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The Application of Intelligent Evaluation Method with Deep Learning in Calligraphy Teaching

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1317-1324页

作者： Wang, Yu Weinan Normal Univ Sch Humanities Weinan 714099 Peoples R China

Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level.

关键词： Deep learning calligraphy teaching BPNN intelligent evaluation sparrow search algorithm

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A Novel Electricity Measurement System Based on Electricity Usage Identification 5th

A Novel Electricity Measurement System Based on Electricity ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Bing Wang, Tao Tao, Xuepan Li, Wenjie Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Detailed user electricity information is crucial for promoting energy conservation, emission reduction, and implementing energy optimization. To address the issue of power line interference in the precise data acquisition process, a novel electricity metering system based on electricity identification has been proposed and designed. The metering system consists of two main parts: the electricity identification end and the collection metering end. At the electricity identification end, each electrical appliance's electricity information is independently encoded through ASK (Amplitude Shift Keying) digital modulation and coupled to the power line via capacitive coupling. Through power line carrier communication, the collection metering end collects and analyzes the independently encoded information to achieve electricity metering. By constructing an SSA-ELM load recognition model, the accuracy of load recognition has been improved, corresponding to fine-grained electricity consumption of various electrical appliances, thereby providing users with more detailed and intuitive electricity usage data.

关键词： Electricity identification ASK electricity metering power line carrier communication FFT load recognition sparrow search algorithm

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An SSA-CNN-AM-BILSTM method for valve opening prediction 14

An SSA-CNN-AM-BILSTM method for valve opening prediction

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14th Asian Control Conference (ASCC)

作者： Yang, Han Li, Xiaolei Shi, Yukun Wang, Youqing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100000 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

Benzene production is an important chemical industry process for producing organic compounds such as styrene and phthalic acid. In this paper, a neural network method based on SSA-CNN-AM-BILSTM is proposed to predict the valve opening in the benzene making process. The method consists of sparrow search algorithm (SSA), Convolutional Neural Network (CNN), Attention Mechanism (AM) and Bidirectional Long and Short-Term Memory Network (BiLSTM). The CNN performs the readout of the data features, and the AM is utilized to capture the effect of the feature states on the single loop at different times in the past, which enables the model to focus on the important information in the input data and enhances the model's ability to represent the data. BiLSTM adds an inverse LSTM layer to the Long Short-Term Memory (LSTM) network to improve the prediction results. For the hyperparameters in the neural network, SSA is applied for training optimization to improve the accuracy of the model.

关键词： sparrow search algorithm Convolutional Neural Networks Bidirectional Long and Short-Term Memory Networks Attention Mechanism

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Spacecraft Orbit Prediction Method Based on SSA-AM-LSTM 9

Spacecraft Orbit Prediction Method Based on SSA-AM-LSTM

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Chen, Yilun Nan, Chunli Yan, Zihan Xi, Xiuchi Li, Xue Li, Jiaojiao Changan Univ Sch Comp Sci Xian Peoples R China Changan Univ Dept Comp Sci Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

In order to improve the accuracy of spacecraft orbit prediction, a spacecraft orbit time series prediction method integrating sparrow search algorithm (SSA), Attention Mechanism (AM) and Long Short-Term Memory (LSTM) network (denoted as SSA-AM-LSTM) is proposed. A nonlinear model between spacecraft orbit time series variables and spacecraft longitude, latitude and elevation is established using the LSTM network;the weights of the LSTM network are optimised based on the Attention Mechanism to solve the multi-value problem;and the AM-LSTM network is structurally optimised by incorporating the sparrow search algorithm to improve the model accuracy. Experiments are conducted using real orbital data from five in-orbit spacecraft. The experimental results show that the proposed model reduces the error evaluation indexes RMSE and MAE by 93.8% and 95.48% and 64.06% and 71.33% respectively compared to the LSTM network and the AM-LSTM model, and the MAPE indexes also decrease by 95.48% and 71.33%, which indicates that the accuracy of the proposed model is greatly improved compared to the traditional neural network.

关键词： LSTM Self-attention mechanism sparrow search algorithm Multivariate time series prediction

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Fuzzy Control of Drilling Rig Winch Motor Based on SAWS-SSA algorithm

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IEEE ACCESS 2023年 11卷 136496-136506页

作者： Feng, Yanwei Wei, Lixin Tianshui Normal Univ Dept Mech Engn Tianshui 741000 Peoples R China Tianshui Elect Transmiss Res Inst Grp Co Ltd Dept Res & Dev Tianshui 741000 Peoples R China

This paper proposes an improved sparrow search algorithm based on the self-avoiding random walk strategy as a fuzzy control method (SAWS-SSA) for rig winch motors in the complex oil rig electric control industry. The SAWS-SSA aims to accurately describe the system dynamics and improve the motor performance despite the large number of control-winch variables. During the startup of the three-phase asynchronous motor of the winch, the error value between the current feedback and thyristor trigger angle coupled with the periodic change rate was used as the input for the fuzzy control start. The fuzzy rules progressively reduce the components of the excitation current and iteratively calculate the difference between the input and feedback of the active power until the motor speed and torque are stabilized. To reduce the evaluation time of the optimization search, we optimized the heuristic affiliation function variables using an improved sparrow search algorithm equipped with a self-avoidance random walks strategy. Experimental verification confirms that SAWS-SSA outperforms other control algorithms in terms of the statistical error in the instantaneous dynamic response of the oil rig winch under a constant load and at different speeds.

关键词： Mathematical models Heuristic algorithms Fuzzy control Winches Drilling Torque Permanent magnet motors self-avoiding random walks strategy sparrow search algorithm fuzzy rules

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Comparative Analysis of Different Prediction Models of Inorganic Fertilizers N, P, and K Under Different Soil Fertility Conditions 36

Comparative Analysis of Different Prediction Models of Inorg...

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36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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Research on Electric Power Load Forecasting Based on SSA-LSTM Model with Variational Mode Decomposition 5

Research on Electric Power Load Forecasting Based on SSA-LST...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Dou, Xinfeng Zhang, Jiacheng Nanjing Inst Technol Sch Econ & Management Nanjing 211167 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Sussex Artificial Intelligence Inst Hangzhou 310018 Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the continuous growth of global energy demand and the widespread application of renewable energy in the power grid, the accuracy of electricity load forecasting has become particularly important for the stable operation of the power grid and effective energy management. This paper proposes a method based on Variational Mode Decomposition (VMD) and sparrow search algorithm (SSA) optimized Long Short-Term Memory (LSTM) network model to improve the accuracy and reliability of electric power load forecasting. Specifically, the VMD method is used to preprocess the electric power load data, decomposing it into multiple Intrinsic Mode Functions (IMFs) to eliminate noise and extract key trends and cyclical features from the data. The SSA algorithm is further used to optimize the LSTM model, enhancing its ability to handle the nonlinear and complex nature of electric power load data. Using actual electric grid load data from a specific area as a case study, experiments show that our model outperforms other advanced models in performance, providing a more accurate and reliable tool for load management and dispatching in power systems.

关键词： Electric Power Load Forecasting LSTM sparrow search algorithm VMD Decomposition

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A control strategy for smoothing the fluctuation of wind power grid connection based on hybrid energy storage system 36

A control strategy for smoothing the fluctuation of wind pow...

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36th Chinese Control and Decision Conference (CCDC)

作者： He, Zijian Pan, Lin Wan, Jiying Huang, Wenhua Sheng, Zihao Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572025 Peoples R China Wuhan Univ Technol Qingdao Inst Qingdao 266200 Peoples R China Hebei Huifeng Network Technol Dev Co Ltd Shijiazhuang 050092 Hebei Peoples R China Wuhan Univ Technol Xiangyang Res Inst Xiangyang 420624 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The stochastic volatility of wind power generation has an impact on grid stability, and a hybrid energy storage system (HESS) based on parameter optimization with variational mode decomposition (VMD) is proposed to suppress the fluctuation of wind power on the grid. control strategy. Firstly, the VMD method optimized by the sparrow search algorithm (SSA) is used to decompose the fluctuating power of wind power that needs to be suppressed. According to the obtained spectrogram, based on the energy ratio of each sub-module and the minimum modal overlap, the high and lowfrequency power is allocated to the supercapacitor and lithium battery to realize the distribution of power in the hybrid energy storage system. Secondly, to prolong the service life of the energy storage system and prevent the overcharge and over-discharge phenomenon from damaging the energy storage components, the power of the energy storage components is corrected by fuzzy control rules. Finally, the method proposed in this paper is applied to wind power data collected in a region, and the experimental results show that the strategy can effectively smooth out the fluctuation of wind power grid connection, reduce the degree of fluctuation of the charge state of the energy storage system, and realize the stable operation of the hybrid energy storage system.

关键词： hybrid energy storage system wind power leveling variational modal decomposition sparrow search algorithm fuzzy control

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The SSA-BP-based potential threat prediction for aerial target considering commander emotion

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Defence Technology（防务技术） 2022年第11期18卷 2097-2106页

作者： Xun Wang Jin Liu Tao Hou Chao Pan College of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China Beijing Xin Li Machinery Limited Liability Company Beijing100039China School of Information and Communication Engineering Hubei University of EconomicsWuhan430205China

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense ***,the traditional threat prediction methods mostly ignore the effect of commander's *** only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex *** aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction ***,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized *** experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.

关键词： Aerial targets Emotional factors Potential threat prediction BiLSTM sparrow search algorithm Neural network

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An integrated decision support system for ship collision avoidance

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JOURNAL OF TRANSPORTATION SAFETY & SECURITY 2025年第3期17卷 272-298页

作者： Sun, Xiaoqing Zhang, Ke Jimei Univ Chengyi Coll Xiamen Peoples R China China Waterborne Transport Res Inst Beijing Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan Peoples R China

The ship encounter process is divided into the stages of free action, risk of collision, close-quarter, and immediate danger according to the four-stage theory of ship encounters. Current research on ship collision avoidance primarily focuses on the risk of collision stage, aiming to prevent ships from entering the close-quarter stage. However, various navigational equipment and environmental factors may cause ships to enter the close-quarter or immediate danger stages. Therefore, this paper establishes an integrated decision support system for the above stages to ensure that ships can handle all situations. First, the system includes a new collision risk detection module based on the ship domain model and the four-stage theory. Subsequently, in the collision avoidance strategy formulation module, two sets of avoidance strategies are established based on the convention on the COLREGs and the most effective avoidance principle for ships in different stages. Finally, in the avoidance action optimization module, a new fitness function is constructed based on ship maneuverability, and the sparrow search algorithm is introduced to solve for the optimal avoidance actions. The results demonstrate the capability of the decision support system presented in this paper to effectively avoid other ships in different stages of the encounter process.

关键词： ship collision avoidance decision support system collision risk model ship avoidance strategy sparrow search algorithm

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