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Optimization and Application of High Performance Algorithm under Data Governance Framework

Optimization and Application of High Performance Algorithm u...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International conference on

作者： Yiming Li Yimin Wang Yang Yu Meng Zhu Yulong Ma Metrology Center State Grid Jiangsu Marketing Service Center Nanjing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

the in the era of big data and information, the optimization and application of high-performance algorithms within a Data Governance framework is crucial. However, issues arise with inaccurate performance characterization, particularly with the Grey Wolf Optimizer (GWO), which struggles to overcome phase limitations in this context, leading to suboptimal outcomes. therefore, we present a study focused on the development and assessment of a digital mining algorithm for optimizing high-performance algorithms under a Data Governance framework in the big data *** initiate our investigation, we employ computational theory to identify the key factors affecting the optimization and application of high-performance algorithms under a Data Governance framework. these factors are then categorized according to the specific requirements of the optimization process, aiming to minimize the influence of extraneous variables. Computational theory is further utilized to design a digital mining algorithm tailored for the optimization and application of high-performance algorithms under a Data Governance framework. the performance of this algorithm is rigorously *** results obtained using MATLAB indicate that, under specified evaluation criteria, the digital mining algorithm surpasses the traditional GWO in terms of both the accuracy of the optimization and the processing time required for influencing variable *** research underscores the importance of developing advanced algorithms that can effectively navigate the complexities of data governance and big data environments, ensuring more accurate and efficient outcomes.

关键词： Accuracy Navigation Big Data Data governance Internet complexity theory Data mining Reliability MATLAB Optimization

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Ship Attitude Prediction Based on VMD-PSO-LSTM

Ship Attitude Prediction Based on VMD-PSO-LSTM

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Mechatronics Technology and Intelligent Manufacturing (ICMTIM), International conference on

作者： Yichen Guo Wenlin Yang Anri Hu Xin Tan Technical Research and Development Department Guangdong Institute of Intelligent Unmanned System Guangdong China

ISBN: (数字)9798350363265

ISBN: (纸本)9798350363272

the marine environment is full of complexity, which makes the ship's posture highly stochastic, Traditional prediction models are less effective in predicting ship attitude therefore, Using variational modal decomposition (VMD) to preprocess ship attitude data can effectively reduce the complexity of ship signals and improve the prediction model with a particle swarm algorithm-optimized long-short time (PSO-LSTM) neural network. First, the ship posture data are decomposed into several intrinsic modal functions (IMFs) as well as residual terms using the VMD algorithm. then, neural network hyperparameter optimization and model training are performed for Each IMF component and the residual term separately by PSO-LSTM model. Finally, the trained model is used to predict each of these decomposition terms, and the above predicted values are summed to obtain the prediction results. the results show that the root mean square error values of the model are reduced by 0.0407m and 0.0121m, and its mean absolute percentage error is reduced by 27.57% and 24.68% compared to the LSTM and PSO-LSTM models, respectively. the effectiveness of the method in improving the prediction accuracy is demonstrated.

关键词： Adaptation models Accuracy Predictive models Prediction algorithms Feature extraction Data models complexity theory

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Research on Optimization Method of Regional Integrated Energy System Considering Synergistic Operation of Multi-Energy Stations

Research on Optimization Method of Regional Integrated Energ...

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Clean Energy and Electric Power Engineering (ICCEPE), International conference on

作者： Zhiwei Wang Dexin Li Ziyue Yang Zhan Su State Grid Jilin Electric Power Co. Ltd China Power Science Research Institute of State Grid Jilin Electric Power Co. China North China Electric Power University Beijing China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

With the complexity of energy demand and the diversification of energy supply, the integrated energy system (IES) has gradually become a popular field of research. the coordinated functioning of multiple energy facilities within a geographical area can augment the impact of IES and improve the balance of energy resources among different regions. therefore, this paper explores the problem of energy interaction and cooperative operation of multiple energy stations in a region and proposes cooperative operation methods. To begin with, the operational optimization framework of multiple energy facilities is examined. Subsequently, building upon the principles of operational optimization, a model for optimizing the operation of these facilities is formulated. Finally, a simulation analysis is carried out for the multi-energy station operation optimization model of the regionally integrated energy system (RIES) to obtain the operation scheme with a better economy.

关键词： Green energy Economics Energy consumption Energy resources Optimization models Buildings Cooperative systems complexity theory Power systems Genetic algorithms

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A D2D Resource Scheduling Algorithm Based on Position Relation in Cellular Network 5th

A D2D Resource Scheduling Algorithm Based on Position Relati...

引用

5th International conference on Machine Learning and Intelligent Communications, MLICOM 2020

作者： Su, Xing Su, Yanyong Communication Research Center Harbin Institute of Technology Harbin50001 China

ISBN: (纸本)9783030667849

With the development of 5G, device-to-device (D2D) and other technologies have emerged one after another, making the communication services faster and wider in coverage. Aiming at the problem of high computational complexity of traditional resource allocation algorithms, by studying the D2D communication network model and the spatial location relationship of users, ignoring parameters that have little impact on the calculation, a D2D resource scheduling algorithm based on location relationship is proposed and optimized system performance. Simulation results show that, compared with traditional algorithms, the proposed resource scheduling algorithm has greatly reduced algorithm complexity, and has good data transfer rate and high system capacity. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computational complexity

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A Comprehensive Survey of Intelligent Control Approaches in Building Energy Management

A Comprehensive Survey of Intelligent Control Approaches in ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Habiba Mahrouch Touria Haidi Mounir Derri Ishak Hbiak Doha Bouabdallaoui Boutaina Talbi Electrical Engineering Department Laboratory LAGES Hassania School of Public Works Casablanca Morocco Civil Engineering Department Laboratory LAGES Hassania School of Public Works Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Effective building energy management is crucial for mitigating climate change and reducing energy costs. this review focuses on intelligent control methods for optimizing building energy consumption, drawing on recent research from international publications. It examines various strategies, including fuzzy logic control, neural network-predictive control, and reinforcement learning, highlighting their features, advantages, and limitations. the review addresses key challenges, such as controlling complex and unpredictable building systems. Future research directions are explored, emphasizing the development of adaptive, robust, and nonlinear control techniques. this includes integrating machine learning and data-driven approaches to enhance performance, adaptability, and resilience.

关键词： Energy consumption Transient response Machine learning algorithms Robustness Computational complexity Sustainable development Optimization Intelligent control Building management systems Climate change

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Navigating complexity: A Deep Learning Approach to Lane Change Decisions in Autonomous Vehicles

Navigating Complexity: A Deep Learning Approach to Lane Chan...

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International conference on Computer Engineering and Applications (ICCEA)

作者： Nada Kassem Sameh Eid Yasser Elshaer School of Mechanical Engineering Arab Academy for Science Technology & Maritime Transport Giza Egypt School of Mechanical Engineering The Knowledge Hub Universities Coventry University Branch Cairo Egypt

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Decision-making plays a critical role in guiding autonomous vehicles through complex environments characterized by dense traffic and dynamic elements. the existing research for decision-making in autonomous driving suffers from safety concerns, high computational power needs, low accuracy in comparison to human drivers, and their outputs cannot be easily interpreted. the paper discusses a deep learning-based decision-making algorithm for autonomous vehicles in dense and dynamic environments. It aims to create an easily interpreted algorithm that ensures safety, efficiency, and accuracy in lane change scenarios. the study introduces a novel approach that can be used within embedded systems and behaves similarly to human drivers. the methodology involves classifying lanes as blocked or free and making decisions based on this classification, mimicking human decision-making processes. the results show the algorithm’s effectiveness in complex scenarios, reaching a safety of 96% and accuracy of 90% which surpasses the Hierarchical Finite State Machine (HFSM) algorithm.

关键词： Accuracy Navigation Heuristic algorithms Decision making Classification algorithms Safety Finite element analysis

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Design of FPGA Deep Neural Network Accelerator Based on High-Level Synthesis

Design of FPGA Deep Neural Network Accelerator Based on High...

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Science and Technology Innovation (IAECST), International Academic Exchange conference on

作者： Hong Fan Haixiang Wang Kai Che Ziyu Wu Pre-research department Aeronautics Computing Technology Research Institute Xian China Laboratory 18th Aeronautics Computing Technology Research Institute Xian China

Hardware accelerators for deep learning algorithms are the cornerstone of intelligence in the aviation field. the adoption of Field Programmable Gate Array (FPGA) as embedded coprocessors for algorithm acceleration has gained wide acceptance. Decision-making games are critical tasks, where their accuracy and real-time performance directly influence the outcome of aerial engagements. Traditional FPGA development using hardware description languages is associated with long development cycles, high complexity, slow iteration, and challenges in swiftly responding to model algorithm updates. In contrast, FPGA design based on High Level Synthesis (HLS) provides an appropriate technology pathway to circumvent these drawbacks. A specific deep learning algorithm related to aircraft decision games is implemented, optimized, and deployed on FPGA via HLS. the FPGA is connected to the host machine via PCIe as a coprocessor, achieving holistic coupled acceleration. Comparative testing against running the algorithm on an ftd2000 eight-core CPU and an Intel i9-9900k processor demonstrates that using FPGA as a coprocessor significantly reduces algorithm execution time, resulting in a noticeable acceleration effect.

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Bond Dissociation Energy and Pattern Spectrumfor Shape Classification 5

Bond Dissociation Energy and Pattern Spectrumfor Shape Class...

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5th International conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Kumar, Ratnesh Mali, Kalyani Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India

ISBN: (纸本)9781665432726

the paper represents an effective, robust and shape-preserving, pattern spectrum based on sigma-pi model. A new idea behind the translation, rotation and scale invariant features are introduced called bond-dissociation energy of sigma and pi bond in a sigma-pi model. In this model, we map the structure of pixels in a shape to the structure of atoms in a crystalline solid-state substance. the model compute the energy required to break the atom from a lattice. the sigma-pi model corresponding to the bond-dissociation energy pattern spectrum provides excellent power, which is demonstrated by excellent retrieval performance on several popular shapes benchmarks, including MPEG-7 CE-Shape-1 data set, Myth dataset, Tool dataset and Kimia's data set. Experimental results obtained from popular databases demonstrate that the proposed model can achieve comparably better results than existing algorithms.

关键词： Sigma-pi model Bond-dissociation energy Shape-size complexity Pattern spectrum

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MAE-CNN: A Multi-Scale Attention Enhanced Convolutional Neural Network for CU partition prediction

MAE-CNN: A Multi-Scale Attention Enhanced Convolutional Neur...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Junwei Chen Chengji Zhao Rongbang An Energy and Electricity Research Centre Jinan University Zhuhai Guangdong China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

With the continual advancements in video encoding technology, the H.265 and H.266 standards have significantly reduced bit rates and enhanced data compression efficiency compared to their predecessors. H.265 employs a quadtree partitioning mechanism, while H.266 further develops this approach with the introduction of a Quad-tree with Nested Multi-type Tree (QTMT) partitioning system. these complex partitioning mechanisms require the exploration of all potential CU division options and the calculation of corresponding rate- distortion (RD) costs, substantially increasing encoding complexity. To address this challenge, this study introduces a Multi-scale Attention Enhanced Convolutional Neural Network (MAE-CNN), specifically designed to reduce the complexity of intra-frame video encoding. this deep convolutional neural network, trained using CU data collected through an official test model, incorporates the Inception module and the Convolutional Block Attention Module (CBAM). It significantly improves the detection of image details and broad area features, while also enhancing the network's ability to identify crucial feature information. Integrating this model into the encoder shows that under the full intra-frame encoding configuration, the algorithm saves an average of 64.07% of encoding time, with less average quality loss and lower bitrate growth, significantly better than traditional encoding methods. this research not only markedly improves encoding efficiency but also broadens the applications of deep learning in the video encoding field.

关键词： Deep learning Bit rate Prediction algorithms Feature extraction Encoding complexity theory Partitioning algorithms

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High-Resolution Remote Sensing Image Registration Leveraging Locally Invariant Feature Points

High-Resolution Remote Sensing Image Registration Leveraging...

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Sensors and Information Technology (ICSI), International conference on

作者： Yue Wu Wenzao Shi Nuoqi Zhang Jiewei Wu Yuchen Zheng Haitao Xie Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou P. R. China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

In order to solve the problem of low registration accuracy in traditional point feature matching algorithms, this paper proposes a high-resolution remote sensing image registration algorithm based on locally invariant feature points. Firstly, a bilateral filter is used to preprocess the reference image and the image to be registered separately, preserving their edge information and removing noise. Secondly, extract local invariant feature points from the preprocessed two images. then, bidirectional matching is performed using the Euclidean distance ratio method of nearest and second nearest neighbors to obtain matching feature points[1]. Finally, perform affine transformation on the registered images. To validate the method's effectiveness, we propose the affine transformation root mean square error (AT-RMSE) as an evaluation metric for accuracy. PLEIADES images from two time phases in Shenzhen are selected, and the results demonstrate that although the bilateral filter increases the running time of the algorithm, it can reduce AT-RMSE by more than 55%; In addition, registration methods based on SURF features have better performance and significantly improved accuracy compared to SIFT based methods.

关键词： Image registration Matched filters Technological innovation Accuracy Urban planning Filtering algorithms Feature extraction Solids Time complexity Remote sensing

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