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arXiv 2023年

作者： Li, Qipeng Zhuang, Yuan Chen, Yiwen Huai, Jianzhu Li, Miao Ma, Tianbing Tang, Yufei Liang, Xinlian The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430206 China The Institute of Technological Sciences Wuhan University Wuhan430206 China The Anhui University of Science and Technology Anhui Huainan232001 China The Department of Computer Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Improved block chain system for high secured IoT integrated supply chain

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Measurement: Sensors 2023年 25卷

作者： N, Jayashri Rampur, Veeresh Gangodkar, Durgaprasad M, Abirami C, Balarengadurai N, Anil Kumar Department of Computer Applications Dr. M.G.R Educational and Research Institute Tamil Nadu Chennai India Dept of Electronics Government First Grade College Naubad Karnataka State Bidar585401 India Dept of Computer Science Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Fairlands Tamil Nadu Salem16 India Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Electronics & Instrumentation Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India

The incredibly complex supply chains in today's world face significant problems in terms of accountability and reliability. Blockchain technology could be able to solve these problems by offering a tamper-proof audit trail of supply chain activities & data on a product lifecycle, but it could resolve the issue of information's inherent lack of reliability. Current Reputation systems offer a practical solution to the confidence issue. Moreover, it is based on a small number of observations, lack granularity or mechanization have considerable overhead, current reputational methods are not suitable for blockchain-based supply chain operations. In this research, we present TrustChain, a three-layered system for managing trust that tracks connections between supply chain actors and automatically calculates trust and reputation scores based on those connections using a consortium blockchain with Internet of Things (IoT). Trustchain would be novel because it supports reputation rankings that distinguish between supply chain participants & goods, allowing the assignment of product-specific reputations for the same participant, (a) the reputation design that assesses the performance of commodities and the trustworthiness of entities based on several observations of supply chain events, (b) the use of smart contracts for transparent, impactful, safe, and automated computation of reputation rating, and (c) reputation scores that differentiate between supply chain members and services. © 2022 The Authors

关键词： Internet of things

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Construction of Low-carbon Economic Development Level Model Based on BP Neural Network Algorithm

Construction of Low-carbon Economic Development Level Model ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Nigmatullayeva Gulchekhra Nurullayevna A.Gnana Soundari I.Parvin Begum Santhosh S Laith H. Alzubaidi Aiman Naqvi Department of «Green» Economy and Sustainable Business Tashkent State University of Economics Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Applications B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India College of Technical Engineering The Islamic University Najaf Iraq M S Ramaiah Institute Of Technology Bangalore

The Low-Carbon Economy (LCE) is an all-encompassing topic that combines economic, social, and environmental concerns with technological ones. It encompasses the ideas of low-carbon industry, low-carbon technology, low-carbon energy, low-carbon consumption, and carbon sinks. The expansion of LCE in India is hampered by a number of causes, one of which is India's illogical industrial structure, which is also an important component. As a result, the BPNN (BP neural network) technique is utilized in this study in order to construct the LCE development level model. First, an index system for controlling total carbon emissions should be created for LCE development. Next, an evaluation method that is based on the BPNN model should be developed. Examine the precision of the second generation prediction model by applying the test sample set to it, and continue this process until you have gotten the final prediction model that satisfies the convergence conditions. The findings of the research indicate that the maximum error that can occur between the actual output value of BPNN and the expected output value of BPNN is 8.36%, and the error is less than 10%, which indicates that the training results of the BPNN evaluation model constructed in this paper are good, and the evaluation results obtained are quite satisfactory.

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Carrier recombination dynamics in green InGaN-LEDs with quantum-dot-like structures

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JOURNAL OF MATERIALS science 2021年第2期56卷 1481-1491页

作者： Tian, Ming Ma, Cangmin Lin, Tao Liu, Jianping Talwar, Devki N. Yang, Hui Cao, Jiehua Huang, Xinying Niu, Wenlong Ferguson, Ian T. Wan, Lingyu Feng, Zhe Chuan Laboratory of Optoelectronic Materials and Detection Technology Center on Nanoenergy Research Guangxi Key Laboratory for the Relativistic Astrophysics School of Physics Science and Technology Guangxi University Nanning China State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou China Department of Physics University of North Florida Jacksonville USA Southern Polytechnic College of Engineering and Engineering Technology Kennesaw University Kennesaw USA

Exciton localization phenomena are considered here to comprehend the high internal quantum efficiency in InGaN/GaN multiple-quantum-well structures having discrete quantum dots (QDs) prepared by metal-organic-chemical-vapor deposition method onc-sapphire substrates. Spectroscopic results from the variable-temperature steady-state-photoluminescence and time-resolved photoluminescence (TRPL) are investigated. While the exciton localization is enhanced by strong localized states within the InGaN/GaN QDs-the impact of free carrier recombination cannot be ignored. The observed non-exponential decay in TRPL measurements is explained using a model by meticulously including localized exciton, non-radiative and free carrier recombination rates. A new method is proposed to calculate the internal quantum efficiency, which is supplementary to the traditional approach based on temperature-dependent photoluminescence measurement.

关键词： Photoluminescence

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Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion

arXiv

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arXiv 2024年

作者： Su, Bowen You, Juntao Cai, HanQin Huang, Longxiu Department of Mathematics Michigan State Univeristy East LansingMI48824 United States Institute for Advanced Study Shenzhen University Shenzhen518000 China Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both methods lack sufficient flexibility for diverse practical applications. To address this, we introduce Tensor Cross-Concentrated Sampling (t-CCS), a novel and straightforward sampling model that advances the matrix cross-concentrated sampling concept within a tensor framework. t-CCS effectively bridges the gap between Bernoulli and t-CUR sampling, offering additional flexibility that can lead to computational savings in various contexts. A key aspect of our work is the comprehensive theoretical analysis provided. We establish a sufficient condition for the successful recovery of a low-rank tensor from its t-CCS samples. In support of this, we also develop a theoretical framework validating the feasibility of t-CUR via uniform random sampling and conduct a detailed theoretical sampling complexity analysis for tensor completion problems utilizing the general Bernoulli sampling model. Moreover, we introduce an efficient non-convex algorithm, the Iterative t-CUR Tensor Completion (ITCURTC) algorithm, specifically designed to tackle the t-CCS-based tensor completion. We have intensively tested and validated the effectiveness of the t-CCS model and the ITCURTC algorithm across both synthetic and real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

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Measurement: Sensors 2023年 27卷

作者： Michael Onyema, Edeh Balasubaramanian, Sundaravadivazhagn Suguna S, Kanimozhi Iwendi, Celestine Prasad, B.V.V. Siva Edeh, Chinecherem Deborah Department of Vocational and Technical Education Faculty of Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Information Technology University of Technology and Applied Sciences AlMussanah Oman Department of Computer Applications ArulmiguArthanareeswarar Arts and Science College Tamil Nadu Tiruchengode India School of Creative Technologies University of Bolton United Kingdom Department of Computer Science and Engineering Anurag University Hyderabad India Faculty of Law Enugu State University of Science and Technology Nigeria

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques. © 2023 The Authors

关键词： Object detection

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AI-Enhanced Performance Monitoring for Saas applications in the Cloud

AI-Enhanced Performance Monitoring for Saas Applications in ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Punit Goel Naga Srirama Narasimha Raviteja Malladi Hina Gandhi Siddharth Choudhary Rajesh Vibhor Goyal Sanjeev Pippal Maharaja Agrasen Himalayan Garhwal University Uttarakhand India California State University Los Angeles CA United States Northeastern University Boston MA USA New York University New York United States Florida State University Tallahassee FL United States Department of Computer Science and Engineering (AI) G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

As SaaS applications in the cloud continue to flourish, effective application performance monitoring and management solutions have become a necessity. AI technology provides a significant, effective solution to this problem by implementing an extensive, on-time execution monitoring system that can comprehend the SaaS applications' acting circumstances. This AI-enhanced performance Monitoring for SaaS applications in the Cloud is an advanced solution that leverages machine learning and artificial intelligence methods to monitor and enhance the performance of SaaS applications. It's trained to receive and analyze extensive data from constant interaction with users, as well as the metrics of servers and the traffic from the network. The algorithms then use that data to spot patterns and anomalies, predict performance issues before they occur, and automatically take proactive steps to optimize application performance. The system is constantly learning and continues to adapt to changing conditions, which allows it to detect and resolve issues quickly, ensuring maximum user experience and minimal disruption to business. AI-enhanced performance Monitoring system also provides detailed insights and analytics to help make better decisions and allocate resources efficiently. This solution allows companies to ensure their SaaS apps continue to work at an optimal level so that users remain happy, operations are efficient, and the company is able to succeed.

关键词： Measurement Shape Companies Telecommunication traffic Prediction algorithms User experience Servers Monitoring Optimization Business

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Unexpected Trade-Offs of Fossil Fuel Reduction on PM2.5and O3Pollution Regulation Can Be Offset by Synergistic Control of VOCs Source

ACS ES&T Air

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ACS ES&T Air 2024年第7期1卷 660-669页

作者： Guoliang Shi Haofei Yu Jie Li Zhang Zhang Feng Wang Junbo Huang Yue Li Yulong Yan Yinchang Feng Armistead G. Russell State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission Research College of Environmental Science and Engineering Nankai University Tianjin 300350 People’s Republic of China China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research College of Environmental Science and Engineering Nankai University Tianjin 300350 China Department of Civil Environmental and Construction Engineering University of Central Florida Orlando Florida 32816 United States School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin 300384 China College of Computer Science Nankai University Tianjin 300350 China School of Environment Beijing Jiaotong University Beijing 100044 China School of Civil and Environmental Engineering Georgia Institute of Technology Atlanta Georgia 30332-0512 United States

Particulate matter (PM2.5) and ozone (O3) are typical air pollutants that require effective simultaneous control. Current PM2.5and O3co-mitigation strategies are primarily focused on the emission optimization of nitrogen oxides (NOx) and volatile organic compounds (VOCs); however, fundamental mechanisms underlying these co-mitigation efforts remain largely unanswered. Here, utilizing an atmospheric chemistry model combined with machine learning technique, we extend the scope of coordinated control efforts beyond just precursors by identifying and controlling common sources of PM2.5and O3, which are fossil fuel combustion sources including coal combustion, industrial emission, and vehicle exhausts. We conducted further sensitivity simulations and found that reducing fossil fuel combustion emissions significantly lowers PM2.5levels but has trade-off effects on O3control, which is attributable to the suppressed aerosol sink of hydroperoxyl radicals and the enhanced atmospheric oxidizing capacity. Furthermore, this unexpected enhancement of O3can be offset by synergistically controlling certain other specific VOCs sources. For example, during days with high levels of both PM2.5and O3pollution, a 40% reduction of VOCs sources at the corresponding level of coal combustion reduction is predicted to lead to an ∼13 ppb (∼14%) reduction in O3concentration. Our results provide the scientific evidence and theoretical framework for mitigating PM2.5and O3pollution through joint control of fossil fuel combustion sources and specific VOCs sources.

关键词： Environmental pollution Fossil fuels Particulate matter Redox reactions Volatile organic compounds

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ACCELERATING ILL-CONDITIONED HANKEL MATRIX RECOVERY VIA STRUCTURED NEWTON-LIKE DESCENT∗

arXiv

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arXiv 2024年

作者： Cai, Hanqin Huang, Longxiu Lu, Xiliang You, Juntao Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48823 United States School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan430072 China Institute for Advanced Study Shenzhen University Shenzhen518000 China

This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem. HSNLD is highly efficient with linear convergence, and its convergence rate is independent of the condition number of the underlying Hankel matrix. The recovery guarantee has been established under some mild conditions. Numerical experiments on both synthetic and real datasets show the superior performance of HSNLD against state-of-the-art *** Codes 15A29, 15A83, 47B35, 90C17, 90C26, 90C53 Copyright © 2024, The Authors. All rights reserved.

关键词： Recovery

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Non-Variational ADAPT algorithm for quantum simulations

arXiv

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arXiv 2024年

作者： Tang, Ho Lun Chen, Yanzhu Biswas, Prakriti Magann, Alicia B. Arenz, Christian Economou, Sophia E. Department of Physics Virginia Tech BlacksburgVA24061 United States Virginia Tech Center for Quantum Information Science and Engineering BlacksburgVA24061 United States Department of Physics Florida State University TallahasseeFL32306 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction. We compare this non-variational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation. We find that despite using deeper circuits, this new algorithm reaches chemical accuracy at a similar measurement cost to ADAPT-VQE. Since it does not rely on a classical optimization subroutine, it may provide robustness against circuit parameter errors due to imperfect control or gate synthesis. © 2024, CC BY.

关键词： Hamiltonians

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