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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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GUEST EDITORIAL

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China Communications 2022年第1期 4-9页

作者： Zhenyu Xiao Qihui Wu Jiajia Liu Ning Zhang Tao Sun the School of Electronic and Information Engineering Beihang University IET China Institute of Communications and of Electronics the Nanjing University of Aeronautics and Astronautics (NUAA) key lab of Ministry of Industry and Information Technology of China the School of Cybersecurity Northwestern Polytechnical University Shaanxi Provincial Engineering Laboratory of Cyber Security Xi’an Unmanned System Security and Intelligent Communications ISTC Center IEEE IOT-AHSN TC IEEE Communications Society IEEE Vehicular Technology Society the Department of Electrical and Computer Engineering at University of Windsor China Mobile Research Institute 3GPP SA2 China Mobile’s 3GPP SA and CT groups

Space-Air-Ground Integrated Network with Native Intelligence(NI-SAGIN): Concept, Architecture, Access and Networking Ubiquitous coverage is one of the most important goals for mobile communication networks. To achieve this, integration of space, air, and ground networks is highly demanded, which expects to become the one of the enabling technologies for 6G networks. However, the integrated network faces the demand of orders of magnitude improvement in coverage,

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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Microcomb-driven photonic convolutional engine

Microcomb-driven photonic convolutional engine

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Conference on Lasers and Electro-Optics (CLEO)

作者： Bowen Bai Qipeng Yang Haowen Shu Lin Chang Yichen Wu Fenghe Yang Bitao Shen Zihan Tao Weiqiang Xie Weiwei Hu John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China Zhangjiang Laboratory Shanghai China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm 2 and a record high weight co... 详细信息

ISBN: (纸本)9781957171258

关键词： Calibration Distributed feedback lasers Edge detection Optical delay lines Parallel processing Silicon modulators

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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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Comprehensive Study of Support Vector Machines with Principal Component Analysis and K-Fold Monte Carlo Cross-Validation for Breast Lesion Detection Using Ultra-Wideband Signals Propagation from Mammowave system

SSRN

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SSRN 2024年

作者： Dudley, Sandra Taparugssanagorn, Attaphongse Tiberi, Gianluigi Wisanmongkol, Juthatip Sánchez-Bayuela, Daniel Álvarez Castellano, Cristina Romero Ghavami, Mohammad Department of ICT School of Engineering and Technology Asian Institute of Technology Pathum Thani10120 Thailand Location and Automatic Identification System Research Team National Electronics and Computer Technology Center Pathum Thani10120 Thailand School of Engineering London South Bank University 103 Borough Road LondonSE1 0AA United Kingdom UBT - Umbria Bioengineering Technologies Perugia Italy Complejo Hospitalario Universitario de Toledo Spain University of Castilla La Mancha Spain

Recently, a new microwave apparatus for breast lesion detection, known as MammoWave, using ultra-wideband signals and dual-antenna rotation has been developed. Machine learning techniques, specifically support vector machines (SVM), undergo comprehensive analysis to examine the frequency spectrum collected by MammoWave. This study includes 352 breasts from 172 patients and incorporates principal component analysis (PCA) for quantitative response extraction. The SVM three-class classifier, utilising a radial basis function (RBF) kernel model, achieves an average accuracy of 87% inPreprint submitted to Artificial Intelligence in Medicine October 13, 2023classifying "benign lesion" and "cancerous lesion" cases, with some misclassifications observed within the "no-lesion" class. Notably, employing a smaller dataset further enhances accuracy to an impressive 93%. Additionally, the investigation shows that incorporating complex values, including the phase component of the measured S21, does not significantly improve classification performance compared to using only real, imaginary, or magnitude components. The phase information carries important characteristics related to signal propagation, spatial arrangement, and frequency response, enhancing the classifier’s resilience to noise. These findings suggest promising accuracy potential of the machine learning-augmented MammoWave in identifying breast lesions. Further exploration of alternative approaches and the potential integration of ensemble learning methods like XGBoost are warranted to further enhance the classification performance and generalization of the model. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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The Frontier of Geospatial AI Deep Learning Applications in Data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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Intelligent systems and Advanced Applications (ICISAA), International Conference on

作者： Ajit Kumar Mishra Vikash Verma R. Manivannan N. Krishna Kumar Mudunuru Srividya Sanskriti Sharma Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P India Dept of Artificial Intelligence & Machine Learning GGITS Jabalpur M.P India School of Computing Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept.of CSE Guru Nanak Institutions Technical Campus Ibrahimpatam Hyderabad Telangana India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Business System GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI’s data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it’s versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN’s cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today’s challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era.

关键词： Deep learning Training Urban areas Transforms Feature extraction Solids Spatial databases Geospatial analysis Data mining Artificial intelligence

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Multifunctional Metasurface Architecture for Amplitude, Polarization and Wave-Front Control

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Physical Review Applied 2022年第6期17卷 064060-064060页

作者： A. Pitilakis M. Seckel A.C. Tasolamprou F. Liu A. Deltsidis D. Manessis A. Ostmann N.V. Kantartzis C. Liaskos C.M. Soukoulis S.A. Tretyakov M. Kafesaki O. Tsilipakos School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki GR-54124 Greece System Integration and Interconnection Technologies Fraunhofer IZM Berlin 13355 Germany Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Crete Heraklion GR-70013 Greece Xi’an Jiaotong University School of Electronic Science and Engineering Xi’an 710049 China Department of Electronics and Nanoengineering Aalto University Aalto FI-00076 Finland Department of Computer Science Engineering University of Ioannina Ioannina Greece Ames Laboratory U.S. Department of Energy and Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Department of Materials Science and Technology University of Crete Crete Heraklion GR-70013 Greece

Metasurfaces (MSs) have been utilized to manipulate different properties of electromagnetic waves. By combining local control over the wave amplitude, phase, and polarization into a single tunable structure, a multifunctional and reconfigurable metasurface can be realized, capable of full control over incident radiation. Here, we experimentally validate a multifunctional metasurface architecture for the microwave regime, where variable loads are connected behind the back plane to reconfigurably shape the complex surface impedance. As a proof-of-concept step, we fabricate several metasurface instances with static loads in different configurations (surface mount capacitors and resistors of different values in different connection topologies) to validate the approach and showcase the different achievable functionalities. Specifically, we show perfect absorption for oblique incidence (both polarizations), broadband linear-polarization conversion, and beam splitting, demonstrating control over the amplitude, polarization state, and wave front, respectively. Measurements are performed in the 4–18-GHz range inside an anechoic chamber and show good agreement with theoretically anticipated results. Our results clearly demonstrate the practical potential of the proposed architecture for reconfigurable electromagnetic wave manipulation.

关键词： Electromagnetism Light-matter interaction Optical & microwave phenomena Optoelectronics Polarization of light Metasurfaces Electromagnetic wave theory Finite-element method

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Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

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Informatics in Medicine Unlocked 2023年 36卷 101158页

作者： Tuncer, Ilknur Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Yeong, Chai Hong Acharya, U. Rajendra Elazig Governorship Interior Ministry Elazig Turkey School of Business (Information System) University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Department of Digital Forensics Engineering College of Technology Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya 47500 Malaysia Ngee Ann Polytechnic Department of Electronics and Computer Engineering 599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate tha

关键词： Computed tomography images COVID-19 classification Swin Swin-textural Textural feature extraction

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Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss

arXiv

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arXiv 2024年

作者： Jeon, Hyungjun Lee, Kyungmin Lee, Dongkyu Kim, Bongsang Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Quantum AI Dept AI Lab CTO LG Electronics Seoul06772 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing. © 2024, CC BY-NC-ND.

关键词： Mapping

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