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A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA

Journal of Mountain Science 2024年第12期21卷 4019-4032页

作者： DONG Hanchuan LIU Shuang PANG Lili LIU Dunlong DENG Longsheng FANG Lide ZHANG Zhonghua College of Quality and Technical Supervision Hebei UniversityBaoding 071002China Technology Innovation Center for Geological Environment Monitoring Ministry of Natural ResourcesTianjin 300304China Center for Hydrogeology and Environmental Geology Survey China Geological SurveyTianjin 300304China Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610299China College of Software Engineering Chengdu University of Information and TechnologyChengdu 610225China School of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054China

Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris ***,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)***-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and *** mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search *** Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the *** on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound *** reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence *** Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.

关键词： Debris flow infrasound Variational Mode Decomposition sparrow search algorithm ARIMA model Hilbert transform

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Multisensor information fusion method for intelligent fault diagnosis of reciprocating compressor in shale gas development

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2024年第1期238卷 16-28页

作者： Tang, Yang Yang, Xin Lei, Bo Yang, Liu Xie, Chong Southwest Petr Univ Sch Mechatron Engn Chengdu Peoples R China Sichuan Changning Nat Gas Dev Co Ltd Chengdu Peoples R China Southwest Petr Univ Sch Mechatron Engn 8 Xindu Rd Chengdu 610500 Peoples R China

To address the problems of the poor feature extraction ability and weak data generalization ability of traditional fault diagnosis methods in reciprocating shale gas compressor fault diagnosis applications, in this study, a fault diagnosis method for reciprocating shale gas was developed. This method uses a novel optimized learning method, free energy in persistent contrastive divergence, in deep belief network learning and training. It solves the problem of the deep belief network classification ability degradation in long-term training. The root mean square error is used as the fitness function to search for the optimal parameter combination of the DBN network by using the sparrow search algorithm. At the same time, the learning rate and batch size of the deep belief network, which have a large impact on the training error, are selected for optimization. Then, the original vibration signal is preprocessed by calculating 13 different time domain indicators, and feature-level data and decision-level data are fused in a parallel superposition method to obtain a fused time domain index dataset. Finally, combined with the powerful adaptive feature extraction and nonlinear mapping ability of deep learning, the constructed sample dataset is input to the deep belief network for training, and the deep belief network based on reciprocating shale gas compressor fault diagnosis model is established.

关键词： Deep belief network sparrow search algorithm reciprocating shale gas compressor fault diagnosis optimization design

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IDHNet: Ultra-Short-Term Wind Power Forecasting With IVMD-DCInformer-HSSA Network

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ENERGY SCIENCE & ENGINEERING 2024年第12期12卷 5566-5589页

作者： Li, Wei Gao, Lu Zhang, Fei Ren, XiaoYing Qin, Ling Inner Mongolia Univ Sci &Technol Sch Digtial & Intelligence Ind Baotou Inner Mongolia Peoples R China Inner Mongolia Univ Sci & Technol Sch Automat & Elect Engn Baotou Inner Mongolia Peoples R China North China Elect Power Univ Coll Renewable Energy Beijing Peoples R China

The variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD-DCInformer-HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R-squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%-3% compared with other models, which has a good prediction effect.

关键词： Informer sparrow search algorithm ultra-short-term wind power forecasting variational mode decomposition

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Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network

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SENSORS 2024年第2期24卷 493页

作者： Yin, Songfeng Zou, Xiang Cheng, Yue Liu, Yunlong Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China Tsinghua Univ Hefei Inst Publ Secur Hefei 230601 Peoples R China Hefei Tsingsensor Technol Co Ltd Hefei 230601 Peoples R China

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Levy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment.

关键词： laser methane sensors temperature compensation isolation forest algorithm BP neural network sparrow search algorithm

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Stochastic model updating based on sub-interval similarity and BP neural network

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第12期31卷 2667-2679页

作者： Zhao, Yanlin Li, Xindong Zhao, Jiahui Yang, Jianhong Yang, Debin Bing, Sun Yao, Wang Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Guangzhou Peoples R China Beijing Key Lab Lightweight Met Forming Beijing Peoples R China China Acad Launch Vehicle Technol Beijing Peoples R China

A stochastic model updating framework is proposed in this work to address the problem of uncertain model calibration. This framework includes an effective uncertainty quantification metric of sub-interval similarity to measure the discrepancy between model predictions and experimental observations. A back propagation neural network is employed as a surrogate model for finite element method models, and a sparrow search algorithm is introduced as an optimization operator. Two typical numerical examples of a 3-degree-of-freedom mass-spring system and a satellite finite element model have been presented to demonstrate the feasibility and the effectiveness of the proposed stochastic model updating algorithm.

关键词： Sub-interval similarity back propagation neural network stochastic model updating sparrow search algorithm uncertainty quantification model calibration

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Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a university community

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ENERGY 2024年 294卷

作者： Zhang, Chengyu Luo, Zhiwen Rezgui, Yacine Zhao, Tianyi Dalian Univ Technol Inst Bldg Energy Dalian Peoples R China Cardiff Univ Welsh Sch Architecture Cardiff Wales Univ Cardiff Sch Engn Cardiff Wales

The escalating energy and environmental crises underline the imperative for sustainable cities and societies. For effective and real-time energy management, this study proposes an enhanced building energy consumption prediction system. It introduces a novel concept named region -wide occupant energy -use behavior probability and incorporates it into the input system, which better reflects real-time and complex energy -occupantenvironment interactions in buildings. In addition, it integrates the squeeze -and -excitation attention mechanism, sparrow search algorithm, and convolutional neural network processes for optimizing data processing and hyperparameter selection. Validation in seven sample buildings demonstrates the proposed prediction system has a better balance between time and accuracy, reducing 36.32% MAPE and 31.20% CV-RMSE on average compared to all other prediction systems, only with 118.354s of extra time consumption increase compared to the least time-consuming method. Furthermore, this study discusses methods for selecting suitable input systems and algorithms based on building type, data collection conditions, accuracy, and time consumption. Finally, the enhanced prediction is applied to forty-five buildings in a university community, yielding a 12.35% MAPE and a 0.1707 CV-RMSE on average, reaffirming its superiority and practicality.

关键词： University community Building energy consumption prediction Region-wide occupant behavior probability Squeeze-and-excitation attention mechanism sparrow search algorithm Priority selection

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Risk Prognosis and Fire Prediction of Urban Utility Tunnels Using a Hybrid SSA-LSTM Method

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年

作者： Xu, Tianlong Huang, Xin Zeng, Ying Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu Sichuan Peoples R China China Univ Petr Beijing Karamay Karamay Xinjiang Peoples R China

This paper presents a prediction model for the fire trend prediction in urban utility tunnels by integrating the sparrow search algorithm (SSA) with the long short-term memory (LSTM) network. An improved LSTM is developed to effectively capture the dynamic nature of fire evolution. The SSA is employed to optimize the parameters of the LSTM, with its global optimization capability enhanced through the incorporation of four meta-heuristic optimization methods. To comprehensively evaluate the model's effectiveness, comparative experiments were conducted against the Support Vector Machine (SVM), Random Forest (RF), Gated Recurrent Unit (GRU), and Transformer models, demonstrating the superiority of the improved SSA-LSTM in multiple evaluation metrics. The advanced SSA-LSTM model is then used to predict fire severity based on fire intensity. The applicability and effectiveness of the proposed model are validated through a practical fire experiment. Comparative analysis with existing approaches indicates that the proposed model achieves an approximately 15% improvement in prediction accuracy. In addition, the model shows potential for broader applications in dynamic fire trend identification and critical point warning systems.

关键词： fire trend prediction long short-term memory network sparrow search algorithm urban utility tunnels

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Low power energy balanced clustering routing scheme based on improved SSA and Multi-Hop transmission in IoT

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Wang, Rui Guo, Xiaoling Sun, Xinghua Yang, Jie Hebei North Univ Sch Informat Sci & Engn Zhangjiakou 075000 Peoples R China Hebei North Univ Fac Sci Zhangjiakou 075000 Peoples R China

In the large scale monitoring application of the IoT, the power carried by a single sensor node is limited, and it may not be replenished in the later stage. Therefore, it is required that the sensor nodes should save power when working, and the power consumption among the sensor nodes should be balanced to avoid the early death of some nodes and the occurrence of monitoring blind spots in the monitor area. Communication is the main part of power loss for sensor nodes. For this reason, this paper introduces a new low power energy balanced clustering routing scheme. Firstly, the sparrow search algorithm is improved by adding adaptive sine function and Cauchy random number to raise the search capability of the discoverer;The standard normal distribution random number is used to enhance the vigilant escape from the limit of local optimal value. Then the enhanced SSA algorithm can be used for cluster head selection. A fitness function with five key parameters is constructed by using the residual energy, the member number within a cluster, the intra-cluster distance in a cluster, the distance among clusters, and the distance from cluster head to base station, which effectively realizes the energy consumption balance among sensors in each round. At the same time, in the data forwarding stage, the multi-hop forwarding rules are designed to further reduce energy loss, greatly delay the arrival of the first dead node, and keep the sensor node working in the low power mode for a long time. Experimental results show that this scheme not only significantly prolongs the sensor lifetime, but also balances the power consumption among sensor nodes. This scheme provides a new idea for IoT sensor nodes to work in low power and ultra long standby mode, especially suitable for large scale and long term deployment scenarios such as ecological monitoring and industrial equipment state perception.

关键词： Low power Energy balanced Clustering routing sparrow search algorithm Multi-hop Network lifetime

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A hybrid approach for lithium-ion battery remaining useful life prediction using signal decomposition and machine learning

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SCIENTIFIC REPORTS 2025年第1期15卷 1-17页

作者： Fan, Yibiao Lin, Zhishan Wang, Fan Zhang, Jianpeng Longyan Univ Sch Phys & Mech & Elect Engn Longyan 361000 Peoples R China Fujian Antong Elect Co Ltd Longyan 361000 Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361102 Peoples R China

Lithium-ion batteries are widely used in many fields, and accurate prediction of their remaining useful life (RUL) was crucial for effective battery management and safety assurance. In order to solve the problem of reduced RUL prediction accuracy caused by the local capacity regeneration phenomenon during battery capacity degradation, this paper proposed a novel RUL prediction method, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique with an innovative hybrid prediction strategy that integrated the support vector regression (SVR) and the long short-term memory (LSTM) networks. First, CEEMDAN was used to decompose the battery capacity data into high-frequency and low-frequency components, thereby reducing the impact of capacity regeneration. Subsequently, the SVR model predicted the low-frequency component that characterized the main degradation trend, and the high-frequency component that contained capacity regeneration features was predicted using an LSTM network optimized by the sparrow search algorithm (SSA). Finally, the final RUL prediction was obtained by combining the predictions of the two models. Experimental results on NASA public datasets showed that the proposed hybrid method significantly outperformed existing methods: the RMSE of the methods proposed in this paper were all less than 0.0086 Ah, the MAE were all less than 0.0060 Ah, the R2 values were all higher than 0.96, and the RUL prediction errors were controlled within one cycle. This method gave full play to the complementary advantages of SVR and LSTM and provided an accurate and reliable solution for RUL prediction of lithium-ion batteries.

关键词： Li-ion batteries Remaining useful life prediction Long short-term memory Support vector regression CEEMDAN sparrow search algorithm

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Study and application of deeply optimized neural network in roof stability evaluation

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EARTH SCIENCE INFORMATICS 2024年第2期17卷 1729-1744页

作者： Yin, Huiyong Li, Shuo Xu, Guoliang Xie, Daolei Jiang, Cheng Dong, Fangying Wang, Houchen Wu, Bin Shandong Univ Sci & Technol Coll Earth Sci & Engn Shandong Prov Key Lab Deposit Mineralizat & Sedime Deposit Mineralizat & Sedimentary Minerals Qingdao 266590 Shandong Peoples R China Geophys Prospecting & Surveying Shandong Coalfield Jinan 250104 Shandong Peoples R China Inner Mongolia Datang Int Jungar Min Co Ltd Hohhot 010090 Inner Mongolia Peoples R China Xiezhuang Coal Mine Xinwen Min Grp Co Ltd Xintai 271200 Peoples R China Qinxin Coal Ind Co Ltd Changzhi 046500 Shanxi Peoples R China

Deep coal seam mining causes instability and collapse of coal seam roof frequently, which seriously affects the safety production and threatens the personal safety of underground personnel. In order to evaluate the stability of coal roof accurately, this paper select 6th coal seam in Kongduigou Coalfield of Jungar Coalfieldas research object, analyzes the geological and hydrogeological data, and study the lithology, rock combination, sandstone thickness, fault, fold, seam inclination, rock quality index, and rock compressive strength on the influence of the roof stability, drawing the main control factor 3D mapping projection surface maps. Select 58 borehole data points as the input samples (50 training sets and 8 test sets), use genetic algorithm (GA) to optimize the network random initial weights and threshold initial and sparrow search algorithm (SSA) for secondary optimization for the BP neural network training and learning, establishing GA-BP neural network based on SSA optimization (SSA-GA-BP neural network) coal roof stability evaluation model, which is used to predict and evaluate the 6th coal roof stability of the research area after the training error accuracy reached the requirements. The fuzzy comprehensive evaluation method, BP neural network, GA-BP neural network and SSA-GA-BP neural network are also used to predict and evaluate the 6th coal roof stability. Compare the evaluation results of each model with the actual value. The results show that the error of coal seam roof stability evaluation of SSA-GA-BP neural network is smallest, with the accuracy 88%, and the model is successfully applied to predict the roof stability of the 6th coal seam in Kongduigou Coalfield, which provides a scientific evaluation method and theoretical basis for the evaluation of coal seam roof stability.

关键词： Stability of coal seam roof Genetic algorithm sparrow search algorithm BP neural network Fuzzy comprehensive evaluation Evaluation and prediction

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