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International Electrical Engineering Congress (iEECON)

作者： Tanee Somwong Pongsathorn Chomtong Jirayut Khamsuthat Nonchanutt Chudpooti Prayoot Akkaraekthalin Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the design of a miniaturized metamaterial reflector composing of an open-loop transmission line unit cell structure. The transmission line ends are connected by using a double interdigital technique, resulting in a large capacitive value compared to the conventional methods. When combined with the open-loop line employing the trisection step-impedance structure with adjusting of the capacitive value, its second and third resonant frequencies will be controllable. As a result, this approach can reduce the unit cell size from a typical value of λ/2 to an approximate value of λ/16, therefore overall size of the reflector is significantly decreased. The designed unit cell is measured to be 1.05 cm × 1.0 cm. We implemented the proposed reflector on an FR-4 PCB with a relative permittivity of about 4.4, a substrate height (h) of 1.6 mm, and a loss tangent of about 0.02. The operating frequency range targets of 5G use cases were set to be at 1.8 GHz, 2.6 GHz and 3.5 GHz, respectively. When used with a dipole antenna, the bandwidths of 390 MHz, 604 MHz, and 1.1 GHz and the antenna gains of about 8.23 dB, 8.14 dB, and 8.19 dB were obtained at the operating frequencies, respectively.

关键词： Wireless communication 5G mobile communication Dipole antennas Simulation Resonant frequency Reflector antennas Transmission line measurements Metamaterials Gain Substrates

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Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020

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Research 2024年第2期2023卷 647-658页

作者： Chao Yang Qingquan Li Xuqing Wang Aihong Cui Junyi Chen Huizeng Liu Wei Ma Xuanyan Dong Tiezhu Shi Fanyi Meng Xiaohu Yan Kai Ding Guofeng Wu MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area&Guangdong Key Laboratory of Urban Informatics&Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Geography Hong Kong Baptist UniversityHong Kong Special Administrative Region 999077China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China institute for Advanced Study and Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China School of Civil Engineering Chongqing Jjiaotong UniversityChongging 400074China Department of Cil and Environmental Engineering Tohoku UniversitySendai 980-8579Japan School of Artificial Intelligence Shenzhen PolytechnicShenzhen 518055China School of Computer Science and Technology Dongguan University of TechnologyDongguan 523419China

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas(PAs)and threatened *** the 21st century,both the highlands and lowlands of Asia have been experiencing the dramatic human ***,the threat degree of human expansion to biodiversity is poorly ***,the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental(Asia),national(48 Asian countries),and hotspot(6,502 Asian terrestrial PAs established before 2000)scales is investigated by integrating multiple large-scale *** results show that human expansion poses widespread threat to biodiversity in Asia,especially in Southeast Asia,with Malaysia,Cambodia,and Vietnam having the largest threat degrees(~1.5 to 1.7 times of the Asian average level).Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries(most Southeast Asian countries).The regions with threats to biodiversity are present in~75%terrestrial PAs(including 4,866 PAs in 26 countries),and human expansion in PAs triggers higher threat degrees to biodiversity than that in *** findings provide novel insight for the Sustainable Development Goal 15(SDG-15 Life on Land)and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of *** reduce the threat degree,Asian developing countries should accelerate economic transformation,and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries(i.e.,trade leading to the loss of wildlife habitats)to alleviate human expansion,especially in PAs and highlands.

关键词： Human terrestrial integrating

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AI-Driven Smart Health Monitoring System Using Wearable IoT Devices and Predictive Analytics

AI-Driven Smart Health Monitoring System Using Wearable IoT ...

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IoT, Communication and Automation technology (ICICAT), International Conference on

作者： Bh. Prashanthi Ashish Sharma M. Mahima K. Navya Ramy Riad Al-Fatlawy Senthilnathan Chidambaranathan Department of CSE GRIET Bachupally Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of IT New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of ECE CMR Institute of Technology Hyderabad Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Architecture and Design Virtusa Corporation New Jersey USA

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended to gather the physiological data; for example, pulse, blood pressure, and oxygen saturation and others via wearables and process it using sophisticated AI techniques and approaches, including deep learning based on CNN and LSTM. According to the systems, there are predictions that need to be offered by means of the programs and health risks that should be anticipated for timely intervention in order to increase chances for the patients’ recovery. Although the system is designed for home use, the writers applied a cloud-based solution that provides scalability and real-time data processing; data is securely transferred from the wearable devices to the cloud through Bluetooth and Wi-Fi. This physiological data is augmented by the other public datasets available in the healthcare domain such that the AI learning algorithms can learn from an extensive health record. From the above research, it manifests how the system was able to predict serious health precursors including; heart attack and respiratory failure with LSTM models having prediction rate of more than 96%. It also delivers on-time alarms to the clinical practitioners, with average response time of less than three seconds for critical states. A set of surveys concerning the usability and comfort of the wearable devices among the patients has been conducted and the results show high satisfaction levels reported by the patients about the accuracy as well as simplicity of the system. Despite the introduced effort of the system, there exist significant concerns involving data protection, compatibility, and AI ethic; therefore, the future study should overcome such shortcomings. With these advancements of an artificial intelligence driven health monitoring system, healthcare systems would shift to being

关键词： Smart healthcare Predictive models Real-time systems Internet of Things Biomedical monitoring Artificial intelligence Wearable devices Predictive analytics Monitoring Long short term memory

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Quantitative Evaluation to Detect Crack Depth in Beams Based on CNN-RNN-LSTM Approach

Quantitative Evaluation to Detect Crack Depth in Beams Based...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mohammad Asif Gandhi A. N. Swaminathen Deepak T. Patil A Ravitheja R. Kamali Akshay Rajput Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Panvel Maharashtra India Adi Shankara Institute of Engineering and Technology Kerala India Bharati Vidyapeeth college of Architecture Navi Mumbai Maharashtra India Department of Civil Engineering SVR Engineering College Kurnool Andhra Pradesh India Department of Computer Science And Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this study, the proposed approach offers a novel hybrid image-based deep learning method for autonomous concrete fracture identification. Cracks are easier to spot and false positives are reduced with hybrid images that include visual and infrared thermography data. In specifically, a Visible and infrared camera as well as a line laser make up a hybrid imaging system for spatially scanning unmanned vehicles can efficiently assess large-scale concrete-made constructions like bridges and dams. Crack detection is a commonplace task in many industrial domains, however the current method of relying on expert judgment is typically laborious, time consuming, and inaccurate. The practical benefits of automated decision-making increase as the size of the target concrete construction grows. Cracks can be automatically identified and seen using the proposed method. Preprocessing, segmentation, feature extraction, and model training are the four components of the proposed method. It performs clever edge recognition, scaling, and greyscale conversion as part of its preparation. Improved K-Means segmentation is used for the separation. It employs GLCM for feature extraction, and CNN-RNN-LSTM for model training. The new method is compared to two established approaches, CNN-RNN and CNN-LSTM, and is found to be superior.

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Handwritten Digit Recognition using Deep Learning Networks

Handwritten Digit Recognition using Deep Learning Networks

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2022 IEEE Conference on Telecommunications, Optics and computer Science, TOCS 2022

作者： Yu, Shiying Liu, Jinmei Shu, Hang Cheng, Zhiwei Wuhan University School of Geodesy and Geomatics Wuhan430000 China Rutgers University School of Arts and Sciences New Jersey NJ08854 United States South West University School of Computer and Information Science Chongqing400010 China Xi'an University of Science and Technology Department of Architecture and Civil Engineering Xi'an710600 China

ISBN: (纸本)9781665470537

Handwritten digit recognition has been widely researched. Numerous classification methods have been proposed. This article improves the recognition of handwritten numbers method based on CNN neural network. Recognition of handwritten digits has made great progress, but due to insufficient recognition accuracy, it will have a certain impact on work efficiency. In this paper, a two-layer CNN network with two fully connected layers can achieve a good prediction performance. In our framework, the activation function used is the ReLU function, to some extent, this avoids the difficulties of gradient disappearance and gradient saturation. At the same time, dropout is added to the full connection layer to discard the neurons in the network with a certain probability, which can avoid certain accidents, and the final trained network will be more robust and the generalization performance will be stronger. This method effectively improves the accuracy of recognition in the final experiment, which achieves 98.75% accuracy on theMNIST dataset and 99.25% on the CIFAR-100 dataset. © 2022 IEEE.

关键词： Deep neural networks

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Power Efficient Video Super-Resolution on Mobile NPUs with...

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17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Zürich Switzerland AI Witchlabs Zürich Switzerland University of Wuerzburg Würzburg Germany MediaTek Inc. Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Video Algorithm Group Camera Department Xiaomi Inc. Beijing China Audio & Video Technology Platform Department ZTE Corporation Shenzhen China Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China MIGU Video Co. Ltd. Beijing China BOE Technology Group Co. Ltd. Beijing China GenGenAI Seoul Korea Republic of North China University of Technology Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Uppsala Sweden Northeastern University Shenyang China

ISBN: (纸本)9783031250651

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Buckling of a Viscoelastic Anisotropic Fiber Reinforced Plate Under Rapidly Increasing Shear Load

SSRN

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SSRN 2023年

作者： Eshmatov, Bakhtiyor Mirsaidov, Mirziyod Abdikarimov, Rustamkhan Department of Mathematics Branch of University of Science and Technology "MISaA" Almalyk Uzbekistan Department of Mechanics and Computer Modeling National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" Tashkent Uzbekistan Department of Civil Engineering Tashkent University of Architecture and Civil Engineering Tashkent Uzbekistan

The paper investigates the dynamic deformation of a viscoelastic anisotropic reinforced plate caused by rapidly increasing shear load, taking into account geometric nonlinearity. This complex problem is formulated as the solution of systems of nonlinear integro-differential equations with singular relaxation kernels. To solve this problem, a combination of the Bubnov-Galerkin method and a numerical approach using quadrature formulas is used. To substantiate the correctness of the choice of this numerical method, a test case was solved, which showed a sufficiently high accuracy of the obtained results. The study is devoted to the dynamic response of the plate, covering extensive variations of its physical, mechanical and geometric characteristics. © 2023, The Authors. All rights reserved.

关键词： Anisotropy

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Transmission Line Age Determination with Statistical Health Index and Failure Probability Curve Fitting Technique

Transmission Line Age Determination with Statistical Health ...

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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

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Energy Analyze Tool for Renewable Energy Assited Data Centers

Energy Analyze Tool for Renewable Energy Assited Data Center...

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2021 IEEE East-West Design and Test Symposium, EWDTS 2021

作者： Gokcul, Furkan Hahanov, Vladimir Gugul, Gul Nihal Olmez, Burak Behlul Kuru, Mustafa Kuveyt Turk Participation Bank Research and Development Center Department of corporate architecture Kocaeli Turkey Computer Engineering Faculty Kharkov National University of Radio Electronics Kharkov Ukraine Faculty of Technology Selcuk University Department of Computer Engineering Konya Turkey Akin Robotics Tatlicak Konya Karatay42030 Turkey Payolog LLC Primefor Technology J. S. C Konya Turkey

ISBN: (纸本)9781665445030

Data centers are estimated to be responsible for 13% of energy consumed in world by 2030. Rapid increase in data storage and server performance requirements are the main causes of increase in energy consumption especially for hyperscale data centers such as in financial and banking industry. However small-scale data centers accounted for 70% of total consumption in 2020, which indicates the energy saving potential due to the old systems and un-specially designed buildings. Unlike standard buildings, data centers have high indoor heat gain, therefore high thermal conductivity in cold climates, low conductivity in hot climates is *** systems analysis, air flow analysis, capacity management and network software have been developed to achieve energy savings in data centers in literature. However, a software that calculates the energy demand by taking into account the building details, location and renewable energy systems installed in the data center is not found. In this study a software is developed to calculate final energy demand of datacenters by taking into account the location, building details, IT devices and renewable energy system. For the verification of developed software, hourly energy consumption data of a data center of a private bank is used. © 2021 IEEE.

关键词： Energy efficiency

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A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors

arXiv

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arXiv 2024年

作者： Wu, Keshu Li, Pei Cheng, Yang Parker, Steven T. Ran, Bin Noyce, David A. Ye, Xinyue Department of Civil and Environmental Engineering The University of Wisconsin-Madison MadisonWI53706 United States Department of Landscape Architecture and Urban Planning The Zachry Department of Civil and Environmental Engineering Texas A&M University College StationTX77840 United States Center for Geospatial Sciences Applications and Technology The Department of Landscape Architecture and Urban Planning Department of Computer Science and Engineering Texas A&M University College StationTX77840 United States

Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, communications, and more remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the proposed DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT’s scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Furthermore, its ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the reliability and safety of transportation systems. © 2024, CC BY.

关键词： Vehicle to infrastructure

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