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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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Online Pattern Recognition and Data Correction of PMU Data Under GPS Spoofing Attack

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Journal of Modern Power systems and Clean Energy 2020年第6期8卷 1240-1249页

作者： Ancheng Xue Feiyang Xu Jingsong Xu Joe H.Chow Shuang Leng Tianshu Bi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China State Grid Ningxia Yinchuan Electric Power Company Yinchuan 750001China the Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Smart grids are increasingly dependent on data with the rapid development of communication and *** one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from *** with cyber attacks,global position system(GPS)spoofing attacks(GSAs)are easier to implement because they can be exploited by portable devices,without the need to access the physical ***,this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference(PAD)***,this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA,i.e.,the step attack and the ramp ***,the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed,which does not require the line *** obtaining the estimated PAD deviations,the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD ***,with the case studies,the effectiveness of the proposed method is demonstrated,and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.

关键词： global position system(GPS) GPS spoofing attack(GSA),phasor measurement,pattern recognition,data correction,line parameter

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

A Development of Time-Varying Weight Model Predictive Contro...

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IEEE International Conference on systems, Man and Cybernetics

作者： Mohammad Reza Chalak Qazani Houshyar Asadi Arian Shajari Zoran Najdovski Chee Peng Lim Saeid Nahavandi Department of the Computer Science and Information Systems Murray State University Murray Murray KY United States Institute for Intelligent System Research and Innovation Deakin University Geelong VIC Australia Swinburne University of Technology Hawthorn VIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University Allston MA USA

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.

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Real-time multiple people gait recognition in the edge

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Scientific reports 2025年第1期15卷 19276页

作者： Paula Ruiz-Barroso José María González-Linares Francisco M Castro Nicolás Guil Department of Computer Architecture Institute for Mechatronics Engineering & Cyber-Physical Systems (IMECH.UMA) Universidad de Málaga Málaga Spain. Department of Computer Architecture Institute for Mechatronics Engineering & Cyber-Physical Systems (IMECH.UMA) Universidad de Málaga Málaga Spain. jgl@uma.es.

Deploying deep learning models on edge devices offers advantages in terms of data security and communication latency. However, optimizing these models to achieve fast computing speeds without sacrificing accuracy can be challenging, especially in video surveillance applications where real-time processing is crucial. In this study, we investigate the deployment of gait recognition models as a multi-objective selection problem in which we seek to simultaneously minimize several objectives, such as latency and energy consumption, while maintaining accuracy. The decision space of a problem comprises all models that can be built by varying parameters, such as the size of the model, the operating frequency of the device, and the precision of the operations. From this problem definition, a subset of Pareto optimal models can be selected to be deployed on the target device. We conducted experiments with two different gait recognition models on NVIDIA Jetson Orin Nano and Jetson AGX Orin to explore their decision spaces. In addition, we investigated different strategies to increase the throughput of the deployed models by taking advantage of batching and concurrent execution. Together, these techniques allowed us to design real-time solutions for gait recognition in scenarios with multiple subjects. These solutions can process between 42 and 188 simultaneous subjects at 25 inferences per second with an energy consumption ranging from 6.31 to 9.71 mJ per inference, depending on the device and the deployed model.

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Electrical Integration of the VenSpec Spectrometer Consortium: An architecture Trade-off 32

Electrical Integration of the VenSpec Spectrometer Consortiu...

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Infrared Remote Sensing and Instrumentation XXXII 2024

作者： Fitzner, Alexander Hafemeister, Lisa Del Togno, Simone Lötzke, Horst-Georg Wendler, Belinda Wolff, Friederike Helbert, Jörn Gutiérrez-Marqués, Pablo Nathues, Andreas Perplies, Henry Loose, Alexander Fischer, Henning Marlur, Vrushabh Hall, Ian Meller, Reinhard Castro, Jose M. Ortega, Jaime Jiménez Lara, Luisa M. Alvarez, Fernando Nogales, Álvaro Mazuecos Fiethe, Björn Gómez, Andrès Buchhorn, Dennis Neefs, Eddy De Cock, Roderick Erwin, Justin Robert, Séverine Vandaele, Ann-Carine Berkenbosch, Sophie Hagelschuer, Till Peter, Gisbert Pertenais, Martin Lustrement, Benjamin Hassan-Khodja, Rafik Vivat, Francis Bertran, Sandrine Marcq, Emmanuel Parro, Vanderlei Cunha de Marca Franҫa, Rodrigo Institute of Planetary Research German Aerospace Center Rutherfordstr. 2 Berlin12489 Germany Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Instituto de Astrofísica de Andalucía Glorieta de la Astronomía s/n Granada18008 Spain Institute of Computer and Network Engineering Hans-Sommer-Str. 66 Braunschweig38106 Germany Royal Belgian Institute for Space Aeronomy Ringlaan 3 Avenue Circulaire Brussels1180 Belgium Institute of Optical Sensor Systems German Aerospace Center Rutherfordstr.2 Berlin12489 Germany Laboratoire Atmosphères Observations Spatiales UVSQ 11 boulevard D’Alembert Guyancourt78280 France Instituto Mauá de Tecnologia Rua Pedro de Toledo Vila Mariana SP São Paulo1071 Brazil Hensoldt Space Consulting 1 Rond-Point du Général Eisenhower Golf Park - Bâtiment F Toulouse31100 France

ISBN: (纸本)9781510679481

For ESA’s EnVision Mission to Venus, a consortium of three spectrometers from across Europe has been formed to collaborate not only on the management and science aspects, but also on the technical implementation. One important technical goal of the VenSpec suite is to implement a clean, simple and robust interface to the spacecraft and to provide an abstraction layer between the channels and the spacecraft. This is achieved by implementing the Central Control Unit (CCU), which provides a harmonized power and data interface to the spacecraft and allows the channels to design for a simple tailored internal interface to the CCU. The CCU consists of two electrical subsystems, the Data Handling Unit (CCU DHU), developed by the Max Planck institute for Solar system Research (MPS) in Göttingen and the institute of computer and Network engineering (IDA) in Braunschweig and the Power Supply Unit (CCU PSU), developed by the Instituto de Astrofísica de Andalucía (IAA-CSIC) in Granada, the system responsibility being at the DLR institute of Planetary Research (DLR-PF) in Berlin. Within this framework, an extended electrical architecture trade-off was performed in 2023 to optimize the system, guaranteeing the requested functionality and complying to requirements from all sides. As a result of the trade-off. a single power and data interface were found to be the most suitable and robust solution considering performance, reliability, Fault Detection Isolation and Recovery (FDIR) and Electromagnetic Compatibility (EMC) considerations as well as the complexity of the associated verification campaign. This paper demonstrates the options that were suggested by the different parties and justifies the final architecture, which has been chosen to achieve the best solution for the VenSpec suite. © 2024 SPIE.

关键词： Interplanetary flight

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FLEE-GNN: A Federated Learning system for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows

arXiv

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arXiv 2023年

作者： Qu, Yuxiao Rao, Jinmeng Gao, Song Zhang, Qianheng Chao, Wei-Lun Su, Yu Miller, Michelle Morales, Alfonso Huber, Patrick R. Department of Computer Science Carnegie Mellon University United States Geospatial Data Science lab University of Wisconsin Madison United States Department of Computer Science and Engineering Ohio State University United States Center for Integrated Agricultural Systems University of Wisconsin Madison United States Department of Planning and Landscape Architecture University of Wisconsin Madison United States Institute of the Environment University of California Davis United States

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning system for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks. FLEE-GNN addresses the limitations of current methodologies, such as entropy-based methods, in terms of generalizability, scalability, and data privacy. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning on food supply network resilience analysis across geographical regions. This paper also discusses FLEE-GNN’s innovative data generation techniques, experimental designs, and future directions for improvement. The results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks, contributing to efforts towards ensuring global food security using AI methods. The developed FLEE-GNN has the potential to be applied in other spatial networks with spatially heterogeneous sub-network distributions. © 2023, CC BY.

关键词： Geographical regions

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Secure Relay Selection for UAV-empowered Two Way Untrusted Relaying Networks

Secure Relay Selection for UAV-empowered Two Way Untrusted R...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Kan Yu Zhiyong Feng Lin Yang Dianxia Chen Dong Li Xiaoshan Ma The School of Computer Science and Engineering Macau University of Science and Technology Taipa P. R. China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R. China School of Computer Science and Engineering Macau University of Science and Technolog China The National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute AMS PLA Beijing P.R. China The Education Tianjin Ren’ai College Tianjin P.R. China The School of Computer Science and Engineering Macau University of Science and Technology P. R. China The Academy of Military Sciences of PLA Beijing P.R. China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In this paper, we examine the security aspects of two-way untrusted relaying networks (TWURNs) through the lens of physical layer security (PLS). These networks involve the exchange of confidential information between two multiple-antenna base stations (BSs) with the aid of unmanned aerial vehicle (UAV)-enabled untrusted relays equipped with a single antenna. Given the shortcomings observed in existing single untrusted relay selection methods, we introduce an enhanced approach grounded in the min-min criterion (MMC) based on received signal strength. Additionally, we present a novel closed-form expression for the secrecy outage probability (SOP), a fundamental metric within the domain of PLS, derived from the cumulative distribution function (CDF) and probability density function (PDF) of received signal strengths. Through simulations, we substantiate the validity of our analytical findings, demonstrating the superior efficacy of the proposed MMC compared to prevailing schemes.

关键词： Measurement Probability density function Probability Physical layer security Minimax techniques Power system reliability Relays

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