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50th Annual Conference of the IEEE Industrial electronics Society, IECON 2024

作者： Komurcugil, Hasan Guler, Naki Bayhan, Sertac Dedeoglu, Seyfullah Department of Computer Engineering Eastern Mediterranean University Famagusta Via Mersin 10 Turkey Department of Electrical-Electronic Engineering Technology Faculty Gazi University Ankara Turkey Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Department of Electrical and Electronics Engineering Faculty of Engineering Hitit University Çorum Turkey

ISBN: (纸本)9781665464543

This paper presents an adaptive rate reaching law based sliding mode control method for a three-phase F-type converter. Unlike the existing reaching laws in which the gain is always constant, the proposed reaching law employs an adaptive gain that is continuously updated with the grid current errors. This means that the gain can reach to its smallest value when the grid current errors are zero. It is shown that the minimum gain reduces the reaching time. As a consequence of using adaptive gain, the dynamic response of system is improved considerably (i.e: the reaching time is reduced). Moreover, it is shown that the three-phase F-type converter can be operated as bidirectional battery charger as well as shunt active power filter (SAPF) for compensating the nonlinear loads connected at the point of common coupling. The proposed control and correct operation of system are validated by MATLAB/Simulink simulations under various operating conditions which include battery charging mode, battery discharging mode, and simultaneous operation as SAPF and battery charging/discharging. © 2024 IEEE.

关键词： Sliding mode control

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Construction, modeling, and control of a three-beam prototype using interactive fiber rubber composites

Construction, modeling, and control of a three-beam prototyp...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Acevedo-Velazquez, Aline Iobana Keshtkar, Najmeh Mersch, Johannes Katzer, Konrad Cherif, Chokri Zimmermann, Martina Gerlach, Gerald Röbenack, Klaus Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Institute of Solid-State Electronics Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Fraunhofer Institute for Material and Beam Technology IWS Dresden Germany Institute for Material Science Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany Institute of Textile Machinery and High Performance Material Technology Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany

In this contribution, we discuss the construction, modeling, and control of a three-beam prototype that utilizes interactive fiber rubber composites integrated with shape memory alloys (SMAs) as actuators. These actuators are integrated into a textile layer and covered with an elastomer layer, providing flexibility and protection to the prototype. A mathematical model was developed to describe the behavior of the prototype, employing system identification techniques. However, precise position control of systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To tackle this challenge, a proportional-integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through both numerical simulations and real experiments. © 2023 The Authors. Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Actuators

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Performance Analysis of Delay-Phase Precoding for THz–Massive MIMO System 7th

Performance Analysis of Delay-Phase Precoding for THz–Massi...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Sur, Samarendra Nath Tran, Huu Q. Kandar, Debdatta Nandi, Sukumar Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Information Technology North-Eastern Hill University Shillong India Department of Computer Science Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9789819753369

The emergence of Terahertz (THz) communication holds promise for enabling high-speed wireless data transmission, particularly in dense urban environments where spectrum congestion is a pressing concern. It is also regarded as a pivotal technology for the evolution of 6G networks, primarily due to its capability to offer ultra-wide bandwidth. However, harnessing the potential of THz frequencies requires innovative solutions to overcome the inherent challenges posed by channel characteristics and hardware constraints. The phenomenon of beam squint presents a formidable challenge for traditional wideband hybrid beamformers when applied to ultra-massive multiple input multiple output (UM-MIMO) systems operating in the THz band. This challenge is particularly pronounced due to the consequential beam gain loss, hindering the optimal exploitation of the vast available bandwidth in this frequency range. To address this issue, a cutting-edge solution has emerged in the form of delay-phase precoding (DPP), aimed at efficiently mitigating the adverse effects of beam squint. Through extensive simulations and analysis, the effectiveness of the DPP technique in enhancing the overall performance of mMIMO–THz systems is demonstrated, providing valuable insights for future THz communication system designs. In this study, the evaluation focuses on the spectral and energy efficiency of the system as performance metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Harnessing the power of functionalized biochar:progress,challenges,and future perspectives in energy,water treatment,and environmental sustainability

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Biochar 2024年第1期6卷 407-486页

作者： Muhammad Zubair Yameen Salman Raza Naqvi Dagmar Juchelková Muhammad Nouman Aslam Khan Laboratory of Alternative Fuels and Sustainability School of Chemical and Materials Engineering(SCME)National University of Sciences and Technology(NUST)Sector H-12Islamabad 44000Pakistan. Department of Electronics Faculty of Electrical Engineering and Computer ScienceVŠB-Technical University of Ostrava17.Listopadu 2172/1570800 OstravaPorubaCzech Republic. Department of Engineering and Chemical Sciences Karlstad UniversityKarlstadSweden.

The swift advancement of sustainable energy technologies,coupled with the urgent need to address environmental challenges,has generated considerable interest in the multifaceted applications of biochar materials to promote energy,water,and environmental *** comprehensive review examines recent advancements in the production and applications of functionalized biochar materials,emphasizing their pivotal roles in energy conversion and storage,wastewater treatment,CO_(2)reduction,soil amelioration,and the promotion of carbon neutrality within a circular economy *** functionalization of biochar materials involves surface chemistry and porosity modifications,achieved through techniques like templating,chemical activation,metal impregnation,or heteroatom *** modifications substantially enhance the catalytic activity,energy storage capacity,and cycling stability of biochar materials,making them particularly effective in diverse energy applications such as water splitting,fuel cells,and ***,functionalized biochar materials demonstrate remarkable efficacy as catalysts and adsorbents in wastewater treatment,proficiently removing pollutants like heavy metals,organic contaminants,and nutrients,thereby facilitating resource recovery from *** review also underscores the potential of functionalized biochar materials in CO_(2)capture and conversion,exploring innovative strategies to augment their CO_(2)adsorption capacity and state-of-the-art catalytic processes for transforming captured CO_(2)into valuable fuels and *** summary,this review offers valuable insights into the recent advancements in biochar research,underscoring its substantial commercial potential as a versatile material contributing to a cleaner and more sustainable future.

关键词： Engineered biochar Sustainable energy technologies Wastewater treatment Carbon reduction Circular economy

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A Global Best-guided Firefly Algorithm for engineering Problems

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Journal of Bionic engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization engineering design

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Secure Encryption of Satellite Images Using S-Box-Based Two-Fish Algorithm and A Novel Chaotic Map 3rd

Secure Encryption of Satellite Images Using S-Box-Based Two-...

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Jansi, R. Ashwini, K. Aishwarya, N. Muthulakshmi, M. Logeshwari, M. Kanna, Mukesh Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Mathematics Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819795772

In recent days, encryption of satellite imagery has attracted a wide range of researchers since these images contain confidential information. Thereby, it is necessary to preserve the privacy of this data to avoid unauthorized access. Two-fish algorithm is one of the widely used image encryption schemes. However, the main drawback of this scheme lies with the static nature of the S-box. To avoid this issue and to improve the quality of encryption, in this paper, we propose a novel algorithm based on chaotic map to create a dynamic S-box structure. In the proposed Chaotic S-Box-based Two-Fish (CSTF) algorithm, a novel chaotic map with improved Lyapunov exponent and approximate entropy has been proposed. The sequences produced by the proposed chaotic map are used to randomize the values inside the static S-box structure of Two-fish algorithm. This encryption scheme is suitable for the encryption of satellite images. The proposed algorithm is validated in terms of S-box evaluation metrics like nonlinearity, bit independence criteria-nonlinearity (BIC-nonlinearity), strict Avalanche criterion (SAC) and bit independence criteria-strict Avalanche criterion (BIC-SAC). To validate the proposed algorithm in terms of key sensitivity, tests like Unified Average Changing Intensity (UACI), Hamming Distance (HD) and Number of Pixels Change Rate (NPCR) have been performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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Quantum-Assisted activation for supervised learning in healthcare-based intrusion detection systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 977-984页

作者： Laxminarayana, Nikhil Mishra, Nimish Tiwari, Prayag Garg, Sahil Behera, Bikash K. Farouk, Ahmed Department of Electronics and Communications Engineering Indian Institute of Information Technology West Bengal 741235 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal 741235 India Department of Computer Science Aalto University Espoo02150 Finland Ecole de Technologie Superieure MontrealQCH3C 1K3 Canada Pvt. Ltd. WB Mohanpur741246 India Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada83523 Egypt

Intrusion detection systems (IDSs) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially in healthcare, where sensitive information (patients medical history, prescriptions, electronic health records, medical bills/debts, and many other sensitive data points) is open to compromise from adversaries. In the big data era, classical machine learning has been applied to train IDS. However, classical IDS tend to be complex: either using several hidden layers susceptible to overfitting on training data or using overly complex architectures such as convolutional neural networks, long-short term memory systems, and recurrent neural networks. This article explored the combination of principles of quantum mechanics and neural networks to train IDS. A hybrid classical-quantum neural architecture is proposed with a quantum-Assisted activation function that successfully captures patterns in the dataset while having less architectural memory footprint than classical solutions. The experimental results are demonstrated on the popular KDD99 dataset while comparing our solution to other classical models. Impact Statement Intrusion detection systems are dynamic defenses against network breach attacks. Lately, machine learning has been leveraged to perform automated intrusion detection. However, classical machine learning needs a large amount of data and overly complex architectures to "learn" patterns from data. In this work, we leverage machine learning concepts to derive a novel quantum activation function that greatly simplifies neural architectural complexity while achieving same level of accuracy and performance. Our architecture is much simpler than state-ofthe-Art based classical systems, simpler to train, and easy to handle. © 2024 Institute of Electrical and electronics Engineers Inc.. All rights reserved.

关键词： Network architecture

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Switching Activity Reduction in Graphene PN Junction Circuits using Circuit Re-structuring

Switching Activity Reduction in Graphene PN Junction Circuit...

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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Das, Subrata Deb, Arighna Fiser, Petr Sister Nivedita University Dept. of Computer Science & Engineering Kolkata India School of Electronics Engineering Kiit Bhubaneswar India Czech Technical University Faculty of Information Technology Prague Czech Republic

ISBN: (纸本)9798350315042

Graphene PN Junction (GPNJ) logic circuits received significant attention from the researchers thanks to the availability of electrostatically doped graphene PN Junction (GPNJ) device- a promising one for designing low-power, high-speed circuits. Several design approaches for GPNJ logic circuits realizing important arithmetic and arbitrary Boolean functions exist in literature. However, no detailed evaluations of the resulting circuit complexities with respect to relevant cost metrics are reported. It remains open, how do we synthesize GPNJ logic circuits with minimal switching activity, an essential cost metric influencing the dynamic power dissipation in the resulting circuits. In this paper, we introduce a synthesis approach for GPNJ logic circuits that interconnects a set of GPNJ devices in parallel. Such parallel circuit structures ensure the reduction of switching activity in the resulting GPNJ logic circuits. To the best of our knowledge, so far only one research work discussed the computation of switching activity of GPNJ logic circuits. Experimental evaluations confirm that an average 67.35% reduction in switching activity can be achieved using the proposed approach over the existing one. © 2023 IEEE.

关键词： Graphene

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