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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis 25

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

引用

25th IEEE China Conference on system Simulation Technology and its application, CCSSTA 2024

作者： Huang, Jiahui Zheng, Xiangxiang Lv, Guodong Li, Xiaojing Wu, Guohua Xu, Liang Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China The First Affiliated Hospital of Xinjiang Medical University State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia Urumqi China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350366600

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached 95.7%. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and 1372 cm-1 might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application. © 2024 IEEE.

关键词： Raman scattering

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Short Term Power Load Forecasting Method Based on VMD-ResNet 5

Short Term Power Load Forecasting Method Based on VMD-ResNet

引用

5th International Conference on Power and Energy Technology, ICPET 2023

作者： Li, Dahua Chen, Wenkai Wang, Sun Tian, He Chen, Hao Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China Tianjin Tongshi Zhiyan Technology Co. Ltd Tianjin China State Grid Tianjin Jinghai Power Supply Co. Ltd Tianjin China

ISBN: (纸本)9798350339673

The load of a region is often influenced by multiple factors, resulting in strong nonlinearity and temporal characteristics of load data, which increases the difficulty of accurately predicting load. To improve the accuracy of power load forecasting, this paper proposes a short-term power load forecasting method based on VMD-ResNet. Firstly, at the data level, the Variational Modal Decomposition (VMD) method is used to decompose historical loads and temperatures to obtain a more stable Intrinsic Mode Function (IMF). Then, different IMFs are integrated and input into the first stage prediction model along with seasonal and holiday information to extract internal features of the data for preliminary prediction;The second stage is a Deep Residual Network (ResNet) that uses the forecast data from the first stage as input to predict the load over the next 24 hours. The data adopts real load data from Panama City from January 2015 to June 2020. The average absolute percentage error (MAPE) of the VMD-ResNet load forecasting model is 1.3%. Compared with load forecasting networks such as SVR, LSTM, CNN-LSTM, ResNet, etc., the MAPE of the test set has decreased by 2.36%, 1.8%, 1.2%, and 0.8%, respectively. Experiments have shown that VMD-ResNet can deeply mine data characteristics and has good predictive performance. © 2023 IEEE.

关键词： deep learning deep residual network load forecasting variational mode decomposition

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Research on application of Innovative Linear Active Disturbance Rejection control in Three-Phase Four-Wire system DSTATCOM

Research on Application of Innovative Linear Active Disturba...

引用

International Joint Conference on Energy, Electrical and Power Engineering, CoEEPE 2021

作者： Ma, Youjie Jiang, Xinyu Zhou, Xuesong Tianjin Key Laboratory of Control Theory and Application for Complex Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789811919213

In order to improve the compensation accuracy and speed of the static synchronous compensator (DSTATCOM) for the unbalanced load, a novel linear active disturbance rejection controller is proposed in this paper. The controller is applied to the current tracking control of DSTATCOM, and its mathematical model is derived. The performance of the new linear active disturbance rejection controller is analyzed by frequency domain analysis to prove its correctness. Finally, the operation performance of DSTATCOM under the action of the new controller is verified by MATLAB/Simulink simulation platform, and compared with the traditional ADRC controller. The results show that the capacitive split DSTATCOM controller has better compensation performance and is more practical. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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Online Reentry Trajectory Optimization for Hypersonic Vehicles Using Radau Pseudospectral Sequential Convex Programming

Online Reentry Trajectory Optimization for Hypersonic Vehicl...

引用

第43届中国控制会议

作者： Zhifei Song Junjie Liu Yuehui Ji Haobo Zhang Junchi Wang Qiang Gao Tianjin Key Laboratory of Control Theory and Application for Complex Systems Maritime College Tianjin University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper,an online trajectory optimization scheme is proposed to address the uncertainties and mission changes in hypersonic vehicle reentry trajectory *** combining Radau pseudospectral method and sequential convex programming(SCP),the discretization accuracy and solution speed of the trajectory optimization algorithm are ***,the downrange angle is used as the independent variable in the equations of motion to implement the free time trajectory optimization *** initial trajectory generation method is proposed,which makes the trajectory's terminal state close to the terminal position and thus reduces the number of algorithm optimization ***,the compensation and regularization terms are used,respectively,to maintain the feasibility of the trajectory optimization problem and to improve the convergence rate of the *** the end,the proper trajectory optimization period is set,and the interior-point method is used to solve the reentry trajectory optimization *** simulations demonstrate the validity of the algorithm and online reentry trajectory optimization scheme proposed in this paper.

关键词： Radau pseudospectral method Hypersonic vehicle Sequential convex programming Online reentry trajectory optimization

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Global Path Planning of Unmanned Surface Vehicles Based on Deep Q Network

Global Path Planning of Unmanned Surface Vehicles Based on D...

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第43届中国控制会议

作者： Shuhao Li Yuehui Ji Junjie Liu Zhongli Bai Jiajun Hu Qiang Gao Tianjin Key Laboratory of Control Theory and Application for Complex Systems Maritime College Tianjin University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The paper proposes an autonomous global path planning algorithm for unmanned surface vehicles(USV) in complex sea areas based on Deep Q Network(DQN).This algorithm integrates the artificial potential field method with the DQN algorithm to design action space and a new greedy *** also introduces a reward function based on potential energy and a penalty item for threat *** results demonstrate that compared to the traditional DQN algorithm,the algorithm proposed in this paper reduces training time by 35.27%,shortens path length by 8.71%,and decreases convergence iterations by 56.36%.Compared to the other three algorithms,such as Q-learning,the paths planned by the algorithm in this paper are more reasonable,smooth,and *** research results validate the practicality and effectiveness of the proposed algorithm,which has significant practical implications for the development of autonomous global path planning technology for USV.

关键词： Path planning Unmanned surface vehicles Deep Q network Artificial potential field Grid method

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Chaos Suppression of Power system Based on Finite-time Adaptive Sliding Mode control 20

Chaos Suppression of Power System Based on Finite-time Adapt...

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Wang, Hongjun Li, Shuyi Zhao, Hui Yue, Youjun Tianjin University of Technology Key Laboratory of Control Theory and Application for Complex Systems in Tianjin Binshui Xidao Extension 391 Tianjin300384 China Tianjin Agricultural University College of Engineering and Technology 22 Jinjing Road Tianjin300384 China

ISBN: (纸本)9798350320831

Under certain conditions, the power system will produce chaotic oscillation, which may impact the safety and stability of the power grid. Under the action of inductive load, the power system will generate a certain amount of reactive power. Excessive reactive power will lead to the deviation of the voltage phase angle of the power system. Usually, reactive compensation equipment is used to absorb the reactive power in the power system and adjust the voltage phase angle. In this article, the dynamics analysis of chaotic oscillation of the second-order power system model (SOPSM) is firstly carried out, and a global adaptive sliding mode controller based on finite time is designed by comprehensively using backstepping, fixed-time controller and adaptive sliding mode control methods (ASM), which not only realizes the suppression of chaotic oscillation of the power system, but also reduces the computational burden and improves the convergence speed of the system, Finally, MATLAB/Simulink numerical simulation shows that the designed controller can quickly restore the power system from chaotic state to equilibrium state, thus verifying the effectiveness and efficiency of the controller. © 2023 IEEE.

关键词： Sliding mode control

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Research on Seismic Bracing Monitoring system Based on Wireless Transmission

Research on Seismic Bracing Monitoring System Based on Wirel...

引用

IEEE International Conference on Mechatronics and Automation

作者： Lujia Wang Baofeng Zhang Wei Wang Zhiwei Li Junchao Zhu Tianjin Key Laboratory of Control Theory & Application of Complex Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Real-time data acquisition and monitoring of seismic bracings can help to maintain their effectiveness during service life and to improve the seismic resistance and disaster mitigation effects when strong earthquakes occur. In order to monitor real-time states of the seismic bracing, an intelligent monitoring system based on wireless transmission technology was designed. The system used CC2530 Zigbee module to establish a ZigBee wireless LAN, and combined MEMS vibration sensors and strain gauges. Meanwhile, the system used ESP32 to upload the measurement data to OneNET cloud platform through MQTT wireless communication protocol. The function of data visualization was designed so as to realize real-time monitoring of seismic bracing states. The experimental results showed that the proposed wireless monitoring system can reliably execute real-time data collection, storage, display, and hazard warning to guarantee the effectiveness of seismic bracings.

关键词： Wireless communication Vibrations Wireless sensor networks Wireless LAN Disasters Data visualization Zigbee

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

引用

system Simulation Technology and its application (CCSSTA), IEEE China Conference on

作者： Jiahui Huang Xiangxiang Zheng Guodong Lv Xiaojing Li Guohua Wu Liang Xu Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia The First Affiliated Hospital of Xinjiang Medical University Urumqi China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached $95.7 \%$. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and $1372 \mathrm{~cm}^{-1}$ might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Biological system modeling Surface resistance Raman scattering Interference Machine learning Kernel

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Short Term Power Load Forecasting Method Based on VMD-ResNet

Short Term Power Load Forecasting Method Based on VMD-ResNet

引用

International Conference on Power and Energy Technology (ICPET)

作者： Dahua Li Wenkai Chen Sun Wang He Tian Hao Chen Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China Tianjin Tongshi Zhiyan Technology Co. Ltd Tianjin China State Grid Tianjin Jinghai Power Supply Co. Ltd Tianjin China

The load of a region is often influenced by multiple factors, resulting in strong nonlinearity and temporal characteristics of load data, which increases the difficulty of accurately predicting load. To improve the accuracy of power load forecasting, this paper proposes a short-term power load forecasting method based on VMD-ResNet. Firstly, at the data level, the Variational Modal Decomposition (VMD) method is used to decompose historical loads and temperatures to obtain a more stable Intrinsic Mode Function (IMF). Then, different IMFs are integrated and input into the first stage prediction model along with seasonal and holiday information to extract internal features of the data for preliminary prediction; The second stage is a Deep Residual Network (ResNet) that uses the forecast data from the first stage as input to predict the load over the next 24 hours. The data adopts real load data from Panama City from January 2015 to June 2020. The average absolute percentage error (MAPE) of the VMD-ResNet load forecasting model is 1.3%. Compared with load forecasting networks such as SVR, LSTM, CNN-LSTM, ResNet, etc., the MAPE of the test set has decreased by 2.36%, 1.8%, 1.2%, and 0.8%, respectively. Experiments have shown that VMD-ResNet can deeply mine data characteristics and has good predictive performance.

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Image Segmentation Method of Crop Diseases Based on Improved Segnet Neural Network 17

Image Segmentation Method of Crop Diseases Based on Improved...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Yue, Youjun Li, Xuesong Zhao, Hui Wang, Hongjun Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters;secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training;in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it;then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network;finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s;compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper. © 2020 IEEE.

关键词： Image segmentation

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