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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Innovation Technology Office Research Center PASCO Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan

Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, how edges contribute to the improvement is unclear. In this study, we propose a method that automatically learns to enhance/suppress edges while keeping the its working mechanism clear. First, we theoretically figure out how edge enhancement/suppression works. Second, we experimentally verify the edge enhancement/suppression behavior. Third, we empirically show that this behavior improves the performance. In general, we observe that the proposed method achieves competitive performance in point cloud classification and segmentation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science Tokyo Institute of Technology Tokyo Japan Innovation Technology Office Research Center PASCO Corporation Tokyo Japan

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various convolution operations. We observe that the convolution can be equivalently decomposed as a weighted combination of a local and a global component. With this observation, we explicitly decouple these two components so that the local one can be enhanced and facilitate the learning of local surface geometry. Specifically, we propose Laplacian Unit (LU), a simple yet effective architectural unit that can enhance the learning of local geometry. Extensive experiments demonstrate that networks equipped with LUs achieve competitive or superior performance on typical point cloud understanding tasks. Moreover, through establishing connections between the mean curvature flow, a further investigation of LU based on curvatures is made to interpret the adaptive smoothing and sharpening effect of LU. The code will be available. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Internet-of-Things technology and applications for clean energy systems

Energy Conversion and Economics

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Energy Conversion and Economics 2021年第4期2卷 183-185页

作者： Strunz, Kai Wang, Xuanyuan Guo, Qinglai Xie, Le Zhang, Song Fang, Xin Wang, Jianxiao School of Electrical Engineering and Computer Science Berlin10587 Germany State Grid Jibei Electric Power Co. LTD. Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Business Architecture and Technology ISO New England Inc. HolyokeMA United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO United States School of Electrical and Electronic Engineering North China Electric Power University Beijing China

[...]future research interests regarding IESSs are summarised. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection.

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Intelligent Techniques for Prediction of Drilling Rate for Percussive Drills in Topically Weathered Limestone

Intelligent Techniques for Prediction of Drilling Rate for P...

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International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021

作者： Bhatawdekar, Ramesh Murlidhar Roy, Bishwajit Changtham, Saksarid Khandelwal, Manoj Armaghani, Danial Jahed Mohamad, Edy Tonnizam Pathak, Pranjal Mondal, Subhrojit Kumar, Radhikesh Md Dan, M.F. Department of Mining Engineering Indian Institute of Technology Kharagpur721302 India Faculty of Civil Engineering University Technology Malaysia Johor UTM Skudai81310 Malaysia Department of Computer and Science Engineering Bennett University Uttar Pradesh Greater Noida201310 India Siam City Concrete Company Limited Bang Sue Bangkok10800 Thailand School of Engineering Information Technology and Physical Sciences Federation University Ballarat Australia Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University 76 Lenin Prospect Chelyabinsk454080 Russia NIT Patna800005 India Faculty of Civil and Built Environment University Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9789811697692

Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weathering. Weathering of limestone takes place very rapidly in tropical (wet) climatic regions. Previous researchers have correlated different individual rock mass properties with rate of drilling. However, single property of limestone is not adequate to correlate with the drilling rate. In this study, sensitivity analysis of different properties of weathered limestone was carried out with respect to drilling rate. Rock density, rock quality designation (RQD), geological strength index (GSI), point load index (PLI) and Schmidt hammer rebound number (SHRN) were identified as crucial input parameters. 113 data sets were collected with the foregoing five input parameters and the output parameter as drilling rate of percussive drills. Data was analysed with multi variable regression analysis (MVRA) which showed R2 value as 0.54. Artificial neural network (ANN) has been widely used for solving various engineering problems. On the other hand, optimization problems are solved by the Biogeography Based Optimization (BBO) model. Further this data was analysed with a hybrid intelligent model namely BBO- ANN. The R2 values for training data set and testing data set 0.638 and 0.761 respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Limestone

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 14784 LNCS卷 v页

作者： Bai, Guangdong Ishikawa, Fuyuki Aït-Ameur, Yamine Papadopoulos, George A. School of Electrical Engineering and Computer Science Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD Australia Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Chiyoda-ku Japan IRIT University of Toulouse Toulouse France Department of Computer Science University of Cyprus Nicosia Cyprus

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

Recommendation System Towards Residential Energy Saving Base...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Shadi Atalla Yassine Himeur Wathiq Mansoor Abbes Amira Fodil Fadli Abigail Copiaco Shahab Saquib Sohail College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah UAE Dept. of Architecture & Urban Planning Qatar University Doha 2713 Qatar School of Engineering Sciences & Technology Jamia Hamdard New Delhi 110062 India

ISBN: (纸本)9781665492669

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users’ activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors’ algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size.

关键词： Training Power demand Machine learning algorithms Shape Signal processing algorithms Signal processing Feature extraction

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Identifying users with wearable sensors based on activity patterns 11

Identifying users with wearable sensors based on activity pa...

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11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Ehatisham-Ul-Haq, Muhammad Malik, Maryam Naseer Azam, Muhammad Awais Naeem, Usman Khalid, Asra Ghazanfar, Mustansar Ali Department of Computer Engineering University of Engineering and Technology Taxila Punjab47050 Pakistan Whitecliffe Technologies Wellington New Zealand School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering and Computer Science Victoria University of Wellington New Zealand School of Architecture Computing and Engineering University of East London United Kingdom

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user's identity based on his/her daily living physical activity patterns. © 2020 The Authors. Published by Elsevier B.V.

关键词： Wearable sensors

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Secure Beamforming for Continuous Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wang, Zhaolin Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a waterfilling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Construction Progress Monitoring In Smart Cities Using Deep Alexnet

Construction Progress Monitoring In Smart Cities Using Deep ...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： A. Rajavel S. Karkuzhali Sarvdaman Sharma Sitesh Kumar Singh Senthilkumar P S. Jhansi Ida Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Business Systems R.M.K Engineering College India

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle.

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Gamma-Ray Burst Light Curve Reconstruction: A Comparative Machine and Deep Learning Analysis

arXiv

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arXiv 2024年

作者： Kaushal, A. Manchanda, A. Dainotti, M.G. Deepu, A. Naqi, S. Felix, J. Indoriya, N. Magesh, S.P. Gupta, H. Gupta, K. Madhan, A. Hartmann, D.H. Pollo, A. Bogdan, M. Prochaska, J.X. Fraija, N. Debnath, D. Department of Computer Science and Engineering UIET-H Panjab University Punjab 146001 India Department of Physics Indian Institute of Technology Telangana Hyderabad502284 India National Astronomical Observatory of Japan 2 Chome-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomical Sciences The Graduate University for Advanced Studies SOKENDAI Shonankokusaimura Miura District Kanagawa Hayama240-0115 Japan Nevada Center for Astrophysics University of Nevada 4505 Maryland Parkway Las VegasNV89154 United States Space Science Institute 4765 Walnut Street Suite B BoulderCO80301 United States Department of Ocean Engineering and Naval Architecture Indian Institute of Technology West Bengal Kharagpur721302 India KNIT Uttar Pradesh Sultanpur228118 India Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering and Computer Science IISER Madhya Pradesh Bhopal462066 India Indian Institute of Technology Madras600036 India MIT WPU School of Computer Science & Engineering Pune411038 India Department of Computer Science PSIT College Uttar Pradesh Kanpur209305 India Visveswaraya Technological University Karnataka Belagavi590018 India Department of Physics and Astronomy Clemson University ClemsonSC29634 United States Astronomical Observatory of Jagiellonian University in Kraków Orla 171 Kraków30-244 Poland National Centre for Nuclear Research Warsaw02-093 Poland Department of Mathematics University of Wroclaw Wroclaw50-384 Poland University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States National Autonoma University of Mexico Circuito Interior Mexico City04510 Mexico NIT Tripura Agartala799046 India

Gamma-Ray Bursts (GRBs), observed at large redshifts, are probes of the evolution of the Universe and can be used as cosmological tools. To this end, we need tight (with small dispersion) correlations among key parameters. To reduce such a dispersion, we will mitigate gaps in light curves (LCs), including the plateau region, key to building the two-dimensional Dainotti relation between the end time of plateau emission (Ta) to its luminosity (La). We reconstruct LCs using nine models: Multi-Layer Perceptron (MLP), Bi-Mamba, Fourier Transform, Gaussian Process–Random Forest Hybrid (GP-RF), Bidirectional Long Short-Term Memory (Bi-LSTM), Conditional GAN (CGAN), SARIMAX-based Kalman filter, Kolmogorov–Arnold Networks (KANs), and Attention U-Net. These methods are compared to the Willingale model (W07) over a sample of 545 GRBs. MLP and Bi-Mamba outperform other methods, with MLP reducing the plateau parameter uncertainties by 25.9% for log Ta, 28.6% for log Fa, and 37.7% for α (the post-plateau slope in the W07 model), achieving the lowest 5-fold cross validation (CV) mean squared error (MSE) of 0.0275. Bi-Mamba achieved the lowest uncertainty of parameters, a 33.3% reduction in log Ta and log Fa and a 41.9% in α, but with a higher MSE of 0.130. Bi-Mamba brings the lowest outlier percentage for log Ta and log Fa (2.70%), while MLP carry α outliers to 0.900%. The other methods yield MSE values ranging from 0.0339 to 0.174. These improvements in parameter precision are needed to use GRBs as standard candles, investigate theoretical models, and predict GRB redshifts through machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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