLAB to synthesize the $H_{\infty}$ controller; Finally, this article adopts the direct form conversion method to implement the controller, and successfully controls the speed of the DC motor through the STM32F407ZGT6 microcontroller. The experimental results show that this algorithm can achieve good speed tracking performance and anti-disturbances ability for DC motors.

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Research on Inverted Pendulum Control Strategy Based on Deep Reinforcement Learning

Research on Inverted Pendulum Control Strategy Based on Deep...

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Electrical engineering and Control science (IC2ECS), International Conference on

作者： Jiehong Zheng Xinzui Wang Yanli Yang Jihua Medical Institute Jihua Laboratory Foshan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China

Aiming at the control planning of inverted pendulum task, the planning strategy based on Soft Actor-Critic (SAC) algorithm was studied. An agent based on Actor-Critic framework is designed, which takes inverted pendulum state as input and planned target position as output as controller. The agent has a total of 5 neural networks, namely, action network actor, 2 current evaluation networks and 2 target evaluation networks. Actor network outputs the next planned position according to the current inverted pendulum state, and the current Critic network and target Critic network respectively output the value evaluation of the current action and the next action according to the reward feedback from the environment. The PD control method is used to convert the planned target position of the mobile network output into the control voltage value, and actually control the inverted swing. Simulation results show that the proposed method is effective and efficient.

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Integrated RFID System for Intralogistics Operations with Industrial Mobile Robots 25

Integrated RFID System for Intralogistics Operations with In...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Pacheco, Fabio Daniel Rebelo, Paulo M. Sousa, Ricardo B. Silva, Manuel F. Mendonca, Helio S. INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto 4200-465 Portugal ISEP Polytechnic of Porto Porto 4249-015 Portugal University of Trás-os-Montes and Alto Douro (UTAD) Vila Real 5000-801 Portugal University of Porto (FEUP) Faculty of Engineering Porto 4200-465 Portugal

ISBN: (纸本)9798331538606

Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository. © 2025 IEEE.

关键词： Industrial Robots Intralogistics Operations Mobile Robots Radio-Frequency IDentification (RFID) Wireless Sensing

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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Automation, Control and Electronics engineering (CACEE), International Conference on

作者： Gloriana Joseph Monko Masaomi Kimura Department of Functional Control Systems Graduate School Shibaura Institute of Technology Tokyo Japan Department of Computer Science and Engineering Shibaura Institute of Technology Tokyo Japan

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities; it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications.

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE International Conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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Finite Sample Analysis of Subspace Identification Methods

arXiv

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arXiv 2025年

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Qin, S. Joe Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States Institute of Data Science Lingnan University Hong Kong

As one of the mainstream approaches in system identification, subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis of SIMs remains an open problem. Amid renewed focus on identifying state-space models in the non-asymptotic regime, this work presents a finite sample analysis for a large class of open-loop SIMs. It establishes high-probability upper bounds for system matrices obtained via SIMs, and reveals that convergence rates for estimating Markov parameters and system matrices are O(1/√N) up to logarithmic terms, in line with classical asymptotic results. Following the key steps of SIMs, we arrive at the above results by a three-step procedure. In Step 1, we begin with a parsimonious SIM (PARSIM) that uses least-squares regression to estimate multiple high-order ARX models in parallel. Leveraging a recent analysis of an individual ARX model, we obtain a union error bound for a bank of ARX models. Step 2 involves model reduction via weighted singular value decomposition (SVD), where we consider different data-dependent weighting matrices and use robustness results for SVD to obtain error bounds on extended controllability and observability matrices, respectively. The final Step 3 focuses on deriving error bounds for system matrices, where two different realization algorithms, the MOESP type and the Larimore type, are considered. Although our study initially focuses on PARSIM, the methodologies apply broadly across many variants of SIMs. Copyright © 2025, The Authors. All rights reserved.

关键词： MIMO systems

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Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class Names

Improving Generalized Zero-Shot Learning by Exploring the Di...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yapeng Li Yong Luo Zengmao Wang Bo Du National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generalized Zero-Shot Learning (GZSL) methods often assume that the unseen classes are similar to seen classes, and thus perform poor when unseen classes are dissimilar to seen classes. Although some existing GZSL approaches can alleviate this issue by leveraging additional semantic information from test unseen classes, their generalization ability to dissimilar unseen classes is still unsatisfactory. This motivates us to study GZSL in the more practical setting, where unseen classes can be either similar or dissimilar to seen classes. In this paper, we propose a simple yet effective GZSL framework by exploring diverse semantics from external class names (DSECN), which is simultaneously robust on the similar and dissimilar unseen classes. This is achieved by introducing diverse semantics from external class names and aligning the introduced semantics to visual space using the classification head of pretrained network. Furthermore, we show that the design idea of DSECN can easily be integrate into other advanced GZSL approaches, such as the generative-based ones, and enhance their robustness for dissimilar unseen classes. Extensive experiments in the practical setting including both similar and dissimilar unseen classes show that our method significantly outperforms the state-of-the-art approaches on all datasets and can be trained very efficiently.

关键词： Visualization computer vision Zero-shot learning Semantics Robustness Pattern recognition

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Fine-Grained Noisy Segment Learning for Fatigue Detection

SSRN

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SSRN 2024年

作者： Wang, Mei Hu, Ruimin Liao, Liang Wang, Xiaochen Chen, Xiaohe Hu, Jinzhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China The Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore The Wuhan Research Institute of Posts and Telecommunications Wuhan430072 China

In fatigue detection, fine-grained labels (seconds-based) commonly inherit coarse-grained labels (minutes-based or more). However, due to the dynamic and time-varying nature of fatigue states, ``Noisy Segments'' often arise in Fine-Grained Fatigue Detection (FD-FGNS). To address this, we propose an Adaptive Fine-Grained Noisy segment learning (AFGN) method, which differs from traditional manual relabeling approaches. AFGN consists of an unsupervised Trustworthy sample Extraction (uTE) method and a Dynamic Noise-tolerant Selective-supervised Contrastive Learning (DNSCL) module. uTE extracts trustworthy samples for positive guidance learning, while DNSCL effectively enhances the model’s tolerance to noise, ensuring robust latent representation learning. Furthermore, we introduce two re-annotated datasets with fine-grained labels, reDROZY and reRLDD. Empirical evidence demonstrates the superiority of AFGN over benchmark methods in mitigating noise in fatigue detection. This study, diving into the unexplored area of fine-grained fatigue detection with noisy labels, opens up new research and application prospects in this field. © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Bacterial population dynamics and biosafety assessment in a closed Bioregenerative Life Support System during the “Lunar Palace 365” experiment

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science China Life sciences 2025年

作者： Yuming Fu Yueying Lu Jianlou Yang Hong Liu Key Laboratory of Biomechanics and Mechanobiology (Beihang University) Ministry of EducationKey Laboratory of Innovation and Transformation of Advanced Medical DevicesMinistry of Industry and Information TechnologyNational Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering)School of Biological Science and Medical EngineeringBeihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University International Joint Research Center of Aerospace Biotechnology&Medical Engineering Beihang University School of Sport Management Shandong Sport University Innovation Center for Medical Engineering&Engineering Medicine Hangzhou International Innovation InstituteBeihang University

Our study investigates the dynamic variation of bacterial populations within the Bioregenerative Life Support System(BLSS), a critical component for sustainable habitats in extraterrestrial environments supporting human survival. We utilized the ground-based BLSS test platform, referred to as “Lunar Palace 1”(LP1), to examine bacterial dynamics in an ecosystem integrating plant production, human cultivation, and equipment maintenance. Our findings reveal distinct bacterial compositions among crew members and functional areas within the LP1 system while preserving temporal stability across the 370-day experiment. Importantly, we observed a low abundance of potential pathogens, minimal antibiotic resistance, and negligible impact of the isolated bacterial strains on equipment materials, suggesting favorable biosafety attributed to plant integration. Our work enhances the understanding of microbial communities in BLSS and underscores the importance of incorporating green plants to bolster environmental biosafety and sustainability in long-term space exploration missions.

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