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International Conference on Contemporary Computing and Informatics (IC3I)

作者： B. Neeraja Hayder M. Salman Ch. Venkata Krishna Reddy Sachin Ghai A Deepak Pramod Kumar Department of Electrical and Electronics Engineering Government Polytechnic Hyderabad Telangana Department of Computer Science Al-Turath Universiy College Baghdad Iraq Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai Tamilnadu India Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The progressive global rise in the usage of renewable energy sources for power generation to lessen the impact of carbon emissions is encouraging. When compared to other renewable energy options, solar has the greatest potential to meet the world’s electrical needs. Solar cell performance is highly sensitive to both the intensity of available sunlight and the surrounding temperature. Maximum Power Point Tracking (MPPT) allows for maximum power to be extracted from solar cells despite their poor efficiency. The current decade has seen a surge in research into photovoltaic (PV) systems. Traditional MPPT methods have a less complicated procedure and are easier to implement, but they can’t compete with AI-based methods when it comes to efficiency, especially in cases of shading and constantly moving climatic conditions. Although several methods exist for MPPT, more recent methods, such as artificial intelligence, can be employed for excellent efficiency in the case of extracting the maximum power available from solar cells. This research proposes a maximum MPPT method for solar PV systems based on artificial neural networks (ANNs). Five-layer, feed-forward topology-based ANNs are designed in MATLAB Simulink. The commonplace PID controller was used as a benchmark. Compared to a PID-based MPPT controller, the ANN-based controller performs better in simulations. The suggested system’s strengths lie in its quick tracking speed, low rising time, and good static and dynamic tracking efficiency.

关键词： Maximum power point trackers Temperature sensors Renewable energy sources Software packages Photovoltaic cells Artificial neural networks Solar panels

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Dynamic cat swarm optimization algorithm for backboard wiring problem

arXiv

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arXiv 2021年

作者： Ahmed, Aram Rashid, Tarik Saeed, Soran Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimanyah Iraq International academic office Kurdistan Institution for Strategic Studies and Scientific Research Sulaimanyah Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

This paper presents a powerful swarm intelligence meta-heuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence", which is the possibility of entrapment in local optima which usually happens due to the off-balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real world scenario are used. In addition, the Dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion as well as future directions to further improve the algorithm are discussed. © 2021, CC BY-NC-ND.

关键词： Heuristic algorithms

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Reconstructions for Analytic Signals Based on Greedy Algorithm

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Journal of Physics: Conference Series 2021年第1期1948卷

作者： Li Shuang Computer Engineering Technical College (Artificial Intelligence College) Guangdong Polytechnic of Science and Technology (GDPST) Zhuhai China

In this paper, we utilize greedy algorithm to approximate analytic signals and transfer functions sparsely. Based on reproducing kernels of Hardy space, a redundant dictionary is constructed. The approximating strategy relies on matching pursuit which is an implementation of greedy algorithm. A numerical example and Python codes are provided to illustrate the idea.

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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BroadSurv: A Novel Broad Learning System-based Approach for Survival Analysis

BroadSurv: A Novel Broad Learning System-based Approach for ...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Guangheng Wu Junwei Duan Jing Wang Lu Wang Cheng Dong Changwei Lv College of Information Science and Technology Jinan University Guangzhou China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China Academic Affairs Office Jinan University Guangzhou China School of Intelligent System Science and Engineering Jinan University Zhuhai China Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9781665402460

Survival analysis (time-to-event analysis) is a set of statistic methods to analyze time-to-event data and is widely used in many fields such as economics, finance and medicine. One of the fundamental problems in survival analysis is to explore the relationship between the covariates and the survival time. Recently, with the development of deep learning-based techniques, various approaches have been proposed for survival analysis. To better handle the censoring, special cost functions or sophisticated network structures are usually designed for these methods. In this paper, a novel two-stage method is proposed to model the survival data. In the first stage, pseudo conditional probabilities are computed, which can act as the quantitative response variables in regression problems. In the second stage, with these pseudo values, a complicated survival analysis problem is transformed into a regression problem that can be effectively solved by broad learning system. The experimental results show that, with a flexible structure and a simple cost function, our proposed method has a better performance in handling the censored problems.

关键词： Learning systems Economics Computational modeling Finance Probability Cost function Hazards

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba

arXiv

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arXiv 2024年

作者： Huang, Jiahao Yang, Liutao Wang, Fanwen Nan, Yang Wu, Weiwen Wang, Chengyan Shi, Kuangyu Aviles-Rivero, Angelica I. Schönlieb, Carola-Bibiane Zhang, Daoqiang Yang, Guang Department of Bioengineering and Imperial-X Imperial College London London United Kingdom National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong China Human Phenome Institute Fudan University Shanghai China Department of Nuclear Medicine Inselspital University of Bern Bern Switzerland Department of Informatics Technical University of Munich Munich Germany Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts

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Tunnelling and Underground Space technology 2021年 116卷 104109-104109页

作者： Mahmoodzadeh, Arsalan Mohammadi, Mokhtar Nariman Abdulhamid, Sazan Nejati, Hamid Reza M Gharrib Noori, Krikar Hashim Ibrahim, Hawkar Farid Hama Ali, Hunar Department of Civil Engineering University of Halabja HalabjaKurdistan Region Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University KR Iraq Civil Engineering Department College of Engineering Salahaddin University-Erbil ErbilKurdistan Region44002 Iraq Rock Mechanics Division School of Engineering Tarbiat Modares University Tehran Iran Petroleum Technology Department/Control and Operation Koya Technical Institute Erbil Polytechnic University ErbilKurdistan Region Iraq

The impact of uncertainty on decision-making is increasingly prioritized, especially for large geotechnical projects such as tunnels. In this article, the effects of geological/geotechnical uncertainties in tunnel construction time and costs were reduced. The forecasting of geological/geotechnical conditions in a tunnel project was based on the Markov chain. To accessing the more accurate input data of the Markov chain and estimate the construction time and cost required for tunnel construction, tunneling experts were interviewed through a questionnaire. Also, to assess the updating effect on obtained outcomes during the tunnel construction, the model was updated for one time. Finally, comparing the predicted results with the actual results, the effects of the interviewing and updating process were investigated. According to the results, the uncertainties in the predictions were found to be reduced significantly. Lastly, this research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues. © 2021 Elsevier Ltd

关键词： Decision making

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Application of face recognition based on CNN in fatigue driving detection 2019

Application of face recognition based on CNN in fatigue driv...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Xing, Jianju Fang, Guoxin Zhong, Juping Li, Jun Department of Computer Meizhou Vocational Technical School Meizhou China Guangdong Hengdian Information Technology Co. Ltd Guangzhou China College of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (纸本)9781450372022

Fatigue driving detection technology based on the external characteristics of the driver has made some progress in many aspects, but the method of driver facial feature extraction needs to be further improved, and the driver's eye location takes a long time, which affects the system recognition rate. The paper uses the fatigue driving detection method to achieve better results than the traditional detection method. The authors applied the convolutional neural network to face recognition, and improve the pupil localization algorithm, effectively overcoming the problem of large calculation of the original algorithm. According to the characteristics of driver's eyes with different width and height ratios in different states, a simple and feasible method of eye state judgment is realized, and the driver's fatigue state is judged by PERCLOS algorithm. The convolutional neural network model is applied to ORL face database, and the face recognition rate is 85%. The improved Hough transform method has a positioning accuracy of 92% for the driver's eyes, respectively, and the recognition rate for the driver's eye state is 83.9%. The authors designed the prototype system of fatigue driving detection based on face recognition which realizes the functions of driver's face feature detection, eye location, eye state judgment and fatigue judgment. The experimental results show that the recognition rate of fatigue is 87.5%. © 2019 Association for Computing Machinery.

关键词： Face recognition

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Thresholding-based approach for segmentation of melanocytic skin lesion in dermoscopic images

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AIP Conference Proceedings 2024年第1期2991卷

作者： Dilovan Asaad Zebari Yusliza Yusoff Adel Al-Zebari Habibollah Haron Nechirvan Asaad Zebari Department of Computer Science College of Science Nawroz University Duhok 42001 Kurdistan Region Iraq Faculty of Computing Universiti Teknologi Malaysia Johor Bahru 81310 Johor Malaysia Department of Information Technology Technical College of Informatics Akre Duhok Polytechnic University Duhok 42001 Kurdistan Region Iraq Department of Computer Engineering Harran University 63300 Sanliurafa Turkey

The early and effective identification of skin lesion relies heavily on skin lesion segmentation. In contrast, automatic lesion segmentation in dermoscopic images is a difficult task due to several issues such as artifacts (hairs, gel bubbles, and ruler markers), indistinct boundaries, low contrast, and differences in sizes and lesion shapes. Nevertheless, this task can be accomplished. This work attempts to segment skin lesions from normal lesions based on the thresholding technique. The proposed work improved the segmented lesion by removing unwanted objects from an image using morphological operation. This work can effectively segment the lesion region from the normal skin. The segmentation accuracy provided by the system is 91.21%, and comparative analysis has been carried out with thresholding techniques. Based on the findings of the investigation, it has been determined that this work can segment lesions effectively.

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