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Joint Identification and Clustering Using Deep Learning Techniques 1

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Sethi, Dimple Prakash, Chandra Bharti, Sourabh Department of Information Technology Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi New Delhi110006 India Department of Computer Science and Engineering National Institute of Technology GT Karnal Road Delhi Delhi110036 India Nimbus Research Centre Munster Technological University Rossa Avenue CorkT12 P928 Ireland

ISBN: (数字)9789819930104

ISBN: (纸本)9789819930098

An effective method for assessing the function of the human limbs is to quantify the angular joint kinematics of the body. Motion-capturing techniques that monitor markers placed on anatomical landmarks are typically used to derive joint angles. The administrative cost, soft tissue artifacts, and intra- and inter-tester variability are disadvantages of this approach. Tracking rigid marker clusters attached to body parts and calibrated concerning anatomical landmarks or known joint angles constitutes an alternate technique. However, a thorough investigation of the accuracy and dependability of using the cluster approach on the body joints has not yet been conducted. We aim to compare three different pose estimation models (Blazepose, Keypoint RCNN, MMPose) by clustering the human joints into four clusters (upper left, upper right, lower left, lower right) using the Expectation Maximization Gaussian Mixture Model. The joint clusters obtained by the cluster models using three pose estimation models were comparable to and correlated to the anatomical model. However, they showed noticeable offsets and differences in sensitivity compared to those from the anatomical model. Overall, the joint clusters generated were accurate, and a cumulative accuracy of 75% was achieved using MMPose, 86% and 67% achieved using BlazePose and Keypoint RCNN, respectively, and they can be substituted for the outputs of anatomical models when calculating kinematic metrics. When evaluating trends in movement behavior, cluster models seem to be an appropriate and even preferable replacement for anatomical models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Maximum principle

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Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction

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Physical Review Letters 2024年第3期132卷 030601-030601页

作者： Yong Liu Yaojian Chen Chu Guo Jiawei Song Xinmin Shi Lin Gan Wenzhao Wu Wei Wu Haohuan Fu Xin Liu Dexun Chen Zhifeng Zhao Guangwen Yang Jiangang Gao Zhejiang Lab Hangzhou 311121 China Tsinghua University Beijing 100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China National Supercomputing Center in Wuxi Wuxi 214000 China Information Engineering University Zhengzhou 450001 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

The quantum supremacy experiment, such as Google Sycamore [F. Arute et al., Nature (London) 574, 505 (2019).], poses a great challenge for classical verification due to the exponentially increasing compute cost. Using a new-generation Sunway supercomputer within 8.5 d, we provide a direct verification by computing 3×106 exact amplitudes for the experimentally generated bitstrings, obtaining a cross-entropy benchmarking fidelity of 0.191% (the estimated value is 0.224%). The leap of simulation capability is built on a multiple-amplitude tensor network contraction algorithm which systematically exploits the “classical advantage” (the inherent “store-and-compute” operation mode of von Neumann machines) of current supercomputers, and a fused tensor network contraction algorithm which drastically increases the compute efficiency on heterogeneous architectures. Our method has a far-reaching impact in solving quantum many-body problems, statistical problems, as well as combinatorial optimization problems.

关键词： Quantum benchmarking Quantum circuits Quantum computation Tensor network methods

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A Novel Interoperability Evaluation Framework for the Warehouse Management System

A Novel Interoperability Evaluation Framework for the Wareho...

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Annual Conference of Industrial Electronics Society

作者： Tijo Thayil Jingbing Zhang Prahlad Vadakkepat Abdullah Al Mamun Krishna Sagar Smart Robotics and Automation Advanced Remanufacturing & Technology Centre (ARTC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore Dept. of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Advanced Remanufacturing & Technology Centre (ARTC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore

Warehouse management is a critical element in the supply chain. The rise of e-commerce demands ‘any-mix any-volume’ customized and personalized orders. Due to the variety of products in ‘any-mix any-volume’ scenario the warehouse operations have become complex. The advent of multi-channel commerce has reduced the minimum order quantity (MOQ), claiming frequent changes. The ‘any-mix any-volume’ operations require flexible automation for product storage, retrieval, packing and shipping. Seamless operations of multiple devices are essential for automating flexible warehouses. For effective flexible operations, the Warehouse Management System (WMS) requires controlling the automation devices and associated auxiliary systems. Heterogenous automation devices with distinct communication protocols, data formats and connectivity are challenges for WMS centered control. An interoperable WMS can enable seamless communication and data exchange among devices. In this work, the interoperability of the WMS and various aspects of interoperability of engineering systems are presented. Different WMS system designs are considered and evaluated for the interoperability requirements. A framework for evaluating the interoperability of a WMS is proposed which is validated through experimental trials at an industry grade warehouse.

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Advances in solar energy harvesting integrated by van der Waals graphene heterojunctions

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RSC Advances 2023年第44期13卷 31273-31291页

作者： Le, Top Khac Mai, The-Hung Iqbal, Muhammad Aamir Vernardou, Dimitra Dao, Van-Duong Ponnusamy, Vinoth Kumar Rout, Chandra Sekhar Pham, Phuong V. Faculty of Materials Science and Technology University of Science Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan School of Materials Science and Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering School of Engineering Hellenic Mediterranean University Heraklion71410 Greece Faculty of Biotechnology Chemistry and Environmental Engineering Phenikaa University Hanoi100000 Viet Nam Department of Medicinal and Applied Chemistry and Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan Department of Chemistry National Sun Yat-sen University Kaohsiung80424 Taiwan Centre for Nano and Material Sciences Jain University Bangalore562112 India

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems. © 2023 The Royal Society of Chemistry.

关键词： Graphene

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Open-Set Identification of Minerals From CRISM Hyperspectral Data

Open-Set Identification of Minerals From CRISM Hyperspectral...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sandeepan Dhoundiyal Moni Shankar Dey Shashikant Singh Pattathal V. Arun Guneshwar Thangjam Alok Porwal Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Group Indian Institute of Information Technology Sri City India School of Earth and Planetary Sciences National Institute of Science Education and Research HBNI Bhubaneswar India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have proven instrumental in analysing the mineralogy of the Martian surface and advanced our understanding of the geological history and habitability of Mars. Recently, machine learning-based methods have been used to analyse CRISM data. However, these methods exhibit limitations such as training difficulties and the need for manual feature selection. Therefore, in the contribution we propose a novel algorithm which combines the Random Forest algorithm with Extreme Value Analysis to classify CRISM spectra under an open-set regime. The algorithm’s effectiveness is demonstrated using ∼ 470 000 labelled spectra from the CRISM machine learning toolkit’s mineral dataset where it returns an accuracy of 86.92 % and a Kappa(κ) of 0.85

关键词： Training Machine learning algorithms Accuracy Reconnaissance Prediction algorithms Feature extraction Minerals

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Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Yelleti, Vivek Vadlamani, Ravi Radha Krishna, P. Centre for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology Castle Hills Road #1 Masab Tank Hyderabad500076 India Department of Computer Science and Engineering National Institute of Technology Warangal506004 India

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution. This unprecedented feature enables them to achieve better diversity and perform global search, effectively yielding a tradeoff between exploration and exploitation. We conducted a comprehensive survey across various publishers and gathered 56 papers. We thoroughly analyzed these publications, focusing on the novelty elements and types of heuristics employed by the extant quantum-inspired evolutionary algorithms (QIEAs) proposed to solve the feature subset selection (FSS) problem. Importantly, we provided a detailed analysis of the different types of objective functions and popular quantum gates, i.e., rotation gates, employed throughout the literature. Additionally, we suggested several open research problems to attract the attention of the *** Codes 68W50, 90C27 © 2024, CC BY-NC-ND.

关键词： Quantum computers

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Design of novel microstrip patch antenna for millimeter-wave B5G communications

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Frontiers in Materials 2024年 11卷

作者： Tiang, Jun Jiat Alsekait, Deema Mohammed Khan, Imran Wang, Pi-Chung Madsen, Dag Øivind Faculty of Engineering Centre for Wireless Technology Multimedia University Cyberjaya Malaysia Department of Computer Science and Information Technology Applied College of Computer and Information Science Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Electrical Engineering University of Engineering and Technology Peshawar Pakistan Islamic University Centre for Scientific Research The Islamic University Najaf Iraq Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan University of South-Eastern Norway Hønefoss Norway

Introduction: The simplicity of integration and co-type features of microstrip antennas make them intriguing for a broad variety of applications, particularly with the growing usage of mmWave bands in wireless communications and the constant rise in data transfer in communication situations. Method: This paper proposes a novel design of micrstrip patch antenna for mmWave B5G communication. The main idea is to realize four-mode antenna the operates in four different frequencies. The geometry is rectangular patch whose resonance frequency is adjusted by varying the walls and pins of the structure. Results: Simulation results show that the proposed antenna design has improved fractional bandwidth and performance as compared with existing antennas. Discussion: The observed curve indicates that, in agreement with the modeling findings, there are four resonance spots in the operational frequency region of 2.5–3.4 GHz: 2.68 GHz, 2.9 GHz, 3.05 GHz, and 3.3 GHz, which correspond to TM1/2,0, TM3/2,0, and TMRS, respectively, and TM1/2,2 four resonant modes, within the frequency range, the observed antenna gain peak is around 9 dBi, which is consistent with the measured results. Copyright © 2024 Tiang, Alsekait, Khan, Wang and Madsen.

关键词： Microstrip antennas

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Exploring Seasonal Coastal Water Quality Parameter Interactions Through the Self-Organizing Map Neural Network in Eastern Mediterranean: Lebanon as a Case Study

SSRN

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SSRN 2025年

作者： Hadjisolomou, Ekaterini El Rahman Hassoun, Abed Fakhri, Milad Ghanem, Abeer Mina, Roula Herodotou, Herodotos Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus National Center for Marine Sciences P.O. Box 189 Jounieh Lebanon GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

Coastal water pollution poses significant environmental challenges, leading to severe ecological and public health consequences. Thus, new assessment tools should be developed and refined to manage water quality more efficiently. In this paper, we present the results of Self-Organizing Map (SOM) neural network models utilized to simulate several water quality parameter interactions monitored from several coastal stations in the South-Eastern Mediterranean Sea, namely in Lebanon. Four seasonal SOM models were developed and calibrated based on the available dataset. The SOM’s visualization of the Component Planes (CPs) enabled the examination of water quality parameter interactions and associations on a seasonal basis. Additionally, Pearson correlation analysis between the SOM’s Best Matching Units was calculated for each monitored parameter to augment the CPs analysis. SOM’s clustering of the data samples and assessment of anthropogenic activities nearby the associated monitoring stations, enabled the evaluation of the potential effects from the existing human activities in nearby areas for each monitoring station. Specifically, the seasonal models revealed that specific land uses nearby the monitoring stations like the ANT-2 (near river mouth), SEL-2 (near a chemical plant), BEY- 6 (public beach) are responsible for the coastal water quality degradation in Lebanon. Overall, this paper demonstrates that the SOM model is not only capable of revealing the parameters’ interactions, but also to identify sources of anthropogenic pollution. Therefore, the SOM models might be used as valuable tools in coastal water quality management. © 2025, The Authors. All rights reserved.

关键词： Eutrophication

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A Study on Effects of Synthetic Data for Predicting the Remaining Useful Life of Aluminium Electrolytic Capacitors Using Bagging-Based Ensemble Learning 2nd

A Study on Effects of Synthetic Data for Predicting the Rema...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Bhattacharyya, Anindya Srijith, K. Behera, R.P. Dasgupta, Arup Chakraborty, R.S. Real Time Systems Division Electronics & Instrumentation Group Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam603102 India Birla Institute of Technology and Science Hyderabad Campus Telangana Secunderabad500078 India Physical Metallurgy Division Materials Characterization Group Metallurgy and Materials Group Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam603102 India Homi Bhabha National Institute Training School Complex Anushaktinagar Mumbai400094 India Department of Computer Science and Engineering IIT Kharagpur Kharagpur721302 India

ISBN: (纸本)9789819995172

Aluminium electrolytic capacitors (AECs) are crucial components used in power supplies, computer motherboards, and variable frequency drives for ripple filtering and energy storage. Their performance is affected by factors like temperature, frequency, ripple current, and ageing. Despite design advancements, AECs remain reliability weak points, demanding thorough analysis. This research proposes a method to predict the Remaining Useful Life (RUL) of AECs under combined temperature and voltage stress. We develop an RUL prediction model using bagged decision trees by ageing 24 AEC samples and tracking degradation via capacitance and ESR changes. Synthetic data strengthens the model due to limited training data. We assess the impact of different minority oversampling methods like SMOTE, SMOTER, and SMOGN on prediction accuracy, using ANOVA to compare outcomes within and between algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Analysis of variance (ANOVA)

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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental Science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei Yu Zongbo Shi Jinxing Liu Yuting Wei Zhongcheng Zhang Yanqi Huangfu Han Xu Yue Li Linlin Zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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