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APPLICATION OF BIG DATA ANALYSIS AND intelligent algorithm IN POWER SYSTEM OPERATION OPTIMIZATION

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1818-1825页

作者： Jin, Huichao Huo, Junyi Wang, Qingfen Li, Dexiong Shijiazhuang Inst Railway Technol Dept Elect Engn Shijiazhuang 050041 Hebei Peoples R China

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi -node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi -node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low -priority data streams because the delay of high -priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.

关键词： Power dispatching Quantum key Dynamic regulation intelligent algorithm Power Grid System

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Research on News Push Mechanism Based on intelligent algorithm 2

Research on News Push Mechanism Based on Intelligent Algorit...

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2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022

作者： Meng, Lingjun College of Humanities Shandong Management University Shandong Jinan250000 China

ISBN: (纸本)9781665485418

In order to build a reasonable news push mechanism through intelligent algorithm, relevant research will be carried out in this paper, mainly discussing the operation process of news push mechanism, and then introducing key technologies and intelligent algorithms in the construction of news push mechanism. Finally, practical research will be conducted to verify the effectiveness of the algorithm. Through the research of this paper, the intelligent algorithm can realize accurate news push and effectively develop news value. © 2022 IEEE.

关键词： intelligent algorithm News information Newsfeed mechanism

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An intelligent algorithm for Negative Sequence Directional Element of DFIG during Ferroresonance in Smart Grid

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TECHNOLOGY AND ECONOMICS OF SMART GRIDS AND SUSTAINABLE ENERGY 2021年第1期6卷 1-17页

作者： Rezaei, Salman Kerman Power Generat Management Co Tech Off Kerman Iran

Smart grid comprises utility system and Distributed renewable power Generations such as wind and solar energy. The smart grid system represents bidirectional flowing of energy and communication facilities among utility system, Distributed Generations (DG) and consumers. Smart grid including wind farm is seriously exposed to high magnitudes of nonlinearities like ferroresonance. It causes mal operation of protective relays in wind farm. This paper investigates impact of ferroresonance in utility system on operation of DFIG (Doubly-Fed Induction Generator) and Negative Sequence Directional Element (NSDE) in Wind Park by means of PSCAD/EMTDC software. As smart grid insists on a self-healing protection, an intelligent algorithm based on wavelet transform, neural network and ferroresonance analysis in time domain is proposed for NSDE to discriminate ferroresonance. The algorithm appropriately conforms to smart grid protection strategy. It discriminates ferroresonance from other nonlinear abnormalities and is able to distinguish different types of ferroresonance. To accord with smart grid requirements, the algorithm is designed to forecast occurrence of ferroresonance in the grid.

关键词： intelligent algorithm Negative sequence directional element Ferroresonance Smart grid Distributed generations

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Dimension shifting based intelligent algorithm framework to solve conditional nonlinear optimal perturbation

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COMPUTERS & GEOSCIENCES 2023年第1期176卷

作者： Yuan, Shijin Liu, Yaxuan Zhang, Huazhen Mu, Bin Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China

Conditional Nonlinear Optimal Perturbation (CNOP) method is an effective way to study the predictability of oceanic and climatic events. A framework combining the Feature Extraction method and intelligent algorithm (FEIA) is frequently used to solve CNOP because it is gradient-free and scalable at high-dimensional scales. However, the fixed latent subspace of FEIA framework makes it challenging to achieve both the quality of so-lutions and the solving efficiency. To overcome this bottleneck, this paper proposes Dimension Shifting based intelligent algorithm (DSIA) framework to solve CNOP in the large-scale model. DSIA framework adopts the dimension shifting strategy, which dynamically shifts search particles in different low-dimensional spaces. To verify the feasibility of DISA framework, we take a Regional Ocean Modeling System (ROMS) model of double-gyre variation as an experimental case. In experiments, we figure out that the selection of feature extraction method and the shifting dimension set are two influential factors for the performance of DSIA framework. Be-sides, in comparative experiments, DSIA framework yields better objective function values and more valid CNOP than FEIA framework. Moreover, convergence experiments demonstrate DSIA framework can solve CNOP with an appropriate number of function evaluations and has better convergence performance. In conclusion, exper-imental results prove that DSIA improves both quality of CNOP and solving efficiency.

关键词： Dimension shifting intelligent algorithm Large-scale optimization Dimension reduction CNOP Regional ocean modeling system

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A data-driven intelligent learning algorithm for simultaneous prediction of aerodynamic heat and thermo-physical property parameters

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 209卷

作者： Li, Yuxuan Sun, Chengbao Cao, Zhenkun Cui, Miao Liu, Kun Dalian Univ Technol State Key Lab Struct Anal Optimizat Key Lab Adv Technol Aerosp Vehicles Liaoning Prov Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Adv Technol Aerosp Vehicles Liaoning Prov CAE Software Ind Equipment Dalian 116024 Peoples R China

It is of great importance and challenging to simultaneously determine time-varying aerodynamic heat and temperature-dependent thermo-physical property parameters with high accuracy, for the optimization of thermal protection systems of hypersonic vehicles. However, it is difficult to directly measure these parameters under high temperature conditions. It is an effective way to determine thermo-physical property parameters and aerodynamic heat by solving inverse problems, based on measurable or easily measured transient temperatures. However, the prediction error of these parameters may be too large, if the measurement error is large, due to the thermal inertia. To deal with this issue, an intelligent algorithm is proposed to simultaneously predict the aerodynamic heat and thermo-physical property parameters for the thermal protection systems of hypersonic vehicles, based on the temperature measurement information. It combines a genetic algorithm and a machine learning algorithm, and the genetic algorithm is employed to update the relevant parameters in the neural network. By training the neural network, the relationship among the predicted parameters and transient temperatures could be established. Thereafter, the aerodynamic heat subjected to the outer surface of the aircraft and the temperature-dependent non-linear thermo-physical property parameters could be predicted. Examples are given to verify the present algorithm. The results show that this work provides an accurate and efficient method for simultaneously determining the aerodynamic heat and thermo-physical property parameters for the thermal protection system of a hypersonic vehicle. The prediction errors of aerodynamic heat and thermo-physical property parameters are much smaller than the measurement errors, when there are relatively large measurement errors in the input data.

关键词： Thermal protection Parameter identification intelligent algorithm Genetic algorithm Machine learning

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SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 79-79页

作者： Wang, Yifei Xu, Julia Zhang, Jie Xu, Hong Sun, Yuzhong Miao, Yuan Wen, Tiancai China Acad Chinese Med Sci Wangjing Hosp Beijing 100102 Peoples R China Univ Melbourne Melbourne Vic 3010 Australia Chinese Acad Sci Inst Informat Engn Beijing 100085 Peoples R China Victoria Univ Coll Engn & Sci Melbourne Vic 3000 Australia Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China China Acad Chinese Med Sci Data Ctr Tradit Chinese Med Beijing 100700 Peoples R China

BackgroundClinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of drugs, including both primary and auxiliary treatments. This complexity and multidimensionality increase the difficulty of extracting knowledge from clinical *** this study, we proposed a subgroup identification algorithm for complex prescriptions (SIAP). We applied the SIAP algorithm to identify the importance level of each drug in complex prescriptions. The algorithm quickly classified and determined valid prescription combinations for patients. The algorithm was validated through classification matching of classical prescriptions in traditional Chinese medicine. We collected 376 formulas and their compositions from a formulary to construct a database of standard prescriptions. We also collected 1438 herbal prescriptions from clinical data for automated prescription identification. The prescriptions were divided into training and test sets. Finally, the parameters of the two sub-algorithms of SIAP and SIAP-All, as well as those of the combination algorithm SIAP + All, were optimized on the training set. A comparison analysis was performed against the baseline intersection set rate (ISR) algorithm. The algorithm for this study was implemented with Python *** SIAP-All and SIAP + All algorithms outperformed the benchmark ISR algorithm in terms of accuracy, recall, and F1 value. The F1 values were 0.7568 for SIAP-All and 0.7799 for SIAP + All, showing improvements of 8.73% and 11.04% over the existing ISR algorithm, *** developed an algorithm, SIAP, to automatically match sub-prescriptions of complex drugs with corresponding standard or classic prescriptions. The matching algorithm weights the drugs in the prescription according to their importance level. The results of this study can help to classify and analyse

关键词： Prescriptions Drug combinations Electronic health records Traditional Chinese medicine Identification model intelligent algorithm

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EdgeVCD: intelligent algorithm-Inspired Content Distribution in Vehicular Edge Computing Network

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IEEE INTERNET OF THINGS JOURNAL 2020年第6期7卷 5562-5579页

作者： Luo, Quyuan Li, Changle Luan, Tom H. Shi, Weisong Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

Vehicular edge computing (VEC), which integrates mobile-edge computing (MEC) into vehicular networks, can provide more capability for executing resource-hungry applications and lower latency for connected vehicles. Distributing the result content to connected vehicles is vital for them to take proper actions based on computing results. However, the increasing number of connected vehicles and the limited communication resources make the content distribution a challenge. Besides, the diversity of connected vehicles and contents makes it more challenging for content distribution. To address this issue, in this article, we propose EdgeVCD, an intelligent algorithm-inspired content distribution scheme. Specifically, we first propose a dual-importance (DI) evaluation approach to reflect the relationship between the Priority of Vehicles (PoV) and the Priority of Contents (PoC). To make use of the limited communication resources, we then formulate an optimization problem to maximize the system utility for content distribution. To solve the complex optimization problem effectively, we first divide the road into small segments. Then, we propose a fuzzy-logic-based method to select the most proper content replica vehicle (CRV) for aiding content distribution and redefine the number of content request vehicles in each segment. Thereafter, the optimization problem is transformed into a nonlinear integer programming problem. Inspired by the artificial immune system, we propose an immune clone-based algorithm to solve it, which has a fast convergence to an optimal solution. Extensive simulations validate the effectiveness of our proposed EdgeVCD in terms of system utility, average utility, and convergence.

关键词： Content distribution dual-importance (DI) evaluation fuzzy logic immune clone intelligent algorithm vehicular edge computing (VEC) network

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PSO-VFA: A Hybrid intelligent algorithm for Coverage Optimization of UAV-Mounted Base Stations

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JOURNAL OF INTERNET TECHNOLOGY 2022年第3期23卷 487-495页

作者： Chen, Xuefeng Tang, Wan Yang, Ximin Zhou, Lingyun Li, Liuhuan South Cent MinZu Univ Sch Comp Sci Beijing Peoples R China

When the number of outdoor wireless users surges and fixed base stations (BSs) can hardly accommodate high-load communication traffic, unmanned aerial vehicles (UAVs) carrying BSs can provide wireless communication services, and the location deployment of the UAV-mounted BSs directly influences the reliability of network communications. For the target area scenario where the UAVs uniformly cover user nodes, we propose a hybrid intelligent coverage algorithm called PSO-VFA to optimize the coverage of a fixed number of UAV-BSs. The PSO-VFA algorithm consists of two phases employing different intelligent algorithms. First, we adopt a particle swarm optimization (PSO) method for a global search of the coverage areas. Then, for local search, a virtual-repulsive-force-based firefly algorithm (VFA) is proposed in this paper to maximize the user coverage. In the VFA algorithm, the users are treated as the objects attracting the UAVs, and the virtual repulsive force is used for UAV location adjustment. Simulation results show that the proposed PSO-VFA hybrid algorithm has faster convergence and significantly increases the communication coverage of UAVmounted BSs compared with individual intelligent algorithms such as VFA, PSO, genetic algorithm (GA), and simulated annealing (SA).

关键词： UAV-mounted base station Deployment coverage intelligent algorithm Firefly algorithm Particle swarm optimization

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Dynamic memory intelligent algorithm used for prediction of thermal error reliability of ball screw system

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APPLIED SOFT COMPUTING 2022年第0期125卷

作者： Li, Tie-jun Sun, Ting-ying Zhang, Yi-min Cui, Shang-yi Zhao, Chun-yu Shenyang Univ Chem Technol Equipment Reliabil Inst Shenyang 110142 Peoples R China Northeastern Univ Sch Mech & Automat Shenyang 110819 Peoples R China

The thermal error of machine tools is one of the main factors affecting the machining accuracy of machine tools. Most of the existed literatures do not consider the randomness of the influencing factors of thermal error, there are still difficulties in accurate prediction and compensation of thermal error. Therefore, the new prediction models with higher accuracy and reliability are urgently needed. Through simulating the dynamic process of memory loss of the human brain after receiving external excitation, a new dynamic time-varying memory intelligent algorithm with external excitation is proposed in this paper. Furthermore, a novel random dynamic time-varying memory intelligent algorithm with external excitation is proposed considering the randomness of factors in this paper. Considering the randomness of factors affecting thermal error, the proposed models are used for the prediction of reliability of thermally-deduced positioning accuracy for machine tool ball screw system under external excitation caused by manufacturing process alternations of machine tools. Finally, the effectiveness of the proposed models is verified by the experiment. Because the presented model can consider the randomness of factors affecting thermal error and the impact and hysteresis phenomenon caused by the alternations of multiple processes, it is suitable for the accurate prediction of the dynamic characteristics under the alternation of arbitrary excitation considering the reliability. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Thermal errors Reliability Time-varying dynamic memory intelligent algorithm Machine tools

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Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm

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APPLIED ENERGY 2022年 328卷

作者： Wang, Xuerui Li, Xiangyu Li, Shaoting Beijing Jiaotong Univ Sch Math & Stat Beijing Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China

Stable and accurate prediction of crude oil prices is critical to national security, economic development, and even international relations, against the background of the COVID-19 and Russia-Ukraine war. Therefore, a memory-based hybrid forecasting system is established for point prediction and interval prediction of crude oil prices in this research, which more thoroughly separates the noise in the raw data and achieves superior prediction performance. There are five main steps in the proposed model: decomposing the raw data into intrinsic mode functions (IMFs) through our new data preprocessing technique complete ensemble extreme -point symmetric mode decomposition with adaptive noise (CEESMDAN), ensemble of IMFs based on memory features, prediction of reconstructed components, error correction via grey wolf optimizer (GWO) for final point prediction, obtaining interval prediction by multi-objective grey wolf optimizer (MOGWO). The empirical results prove that the proposed model has excellent accuracy, robustness, and generalization in both point prediction and interval prediction, compared with various baseline models.

关键词： Crude oil price Interval prediction CEESMDAN intelligent algorithm

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