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Solar Radiation Prediction Using the Random Forest Regression Algorithm 4th

Solar Radiation Prediction Using the Random Forest Regressio...

引用

4th EAI international conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2021

作者： Thota, Sandeep Kumar Reddy Mala, C. Chandamuri, Praneetha Nooka, Chandana Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies- RK Valley Campus Andhra Pradesh Kadapa India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9783031076534

Solar radiation data is critical for solar energy research and development. It is extremely valuable for design and simulation of solar thermal technologies and solar photovoltaic applications because it offers essential data about the energy that impacts the earth. Because conventional energy sources are rapidly decreasing, sustainable energy sources such as solar, biomass, and wind are growing more prominent. Solar energy is the most generally acknowledged renewable energy source since it is abundant all around the planet. As a result, this project examines the model and methodologies utilized to forecast solar radiation. Random forest algorithm is used in solar radiation prediction;this algorithm combines the benefits of both techniques utilized, resulting in faster calculation and convergence to optimal results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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On Some Applications of the Orthogonal Systems of Functions 3

On Some Applications of the Orthogonal Systems of Functions

引用

3rd IEEE international conference on System Analysis and Intelligent computing, SAIC 2022

作者： Voloshyna, Tetiana Karakhanov, Denys Voloshyn, Mychaylo Lesya Ukrainka Volyn National University Faculty of Information Technology and Mathematics Lutsk Ukraine Lesya Ukrainka Volyn National University Faculty of Geography Lutsk Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

When developing automatic signal processing systems, it is important to determine a set of parameters in order to optimize them and increase their efficiency. In the paper the modified Fourier transformation methods for digital signal processing have been used. A new approach to the construction of special polynomials has been offered by means of which the summing function is realized. The matrix methods of summation with rectangular matrices and their efficiency in comparison with the methods based on the use of triangular matrices have been investigated. Necessary and sufficient conditions for uniform convergence of the constructed trigonometric series for any continuous function on a segment in terms of a system of numerical coefficients have been established. The Lebesgue convergence of the constructed series for any summable periodic function has been studied. The obtained results can be used in digital signal processing systems based on the use of Fourier transformations. © 2022 IEEE.

关键词： Digital signal processing

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Stacking Ensembles-based Approach for Pattern Diagnosis of Tabular Data 14

Stacking Ensembles-based Approach for Pattern Diagnosis of T...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Lee, JunKoo Chung, Tai-Myoung Sungkyunkwan University Artificial Intelligence Major Suwon Korea Republic of Sungkyunkwan University College of Computing and Informatics Suwon Korea Republic of

ISBN: (纸本)9798350313277

The introduction of an automation system in facilities has enabled the continuous flow of data streams, facilitating easier and more accessible data collection. Moreover, advancements in machine learning and deep learning techniques have made it possible to detect abnormalities in data streams, such as electrical energy and equipment diagnosis data, almost in real-time. Early detection of such anomalies allows for proactive maintenance and cost savings, ensuring stable operation of the entire system. Conventionally, the State of Charge (SOC) and State of Health (SOH) have been used as major criteria for assessing facility abnormality in electrical energy data. However, these measures are not suitable for real-time diagnostics due to their design for post-discharge battery analysis. In this paper, we propose the utilization of ensemble stacking technique from the ensemble models to enable accurate and real-time health condition monitoring of electrical equipment using open data sources. Additionally, we perform a comparative analysis among various machine learning and deep learning techniques and optimize the model using a grid search algorithm to achieve a high-performing, robust, and generalizable health condition monitoring system. © 2023 IEEE.

关键词： Ensemble Learning Machine Learning Pattern Diagnosis Stacking Tabular Data

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Introduction to Heterogeneous Knowledge Transfer for Quantum Convolutional Neural Networks

Introduction to Heterogeneous Knowledge Transfer for Quantum...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Emily Jimin Roh Kihun Lee Joongheon Kim Department of Electrical and Computer Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The convolutional neural network (CNN) is widely utilized in computer vision due to its ability to effectively harness correlation information within data. However, when the data's dimensionality or the model's complexity becomes excessively large, CNN faces significant challenges in maintaining efficient learning. The quantum convolutional neural network (QCNN) offers an innovative solution by leveraging quantum computing environments, to address problems conventionally solved by CNN or enhance the performance of existing learning models. This paper proposes a model of the CNN structure for quantum computing and introduces a method for enhancing model performance by integrating heterogeneous knowledge transfer (HKT) for QCNN with the CNN model used in conventional computer vision tasks. Furthermore, this paper examines the feasibility of the QCNN-HKT model in comparison to CNN and QCNN by conducting training experiments on the KITTI dataset with quantum computing simulation libraries Torchquantum.

关键词： Training Computer vision Quantum computing Correlation Computational modeling Libraries Information and communication technology Convolutional neural networks Knowledge transfer Faces

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Efficient Implementation of the Classic McEliece on ARMv8 Processors 1

引用

24th World international conference on Information Security Applications (WISA)

作者： Sim, Minjoo Kwon, Hyeokdong Eum, Siwoo Song, Gyeongju Lee, Minwoo Seo, Hwajeong Hansung Univ Dept Informat Comp Engn Seoul 02876 South Korea Hansung Univ Dept Convergence Secur Seoul 02876 South Korea

ISBN: (数字)9789819980246

ISBN: (纸本)9789819980239;9789819980246

Classic McEliece is a Code-based Key Encapsulation Mechanisms (KEM) and one of the candidate algorithms in the NIST PQC competition. Based on the McEliece cryptosystem developed in 1978, this system relies on the Niederreiter variant of McEliece. It consists of three phases: Key Generation, Encapsulation, and Decapsulation. In this paper, we propose an optimized implementation of the internal multiplication operations of Classic McEliece on the ARMv8 processor. We utilize parallel computing techniques using vector registers and vector instructions of the ARMv8 processor. We specifically focus on optimizing the multiplication operation, which is a major contributor to the overall execution time of the Classic McEliece algorithm, by leveraging the commutative property and implementing an parallelization technique. As a result, our approach achieves a maximum performance improvement of 2.82x compared to the reference implementation in the multiplication operation.

关键词： 64-bit ARMv8 Processors Classic McEliece Post-quantum Cryptography Parallel implementation KEM Software implementation

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Elimination of Interference Analysis for RIS Element Demage

Elimination of Interference Analysis for RIS Element Demage

引用

IEEE international conference on Electronic Information and Communication technology (ICEICT)

作者： Sipei Min Canping Yu Liping Li Yingsong Li Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This paper aims to investigate the ability of reconfigurable intelligent surfaces (RIS) damaged in multi-user environments to eliminate interference. Our research found that even if the RIS is damaged, interference can still be eliminated in the absence of a direct path and when the number of RIS is sufficient. In order to solve interference cancellation, we adopted the alternating projection algorithm, which ensures local convergence and good convergence speed.

关键词： Base stations Interference cancellation Reconfigurable intelligent surfaces Information and communication technology Projection algorithms convergence

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Adaptive Fixed-time Fuzzy Tracking Control of Uncertain Robot 27

Adaptive Fixed-time Fuzzy Tracking Control of Uncertain Robo...

引用

27th international conference on Automation and computing, ICAC 2022

作者： Zhou, Beibei Zhu, Chengzhi Jiang, Yiming Guangzhou Jianxin Technology Co. Ltd Guangzhou510640 China South China University of Technology Key Laboratory of Autonomous Systems and Net-worked Control College of Automation Science and Engineering Guangzhou510640 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Hunan Changsha410082 China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

We propose a novel fixed-time fuzzy control strategy to achieve the trajectory tracking of the robot systems with uncertainties in this article. The tracking errors of the system can be convergent to a small area around zero with fixed-time convergence rate, combining with the fuzzy logic system and the backstepping technique. Besides, the boundedness of all the closed-loop system signals can be proved under the proposed controller. The superiority performance of the proposed control strategy has been shown by the simulation results. © 2022 IEEE.

关键词： Closed loop systems

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VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Road 2

VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Ro...

引用

2nd international conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Lu, Xu Wang, Linfei Qi, Chongchong Zhang, Sen Zhao, Zhengpeng Yunnan University School of Information Science and Engineering Yunnan Key Laboratory of Media Convergence Kunming China Yunnan United Vision Technology Company Limited Technology Center Kunming China Yunnan Provincial Highway Science and Technology Research Institute Planning Office Kunming China Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350331417

While object detection has achieved remarkable success in tasks such as autonomous driving and intelligent transportation, there is still a gap in meeting the fine-grained detection requirements for specialized road traffic survey tasks. To address this, we built a dedicated vehicle detection dataset tailored for traffic surveys of roads, named the VDD4TS. Within monitoring scenarios, VDD4TS aims to perform detection research on ten vehicle categories, including sedans, small trucks, buses, medium-sized trucks, large trucks, extra-large vehicles, container trucks, tractors, motorcycles, and electric cars. It comprises 12,535 high-quality images, encompassing 59,242 bounding boxes. It accounts for diverse lighting and weather conditions. Furthermore, the dataset possesses three noteworthy attributes: more detailed vehicle classification, a specific focus on suburban and rural scenes that road network detection systems relatively less represent, and distinct and informative annotation details. Extensive baseline experiments were conducted in object detection tasks to validate the dataset's effectiveness. The experimental results indicate that the VDD4TS dataset poses challenges for current mainstream object detection algorithms and is suitable for evaluating more complex advanced algorithmic models. © 2023 IEEE.

关键词： dataset object detection rural roads traffic survey

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Dynamic Task Offloading Approach for Task Delay Reduction in the IoT-enabled Fog computing Systems 20

Dynamic Task Offloading Approach for Task Delay Reduction in...

引用

20th IEEE international conference on Industrial Informatics (INDIN)

作者： Hoa Tran-Dang Kim, Dong-Seong Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi Si South Korea

ISBN: (数字)9781728175683

ISBN: (纸本)9781728175683

Fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, to achieve the objective of delay reduction remains challenging for offloading strategies due to the resource limitation of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of workload distribution among the heterogeneous fog devices. To cope with the situation, this paper proposes a dynamic task offloading (DTO) approach, which is based on the resource states of fog devices to derive the task offloading policy dynamically. Accordingly, a task can be executed by either a single fog or multiple fog devices through parallel computation of subtasks to reduce the task execution delay. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.

关键词： Fog computing System Task Offloading Task Scheduling Parallel communication and computation Task Division

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UAV Networks Geometry Configuration Aided Cooperative Positioning Algorithm

UAV Networks Geometry Configuration Aided Cooperative Positi...

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IEEE international conference on Signal Processing, Communications and computing (ICSPCC)

作者： Zhe Song Yi Zhang Xingxing Zhu Yang Yu Zeyu Cheng Chengkai Tang School of Electronics and Information Northwestern Polytechnical University Xi'an China Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Due to their flexible and lightweight nature, unmanned aerial vehicles (UAVs) find extensive application in cooperative navigation and positioning technology. Additionally, wireless sensor networks (WSNs) serve as a conduit for exchanging and merging information among cooperative UAV nodes. Addressing the need for lightweight design and real-time adaptability in UAV networks, this study introduces a cooperative positioning algorithm employing manifold gradient filtering assisted by Geometric Dilution of Precision (GDOP). The algorithm leverages the measurement model among cooperative sensor nodes within the UAV network to construct a Riemannian manifold. By computing the gradient on this manifold, the algorithm identifies the steepest descent direction during iteration. Furthermore, it derives the GDOP corresponding to the geometric configuration of cooperative UAV nodes, thereby adjusting the iterative descent rate to expedite convergence. Simulation results demonstrate the algorithm's rapid convergence and high accuracy.

关键词： Manifolds Wireless sensor networks Accuracy Filtering Navigation Simulation Signal processing algorithms Signal processing Autonomous aerial vehicles convergence

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