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Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

npj Computational Materials 2023年第1期9卷 2155-2166页

作者： Alejandro Rodriguez Changpeng Lin Hongao Yang Mohammed Al-Fahdi Chen Shen Kamal Choudhary Yong Zhao Jianjun Hu Bingyang Cao Hongbin Zhang Ming Hu Department of Mechanical Engineering University of South CarolinaColumbiaSC 29208USA Theory and Simulation of Materials(THEOS) École Polytechnique Fédérale de LausanneCH-1015 LausanneSwitzerland Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Materials Science Technical University of DarmstadtDarmstadt 64287Germany Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29208USA

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneckwith the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness andprecision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learningand data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralowlattice thermal conductivity (<1 Wm^(−1) K^(−1)) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, aclass of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550quaternary Heuslers, respectively.

关键词： properties phonon spanning

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Studying the Performance of Digital Signature Algorithms in Information Security Models for Networking Applications 2

Studying the Performance of Digital Signature Algorithms in ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sharma, Mohit Kumar Gopalakrishna, K. Kumari, Aarsi Vivekananda Global University Department of Electrical Engineering Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Latest advances in records generation have enabled virtual signatures for use in a variety of net-primarily based and network-primarily based applications. Digital signatures are used to authenticate the identity of a user, as well as to affirm the integrity and facts confidentiality of the transmitted information. However, virtual signatures are a challenge to numerous safety and overall performance problems, some of which may be exploited by malicious attackers. This paper seeks to study the performance of digital signature algorithms in a diffusion of records protection fashions implemented in networking programs. The paper will, in particular survey, present digital signature algorithms used for authentication and encryption of statistics in networking applications, as well as contemporary studies associated with enhancing their security and overall performance. Furthermore, the paper will identify the top-of-the-line algorithms and fashions encouraged for use in specific eventualities and applications. Moreover, the paper will analyze the usability of these fashions in phrases of their complexity, scalability, and user requirements. Eventually, the paper will gift future research directions and guidelines for similarly enhancing the safety and performance of digital signatures in the context of networking programs. © 2024 IEEE.

关键词： Authentication

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Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems

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Neural Computing and Applications 2024年第26期36卷 16195-16212页

作者： González-Afanador, Ian Chen, Claudia Morales-Torres, Gerardo Meihls, Scott Shi, Hongyang Tan, Xiaobo Sepúlveda, Nelson Department of Electrical and Computer Engineering Michigan State University 426 Auditorium Road East LansingMI48824 United States Great Lakes Science Center U.S. Geological Survey MillersburgMI49759 United States

Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision trees

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SOS: Dynamic Secure Code Offloading for Power Minimization in LEO Satellite Edge Computing

SOS: Dynamic Secure Code Offloading for Power Minimization i...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Jeongsoo Kim Suhyeon Jeon Jeongho Kwak Department of Electrical Engineering and Computer Science DGIST Korea

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

The satellite edge computing (SEC) has recently received considerable attention thanks to its wide area service around the world. However, this also creates a risk of exposing private user data to eavesdroppers. Physical layer security can help prevent this, yet it requires extra usage of network resources. Hence, efficient management of these resources is essential for saving power and ensuring secure code offloading. Moreover, from the perspective of mobile devices that request services, the level of security demands is quite different for various services, yet current studies have not fully considered this aspect. In this paper, we propose a secure code offloading framework for an SEC system with a jamming strategy in the existence of eavesdropping satellite. We formulate an average power minimization problem of an LEO satellite, a gateway, and a mobile device while ensuring security and the stability of queues. This includes making decisions of code offloading, computing/network resource allocation, and jamming unit selection. As a solution of this problem, we propose an SOS algorithm by invoking stochastic optimization theory. Finally, via extensive simulations, we demonstrate that the proposed SOS algorithm can save up to 60% of average power compared to existing algorithms while maintaining the same delay and zero leakage of information toward eavesdropper.

关键词： Satellites Codes Heuristic algorithms Low earth orbit satellites Minimization Mobile handsets Security Jamming Optimization Edge computing

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An Improved Fuzzy Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization Problems with Complicated Pareto Sets 12th

An Improved Fuzzy Classifier-Based Evolutionary Algorithm fo...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Zhang, Jinyuan He, Linjun Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Various surrogate-based multiobjective evolutionary algori-thms (MOEAs) have been proposed to solve expensive multiobjective optimization problems (MOPs). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are challenging to construct reliable surrogates for surrogate-based MOEAs. Constructed surrogates with low accuracy are likely to make incorrect predictions and even mislead the search direction. In this paper, we propose an improved fuzzy classifier-based MOEA by leveraging the accuracy information of the classifier. The proposed algorithm is compared with five state-of-the-art algorithms on two well-known test suites with complicated Pareto sets and four real-world problems. Experimental results demonstrate the effectiveness of the proposed algorithm in solving realistic MOPs with complicated Pareto sets when only a limited number of function evaluations are available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

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computer Systems science & engineering 2022年第6期41卷 1223-1240页

作者： Anika Tahsin Meem Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

关键词： Covid-19 prediction covid-19 coronavirus normal deep learning convolutional neural network image processing chest x-ray

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Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 348-363页

作者： Shaik, Reddi Khasim Priya, S. Shanmuga Saranya, N. R, Kotteeswaran Ramya, S. Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India SRM Institute of Science and Technology Tiruchirappalli India Department of EIE St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Information Technology Prathyusha Engineering College Chennai India

Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variables such as temperature, precipitation, and soil moisture is crucial for a wide range of applications, including agriculture, water resource management, and disaster preparedness. Traditional models have often struggled to capture the complex, nonlinear relationships and temporal dependencies inherent in climate data. In this study, we present a novel Hybrid MLP-GRU Network for climate prediction, leveraging the combined strengths of Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models. This hybrid approach aims to capture both complex, nonlinear relationships and temporal dependencies in climate data, enhancing the accuracy and reliability of predictions. We utilized comprehensive datasets from the Copernicus Climate Change Service (C3S), focusing on essential variables such as temperature, precipitation, soil moisture, and vegetation indices. The data underwent rigorous preprocessing, including normalization, temporal alignment, and feature extraction, to ensure high-quality inputs for the model. The model was trained and validated on extensive climate datasets, demonstrating superior performance metrics with an accuracy of 97.77%, RMSE of 0.140, and MAE of 0.120 on validation data. Comparative analysis with other models, including Linear Regression, Random Forest, LSTM, and CNN, highlights the efficacy of our hybrid approach. The Hybrid MLP-GRU Network consistently outperformed these models in terms of RMSE, MAE, and accuracy. Additionally, hyperparameter tuning, cross-validation, and deployment assessments were conducted to optimize the model’s performance and ensure robust predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate data Climate prediction Data preprocessing Feature extraction Gated Recurrent Unit Hybrid MLP-GRU Network Machine learning Multi-Layer Perceptron Nonlinear relationships Temporal dependencies

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Automatic search for photoacoustic marker using automated transrectal ultrasound

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Biomedical Optics Express 2023年第11期14卷 6016-6030页

作者： Wu, Zijian Moradi, Hamid Yang, Shuojue Song, Hyunwoo Boctor, Emad M. Salcudean, Septimiu E. Johns Hopkins University Laboratory for Computational Sensing and Robotics BaltimoreMD United States University of British Columbia Dept. of Electrical and Computer Engineering VancouverBC Canada University of Texas at Austin Walker Department of Mechanical Engineering AustinTX United States Johns Hopkins University Dept. of Computer Science BaltimoreMD United States

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [IEEE Robot. Autom. Lett 8, 1287 (2023).]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53°±0.30° average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29◦±0.31◦ and 0.48°±0.32° average errors after 28 data acquisitions. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Urology

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Sensing Metal Coil Vibration of Headsets for Eavesdropping on Online Conversations With Out-of-Vocabulary Words Using RFID

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Yunzhong Yu, Jiadi Chen, Yingying Kong, Linghe Zhu, Yanmin Chen, Yichao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Rutgers University Department of Electrical and Computer Engineering United States

As one of the most essential accessories, headsets have been widely used in common online conversations. The metal coil vibration patterns of headset speakers/microphones have been proven to be highly correlated with the speaker-produced/microphone-received sound. This paper presents an online conversation eavesdropping system, RFSpy, which uses only one RFID tag attached on a headset to alternately sense metal coil vibrations of headset speaker and microphone for eavesdropping on speaker-produced and microphone-received sound. In some accessible scenarios, assuming attackers secretly attach a small, battery-free RFID tag under one ear cushion of an eavesdropped user's headset without being noticed. Meanwhile, RFID readers are camouflaged as decorations placed in/out of rooms to transmit and receive RF signals. When the eavesdropped user talks with other users online through the headset, RFSpy first activates the RFID tag to capture the metal coil vibration patterns of headset speaker and microphone upon RF signals. Then, RFSpy reconstructs sound spectrograms from the RF signal-based vibration patterns for not only trained words but also untrained (i.e., out-of-vocabulary) words utilizing designed SSR network. Finally, RFSpy converts the sound spectrograms to conversation content through sound recognition API. Extensive experiments demonstrate that RFSpy can eavesdrop on online conversations with out-of-vocabulary words effectively. © 2002-2012 IEEE.

关键词： Microphones

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A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

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IEEE Transactions on Industrial Informatics 2022年 1-12页

作者： Wang, Xiaokang Yang, Laurence T. Cao, Enyuan Guo, Luyang Ren, Lei Deen, M. Jamal School of Computer Science and Technology Hainan University Haikou China School of Artificial Intelligence University of the Chinese Academy of Sciences Beijing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering McMaster University Hamilton Canada

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model. IEEE

关键词： Time domain analysis

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