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ENGINEERING REsearch EXPRESS 2025年第1期7卷

作者： Wang, Ronglin Wang, Haibo Liu, Jieting Li, Pengtao Zhao, Chuanzhe Song, Yadi Jilin Inst Chem Technol Coll Mech & Elect Engn Jilin 132022 Peoples R China Jilin Inst Chem Technol Coll Informat & Control Engn Jilin 132022 Peoples R China

To address the challenges posed by time delay and nonlinearity in the production process of typical thermal engineering systems such as continuous stirred tank reactors (CSTR), which make system parameter identification and temperature control difficult, an improved firefly algorithm is proposed for system parameter identification. This algorithm demonstrates higher identification accuracy compared to the original firefly algorithm. Subsequently, a combination of an improved sparrow search algorithm (BCGSSA) and PID control is applied to the temperature control of the CSTR system. This approach significantly enhances the dynamic performance of the CSTR system. The improved algorithm (BCGSSA) exhibits excellent control accuracy and robustness.

关键词： sparrow search algorithm firefly algorithm parameter identification temperature control PID control

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Research on Neural Network-based Automatic Music Multi-Instrument Classification Approach

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 792-798页

作者： Guo, Ribin Shanxi Coll Appl Sci & Technol Taiyuan 030062 Shanxi Peoples R China

The automatic classification of multi-instruments plays a crucial role in providing services for music retrieval and recommendation. This paper focuses on automatic multi-instrument classification. Firstly, instrument features were analyzed, and Mel -frequency cepstral coefficient (MFCC) and perceptual linear predictive coefficient (PLPC) were extracted from instrument signals. Features were selected using the entropy weight method. The optimal initial weight threshold of a back-propagation neural network (BPNN) was obtained by utilizing the sparrow search algorithm (SSA), achieving a SSABPNN classifier. Experiments were conducted using the IRMAS dataset. The results demonstrated that the combination of MFCC and PLPC selected through the entropy weight method achieved the best performance in automatic multi-instrument classification. The method yielded high P value, recall rate, and F1 value, 0.72, 0.71, and 0.71, respectively. Moreover, it outperformed other algorithms such as support vector machine and XGBoost. These results confirm the reliability of the automatic multi -instrument classification method proposed in this paper, making it suitable for practical applications.

关键词： Neural network musical instrument automatic classification auditory feature sparrow search algorithm

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A novel NSSA-LSTM-based forecasting model for ship delays in the Beijing-Hangzhou Grand Canal

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JOURNAL OF OCEAN ENGINEERING AND MARINE ENERGY 2024年 1-14页

作者： Sun, Zhengchun Xu, Sudong Southeast Univ Coll Transportat Nanjing 210000 Peoples R China

The Beijing-Hangzhou Grand Canal serves as a crucial corridor for cargo transportation, connecting the north and south parts of China. With the continuous growth of inland waterway transportation demand, the vessel traffic flow of the Beijing-Hangzhou Grand Canal has become more intensive. The ship delays at busy locks which are featured as nonlinear and unsteady, have significantly affected the canal's navigation efficiency and exerted more pressure on the maritime safety administration. To optimize the lock operational strategies and ensure navigation efficiency, a novel ship delay forecasting model based on the optimized sparrow search algorithm (NSSA) and the Long Short-Term Memory (LSTM) network has been proposed. The exploration and utilization capability of the traditional sparrow search algorithm has been improved by introducing the niching techniques. The optimized sparrow search algorithm is subsequently integrated with the LSTM network, leveraging the algorithm to achieve intelligent selection of hyperparameters as well as improving the model accuracy. Finally, a case study has been conducted at Shiqiao Lock in the northern Jiangsu section of the Grand Canal. Ship delay data ranging from 2017 to 2020 were collected for model validation and analysis. The present study demonstrates that the proposed NSSA-LSTM model exhibits better performance and stability in the forecasting of ship waiting time, with an average reduction of 4.1% in Mean Absolute Error (MAE), 52.8% in Mean Squared Error (MSE), 34% in Mean Relative Error (MRE), 29.6% in Root Mean Square Error (RMSE), and an improved goodness of fit (2.8%). The present study would provide a guidance for the intelligent management of ship locks and maritime safety administration in canals.

关键词： Niching technique sparrow search algorithm LSTM neural network Ship delay prediction Beijing-Hangzhou Grand Canal

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Multi-parameter determination method for rock discontinuity roughness based on geometric properties intelligent extraction

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2025年 161卷

作者： Dong, Furui Wang, Shuhong Yin, Hong Jeon, Seokwon Northeastern Univ Sch Resource & Civil Engn Shenyang 110819 Peoples R China Seoul Natl Univ Dept Energy Syst Engn Seoul 151744 South Korea

To accurately and efficiently determine the rock joint roughness coefficient (JRC), a multi-parameter determination method for rock discontinuity roughness based on intelligent extraction of geometric properties was proposed. Firstly, a morphological characterization analysis program for rock discontinuities based on 3D point cloud reconstruction was developed. This program achieved the automatic extraction of arbitrary 2D profile lines and accurate calculation of multiple 2D characterization parameters. Then, two comprehensive characterization parameters were extracted from eight commonly used roughness characterization parameters by principal component analysis method. A multi-parameter determination method for rock discontinuity roughness based on the adaptive vigilance chaotic sparrow search algorithm optimizing extreme limit learning machine (ACSSAELM) was established to construct a nonlinear mapping relationship between the comprehensive characterization parameters and JRC to achieve an accurate prediction of discontinuity roughness. The proposed method's performance was validated using 112 2D profile samples and three granite discontinuity samples, and the reliability of the method was demonstrated by comparing it with other methods. The sensitivity of the method to sampling interval, anisotropy, and sampling size was discussed. Finally, the proposed method was applied to the construction of the Xinglong tunnel, providing effective guidance for practical construction activities.

关键词： Rock discontinuity Roughness Characterization parameters sparrow search algorithm Extreme learning machine

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A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method

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BIOTECHNOLOGY AND GENETIC ENGINEERING REVIEWS 2024年第3期40卷 2180-2195页

作者： Yang, Jieyu Li, Yong Kunming Univ Sci & Technol Fac Informat Engn & Automation Kunming Peoples R China 727 South Jingming Rd Kunming 650500 Yunnan Peoples R China

To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the hysteresis phenomenon of drug effect lagging behind plasma drug concentration, this paper proposes a pharmacokinetic-pharmacodynamic (PK-PD) model based on the SSA-1DCNN-Attention network and the semicompartment method. First, a one-dimensional convolutional neural network (1DCNN) is established, and the attention mechanism is introduced to determine the importance of each physiological and biochemical parameter. The sparrow search algorithm (SSA) is used to optimize the parameters of the network to improve the prediction accuracy after data enhancement through the synthetic minority oversampling technique (SMOTE) method. After constructing the time-concentration relationship of the drug through the SSA-1DCNN-Attention network, the concentration-effect relationship of the drug is established by using the semicompartment method to synchronize the drug effect with the concentration. At last, the phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy was taken as an example to validate the proposed method.

关键词： One-dimensional convolutional neural network attention mechanism sparrow search algorithm pharmacokinetic pharmacodynamic

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Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy

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JOURNAL OF HAZARDOUS MATERIALS 2024年 471卷 134309页

作者： Jiang, Siyuan Xu, Wen Xia, Qi Yi, Ming Zhou, Yuerong Shang, Jiangwei Cheng, Xiuwen Lanzhou Univ Coll Earth & Environm Sci Key Lab Environm Pollut Predict & Control Lanzhou 730000 Gansu Peoples R China

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R 2 value of 0.93 in regression, showcasing strong predictive power. A key innovation is the sparrow search algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA 's efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.

关键词： Antibiotic Pollution Machine Learning Inorganic Catalysts Reverse Synthesis Strategy sparrow search algorithm

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A novel framework based on two-stage multi-view feature optimization and improved support vector data description for aeroengine bearing early fault detection

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 245卷

作者： Hou, Zhaoguo Wang, Huawei Yue, Yubin Xiong, Minglan Zhang, Wenxuan Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China

Early fault detection is crucial to avoid catastrophic flight accidents caused by unplanned downtime of equipment. Aimed at the shortcomings of feature selection and early fault detection, this paper proposes a method combining two -stage multi -view feature optimization and improved support vector data description for aeroengine bearing early fault detection. First, a comprehensive evaluation index is constructed via combining monotonicity, correlation, robustness index and entropy weight method to be used for initial screening sensitive degradation features from multi -view candidate degradation feature. After that, the hierarchical clustering algorithm and local preserving projection algorithm is introduced to realize sensitive degradation features clustering and dimensionality reduction. On this basis, the Euclidean distance sample reduction and the sparrow search algorithm is utilized to complete the training sample selection and model parameter optimization of different clusters and further use the verification samples to filter the optimal features and model. Finally, based on the optimal degradation features and models, a fusion performance degradation index is built to achieve intelligent detection of early fault. Comparisons with some existing fault detection methods, the proposed method improves degradation trend sensibility and demonstrates potential of good early fault detection engineering applications.

关键词： Early fault detection Aeroengine bearing Two-stage feature optimization Support vector data description Sample reduction sparrow search algorithm

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The double-feature extraction method based on slope entropy and symbolic dynamic entropy for the fault diagnosis of rolling bearing

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 211-226页

作者： Zhang, Zhe Liu, Yingwei Han, Yuxuan Huangfu, Pengfei Ma, Zhiyuan Shi, Weichen Feng, Ke Xian Univ Technol Sch Mech & Precis Instrument Engn Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Automat Xian Shaanxi Peoples R China

This paper explores the application of slope entropy in fault diagnosis. In order to improve the recognition rate of faults, double-feature extraction is proposed, which combines slope entropy (SLEn) and symbolic dynamic entropy (SDE). There are five methods of entropy (slope entropy, permutation entropy, symbolic dynamic entropy, and sample entropy) are combined two by two in the double-feature experimental extraction. The K-Nearest Neighbor (KNN) is selected as the recognizer to calculate their recognition rates. The slope entropy and symbolic dynamic entropy having the highest recognition rates at 100%. It is followed by the combination of slope entropy and permutation entropy with an identification rate of 99%. In order to compensate for the lack of adaptive parameter selection in slope entropy and divide the parameter alpha and beta ranges of slope entropy more effectively, a new criterion is proposed to optimize the slope entropy by using the sparrow search algorithm (SSA-SLEn). Its state pattern reflects the characteristics of the fault signals in a more reasonable way.

关键词： Slope entropy Symbolic dynamic entropy sparrow search algorithm S_Dbw Fault diagnosis

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State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model

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ELECTROCHIMICA ACTA 2024年 487卷

作者： Zhou, Jiani Wang, Shunli Cao, Wen Xie, Yanxin Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction based on a sparrow search algorithm (SSA) optimized convolutional bidirectional long short-term memory neural network (CNN-Bi-LSTM) is proposed. Firstly, by analyzing the battery aging data, several feature indicators with highly correlated battery life degradation are constructed. Secondly, the CNN-Bi-LSTM model is used to extract the battery aging data features and the latent timing laws. Finally, the SSA optimizes the parameters to improve the model accuracy. Experimental results based on the NASA-Pcoe battery dataset show that the SSA-CNN-Bi-LSTM model outperforms other models, and the rootmean-square errors of the SOH prediction results are all less than 0.6 %. It indicates that the proposed SSACNN-Bi-LSTM model is capable of predicting SOH accurately and with high precision.

关键词： Lithium -ion batteries State of health Convolutional neural network Bi-directional long short-term memory sparrow search algorithm

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Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost

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MATERIALS 2024年第23期17卷 5727页

作者： Li, Ping Zhang, Zichen Gu, Jiming Anhui Univ Technol Sch Management Sci & Engn Maanshan 243002 Peoples R China

Strength testing of concrete mainly relies on physical experiments, which are not only time-consuming but also costly. To solve this problem, machine learning has proven to be a promising technological tool in concrete strength prediction. In order to improve the accuracy of the model in predicting the compressive strength of concrete, this paper chooses to optimize the base learner of the ensemble learning model. The position update formula in the search phase of the sparrow search algorithm (SSA) is improved, and piecewise chaotic mapping and adaptive t-distribution variation are added, which enhances the diversity of the population and improves the algorithm's global search and convergence abilities. Subsequently, the effectiveness of the improvement strategy was demonstrated by comparing improved sparrow search algorithm (ISSA) with some commonly used intelligent optimization algorithms on 10 test functions. A back propagation neural network (BPNN) optimized with ISSA was used as the base learner, and the adaptive boosting (AdaBoost) algorithm was used to train and integrate multiple base learners, thus establishing an adaptive boosting algorithm based on back propagation neural network improved by the improved sparrow search algorithm (ISSA-BPNN-AdaBoost) concrete compressive strength prediction model. Then comparison experiments were conducted with other ensemble models and single models on two strength prediction datasets. The experimental results show that the ISSA-BPNN-AdaBoost model exhibits excellent results on both datasets and can accurately perform the prediction of concrete compressive strength, demonstrating the superiority of ensemble learning in predicting concrete compressive strength.

关键词： back propagation neural network sparrow search algorithm concrete compressive strength prediction adaptive boosting

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