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SSRN 2024年

作者： Wang, Jianhui Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai264000 China

Significant wave height (WVHT) is one of the important parameters applied in the field of ocean engineering. Accurate predictions of WVHT can help improve wave energy conversion efficiency, coastal facility management, and disaster prevention and mitigation. However, extracting features from complex ocean parameters to accurately predict wave heights remains a challenge. A hybrid deep learning framework is proposed for precise prediction of WVHT, which combining the complementary strengths of deep learning architectures and gradient boosting methods. Incorporating an attention mechanism (AM), a convolutional neural network (CNN), and a bidirectional long and short-term memory network (BiLSTM), the framework is designed to deeply learn the information contained in historical data. Furthermore, the extreme gradient boosting (XGBoost) algorithm is integrated using a multi-model fusion strategy based on weighted ensemble techniques. First, input features are selected using Pearson's correlation coefficient and XGBoost feature importance score. The feature set is then fed into AM-based CNN-BiLSTM (CBLA) and XGBoost for training to predict WVHT. Finally, the predictions of the above models are fused to produce the final predictions. We predict the WVHT at three stations in the North Pacific at 1, 2, 4, and 6 hours in advance. The results show that the prediction performance and robustness of hybrid framework outperforms that of CBLA and XGBoost, and significantly outperforms that of other benchmark models. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Fault Detection Method of Maintenance Equipment Based on ECAT 5

Fault Detection Method of Maintenance Equipment Based on ECA...

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5th International Conference on Smart Grid and Energy engineering, SGEE 2024

作者： Qin, Shuo Zhang, Yimei Zhang, Yinghao Wang, Hongchao Chang, Bo Feng, Hao State Grid Beijing Electric Power Company Changping Power Supply Company Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331530686

To improve the fault detection performance of power system operation and maintenance equipment, this paper studies the ECAT model through the method of integrating Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Attention mechanisms. The results show that: the ECAT model significantly outperforms traditional methods in fault detection accuracy and robustness on multiple public datasets, with an average accuracy improvement of about 10%;the model parameters are globally optimized using the Zebra Optimization Algorithm (ZOA), further enhancing detection performance;and the model effectively captures critical information and long-range dependencies. It can be seen that ECAT provides an efficient and accurate solution for intelligent fault detection of power system operation and maintenance equipment, demonstrating good application prospects. © 2024 IEEE.

关键词： Empirical mode decomposition

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Research on the Probability Interval and Ramping Prediction Method of Wind Turbine Power

Research on the Probability Interval and Ramping Prediction ...

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第43届中国控制会议

作者： Ketao Dong Yali Zhao Qiankun Hu Peng Guo School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The wind power ramp event refers to the large fluctuation of wind power caused by the sudden increase or decrease of wind power in a short time interval,which affects the safe and stable operation of the power grid *** article proposes a wind power prediction method based on the CNN-LSTM model,and the prediction interval of wind power is obtained by using a non-parametric kernel density estimation *** using interval prediction lower limit data combined with a detection method based on statistical power fluctuations and a Swing Door Trending algorithm proposed to identify wind power ramp events,wind power ramp event prediction is ultimately achieved,and so as to make countermeasures in *** results show that the proposed method is more beneficial to the prediction of wind power ramp events.

关键词： Wind power ramp event prediction wind power prediction non-parametric kernel density estimation

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Research on Intelligent Management and Assisted Decision Making Technology of Electric Power Research Enterprise Based on Knowledge engineering 24

Research on Intelligent Management and Assisted Decision Mak...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Zhang, Baoliang Xu, Yinghui Li, Chengyuan Jiang, Yuan Cui, Wenchao Yue, Hongxu Hui, Chen Hu, Ziwei Digitisation Department China Electric Power Research Institute Beijing China School of Mechanical and Electrical Engineering China University of Mining and Technology Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798400712692

Currently, the internal management knowledge of electric power research enterprises is difficult to integrate and structured organization, resulting in greatly reduced decision-making efficiency. Knowledge Graph(KG), as an efficient knowledge organization, helps to connect the complex knowledge network. However, for a Q&A platform based solely on knowledge graphs, it can be challenging to interpret and analyze natural language input, especially when considering historical Q&A data. In this paper, we combine the KG with a Large Language Model(LLM), choose the ERNIE model fine-tuned by the text of electric power domain knowledge, and use the TransE method to integrate the KG into the model's structure to build a knowledge-based Q&A system that assists researchers in decision-making. The experimental results show that the ERNIE model combined with KG outperforms the comparison model in terms of precision, recall, and F1 value in entity and relation classification tasks, and the performance is further improved by increasing the number of attention heads. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Risk perception

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Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 882-894页

作者： Qin, Shujin Zhang, Shancheng Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang School of Economics and Management Shangqiu Normal University Shangqiu476000 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China Monmouth University West Long Branch NJ07764 United States College of Information Science and Engineering Northeastern University Shenyang110819 China Department of Computer Science and Technology Shandong University of Science and Technology Qingdao266590 China

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the multirobotic multiproduct U-shaped disassembly line balancing problem (M2UDP), in which different products are disassembled on a U-shaped model in a preset cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multiobjective multiverse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multiobjective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems. © 2023 IEEE.

关键词： Profitability

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Medical Named Entity Recognition Based on Multiple Features and Dynamic Fusion

Medical Named Entity Recognition Based on Multiple Features ...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Yi Ren Wei Mao School of Computer and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

To Address the challenges of unclear entity delineations and insufficient utilization of Semantic data, This study introduces a novel fusion approach leveraging multiple features for dynamic integration. To enrich the semantic representation of text, Textual model's embedding layer incorporates diverse techniques. Firstly, convolutional neural networks are used to implement font embedding, enriching the character representation of text through Chinese character fonts. Secondly, SoftLexicon is used to fuse word information from the dictionary and enhance entity boundary information. To achieve multi feature embedding, the word vector's semantic information is modeled using McBERT. In the feature extraction layer, long-distance inter-character semantic information is obtained by IDCNN, whereas contextual semantic information is obtained via BiLSTM. At the conclusion, the utilization of the dynamic fusion methodology is employed to accomplish the task of recognizing named entities, leveraging the conditional random field model. The model demonstrated an F1 score of 88.96% on the Chinese medical information evaluation dataset, surpassing the BERT BiLSTM CRF model by 3.81%, thereby validating its effectiveness.

关键词： Technological innovation Dictionaries Text recognition Semantics Named entity recognition Network security Feature extraction

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MMLN: Multi-directional and Multi-constraint Learning Network for Remote Sensing Imagery Semantic Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 1-16页

作者： Sun, Hang Xie, Yongchang Ren, Dong Wen, Fangqing Tong, Lyuyang Chang, Laibin Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive Technology Shiyan China School of Computer Science Wuhan University Wuhan China

Recent advances in satellite remote sensing technology and computer technology have significantly impacted practical applications in remote sensing image segmentation. However, the prevalent hybrid segmentation models that combine Convolutional Neural Networks (CNNs) and Transformers, often overlook the critical exploration of local and global feature correlations across various scales. This exploration is essential for learning more representative features and strengthening context modeling capabilities. Additionally, the decoding layers of these models do not effectively exploit the pixel-level semantic relationships within cross-layer feature maps, thereby limiting the models' ability to discern small object features. To address these challenges, this paper introduces a Multi-directional and Multi-constraint Learning Network (MMLN) designed for semantic segmentation of remote sensing imagery. This network features a Multi-directional Dynamic Complement Decoder (MDCD), which enhances the interaction between local and global features in the feature space, and subsequently improves the feature discrimination within the segmentation network. Moreover, a Multi-constraint Saliency Boundary-adaptive Module (MSBM) is implemented to reinforce the spatial constraints on saliency at the edge regions and ensure semantic consistency along the mask boundaries. This, in turn, augments the segmentation model's capability to detect small objects. The evaluation on four datasets reveals that the MMLN outperforms the existing state-of-the-art methods in remote sensing imagery segmentation. The code is available at https://***/zhongyas/MMLN. Authors

关键词： Semantics

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Automation Detection, Segmentation, and Classification for Ocean Reinforced Concrete Corrosion Based on Artificial Intelligence

SSRN

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SSRN 2024年

作者： Cui, Zhendong Jia, Zhitong Zhao, Yang Fu, Mingjiao School of Computer and Control Engineering Yantai University Yantai264000 China

Based on artificial intelligence technology, it is of great significance to automatically identify and determine the degree of corrosion damage for Ocean reinforced ***, an enhanced and comprehensive non-destructive testing framework was proposed for the automatic detection of corrosion in ocean concrete and the evaluation of its corrosion ***, a dataset containing 3,700 images of offshore ocean concrete corrosion has been established, which captured in various geographical locations, weather conditions, lighting situations, and engineering ***, a Faster-DeepLabV3+ model for damage segmentation and grade classification was present. An Efficient Channel Attention structure was introduced at the front of the inverted residual module of MobileNetV2 to reduce the model parameters and computation while maintaining high-accuracy segmentation results;a stick pooling layer was introduced in Atrous Spatial Pyramid Pooling to improve the model's ability to perceive damage at different scales;a combination of loss functions with Dice loss function and focal loss function was used to enhance the model's capacity to detect outlines and finer ***, different corrosion levels were identified to categorize cracks, spalling, and rebar corrosion in marine concrete into three conditions, and accurate delineation of corroded areas and rebar rusting was successfully ***, experiments are carried out on the constructed data set, and by comparing the intersection merging ratio, average pixel accuracy, pixel accuracy and frames per second, the proposed model is proved to be accurate and efficient. © 2024, The Authors. All rights reserved.

关键词： Nondestructive examination

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Intelligent Decision Algorithm for Photovoltaic Power Generation control Based on Feature Neural Computing Modeling

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Tang, Mingze School of Control and Computer Engineering North China Electric Power University Beijing100096 China

The integration of renewable energy sources into the power grid, especially photovoltaic (PV) systems, has seen a significant upsurge due to the global push for sustainable energy. However, the variable nature of solar energy poses unique challenges in the effective management and control of PV power generation. This study introduces an innovative approach to intelligent decision-making in PV power generation control, leveraging deep learning techniques for temporal data feature modeling. By collecting and analyzing time-series data from PV systems, the proposed method utilizes feature neural computing to model the inherent characteristics of the data, facilitating effective temporal feature classification. The classification results are then employed to inform intelligent control strategies, optimizing the efficiency and reliability of PV power generation. This article proposes an end-to-end neural network structure that can simultaneously mine multi-scale local information and global temporal correlation information in sequences. This structure is primarily achieved through a module. The module consists of two parallel branches. One branch improves the InceptionTime module with depthwise separable convolution to extract multi-scale subsequence information. The other branch employs multi-head self-attention technology with added layer positional encoding to extract global temporal correlation information in sequences. © 2024 Mingze Tang, published by Sciendo.

关键词： Solar energy

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Hierarchical extended parameter estimation algorithms for finite impulse response moving average models

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IFAC-PapersOnLine 2024年第14期58卷 416-421页

作者： Feng Ding Xiao Zhang Ling Xu Xingchen Liu School of Internet of Things Engineering Jiangnan University Wuxi 214122 PR China School of Microelectronics and Control Engineering Changzhou University Changzhou 213164 China College of Computer and Control Engineering Qiqihar University Qiqihar 161006 China

This paper explores some hierarchical extended parameter estimation algorithms for finite impulse response moving average (FIR-MA) model from observation data, including the hierarchical extended stochastic gradient algorithm, the hierarchical multi-innovation extended stochastic gradient algorithm, the hierarchical extended gradient algorithm, the hierarchical multi-innovation extended gradient algorithm, the hierarchical extended least squares algorithm and the hierarchical multi-innovation extended least squares algorithm. The proposed hierarchical algorithms for the FIR-MA systems can be extended to other stochastic systems with colored noises.

关键词： Recursive identification Hierarchical identification Multi-innovation identification

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