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

作者： Wu, ChongRui Xie, ZhiJun Chen, KeWei Xin, Yu Zarei, Roozbeh Xie, YunTao Ningbo University Faculty of Electrical Engineering and Computer Science NingBo China Ningbo University Falculty of Mechanical Engineering and Mechanis NingBo China Deakin University School of Information Technology Melbourne Australia New South Wales University School of Computer Science and Engineer NSW Australia

ISBN: (纸本)9798350337020

The flexible job shop scheduling problem (FJSP) is a complex problem with significant applications in modern manufacturing. While various metainspired algorithms are widely used in FJSP, they of-ten converge to local optima, especially as the problem size increases. To overcome this, we propose an adaptive hybrid algorithm with three stages of 'explore-exploit-escape' (E3HA). In the first stage, we design a Simplified Variable Neighborhood Search (Sim-VNS) algorithm and introduce a Simplified Nopt1 Neighborhood for extensive exploration of solution spaces. In the second stage, we introduce the crossover operation from genetic algorithms to better exploit the elite solutions obtained in the first stage, and we also use mutation operations to improve the quality of regular solutions. Finally, in the third stage, we design a hybrid mechanism of Reverse Learning with Path Relinking (RLPR) and introduce a critical path neighborhood structure to increase the effectiveness of solutions in avoiding local optima and premature convergence. We perform ablation experiments to confirm the effectiveness of each stage of E3HA and test the algorithm on all BRdata and Fdata instances, comparing its performance to relevant existing state-of-the-art algorithms. The results show the effectiveness and stability of our algorithm for FJSP. © 2023 IEEE.

关键词： critical path flexible job shop scheduling problem genetic algorithm Neighborhood Search

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Acute lymphoblastic leukemia Classification Based on Convolutional Neural Network 6

Acute lymphoblastic leukemia Classification Based on Convolu...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Fan, Qile Qu, Boan Teng, Jingjing Hui, Anqi Toe, Teoh Teik Nanjing University of Post and Telecommunications School of Communication and Information Engineering Nanjing China Beijing University of Chemical Technology School of Electrical and Mechanical Engineering Beijing China Suzhou University of Science and Technology Electrical Engineering and the Automatization Specialty Suzhou China Xidian University of Computer Science and Technology Computer Science AndTechnology Xi'an China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9781665491259

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that affects white blood cells, primarily affecting children. Early diagnosis is crucial for successful treatment and recovery. In recent years, convolutional neural networks (CNNs) have been increasingly used in medical image analysis, including the detection and classification of ALL from medical images. In this paper, we propose a model called QCResNet for the classification of ALL from peripheral blood smear images. Our proposed model achieved a high accuracy of 98.9% on a dataset of 15,135 images, outperforming several state-of-the-art methods. Our results demonstrate the potential of QCResNet for accurate and rapid effective diagnosis of acute lymphocytes. © 2023 IEEE.

关键词： Convolutional neural networks

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A Distributed Pricing Mechanism and Optimal Schedulingin Smart Grid using Improved Grey Wolf Optimization

A Distributed Pricing Mechanism and Optimal Schedulingin Sma...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nagendar, Y. Alabdeli, Haider Prashanth, V. Aarthy, G. Thangavel, C. Sr University School of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mechanical Engineering Salem India

ISBN: (纸本)9798350360660

In recent days, energy is most important resource new techniques and methods which introduced to fulfill energy demand for price-based demand response (DR). The preface to this study has shown that load management strategies implemented based on Costlier scheduling for low or consistent energy users results from price-based demand response strategies. Suggested work has taken into account and broadened the same concept to include numerous EV charging regions and corresponding loads from an analytical and implementation point of view. This research provides an innovative approach called Improved Grey Wolf Optimization (IGWO) that uses the distinct energy consumption patterns of EVs in each location to calculate EV charging costs. Dynamic price signal that is non-discriminatory is sent to all EV regions and stations. First, a brand-new mathematical model based on market dynamics and real-time load demand is provided to determine charging prices. The global optimum solution produced by the optimization algorithm successfully converges, confirming the efficacy of the suggested mechanism. Lastly, analysis and simulation outcomes are performed to demonstrate the accomplishments of our suggested task in standings of reasonable cost high user satisfaction distribution. IGWO demonstrated usefulness's revenue is modest by either mechanism or cost saving is 0.69-6.89%. © 2024 IEEE.

关键词： Demand response

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AERODYNAMIC ANALYSIS OF CAMBER MORPHING AIRFOILS USING PARTICLE IMAGE VELOCIMETRY AND COMPUTATIONAL FLUID DYNAMICS METHODS 34th

AERODYNAMIC ANALYSIS OF CAMBER MORPHING AIRFOILS USING PARTI...

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34th Congress of the International Council of the Aeronautical sciences, ICAS 2024

作者： Marciniuk, Marta Kiszkowiak, Lukasz Piskur, Pawel Malicki, Lukasz Strzelecka, Katarzyna Sibilski, Krzysztof Kachel, Stanislaw Wroclaw University of Science and Technology Faculty of Mechanical and Power Engineering Department of Cryogenics and Aerospace Engineering 27 Wybrzeże Stanislawa Wyspiańskiego Str. Wroclaw50-370 Poland Military University of Technology Faculty of Mechatronics Armament and Aerospace gen. Sylwestra Kaliskiego 2 Warsaw00-908 Poland Polish Naval Academy of the Heroes of Westerplatte Faculty of Mechanical and Electrical Engineering Śmidowicza Street 69 Gdynia81-127 Poland

Morphing airfoils are a wide range researched branch of aviation these days. They could efficiently reduce drag and provide better aerodynamic performance due to a smooth transition between the leading and trailing edges when slightly deflected. One interesting approach to modifying aerodynamic performance is drag production. The creation of drag is especially useful when it comes to the landing phase of an aircraft, as it allows the reduction of the approach speed and landing distance. Micro UAVs are burdened with the risk of damage during landing. Existing solutions for micro UAV landing show a number of disadvantages. This paper presents an innovative approach to camber morphing airfoils utilizing not only their abilities to improve aerodynamic performance but also to create drag when extended significantly. The authors performed experiments in a hydrodynamic tunnel using Particle Image Velocimetry (PIV) and then compared the results with Computational Fluid Dynamics (CFD) simulation solutions. The results show a nearly linear increase of drag coefficient with the increase of setting angle and a non-linear increase of lift force coefficient for setting angles from −6 ◦ to 6 ◦. © 2024, International Council of the Aeronautical sciences. All rights reserved.

关键词： Computational fluid dynamics

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Monitoring Early-Stage Corrosion Damage Using a MIMO GPR Array

Monitoring Early-Stage Corrosion Damage Using a MIMO GPR Arr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Weixia Cheng Hai-Han Sun Zheng Fan School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Wisconsin-Madison United States

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Corrosion of reinforcing bars is a leading cause of deterioration of reinforced concrete (RC) structures. There is a need for non-destructive testing methods capable of promptly detecting potential corrosion damage to prevent catastrophic structural failures. Although conventional single-input-single-output ground-penetrating radar (GPR) methods can indicate severe corrosion damage, they are inadequate for identifying early-stage corrosion due to their limited sensitivity to subtle changes in corroded reinforcing bars and their surrounding environment. To address this issue, in this work, we explore the possibility of using multiple-input-multiple-output (MIMO) GPR array to enhance the detection sensitivity of early-stage corrosion. Our approach involves using a linear antenna array to collect the full scattering matrix of signals reflected by the bar. The diffraction stacking algorithm is then applied to the data to reconstruct a high-resolution image of the reinforcing bar. This image undergoes variations during the corrosion process due to the formation of corrosion products with different permittivity and conductivity. By analyzing the difference in the image using the structural similarity (SSIM) index, the progression of corrosion can be effectively monitored. The efficacy of the method has been validated through experimental corrosion monitoring tests. The MIMO array method exhibits promising sensitivity in monitoring early-stage corrosion.

关键词： Sensitivity Diffraction Corrosion Stacking Arrays Indexes Linear antenna arrays

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The MyoPassivity Puzzle: How Does Muscle Fatigue Affect Energetic Behavior of the Human Upper-Limb During Physical Interaction with Robots?

The MyoPassivity Puzzle: How Does Muscle Fatigue Affect Ener...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Suzanne Oliver Peter Paik Xingyuan Zhou S. Farokh Atashzar Department of Mechanical and Aerospace Engineering New York University (NYU) New York NY USA Department of Electrical and Computer Engineering NYU Department of Electrical and Computer Engineering Mechanical and Aerospace Engineering Biomedical Engineering NYU NYU WIRELESS Center and NYU CUSP

The human limb possesses a remarkable capacity to absorb energy during physical human-robot interaction (pHRI), which can be quantified as the biomechanical “Excess of Passivity” (EoP) using non-linear control theory. This biome-chanical passivity index can be used to reduce conservatism and increase the transparency of pHRI stabilizers. Previous work on EoP has used system identification techniques to compute EoP offline. However, for use in real-time controllers, an instantaneous method for EoP estimation would be desired. This paper hypothesizes that muscle fatigue can potentially be a complicating factor which can cumulatively affect the ability of human biomechanics to absorb mechanical energy over time during physical interaction with robots. In this work, we focused on the energetic behavior of the human wrist during pHRI, and, for the first time, we investigated the effect of fatigue on EoP. The EoP for five participants was computed throughout one hundred-second trials of high-frequency wrist perturbations in four directions. Subjects maintained a stiff and consistent grip throughout each trial, causing an accumulation of fatigue in the forearm muscles. Muscle activity was recorded using an array of sixteen sEMG sensors. It was found that the EoP degraded (in a statistically significant manner) with increased muscle fatigue in all directions, even when the level of muscle co-contraction was controlled consistently through a visual myofeedback mechanism. 100% of the subjects exhibited this decline in energy absorption capacity in all directions studied. The median drop in EoP after one-hundred seconds of perturbation was 11% for trials in the abduction and adduction directions and 22% in the pronation and supination directions. These results indicate a need for more robust estimation methods or new modalities to account for muscle fatigue in the control architectures of physical human-robot interaction.

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Many-Objective Multi-Verse Optimizer (MaOMVO): A Novel Algorithm for Solving Complex Many-Objective engineering Problems

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Journal of The Institution of Engineers (India): Series C 2024年第6期105卷 1467-1502页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Shanmugasundar, G. Chohan, Jasgurpreet Singh Abualigah, Laith Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Avadi600062 India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Ostrava70800 Czech Republic Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electrical Engineering Shri K.J. Polytechnic Bharuch392001 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Centre for Research and Development Chandigarh University Mohali140413 India Computer Science Department Al-Bayt University Mafraq25113 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Research has demonstrated the effectiveness of metaheuristic algorithms in addressing optimization challenges involving multi-objectives. These algorithms excel in generating a range of solutions that not only approach the Pareto front but are also evenly distributed. Multi-objective algorithms are effective for problems with two or three objectives. However, their efficiency diminishes in scenarios with many objectives, as the diversity selection and convergence pressure become less effective. Balancing convergence and diversity in multi-objective optimization pose a significant challenge. In response, this study introduces a novel many-objective multi-verse optimizer algorithm named MaOMVO for addressing many-objective problems. It integrates reference point and niche preserve to improve convergence and diversity and employs an innovative information feedback mechanism technique for population renewal. The superiority of the MaOMVO algorithm is evident in tests with MaF problems having 5, 8 and 15 objectives, as well as five real-world problems (RWMaOP1—RWMaOP5). It outperforms four leading algorithms Many-objective moth flame optimization, many-objective particle swarm optimization, non-dominated sorting genetic algorithm-III and reference vector guided evolutionary algorithm in terms of generational distance by 70%, inverted generational distance by 52%, spacing by 46.66%, spread by 55.55%, hypervolume by 52% and running time by 52% with concave, convex and mixed pareto fronts, confirming its robustness in diverse optimization scenarios. © The Institution of Engineers (India) 2024.

关键词： Genetic algorithms

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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Bubble-pen lithography:Fundamentals and applications

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Aggregate 2022年第4期3卷 85-100页

作者： Pavana Siddhartha Kollipara Ritvik Mahendra Jingang Li Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTexasUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTexasUSA Material Science and Engineering Program Texas Materials InstituteThe University of Texas at AustinAustinTexasUSA

Developing on-chip functional devices requires reliable fabrication methods with high resolution for miniaturization,desired components for enhanced performance,and high throughput for fast prototyping and mass ***,laser-based bubble-pen lithography(BPL)has been developed to enable sub-micron linewidths,in situ synthesis of custom materials,and on-demand patterning for various functional components and *** exploits Marangoni convection induced by a laser-controlled microbubble to attract,accumulate,and immobilize particles,ions,and molecules onto different *** years have witnessed tremendous progress in theory,engineering,and application of BPL,which motivated us to write this ***,an overview of experimental demonstrations and theoretical understandings of BPL is ***,we discuss the advantages of BPL and its diverse applications in quantum dot displays,biological and chemical sensing,clinical diagnosis,nanoalloy synthesis,and *** conclude this review with our perspective on the challenges and future directions of BPL.

关键词： additive manufacturing capillary flow lithography marangoni convection microbubbles sensing

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ES-ATF: Early Smoke Detection based on Attention-aggregated Temporal Feature Extraction 10

ES-ATF: Early Smoke Detection based on Attention-aggregated ...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Li, Pengfei Radi, Muaz Al Huang, Xueting Boumaraf, Said Shen, Yuhang Guo, Fusen Al Awadhi, Khalid Yousef Dias, Jorge Javed, Sajid Karki, Hamad Hassan, Taimur Werghi, Naoufel Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Abu Dhabi United Arab Emirates Khalifa University Department of Mechanical Engineering Abu Dhabi United Arab Emirates United Arab Emirates

ISBN: (纸本)9798331532895

Smoke detection is an area where strong research interest was put since its practical meaning in extinguishing fire in the early stage. Detecting smoke not only reduces the life or property loss but also has environment-protection value as inefficient combustion can be restricted. In the literature, there are bunches of investigations on single-frame smoke detection via YOLO-based methods, however, these methods are always attenuated by the transparency and imperceptibility nature of smoke. Therefore, more useful information that can work as an extra prompt to the model is expected. To strengthen the information density of inputs, one possible solution is to explore inter-frame correlation within a smoke snippet. With this added temporal information, a boost in model performance can be expected. This paper adopts an attention-aggregated temporal feature extraction method, by which, the inter-image feature can be better exploited, and more frames can be detected at one time. Consequently, the smoke detection process is speeding up while simultaneously, the model's capability is enhanced in terms of alarming a potential smoke before it evolves to the late stage. © 2024 IEEE.

关键词： Smoke detectors

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