检索结果-内蒙古大学图书馆

Research on feature extraction method for underwater acoustic signal using secondary decomposition

OCEAN ENGINEERING 2024年 306卷

作者： Li, Guohui Liu, Bo Yang, Hong Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Due to the high complexity of the marine environment, underwater acoustic signal (UAS) contains a lot of background noise, which makes it more difficult to extract its features. To accurately extract target features, a feature extraction method for UAS using secondary decomposition and mixed features is proposed. Firstly, an improved CEEMDAN (ICEEMDAN) is proposed to alleviate the mode aliasing problem. Secondly, the threshold for secondary decomposition is determined adaptively, and the UAS is secondarily decomposed using the enhanced beluga whale optimizer (EBWO) improved time-varying filtering empirical mode decomposition to obtain dual mode components. Thirdly, two eigenvectors are selected from the dual mode components, respectively. Fourthly, refined time-shift multiscale fuzzy dispersion entropy (RTSMFuDE) is proposed, and RTSMFuDE of the eigenvector and the original UAS are calculated to construct the three-dimensional eigenvalues. Finally, support vector machine is used to identify three-dimensional eigenvalues. The simulation results of the measured ship radiated noise show that the recognition rate of the proposed method reaches 98.43%, which proves that it can accurately classify ship signals. In addition, the proposed method has been successfully applied to feature extraction of underwater acoustic biological signals, which proves its universality.

关键词： Underwater acoustic signal Secondary decomposition Feature extraction Entropy Mode decomposition optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning

引用

ENERGY 2024年 305卷

作者： Yu, Jie Chen, Lu Wang, Qiong Zhang, Xi Sun, Qinghe Southeast Univ Sch Elect Engn Nanjing 210000 Jiangsu Peoples R China State Grid Beijing Municipal Elect Power Co Beijing 100031 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China State Grid Smart Grid Res Inst Beijing 102209 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

With dynamic changes in the energy market and continuous advances in smart energy technology, regional integrated energy systems (RIESs) have emerged as a vital direction in the evolution of energy systems. They are increasingly gaining significance in the realm of energy supply (ES) and demand response initiatives. In this context, this article introduces a novel optimal operational model for an integrated energy system (IES) that incorporates energy price responsiveness. The paper commences by delivering a comprehensive overview of the typical structure of an IES, presenting detailed models for each system module, encompassing the electrical energy supplier, heating, and cooling components. The proposed system model comprises three distinct subsystems, each catering to specific energy requirements. The primary emphasis is on devising an efficient planning scheme tailored to the energy consumption patterns and operational aspects of the IES. Through the presented model, there is substantial potential for significantly reducing the overall system cost without inducing a noteworthy upsurge in environmental pollution. Moreover, the energy efficiency of the system can experience considerable enhancement. The desired optimization problem has been solved using the proposed multiobjective Horse Herd optimization algorithm (MOHHOA). Additionally, in this strategy, Pareto front and fuzzy selection are employed to make better decisions among all Pareto solutions. The optimization strategy devised in this research not only enables the integrated operation of the IES but also ensures its compatibility with ongoing energy development initiatives. The proposed method yields a diverse set of Pareto solutions across the objective space, providing various trade-offs between economic, environmental, and reliability considerations. Utilizing MOHHOA, the algorithm efficiently balances exploration and exploitation during optimization, resulting in informed decision-making. In optimal p

关键词： Integrated regional energy systems Optimal operating model Response to energy price optimization algorithm Sustainable energy supply

来源：评论

学校读者我要写书评

暂无评论

Robust Trajectory Control for Multitarget Tracking in Proximity Operations

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第10期47卷 2194-2203页

作者： Zhang, Jingrui Xue, Zhirun Cai, Han Houssineau, Jeremie Li, Lincheng Nanyang Technol Univ Sch Aerosp Engn Singapore 639798 Singapore Nanyang Technol Univ Div Math Sci Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanyang Technol Univ Sch Astronaut Singapore 639798 Singapore

来源：评论

学校读者我要写书评

暂无评论

Cardiac Arrhythmia multiclass classification using optimized FLS-based 3D-CNN

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1543-1566页

作者： Pashikanti, Rajesh Patil, C. Y. Shinde, Amita Coll Engn Dept Instrumentat & Control Engn Pune 411005 Maharashtra India AISSMS IOIT Dept Instrumentat Engn Pune Maharashtra India

Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems. Analyzing a huge number of ECG data manually by medical professionals uses excessive medical resources. Consequently, identifying ECG characteristics based on machine learning has become increasingly popular. However, these conventional methods have some limitations, including the need for manual feature recognition, complex models, and lengthy training periods. This research offers a unique hybrid POA-F3DCNN method for arrhythmia classification that combines the Pelican Optimisation algorithm with fuzzy-based 3D-CNN (F3DCNN) to alleviate the shortcomings of the existing methods. The POA is applied to hyper-tune the parameters of 3DCNN and determine the ideal parameters of the Gaussian Membership Functions used for FLSs. The experimental results were obtained by testing the performance of five and thirteen categories of arrhythmia classification, respectively, on UCI-arrhythmia and the MIT-BIH Arrhythmia datasets. Standard measures such as F1-score, Precision, Accuracy, Specificity, and Recall enabled the classification results to be expressed appropriately. The outcomes of the novel framework achieved testing average accuracies after ten-fold crossvalidation are 98.96 % on the MIT-BIH dataset and 99.4% on the UCI arrhythmia datasets compared to state-of-the-art approaches.

关键词： Deep learning optimization algorithm ECG classification cardiac arrhythmia feature extraction 3D-CNN Pelican optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Risk Analysis and Prediction During the Implementation of Carbon Neutrality Goals

引用

JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2025年第1期37卷

作者： Long, Huizhen Li, Meng Dong, Zhen Meng, Yuan Zhang, Fengrui Hong Kong Polytech Univ Hong Kong & Dongguan Univ Technol Sch Tourism & Hospitality Management Hong Kong Peoples R China Henan Open Univ Acad Marxism Kaifeng Peoples R China Sichuan Tourism Univ Sch Marxism Chengdu Peoples R China Jinan Ecol Environm Monitoring Ctr Jinan Peoples R China Sichuan Agr Univ Coll Life Sci Chengdu Peoples R China

Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability. Therefore, it is imperative to explore new approaches to better address risks. In response to this need, our study introduces an innovative risk prediction model known as WOA-FPALSTM. What sets this model apart is its seamless integration of deep learning and heuristic algorithms, designed to overcome the limitations of existing approaches. The core component of deep learning, LSTM, excels in sequence modeling by capturing long-term and short-term dependencies in time series data, thereby enhancing the model's ability to model temporal data. Meanwhile, the heuristic algorithm, WOA (Whale optimization algorithm), equips our model with global search capabilities, facilitating the discovery of optimal model configurations and significantly improving predictive performance.

关键词： Artificial Intelligence LSTM optimization algorithm Risk Emergency Management and Treatment Risk Forecasting WOA

来源：评论

学校读者我要写书评

暂无评论

A data-driven model for predicting metal ductile fracture at various stress states

引用

ENGINEERING FAILURE ANALYSIS 2025年 175卷

作者： Liu, Wen Chen, Geng Liu, Jianhui Zhang, Lele Lanzhou Univ Technol Sch Mech & Elect Engn 36 Pengjiaping St Lanzhou 730050 Gansu Peoples R China Beijing Jiaotong Univ Sch Mech & Elect Control Engn 3 Shangyuan Village Beijing 100044 Peoples R China Beijing Jiaotong Univ Natl Int Sci & Technol Cooperat Base 3 Shangyuan Village Beijing 100044 Peoples R China

Metal ductile fracture often occurs in stamping forming and collision accidents, and accurate prediction of material fracture is essential in the design of stamping parts and passive safety structures. The existing fracture models are based on the general fracture law of materials and can predict fracture in all stress states with fewer parameters. However, the fracture prediction at certain stress states is not accurate enough due to configuration limitations. To this end, a datadriven fracture model was proposed in this paper, which determines the relationship between the fracture strain and the stress triaxiality entirely based on the material fracture behaviour. Thus, high accuracy of fracture prediction under all stress states can be achieved, but the model has more parameters. To calibrate these parameters, tensile experiments were conducted with a series of notched specimens, and the optimization framework of parameters inverse identification was established, including optimization problem establishment, parameter normalization, transformation to an unconstraint problem and solving with the simplex method. The calibration results show that the simulation error of the data-driven model is reduced from 30.64 % to 10.59 % in the 59 iterations, much lower than 18.20 % of the Johnson-Cook model. In the simulation of the modified compact-tension specimen, loads decrease earlier in the Johnson-Cook model, which further highlights the ability of the data-driven model describing the fracture under complex stress states. The accurate fracture prediction of the data-driven fracture model provides a basis for the reasonable design of stamping parts and passive safety structures.

关键词： Data-driven fracture model Stress state Johnson-Cook model Inverse parameter identification framework optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Back analysis of mechanical parameters based on GPSO-BP neural network and its application

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Shi, Song Miao, Yichen Di, Cheng Zhao, Quanchao Zheng, Yantao Liu, Changwu Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Peoples R China China Railway Eryuan Engn Grp Co Ltd Chengdu 610000 Peoples R China

Rock mass mechanical parameters are essential for the design and construction of underground engineering projects, but parameters obtained through traditional methods are often unsuitable for direct use in numerical simulations. The back analysis method, based on displacement monitoring, has emerged as a new approach for determining rock mass parameters. In this study, an experimental scheme was developed using orthogonal and uniform experimental designs to obtain training samples for the neural network. A GA-PSO-BP neural network model (GPSO-BP) was proposed, combining the fast convergence of the particle swarm optimization (PSO) algorithm and the global optimization capability of the genetic algorithm (GA). This model was applied to invert the rock mass parameters E, mu, phi, and c for deep-buried tunnels. The results indicate that the GPSO-BP neural network model outperforms the BP, GA-BP, and PSO-BP neural network models in terms of faster convergence and higher accuracy. It also shows superior performance in handling small datasets and complex problems, achieving better data fitting and the highest score in rank analysis. The DDR curve further confirms the GPSO-BP model's computational efficiency. When the rock mass parameters derived from this model are applied to forward numerical simulations, the average error across four monitoring projects is only 4.34%, outperforming the other three models. Thus, this study provides an effective method for improving the accuracy of rock mass parameter inversion in underground engineering.

关键词： Rock mass parameters BP neural network Back analysis optimization algorithm Displacement prediction

来源：评论

学校读者我要写书评

暂无评论

Research on Fractional-Order Sliding Mode PMSM Speed Regulation Based on Load Observer

引用

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第5期19卷 3429-3438页

作者： Zhu, Maoyun Cao, Yong He, Zhiqin Wu, Qinmu Ma, Jiaqing Qiu, Bing Luo, Chunshan Guizhou Univ Elect Engn Coll Guiyang 550025 Peoples R China Guizhou Prov Orthoped Hosp Guiyang 550002 Peoples R China

In this paper, a fractional-order sliding mode control (FOSMC) strategy based on load observer is proposed. First, a FOSMC strategy is designed to control the speed loop of the permanent magnet synchronous motor (PMSM). The fractional-order calculus is introduced and the exponential reaching law is improved to enhance the chatter suppression capability of the system, which solves the contradiction between fastness and chatter in the traditional sliding mode control (SMC). Second, a load observer with a state error differential term is designed to observe the system disturbances and compensate the observed values into the control system to improve the anti-disturbance capability of the system. In addition, to avoid trial and error of the arrival method parameters, an improved particle swarm optimization (PSO) algorithm is designed to optimize the controller parameters. By simulation, the proposed method is superior to PI and integer-order sliding mode control (IOSMC) in anti-disturbance capability, which verifies the validity of the proposed method.

关键词： PMSM Fractional-order integral sliding mode optimization algorithm Observer Speed regulation

来源：评论

学校读者我要写书评

暂无评论

A Digital Closed-Loop Control System for MEMS Resonant Pressure Sensors Based on a Quadrature Phase-Locked Loop

引用

IEEE SENSORS JOURNAL 2024年第7期24卷 10355-10363页

作者： Zhou, Mengyang Xia, Wenliang Lu, Yulan Xie, Bo Chen, Jian Wang, Junbo Chen, Deyong Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Transducer Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

This article presents a quadrature phase-locked-loop (QPLL)-based digital system for closed-loop control of micro-electromechanical system (MEMS) resonant sensors. The system estimates the sensing signal of the resonator in the form of in-phase and quadrature-phase components by an optimization algorithm, which extends the frequency tracking range and automatically adjusts the amplitude to ensure the linear vibration of the resonator. According to the simulation results, the system has the advantages of fast response and good tracking accuracy. The system has been implemented on a field-programmable gate array (FPGA) development board and validated on an MEMS resonant pressure sensor working by electrostatic excitation and capacitive detection. Experiments show that the proposed system exhibits high linearity, faster startup response, and better frequency stability compared with the previous automatic gain control (AGC)-based analog circuit. Furthermore, the system is widely applicable to sensors with similar resonant characteristics. It has great potential for resonant sensors that require amplitude detection, such as weakly coupled sensors based on the mode-localized phenomenon or parametric coupled sensors subject to blue-sideband excitation.

关键词： Sensors Pressure sensors Sensor systems Resonant frequency Micromechanical devices Sensor phenomena and characterization Closed loop systems Closed-loop system field-programmable gate array (FPGA) micro-electromechanical system (MEMS) resonant sensor optimization algorithm phase-locked loop (PLL)

来源：评论

学校读者我要写书评

暂无评论

Polynomial Attitude Trajectory Planning for Spacecraft with Movable Parts Using Decoupled Strategy

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第1期47卷 72-86页

作者： Fan, Yuqi Xu, Rui Li, Zhaoyu Zhu, Shengying Shang, Haibin Gao, Ai Gao, Yue Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Autonomous Nav & Control Deep Space Explor Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China Space Star Technol Co Ltd Beijing 100081 Peoples R China

The problem of attitude planning for spacecraft with movable parts under complex pointing constraints is addressed in this paper. To avoid tackling the complex pointing constraints in the high-dimensional system, a novel decoupled strategy is proposed to map the pointing space to the attitude space. Path-planning algorithm Rapidly-exploring Random Tree (RRT)*-Smart is employed in pointing space to generate a collision-free pointing path, and piecewise quintic polynomials are used to represent the attitude path. By proving the differential flatness of spacecraft with movable parts, the inverse dynamics is used to acquire attitude state trajectory in state space. Benefiting from the decoupled strategy, the attitude state trajectory is optimized by a new heuristic time allocation algorithm using binary search with no gradient information needed. The simulation results are discussed, and the performance during each scenario is analyzed.

关键词： Spacecrafts Robot Kinematics Energy Consumption Spacecraft Attitude Control optimization algorithm Satellite Formation Attitude Planning Decoupled Strategy Polynomial Movable Telescope

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：