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Logging identification of lithology in fine-grained sedimentary rocks based on the FSSA-HKELM model: a case study of the Qingshankou Formation in the Songliao Basin (NE China)

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OIL SHALE 2024年第3期41卷 163-188页

作者： Tang, Baiqiang Meng, Qingtao Liu, Zhaojun Hu, Fei Zhang, Penglin Dang, Wei Wang, Junxian Jilin Univ Coll Earth Sci Changchun 130061 Jilin Peoples R China Key Lab Oil Shale & Paragenet Minerals Jilin Prov Changchun 130061 Jilin Peoples R China China Natl Petr Corp PetroChina Jilin Oilfield Co Songyuan 138000 Peoples R China Beijing Res Inst Uranium Geol Beijing 100029 Peoples R China

The lithology distribution of fine-grained sedimentary rocks in regional space plays a crucial role in guiding the exploration of oil shale layers. Identifying lithology by conventional logging information is efficient and costeffective. However, the strong inhomogeneity of fine-grained sedimentary rocks leads to a complex nonlinear relationship between lithology and logging data, making conventional linear methods no longer applicable. This study proposes a hybrid model for logging lithology identification of fine-grained sedimentary rocks from the first member of Qingshankou Formation in the Songliao Basin, NE China. This model is based on the hybrid kernel extreme learning machine (HKELM), and the firefly perturbation strategy is introduced into the sparrow search algorithm (FSSA) for optimization. The lithologic distribution is determined using cores, thin sections, and total organic carbon (TOC), while a total of four logging curves, the acoustic (AC), density (DEN), resistivity (RT), and natural gamma (GR) curves, were collected. FSSA-HKELM was compared with five algorithms, ELM, KELM, HKELM, PSO-HKELM, and SSA-HKELM, for lithology prediction effectiveness. The proposed hybrid method outperformed the other algorithms, achieving an accuracy of 81.78%, precision of 81.65%, recall of 87.78%, and a weighted F1 score of 82.16%. FSSA-HKELM is a very effective lithological identification method, which provides a basis for the lithological prediction of oil shale-bearing formations.

关键词： hybrid kernel extreme learning machine lithology identification sparrow search algorithm firefly perturbation strategy fine-grained sedimentary rocks

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A hybridized approach for minimizing energy in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 53-70页

作者： Kak, Sanna Mehraj Agarwal, Parul Alam, M. Afshar Siddiqui, Farheen Jamia Hamdard Dept Comp Sci & Engn Hamdard Nagar New Delhi 110062 India

Traditional scheduling techniques are designed to reduce processing times while disregarding energy costs. One way of lowering energy usage is to implement scheduling strategies that distribute tasks to specified resources, which influence the processing time and power usage. Among the primary objectives to be achieved in cloud computing, power and energy consumption of the cloud environment have become issues due to ecological and economic reasons. Despite the existence of research efforts from the past pertaining to the same topic, an ideal solution to this problem has not yet been found. One of the main drawbacks of utilizing cloud computing is that the cloud data centers hosting cloud computing applications use higher volumes of energy, adding to the increased functioning cost and carbon footprint in the environment, which in turn increases the need for energy-efficient systems. In this research work, a hybrid optimization algorithm is presented, which is intended to minimize energy in the cloud computing environment. Thus, the crow search algorithm (CSA) and the sparrow search algorithm (SSA) are combined to obtain the proposed hybrid model. The hybrid approach achieves the optimal position in the shortest period of time and with the least amount of energy, load, and makespan, hence improving system performance. The experiments were conducted using three setups with different task sizes. The analysis while using the task size = 300 shows that the proposed method improved QoS, Resource Utilization (RU) and decreased makespan, energy usage, and load at a rate of 0.5073, 4.4035, 0.0331, and 0.0014, respectively.

关键词： Carbon footprint Cloud service provider Crow search algorithm Dynamic scheduling Energy consumption Energy-aware scheduling Evolutionary algorithms Green cloud computing Load Makespan Multi-heuristics Multi-cloud Optimization Quality of service Resource allocation Resource utilization sparrow search algorithm Sustainable Task scheduling

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Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networks

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ALEXANDRIA ENGINEERING JOURNAL 2024年 102卷 169-178页

作者： Alkhonaini, Mimouna Abdullah Al Mazroa, Alanoud Aljebreen, Mohammed Hassine, Siwar Ben Haj Allafi, Randa Dutta, Ashit Kumar Alsubai, Shtwai Khamparia, Aditya Prince Sultan Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci & Arts Dept Comp & Informat Technol Ar Ar Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh 13713 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci PB 151 Al Kharj 11942 Norway Babasaheb Bhimrao Ambedkar Univ Dept Comp Sci Amethi UP India

Internet of Things (IoT) sensor networks are connected systems of physical devices set with actuators, sensors, and communication abilities, allowing them to gather, spread, and exchange information with centralized methods. These networks are essential in numerous businesses, such as healthcare, manufacturing, agriculture, and smart cities, as they deliver real-time observation, data-driven insights, and automation. Threat recognition in IoT sensor networks is a vital feature of safeguarding the protection and consistency of interconnected systems in the IoT. As IoT sensor networks endure to increase across various industries, the vulnerability to malicious actions and cyber-attacks increases. Threat recognition utilizing deep learning (DL) leverages neural networks to examine complex patterns and anomalies in data, permitting the identification of potential safety threats. DL techniques like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) excel at learning complex representations of data and feature extraction, making them suitable for identifying sophisticated attacks in different fields, including cybersecurity. This research develops a Hybrid Sine-Cosine Chimp Optimization Feature Selection with a Deep Learning (HSCCOFS-DL) approach for Threat Recognition in IoT Sensor Networks. The foremost aim of the HSCCOFS-DL system lies in the automated detection of threats using DL models. To accomplish this, the HSCCOFS-DL approach undergoes a data normalization process. Besides, the selection of features can be performed using the HSCCO algorithm. Meanwhile, the symmetrical autoencoder (SAE) technique effectively classifies threats. Finally, the sparrow search algorithm (SSA) can be applied to the selection of the hyperparameter of the SAE system. The experimental assessment of the HSCCOFS-DL technique takes place on a benchmark dataset. The simulation results indicated that the HSCCOFS-DL approach attains enhanced performance over other methods

关键词： Saudi Arabia Internet of things Feature selection sparrow search algorithm Deep learning Intrusion detection system

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Optimising Neural Networks for Enhanced Fracture Density Prediction in Surrounding Rock of Coalbed Methane Reservoir

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GEOLOGICAL JOURNAL 2025年第1期60卷 73-86页

作者： Men, Xinyang Chen, Shida Wu, Heng Zhang, Bin Zhang, Yafei Tao, Shu China Univ Geosci Beijing Sch Energy Resources Beijing Peoples R China China United Coalbed Methane Corp Ltd Beijing Peoples R China

Fractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back-propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY-1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the sparrow search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY-2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction. The optimised neural network combines the advantages of Tent chaotic mapping and SSA algorithm, and the model improves the accuracy of predicting fracture in the Surrounding Rock of coalbed methane reservoir compared with the conventional neural netwo

关键词： fracture density machine learning neural network model sparrow search algorithm tent chaos mapping

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Enhanced infrasound denoising for debris flow analysis: Integrating empirical mode decomposition with an improved wavelet threshold algorithm

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MEASUREMENT 2024年 235卷

作者： Dong, Hanchuan Liu, Shuang Liu, Dunlong Tao, Zhigang Fang, Lide Pang, Lili Zhang, Zhonghua Hebei Univ Coll Qual & Tech Supervis Baoding 071002 Peoples R China China Geol Survey Ctr Hydrogeol & Environm Geol Survey Tianjin 300304 Peoples R China Hebei Key Lab Energy Metering & Safety Testing Tec Baoding 071002 Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu 610299 Peoples R China Chengdu Univ Informat & Technol Coll Software Engn Chengdu 610225 Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Beijing 100083 Peoples R China

The measurement and analysis of infrasound are widely utilized in debris flow monitoring and early warning. However, the infrasound signals are often contaminated by noise across various frequencies, posing significant challenges for feature extraction. The current methods for infrasound denoising are somewhat rudimentary and face several constraints. This study introduces a novel method for enhancing infrasound signals through denoising. This approach integrates empirical mode decomposition with an enhanced wavelet threshold algorithm, and improves the conventional wavelet threshold function. This study uses the Signal -to -Noise ratio, smoothness and correlation coefficient to fine-tune the wavelet parameters. Experiments in flumes with varying speeds and volumes were conducted to test the effectiveness of the denoising method. The results demonstrate that, compared to traditional methods, the proposed method improves the SNR by averages of 69.56%, 60.91%, and 55.63%. It offers a new alternative for the noise reduction of debris flow infrasound signals.

关键词： Debris flow infrasound Empirical Mode Decomposition Wavelet threshold Noise reduction sparrow search algorithm

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Fault DiagnosisMethod of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

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Energy Engineering 2024年第7期121卷 1991-2007页

作者： Tengfei Li Wenhui Zhang Ke Mi Qingming Lin Shuangwei Zhao Jiayi Song Puneng Electric Power Technology Engineering Branch Shanghai Hengnengtai Enterprise Management Co.Ltd.Shanghai200437China School of Electrical Engineering Sichuan UniversityChengdu610065China

Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved sparrow search algorithm(ISSA)optimized Backpropagation Neural Network(BPNN)is proposed to improve the operational safety of *** the 1.5kV/4000A/75kA LVCB as an *** to the current operating characteristics of the energy storage motor,fault characteristics are extracted based on Empirical Wavelet Transform(EWT).Traditional BPNN has problems such as difficulty adjusting network weights and thresholds,being sensitive to initial weights,and quickly falling into local optimal *** sparrow search algorithm(SSA)with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN *** results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction:motor voltage increase,motor voltage decrease,energy storage spring stuck,transmission gear stuck,regular state and energy storage spring not *** is suitable for fault diagnosis and detection of the energy storage part of LVCB.

关键词： Low voltage circuit breakers energy storage motor current sparrow search algorithm empirical wavelet transform fault diagnosis

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Fault Diagnosis of Wind Turbine Bolts based on ICEEMD-SSA-SVM Model

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第3期17卷 269-282页

作者： Ge, Qianhua Wang, Dexing Sun, Kai Wang, Dongli SPIC Nantong New Energy Co Ltd Beijing Peoples R China Shaanxi Zhongke QIHANG Technol Co Ltd Beijing 100176 Peoples R China

Background Compared with traditional power generation systems, wind turbines have more units and work in a more harsh environment, and thus have a relatively high failure rate. Among blade faults, the faults of high-strength bolts are often difficult to detect and need to be analyzed with high-precision sensors and other equipment. However, there is still little research on blade *** The improved complete ensemble empirical mode decomposition (ICEEMD) model is used to extract the fault features from the time series data, and then combined with the support vector machine optimized by sparrow search algorithm (SSA-SVM) to diagnose the bolt faults of different degrees, so as to achieve the purpose of early *** The results show that the ICEEMD model used in this paper can extract the bolt fault signals well, and the SSA-SVM model has a shorter optimization time and more accurate classification compared with models such as *** The hybrid model proposed in this paper is important for bolt fault diagnosis of operation monitoring class.

关键词： Improved complete ensemble empirical mode decomposition sparrow search algorithm support vector machine fault diagnosis wind turbine feature extraction

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Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

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Energy Engineering 2024年第3期121卷 821-834页

作者： Qi Zhuang Dong Liu Zhuo Chen PetroChina Changqing Oilfield Company The Second Gas Production PlantXi’an710000China PetroChina Changqing Oilfield Company Safety and Environmental Supervision Department Co.Ltd.Xi’an710000China Sinopec Northwest Oilfield Company The Second Oil Production Plant Co.Ltd.Urumqi830016China

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline *** prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety *** at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved sparrow search algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is *** 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN *** results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison *** the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

关键词： Oil and gas pipeline corrosion defect failure pressure prediction sparrow search algorithm BP neural network logistic chaotic map

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A novel LFSSA-LSTM algorithm for the remaining useful life prediction of lithium-ion batteries in whole life cycle

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2024年第9期19卷

作者： Shi, Peng Hu, Rongchun He, Yisheng Chen, Xing Huang, Haozuo Liu, Zhen Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Chengdu Univ Informat Technol Coll Resources & Environm Chengdu 610225 Peoples R China

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential to improve the safety of battery systems and optimize battery management strategies. Therefore, a new algorithm combining sparrow search algorithm (SSA) improved by the Levy flight method and long short-term memory neural network (LSTM) is proposed in this paper. By extracting the National Aeronautics and Space Administration (NASA) battery aging datasets, the battery correlated health characteristics are obtained, and the deterioration trend of battery performance is effectively described. The sparrow search algorithm improved by the Levy flight method enhances the ability of the algorithm to jump out of the optimal solution. By using the improved algorithm, the hyper-parameter optimization problem of the long short-term memory network is solved, and the accuracy of the algorithm is improved. Finally, the probability density function is used to predict the remaining useful life of lithium-ion batteries, which effectively prevents the uncertainty caused by a single prediction result. Compared with long short-term memory neural network and the one optimized by a traditional sparrow search algorithm, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed algorithm are kept at 1.77 %, 2.24%, and 1.25%, respectively. The results indicate that the algorithm has better accuracy and fitting performance.

关键词： Lithium-ion battery Remaining useful life sparrow search algorithm Long short-term memory neural network Levy flight method

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Thermal error modeling and compensation of spindle based on gate recurrent unit network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期128卷 5519-5528页

作者： Li, Yang Bai, Yinming Hou, Zhaoyang Nie, Zhe Zhang, Huijie Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China

In view of the serious hysteresis and nonlinear relationship between the thermal error of CNC machine tool spindle and the temperature rise of spindle measuring points, a spindle thermal error prediction model combining sparrow search algorithm (SSA) and gate recurrent unit (GRU) is proposed. Taking the spindle of a precision machine tool as the research object, the thermal error and the temperature field of the spindle in idling state are measured. Select the temperature of the measuring point of the spindle as the input and the thermal error in Z-direction as the output, the thermal error prediction model is established by using GRU network. SSA is used to optimize the training parameters of GRU network, and finally a prediction model of SSA-GRU spindle Z-direction thermal error considering the influence of natural environment is established. The performance of the established model is verified by taking the test data of variable speed working condition as the robustness test set. The results show that SSA-GRU can be used for thermal error modeling and compensation, and the Z-direction thermal error of the machine tool spindle can be controlled within 8 & mu;m, which has better prediction accuracy than the traditional network model.

关键词： Spindle Thermal error modeling Gate recurrent unit network sparrow search algorithm

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