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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

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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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Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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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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Fault Type identification in Transmission Lines based on VMD-FE and IGWO-KELM 17

Fault Type identification in Transmission Lines based on VMD...

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

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

ISBN: (纸本)9781728164151

In view of the damage of transmission line short-circuit faults and low fault identification rate, A novel fault identification method based on variational mode decomposition-fuzzy entropy and improved grey wolf algorithm - kernel extreme learning machine is proposed. First, the VMD is used to decompose the three-phase current after the fault to obtain a series of three-phase stable modal components;secondly, the fuzzy entropy value of each group of components is calculated separately as the fault characteristics of the transmission line;Finally, The fault characteristics of the transmission line are input to the kernel extreme learning machine for training to obtain the diagnosis model. The experimental results show that the method is not affected by the distance of the fault, the initial phase angle of the fault and the transition resistance, and can accurately identify the fault type. © 2020 IEEE.

关键词： Variational mode decomposition

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UAV Remote Sensing Image Mosaic Technology Combined with Improved SPHP Algorithm 17

UAV Remote Sensing Image Mosaic Technology Combined with Imp...

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

作者： Leng, Jianwei Wang, Sining Tianjin University of Technology Tianjin Key Laboratory of Complex Control Theory Application Binshui Xidao 391 Tianjin300384 China

ISBN: (纸本)9781728164151

The paper proposes an improved algorithm, for the problem that SPHP algorithm is not suitable for the UAV Remote sensing image mosaic algorithm because it is susceptible to geomorphic factors, resulting in deformation or ghosting. The algorithm firstly extracts feature points by combining SURF algorithm and Harris algorithm. The obtained feature points are coarsely matched by KNN algorithm, then matched by PROSAC algorithm. Finally, the weight coefficient is introduced to calculate the spatial transformation model of the image overlap region. This model replaces the original spatial model of SPHP algorithm. The ghosting of the image overlap area is reduced, and the stitched image is caused to have a smaller deformation. The final result shows that this improved SPHP algorithm can effectively remove the ghost image of the stitched image and generate better stitching results. © 2020 IEEE.

关键词： Image enhancement

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Study on anti-interference performance of three-phase voltage mode PWM rectifier

Study on anti-interference performance of three-phase voltag...

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2021 China Automation Congress, CAC 2021

作者： Li, Guohong Yang, Weiyue Wei, Pengfei Gao, Ran Tianjin Key Laboratory for Control Theory and Applications in Complex Systems Tianjin University of Technology Binshui Xidao Extension 391 Tianjin300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9781665426473

The operation of three-phase voltage type PWM rectifier often contains harmonics, voltage fluctuations and other problems. In this paper, based on the traditional sliding mode controller, the first-order active disturbance rejection control (ADRC) is introduced to control the outer voltage loop, and the state variables observed by the linear extended state observer (LESO) are fed back to the sliding mode controller for feedforward compensation. The controller has resonance suppression ability and strong disturbance immunity, and does not depend on the accurate mathematical model. The effectiveness of the control strategy is verified by simulation and experiment. The results show that the control strategy has the advantages of good dynamic performance, strong robustness and strong anti-interference ability. © 2021 IEEE

关键词： Sliding mode control

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Malodorous Gas Sensor Array System for the Olfactory Robot 2

Malodorous Gas Sensor Array System for the Olfactory Robot

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2nd International Seminar on Computer Science and Engineering Technology, SCSET 2020

作者： Zhang, Xu Zhang, Sixiang Ma, Xu School of Mechanical Engineering Tianjin University of Technology and Education Tianjin300222 China School of Mechanical Engineering Hebei University of Technology Tianjin300100 China The School of Electrical Engineering Tianjin Key Laboratory for Control Theory and Application in Complicated Systems Tianjin University of Technology Tianjin China

Malodorous gas sensor array system for the olfaction robot is developed with four sensors including two semiconductor metal oxide sensors, a electrochemical sensor and a photoionization detector (PID). The system structure and experiments analysis are introduced in this paper. Result of experiments show that the steady system can keep ±0.03V fluctuation limited from 0 to 5 V after it stabilized and the maximum difference of response less than 0.02V. It means that the developed system can be satisfied for the olfaction application of robots in malodorous environment. © Published under licence by IOP Publishing Ltd.

关键词： Robots

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application of Hybrid Energy Storage Device in High Power Direct-current Power Supply 17

Application of Hybrid Energy Storage Device in High Power Di...

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

作者： Liu, Yue Wang, Chunjie Chen, Peng Yin, Jinliang School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Complex System Control Theory and Application Key Laboratory Tianjin China

ISBN: (纸本)9781728164151

The drop-away current of 1500V low-voltage direct-current circuit breaker used in metro DC traction power supply system needs to be checked regularly. The instantaneous power is very high when the detection device is working, and the field power supply is often difficult to meet the requirements. Therefore, an energy storage unit is needed to supply power to the detector. In this paper, a design scheme of high-power DC power supply with supercapacitor and battery as energy storage is proposed, and the energy management and discharge control strategies are studied in theory and simulation, which verify the effectiveness and feasibility of the scheme. © 2020 IEEE.

关键词： Drops

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The junction temperature measurement of insulated gate bipolar transistor based on multi-layer feed -forward neural network is presented 17

The junction temperature measurement of insulated gate bipol...

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

作者： Dong, Chao Mao, Peiye Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

For large power flow type insulated gate bipolar transistor (IGBT) saturation voltage drop and non-linear relation between the collector current and junction temperature of junction temperature prediction problem, set up a big electric saturated pressure drop down IGBT test system, access to settle moderate collector current and saturation nonlinear relation between the pressure drop curve, analyses the relation curve change rule the corresponding physical mechanism. The error back propagation (BP) neural network model was established by using Matlab software to predict the junction temperature. The comparison with the prediction results of the polynomial mathematical model shows that the relative error and prediction error of the neural network model are smaller than that of the polynomial mathematical model, and the prediction accuracy of the junction temperature is higher. It shows the feasibility of neural network for junction temperature prediction, so BP neural network model can be given priority when selecting junction temperature prediction model. © 2020 IEEE.

关键词： Junction temperature

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