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Method to Solve Underwater Laser Weak Waves and Superimposed Waves

SENSORS 2023年第13期23卷 6058-6058页

作者： Kang, Chuanli Lin, Zitao Wu, Siyi Yang, Jiale Zhang, Siyao Zhang, Sai Li, Xuanhao Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China Guilin Univ Technol Key Lab Spatial Informat & Geomat Guilin 541004 Peoples R China

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R-2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (lm) to optimize the estimated parameters;the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms;the final RMSE is 0.0016, R-2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

关键词： laser radar waveform solution lm algorithm underwater exploration

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Optimization of Levenberg Marquardt algorithm Applied to Nonlinear Systems

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PROCESSES 2023年第6期11卷 1794页

作者： Huang, Xinyi Cao, Hao Jia, Bingjing Anhui Sci & Technol Univ Coll Informat & NetWork Engn Chuzhou 233100 Peoples R China

As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the lm algorithm proved the algorithm's efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study's findings demonstrate that the lm optimization algorithm's identification error exceeds 10-5. The research's suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the accuracy study of neural network models and the application of nonlinear systems, the suggested lm-BP optimization algorithm is evidently more appropriate for nonlinear systems.

关键词： lm algorithm BP neural network nonlinear systems algorithm optimization CSTR control system

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Optimal Control of Rural Water Supply Network Based on Intelligent algorithm

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PROCESSES 2023年第4期11卷 1190页

作者： Wang, Bo Yang, Qi Sun, Ruiyang Chen, Zihan Nie, Xiangtian North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China Collaborat Innovat Ctr Water Resources Efficient U Zhengzhou 450046 Peoples R China Henan Key Lab Water Environm Simulat & Treatment Zhengzhou 450046 Peoples R China

Optimizing Rural Water Supply Network (RWSN) is the basis for improving rural people's lives and improving people's health. Currently, the RWSN in China is relatively backward and can no longer meet the needs of the unified management of rural water resources. To optimize the RWSN, this study innovatively established a Multi-Objective Optimization Mathematical Model (MOMM) of RWSN, combining economic factors and reliability. This experiment first analyzes the characteristics of the RWSN system and then establishes a MOMM of a water supply network. NSGA-II algorithm and lm algorithm are introduced to handle the multi-objective model. The research results show that compared to Web decision tools, the RWSN based on the lm-NSGA-II algorithm can save 5.4% of the total annual cost of water supply pipelines. Therefore, the MOMM of the rural water supply pipeline based on the lm-NSGA-II algorithm has better economy and reliability. The experiment aims to provide certain reference values for the optimal control of RWSN through this study.

关键词： NSGA-II algorithm water supply network economy reliability lm algorithm

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Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter

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APPLIED SCIENCES-BASEL 2024年第13期14卷 5868页

作者： Wang, Xuetao Gao, Yijun Lu, Dawei Li, Yanbo Du, Kai Liu, Weiyu Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Harbin Inst Technol Sch Automot Engn Weihai 264209 Peoples R China Changan Univ Sch Energy & Elect Engn Xian 710064 Peoples R China

Featured Application The lm-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the lm algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current *** With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg-Marquardt (lm) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved lm-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

关键词： lithium-ion battery new energy vehicle state of charge equivalent circuit model iterated extended Kalman filter algorithm lm algorithm FUDS working condition BJDST working condition

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Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

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Journal on Internet of Things 2020年第3期2卷 109-120页

作者： Yanming Wang Kebin Jia Pengyu Liu Beijing University of Technology Beijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is *** at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure *** experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

关键词： Depth neural network pressure sensor nonlinearity correction wavelet transform lm algorithm

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Temperature Control Strategy of Incubator based on RBF Neural Network PID

World Scientific Research Journal

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World Scientific Research Journal 2020年第1期6卷 7-15页

作者： Jian Zhang Hongchang Sun Yuming Qi Sanpeng Deng

Taking the incubator temperature control system as the research object, the author studied the hardware structure of the incubator temperature control system and established the mathematical model of the temperature control system. Because the culture object is very sensitive to temperature changes, the traditional PID temperature control algorithm has slow response speed, weak anti-interference ability, and the control parameters are not easy to be set, the author adopts the strategy combining radial basis function (RBF) neural network and traditional PID to adjust the temperature of the incubator. In order to further improve the stability and dynamic response characteristics of the system, the lm algorithm was used instead of the gradient descent method to adjust the PID control parameters. The simulation results show that the control algorithm has faster response speed and stronger anti-interference ability, and can adjust the PID parameters in real time, which can meet the temperature control requirements of the incubator.

关键词： Temperature control system RBF neural network PID control lm algorithm

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Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

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REMOTE SENSING 2022年第2期14卷 295-295页

作者： Yu, Kunyong Hao, Zhenbang Post, Christopher J. Mikhailova, Elena A. Lin, Lili Zhao, Gejin Tian, Shangfeng Liu, Jian Fujian Agr & Forestry Univ Coll Forestry Fuzhou 350002 Peoples R China Univ Key Lab Geomat Technol & Optimized Resources Fuzhou 350002 Peoples R China Clemson Univ Dept Forestry & Environm Conservat Clemson SC 29634 USA Minnan Normal Univ Sch Biol Sci & Biotechnol Zhangzhou 363000 Peoples R China

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (lm) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the lm and MCWS algorithms. The lm algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for lm algorithm, F1 score = 85.92% for MCWS algorithm). The lm and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the i

关键词： lm algorithm Mask R-CNN MCWS algorithm plantation forest UAV imagery

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Application of Optimized BP Neural Network in Addressing for Garbage Power Plant

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Electricity 2005年第A4期16卷 52-55页

作者： By Zheng Yan, Huang Yuansheng, Qi Jianxun and Tang Jing School of Business Administration, North China Electric Power University School of Electrical Engineering, North China Electric Power University School of Business Administration North China Electric Power University School of Electrical Engineering North China Electric Power University

Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.

关键词： garbage power plant lm algorithm neural network site selecdon principle component analysis

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An Improved Calibration Method for a Rotating 2D LIDAR System

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SENSORS 2018年第2期18卷 497-497页

作者： Zeng, Yadan Yu, Heng Dai, Houde Song, Shuang Lin, Mingqiang Sun, Bo Jiang, Wei Meng, Max Q. -H. Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Jinjiang 362200 Peoples R China North Univ China Coll Elect & Control Engn Taiyuan 030051 Shanxi Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518000 Peoples R China Suzhou SinoGermany Robooster Intelligent Technol Suzhou 215000 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg-Marquardt (lm) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from -15 mm to 15 mm for the performance of capturing scans.

关键词： light detection and ranging (LIDAR) rotating unit Pan-Tilt Unit (PTU) bias angle calibration lm algorithm

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Prediction of Oilfield Cementing Quality Based on Levenberg-Marquardt BP algorithm

Prediction of Oilfield Cementing Quality Based on Levenberg-...

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2011 First International Conference on High Performance Structures and Materials Engineering (ICHPSM 2011)

作者： WANG Yuxue ZHOU Shaohua School of Mathematical Science and Engineering Northeast Petroleum University

In this paper an improved algorithm of BP neural network—— Levenberg-Marquardt(lm) algorithm is introduced, and the simulation predictions of oilfield cementing quality is done by using this method. Finally, a pract... 详细信息

关键词： BP algorithm lm algorithm Cementing Quality Prediction

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