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arXiv 2024年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator *** Codes 37M15 © 2024, CC BY.

关键词： Continuous time systems

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Dafi Hisbullah Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Chaerul Umam Folasade Isinkaye Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR.

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AI-Powered Conceptual Model for Scrum Framework

AI-Powered Conceptual Model for Scrum Framework

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Rana Mdallal Dania Alsawalmeh Dania Jarad Ammar Odeh Department of Software Engineering/Enterprise Systems Engineering (ESE) Princess Sumaya University for Technology (PSUT) Department of Computer Science/Cybersecurity Princess Sumaya University for Technology (PSUT)

Scrum is an incremental, interactive agile framework widely used to develop practical problems; the Scrum team focuses on achieving a common goal each cycle, "Sprint Goal" instead of the traditional sequential technique for delivering products. Although many organizations are adopting Scrum, it is still following the traditional manual approaches. Even though many tools are available in the market to facilitate project management and Scrum master tasks, the support is still limited and lacks the utilization of Artificial Intelligence science. This study focuses on integrating the Scrum framework with Artificial Intelligence to build a predicting model that can assist project management "in particular the Scrum master" to predict the achievement of the Scrum Team per Sprint using Story Points; this prediction is built based on several factors that can affect the burndown charts per Sprint.

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An Intelligent Face Detection by Corner Detection using Special Morphological Masking System and Fast Algorithm 2

An Intelligent Face Detection by Corner Detection using Spec...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Arulkumar, V. Jaya Prakash, S. Subramanian, E.K. Thangadurai, N. Sri Krishna College of Engineering and Technology Computer Science and Business Systems Coimbatore India Sir CR Reddy College of Engineering Department of Computer Science and Engineering Andhra Pradesh Eluru India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Electronic and Communication Engineering Faculty of Engineering and Technology !!!Centre For Emerging Technologies Karnataka India

ISBN: (纸本)9781665433686

This research study has applied facial recognition techniques using the angle detection algorithm. Also, a fast angle detection algorithm has been used here, but modified it by applying a shielding technique to create a technique related to loud noise. This article describes twelve facial signs that include the corner of the left eye, the corner of the right eye, the left eyebrow, the right eyebrow, the corner of the lip, and the nostril. It consists of two parts;first, a private browsing technique has been performed to filter the image from noise. The proposed method is based on the assumption that an image is available from the front (fully front). Skin areas were first detected using a color-based learning algorithm and six sigma techniques on RGB, HSV, and NTSC scales. Other analyzes involve morphological processing using the detection of the borderline and the detection of the reflection from the light source of the eye commonly referred to as the eye point. In the second step, a fast angle detection algorithm has been used to detect the placeholders on the face. The Fast Angle Finder works on the Angular Response Function (CRF) which is calculated as the minimum change in intensity in all possible directions. Finally, a comparison has been made with other filtering techniques based on the proposed protection techniques. This article has performed different experiments by using the IRIS Face Database, BioID, and the Cohn Canada Database. The recognition rate obtained by the proposed method is appreciable. © 2021 IEEE.

关键词： Face recognition

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

arXiv

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arXiv 2024年

作者： Barahimi, Borna Singh, Hakam Tabassum, Hina Waqar, Omer Omer, Mohammad Electrical Engineering and Computer Science Department York University Canada School of Computing University of the Fraser Valley BC Canada Cognitive Systems Corp. Waterloo Canada

WiFi-enabled Internet-of-Things (IoT) devices are evolving from mere communication devices to sensing instruments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI;thereby reducing the communication overheads. RSCNet facilitates optimization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4% with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames. © 2024, CC BY.

关键词： Internet of things

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Quantifying, Activating and Rewarding Flexibility in Renewable Energy Communities

Quantifying, Activating and Rewarding Flexibility in Renewab...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julien Allard François Vallée Zacharie De Grève Thomas Stegen Mevludin Glavic Bertrand Cornélusse Power Systems and Markets Research Group Electrical Power Engineering Unit University of Mons Mons Belgium Department of Electrical Engineering and Computer Science University of Liège Liège Belgium

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand.

关键词： Renewable energy sources Europe Tariffs Production Pricing Thermal management Electric vehicles Smart grids Thermal loading Load modeling

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AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design

arXiv

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arXiv 2023年

作者： Zhao, Qi Yan, Bai Hu, Taiwei Chen, Xianglong Duan, Qiqi Yang, Jian Shi, Yuhui The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science Johns Hopkins University BaltimoreMD United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China

Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual optimizer design could be laborious, untraceable and error-prone, let alone human experts are not always available. This arises increasing interest and demand in automating the optimizer design process. In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers. AutoOptLib leverages computing resources to conceive, build up, and verify the design choices of the optimizers. It requires much less labor resources and expertise than manual design, democratizing satisfactory metaheuristic optimizers to a much broader range of researchers and practitioners. Furthermore, by fully exploring the design choices with computing resources, AutoOptLib has the potential to surpass human experience, subsequently gaining enhanced performance compared with human problem-solving. To realize the automated design, AutoOptLib provides 1) a rich library of metaheuristic components for continuous, discrete, and permutation problems;2) a flexible algorithm representation for evolving diverse algorithm structures;3) different design objectives and techniques for different optimization scenarios;and 4) a graphic user interface for accessibility and practicability. AutoOptLib is fully written in Matlab/Octave;its source code and documentation are available at https://***/qz89/AutoOpt and https://***/, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： User interfaces

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Optimizing MapReduce efficiency and reducing complexity with enhanced particle Swarm Optimization (MR-MPSO)

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Swarm and Evolutionary Computation 2025年 95卷

作者： Diwaker, Chander Hasanpuri, Vijay Gulzar, Yonis Sharma, Bhanu Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Kurukshetra India Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Big data's explosive growth poses serious data management difficulties, especially given the data's unequal distribution across massive databases. Because of this mismatch, traditional software systems are less effective, which leads to complex and wasteful data processing. We provide MapReduce-Modified Particle Swarm Optimization (MR-MPSO), a unique optimization technique, to tackle this problem. This strategy not only improves the administration of enormous datasets but also tackles the complexity issue of data imbalance. The MR framework is used to handle large-scale data processing tasks, with MR-MPSO driving the map and reducing functions. Our technique combines adaptive inertia weight with Particle Swarm Optimization (PSO) to improve the accuracy and efficiency of optimization for 10 unimodal and multimodal benchmark functions. MR-MPSO outperforms four optimization algorithms—MR K-means, MR bat, MR whale, and regular MR-on measures such as fitness value mean, median, and standard deviation. Furthermore, MR-MPSO regularly enhances throughput and average I/O rate, especially in complex write operations, with gains ranging from 1.4 % to 28.9 % in throughput and 2.1 % to 17.7 % in I/O rate over typical MR approaches. © 2025

关键词： MapReduce

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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

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computers, Materials & Continua 2021年第9期68卷 2961-2977页

作者： Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Capability Systems Centre School of Engineering and ITUNSW CanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the ***,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels ***,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal *** improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive ***-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed *** develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local *** two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR *** compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and *** was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with

关键词： Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method

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