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SSRN 2024年

作者： Eita, Mohammad A. Elaziz, Mohamed Abd Department of Computer and Control Engineering Faculty of Engineering Tanta University Tanta31733 Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

In this paper, the exploitation capability and balance of Heap-Based Optimizer (HBO) are improved by making some modifications to propose two variants of the improved heap-based optimizer. To improve the exploitative behavior, HBO is integrated with two well-known algorithm which have an excellent exploitation. The first one is Interior Point (IP) which is a derivative-based optimizer. The second one is the Nelder-Mead (NM) algorithm which is a derivative-free optimizer. Moreover, the two variants maintain the excellent exploratory behavior of HBO. Subsequently, The proposed algorithms perform a superior balance between the exploration and exploitation. The performance of proposed algorithms is evaluated through ninety seven benchmark functions including twenty nine very challenging CEC-BC2017 benchmark functions versus eight highly-cited optimizers including the best algorithm of the CEC-BC-2017 competition (EBO-CMAR). The exploration and exploitation capability of the proposed algorithms is tested using forty four multimodal and twenty four unimodal functions, respectively. In the first two experiments, Friedman mean rank test shows HBO-IP and HBO-NM outperforms and achieve 1st and 2nd ranks, respectively. In the last experiment, twenty nine CEC-BC-2017 benchmark functions are used. Friedman test shows that HBO-IP and HBO-NM secure 2nd and 3rd ranks, respectively, after EBO-CMAR;however, Bonferroni method based multiple comparison test (MCT) shows that the difference in ranks between EBO-CMAR and the proposed algorithms is statistically insignificant. Furthermore, Friedman test shows that the overall ranks of the HBO-IP and HBO-NM are 1st and 2nd for all ninety seven functions. The applicability of the proposed algorithms is shown through solving three engineering problems. The behavior is superior or equivalent to the other optimizers. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Improvement of Random Undersampling to Avoid Excessive Removal of Points from a Given Area of the Majority Class 21st

Improvement of Random Undersampling to Avoid Excessive Remov...

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21st International Conference on Computational science, ICCS 2021

作者： Bach, Malgorzata Werner, Aleksandra Department of Applied Informatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783030779665

In this paper we focus on class imbalance issue which often leads to sub-optimal performance of classifiers. Despite many attempts to solve this problem, there is still a need to look for better ones, which can overcome the limitations of known methods. For this reason we developed a new algorithm that in contrast to traditional random undersampling removes maximum k nearest neighbors of the samples which belong to the majority class. In such a way, there has been achieved not only the effect of reduction in size of the majority set but also the excessive removal of too many points from the given area has been successfully prevented. The conducted experiments are provided for eighteen imbalanced datasets, and confirm the usefulness of the proposed method to improve the results of the classification task, as compared to other undersampling methods. Non-parametric statistical tests show that these differences are usually statistically significant. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization

arXiv

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arXiv 2025年

作者： Pezone, Francesco Barbarossa, Sergio Caire, Giuseppe Di Lorenzo, Paolo Kountouris, Marios Bennis, Mehdi Sapienza University of Rome Department of Computer Control and Management Engineering Italy Technische Universität Berlin Faculty 4 - Electrical Engineering and Computer Science Germany

As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart sensors, IoT systems etc., are gaining more and more interest and new communication paradigms are needed. This thesis addresses these challenges by combining semantic communication with generative models to optimize image compression and resource allocation in edge networks. Unlike traditional bit-centric communication systems, semantic communication prioritizes the transmission of meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit of the focus solely on the relevant parts of the data due to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression algorithms, utilizing advanced generative models such as Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These algorithms compress images by encoding only semantically relevant features and exploiting the generative power at the receiver side. This allows for the accurate reconstruction of high-quality images with minimal data transmission. The thesis also introduces a Goal-Oriented edge network optimization framework based on the Information Bottleneck problem and stochastic optimization, ensuring that communication resources are dynamically allocated to maximize efficiency and task performance. By integrating semantic communication into edge networks, the proposed system achieves a balance between computational efficiency and communication effectiveness, making it particularly suited for real-time applications. The thesis compares the performance of these semantic communication models with conventional image compression techniques, using both classical and semantic-awar

关键词： Image compression

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation 3

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Zhang, Jiusi Tian, Jilun Jiang, Yuchen Luo, Hao Huang, Cong-Sheng Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Norwegian University of Science and Technology Faculty of Engineering Trondheim7034 Norway

ISBN: (纸本)9798350324754

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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Weighting Attributes Based on the Greedy Algorithm Properties

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Procedia computer science 2024年 246卷 4883-4892页

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Bȩdzińska 39 41-200 Sosnowiec Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Wireless control of a D.C. Motor Used for Driving a Conveyor Belt

Wireless control of a D.C. Motor Used for Driving a Conveyor...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Livint, Petru Banu, Ioan-Viorel Culea, George Manale, Bouderbala Majout, Btissam Dusa, Alexandru University 'Vasile Alecsandri' of Bacau Department of Energetics and Science Computer Romania Sidi Mohammed Ben Abdellah University Fez Limas Laboratory Faculty of Sciences Dhar El Mahraz Morocco Sidi Mohammed Ben Abdellah University Fez Laboratory of Engineering Modellingand Systems Analysis Morocco 'Dunarea de Jos' University of Galati Department of Automatic and Electrical Engineering Romania

ISBN: (纸本)9798350365306

This article presents an experimental stand for the remote control of a D.C. motor used to drive a conveyor belt. This is based on two development boards Arduino Uno and two shields NRF24L01. The D. C. motor is supplied from a D.C. voltage source by means of the H type bridge. The emitter circuit sends the control signal generated through a potentiometer to the receptor circuit by means of radio waves at a frequency of 2.4 GHz. The receptor circuit receives the analogue signal emitted by the emitting circuit, transforms it into a PWM (pulse width modulation) signal and uses it for controlling the transistors of the H-bridge. For implementing and validating the remote control system of the D.C. motor two programs have been issued in the programming environment Arduino IDE for the two circuits. emitter and receptor. The remote control system of the D.C. motor has been running in the same way as the direct control of this. The remote control is used in the case of the electric actuation systems made with D.C. motors continuous used in spaces inaccessible to the human operator. © 2024 IEEE.

关键词： Belts

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Bluetooth Communications in Educational Robotics 23

Bluetooth Communications in Educational Robotics

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Oprea, Marin Mocanu, Mariana University Politehnica of Bucharest Calarasi Romania Faculty of Automatic Control and Computer Science Computer Department University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

In a world in a continuous and rapid change, it is absolutely necessary for our students to keep up with the rapid progress of new technologies: Internet of Things (IoT), Robotics, Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR) etc. The rapid evolution and diversification of these emerging technologies has recently led to their introduction into the educational offer of the school curriculum for the gymnasium. The discipline of Information and Communication Technology (ICT) has already been implemented, a discipline that involves both the formation of skills to use new technologies and the formation of computational thinking necessary for the efficient and intelligent use of these technologies. In order to teach and learn Physics from a STEM (science, Technology, engineering and Mathematics) educational perspective, we initiated optional school courses of IoT, Robotics and AI (approached through Machine Learning). These courses stimulate, at the level of students, computational thinking, creativity and innovation and lead, from an interdisciplinary perspective, to the development of emerging specializations such as Mathematics-Physics-Automation, Mathematics-Physics-Electronics, Mathematics-Physics-Informatics-Robotics etc. In this paper we presented a method of approaching, in the school educational space, the study of wireless communication technologies between smart devices, through an Educational Robotics project. The project consisted of creating a wireless controlled mobile robotic platform (robot car) via a Bluetooth module connected to an Arduino Uno board. © 2021 IEEE.

关键词： Students

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H control for General Conformable Polynomial Fuzzy Models

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International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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