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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Wu, YuXuan Wang, Jing Zhou, Meng Dong, Zhe Chen, YangQuan College of Information Science and Technology Beijing University of Chemical Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China Mechatronics Embedded Systems and Automation Lab University of California MercedCA95343 United States

ISBN: (纸本)9780738131153

Unmanned aerial vehicles (UAVs) can provide vision at high altitude. In this paper, multi-UAVs and ground vehicle are utilized to explore a complex environment with obstacles. The desired UAV formation is proposed around ground vehicle to provide wider vision. The Interfered Fluid Dynamical System (IFDS) method is utilized to guide UAVs avoid obstacles, while the desired formation need be maintained. Then formation control scheme of multi-UAVs is updated to combine with IFDS method according to original formation and obstacles avoidance. Simulation results shows that multi-UAVs can avoid obstacles and maintain the desired formation around ground vehicle during exploration. © 2021 IEEE.

关键词： Ground vehicles

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FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors

arXiv

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arXiv 2025年

作者： Lin, Tao Wang, Qingwang Liang, Qiwei Tang, Minghua Sun, Yuxuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

Shadow removal is challenging due to the complex interaction of geometry, lighting, and environmental factors. Existing unsupervised methods often overlook shadow-specific priors, leading to incomplete shadow recovery. To address this issue, we propose a novel unsupervised Frequency Aware Shadow Removal Network (FASR-Net), which leverages the inherent frequency characteristics of shadow regions. Specifically, the proposed Wavelet Attention Downsampling Module (WADM) integrates wavelet-based image decomposition and deformable attention, effectively breaking down the image into frequency components to enhance shadow details within specific frequency bands. We also introduce several new loss functions for precise shadow-free image reproduction: a frequency loss to capture image component details, a brightness-chromaticity loss that references the chromaticity of shadow-free regions, and an alignment loss to ensure smooth transitions between shadowed and shadow-free regions. Experimental results on the AISTD and SRD datasets demonstrate that our method achieves superior shadow removal performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet decomposition

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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The Effective Solar Panel Tracker to Obtain Maximum Energy Optimization by Using Innovative Machine Learning

The Effective Solar Panel Tracker to Obtain Maximum Energy O...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Rajanish Kumar Kaushal V. Velmurugan S. Mahendran Arul Prakash M Balvender Singh Joshuva Arockia Dhanraj Department of Electrical Engineering Chandigarh University Mohali Punjab India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr. Sagunthala R and D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Er. Perumal Manimekalai College of Engineering Hosur India Department of Mechanical Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Electrical Engineering Government Women Engineering College Ajmer Rajasthan India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The heat from solar energy can be used to heat the air and use that heat for drying. Solar trackers are used to effectively track the solar collector to heat the air through a solar thermal collector. The drying hot air can be arranged as layer type trackers as per our requirement.A geo thermal heat pump uses geothermal energy to heat your home in winter and cool it in summer in a cost-effective and renewable way. A mini-split heat pump is the second best way to heat or cool your home or commercial space. In this paper an innovative machine learning model was proposed to enhance the tracking of solar panels. The main objective is to obtain the maximum energy of the solar thermal collector. A tracker uses many times more energy than a washing machine. So the efficiency of both can be improved by researching and selecting more energy efficient machines.A portable solar powered cooling unit uses a solar panel and inverter to provide relief from overheating by converting the DC power provided by the panel into 3-phase AC power. In a saturation point the proposed model achieved 87.20% of integrated tracking, 91.59% of distributed tracking, 90.20% of combined tracking, 89.11% of heat based tracking and 93.77% of high power solar tracking. The proposed model achieved higher level of energy optimization. This will possible because the proposed model monitor and generate the high level of energy because of the machine monitoring model.

关键词： Integrated optics Heat pumps Cogeneration Optical saturation Adaptive optics Solar panels Solar heating

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Possibilities for the Use of Heliothermal Plants for the Generation of Green Hydrogen via Desalinated Water

Possibilities for the Use of Heliothermal Plants for the Gen...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： S.P. Kanniyappan Asmita Marathe B Kiran Kumar Sanjeev Kumar T. Sathish Joshuva Arockia Dhanraj Department of Civil Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Centre for Energy Studies School of Technology Savitribai Phule Pune University Pune Maharashtra India Department of Mechanical Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

These days, the substance business utilizes hydrogen (H 2 ) principally to create methanol and smelling salts. However, hydrogen is expected to rapidly secure itself as a significant fuel that will fundamentally work on the nature of the climate. This study looks at the viability of involving sun powered energy in a one of a kind plan intended to deliver desalinated water and hydrogen at the same time while using illustrative box sun based gatherers (essential mover) in three sun oriented radiation draws near: low radiation, high light, and no radiation. This framework, expected for beach front districts, produces water that has been desalinated utilizing humidification and dehumidification procedures utilizing the waste intensity from the natural Rankine cycle that delivers the power. Hydrogen is produced using a low-temperature electrolyser powered by electricity and water, known as a proton exchange film electrolyser. The formation of hydrogen utilizing power and desalinated water acquired from before stages is one of the framework's principles. A parametric exploration in view of the impact of critical variables on creation rates and different efficiencies has been done fully intent on assessing the proficiency of this arrangement.

关键词： Water Storage management Water heating Solar energy Green hydrogen Waste heat Sun

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Daniel Addo Mugahed A. Al-antari Shijie Zhou Kwabena Sarpong Ertan Bütün Muhammed Talo Humam AbuAlkebash Chiagoziem C. Ukwuoma School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Artificial Intelligence DaeyangAI Center Sejong University Seoul Republic of Korea Department of Computer Engineering Firat University Elazığ Turkey Department of Software Engineering Firat University Elazig Turkey Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (Vi $T$ ). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile Vi $T$ is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs.

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Design and Intelligent Control of Flexible Limbs of Tower Climbing Robot

Design and Intelligent Control of Flexible Limbs of Tower Cl...

引用

第四届材料科学应用与能源材料国际研讨会

作者： Wei Zhang Ehui Huang Lizhi Gu Lihong Huang Yongying Fang Sisi Chen Quanzhou University of Information Engineering Key Laboratory of Virtual Manufacturing Technology in Colleges and Universities of Fujian Province College of Mechatronics Engineering and Automation Huaqiao University

In the gecko-like tower climbing robot system, robot limbs play an important role. Based on the system engineering principle, a robot with four flexible limbs is proposed and designed with the functional *** structure, position,and state of the hinge end of the robot flexible limb are planned to ensure that any combination of three hinge points from the four determine the correct position of the chassis at any time without interference. During the climbing process, the precise screw mechanism and the front-end sensors are used to judge and obtain the next holding target, and the "W" type sensing region is set to make the discrete small sensing elements form a continuum, so as to ensure that the system can obtain the target information of the handle in real time. Due to help handle arranged on the tower and uneven distribution of handles on the tower, each climbing step, the flexible limb should make readjustment, and the palm and fingers will grasp the handle properly with the pressure-uniform algorithm, not only making the palm center and the handle center overlap, and also adjusting grip strength in reliability.

关键词： Design and Intelligent Control of Flexible Limbs of Tower Climbing Robot

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Advancements in Underground Object Detection: Exploring Emerging Techniques and Technologies

Advancements in Underground Object Detection: Exploring Emer...

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Telecommunications Conference (ITC-Egypt), International

作者： Sara Yehia Abdel Fatah Fatma Taher Mohamed Khaled Gad Attya Karim Ahmed Hosni Moath Alathbah Thamer Alghamdi Mohamed Fathy Abo Sree M. Abdel-Aleim M Department of Mechatronics Engineering and Automation Faculty of Engineering Egyptian Chinese University Higher Institute of Engineering and Technology El-Tagmoe EL-Khames Cairo Egypt College of Technological Innovation Zayed University Dubai UAE Department of Electronics and Communications Engineering Faculty of Engineering Team leader for the graduation project at AASTMT Cairo Egypt Air defence colleage Team leader for the graduation project at ADC Alexandria Egypt Wolfson Centre for Magnetics School of Engineering Cardiff University Cardiff U.K. Department of Electronics and Communications Engineering Faculty of Engineering Ahram Candian University Cairo Egypt Department of Electrical Engineering College of Engineering King Saud University Riyadh Saudi Arabia Department of Electronics and Communications Engineering Arab Academy for Science Technology and Maritime Transport Cairo Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

In order to better understand the significance of subterranean item detection in a variety of fields, including military operations, construction, archaeology, and utility management, this study looks at recent advancements in this field. Ground Penetrating Radar (GPR), Electromagnetic Induction (EMI), Magnetic Methods, Seismic Techniques, Acoustic Methods, Thermal Imaging, Electrical Resistivity Tomography (ERT), and Remote Sensing are just a few of the cutting-edge techniques covered in this overview, along with their principles, uses, advantages, and disadvantages. The importance of these developments is highlighted in terms of improving the efficiency, accuracy, and security of subterranean exploration and administration. The article highlights current research and development initiatives to improve subterranean item identification, provide deeper understanding of subsurface environments, and support well-informed decision-making in a range of businesses. Using a Patch Antenna to detect Iron beneath the earth's surface as an application for detecting Iron inside explosive materials or finding meteorites that have settled underground for several years that contain Iron elements which is essential in Industry. This paper will discuss the developed methods used in underground exploration.

关键词： Thermal resistance Patch antennas Tomography Radar imaging Iron Telecommunications Security

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A Frequency Up-Conversion Rotational Energy Harvester with Auxetic Structures for High Power Output

SSRN

引用

SSRN 2022年

作者： Chen, Keyu Fang, Shitong Lai, Zhihui Liao, Wei-Hsin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Institute of Intelligent Design and Manufacturing The Chinese University of Hong Kong Shatin Hong Kong College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

Rotational energy harvesters (REH) have been explored to substitute conventional batteries for small electronic sensors. However, there still exists a challenge for rotational energy harvesters to scavenge sufficient energy under low-rotational-frequency excitations. In this paper, we propose a plucking rotational energy harvester with auxetic structures, which utilizes the frequency up-conversion and auxetic structures to enhance the power output of the rotational energy harvesting under low-rotationalfrequency excitations. Finite element simulation is performed to analyze the performance of the proposed rotational energy harvester. The simulation results match well with the experimental ones. When the magnet distance is set to be 2.5 mm, the maximum power output of the auxetic plucking rotational energy harvester (APREH) is found to be 1.43 mW at 1.1 Hz, which is increased by 686% compared with the conventional plain plucking rotational energy harvester (PPREH). In addition, compared with typical plucking rotational energy harvesters, the proposed auxetic plucking rotational energy harvester achieves the highest power output under low-rotational-frequency excitations, which validates the advantage of adopting auxetic structures in plucking rotational energy harvesters. © 2022, The Authors. All rights reserved.

关键词： Energy harvesting

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Optimal Tracking Control of Injection Speed in Injection Molding Machine with Gaussian Process Learning and Model Predictive Control

Optimal Tracking Control of Injection Speed in Injection Mol...

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第43届中国控制会议

作者： Jianpu Cai Zhigang Ren Bo Zhang Zongze Wu School of Automation Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Injection molding, as a common plastic processing technology, has significant importance in modern manufacturing industry. However, due to the nonlinear dynamic characteristics of injection molding machines(IMMs) and the influence of various factors such as raw materials, equipment, and environment during the injection molding process(IMP), uncertainty residuals are generated, which makes it difficult to achieve optimal tracking control of injection speed, thereby affecting product quality and production efficiency. To address this issue, we propose a learning-based model predictive control(LMPC) *** scheme uses Gaussian process regression(GPR) to predict and model uncertainty residuals in the IMP. The residual model of IMM is learned through historical injection molding data, and the model predictive control(MPC) is used to achieve optimal tracking control of injection speed based on the predicted model, thereby automatically adjusting the injection speed according to requirements. In addition, we use differential dynamic programming with control constraints to solve the optimization ***, we conduct comprehensive numerical simulation experiments to prove the effectiveness and efficiency of the proposed LMPC scheme in injection speed control of IMM.

关键词： Injection molding model predictive control optimal control gaussian process learning control

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