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Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

Fluid Dynamics & Materials Processing 2023年第6期19卷 1369-1383页

作者： Xing Deng Feipeng Da Haijian Shao School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.

关键词： Blood cell analysis deep learning models classification-detection

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Short Term Inbound Passenger Flow Prediction of Urban Rail Transit Based on Data Denoising

Short Term Inbound Passenger Flow Prediction of Urban Rail T...

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2023 International Conference on Smart Transportation and City engineering, STCE 2023

作者： Wang, Zhijian Li, Qingyun Wang, Jiuzeng Zhang, Qiang Wei, Yaoquan School of Electrical and Control Engineering North China University of Technology Beijing100144 China Tangshan Expressway Group Co. Ltd. Tangshan063000 China Beijing Aerospace Measurement & Control Technology Co. Ltd. Beijing100041 China

ISBN: (纸本)9781510673540

In order to improve the accuracy of short-term traffic flow prediction, a combined prediction model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Seagull Optimization Algorithm optimized Long-Short Term Memory neural network (SOA-LSTM) was proposed. Firstly, CEEMDAN is used to reduce the interference of data noise, decompose the original passenger flow sequence into multiple characteristic components and residual, and optimize the hyperparameters of the LSTM neural network using SOA. Each feature component is predicted, and the prediction results of each IMFs are summed to obtain the short-term entrance and exit passenger flow prediction results of each station. Extracting four consecutive weeks inbound passenger flow data of Beijing's urban rail transit network in 2019, and validated and compared the proposed short-term passenger flow prediction model. The experimental results demonstrate that compared with the transfer passenger flow prediction results of LSTM, ARIMA-RBF, LSTM-BP, and SOA-LSTM models, the proposed CEEMDAN-SOA-LSTM model has lower RMSE, MAE, and MAPE error values than other models, with R2 increased by 1.63%, 1.2%, 1.18%, and 0.72%, respectively. © 2024 SPIE.

关键词： Empirical mode decomposition

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Enhanced Responsivity of Solar Blind Ultraviolet Photodetector by PEALD Deposited Zn-Doped Ga2O3Thin Films

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IEEE Transactions on Electron Devices 2024年第1期71卷 664-669页

作者： Fan, Hui-Chen Wang, Chen Ruan, Yu-Jiao Shen, Kun-Ching Wu, Wan-Yu Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Zhu, Wen-Zhang Xiamen Institute of Measurement and Testing National Measurement and Testing Center for Flat Panel Display Industry Xiamen361024 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China National United University Department of Materials Science and Engineering Miaoli36063 Taiwan National Chi Nan University Department of Applied Materials and Optoelectronic Engineering Nantou54561 Taiwan Xiamen University of Technology Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen361026 China Da-Yeh University Department of Biomedical Engineering Changhua51591 Taiwan

In this article, the tunable Zn-doped Ga2O3 films were obtained by introducing ZnO intercalation during the film deposition using plasma-enhanced atomic layer deposition (PEALD). The effect of the ZnO cycle ratio on the performance of solar-blind photodetectors (PDs) was first analyzed. Compared to the pure Ga2O3, an obviously enhanced performance of Zn-doped Ga2O3 films PDs was observed by the introduction of Zn dopants, especially photocurrent or responsivity, even if the light intensity is low. A high sensitivity of Zn-doped Ga2O3 PD has been achieved with superior photoelectric characteristics for an extremely low dark current of 2.22 × 10-13 A, an ultrahigh light ON/ OFF current ratio of 2.89 × 106, and a satisfactory responsivity of 104 mA/W when the ZnO cycle ratio is 5%. The above excellent results imply that the Zn-doped Ga2O3 PD can be greatly beneficial to the application in deep-ultraviolet (DUV) detection. © 1963-2012 IEEE.

关键词： II-VI semiconductors

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engineering Vehicle Recognition and Multi-Object Tracking System based on YOLOv5 and DeepSort Algorithm 5

Engineering Vehicle Recognition and Multi-Object Tracking Sy...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Wu, Yanbo Liang, Xintao Cui, Quan Wang, Qingyan Heilongjiang Government Affairs Big Data Center Harbin150028 China School Of Measurement And Control Technology And Telecommunications Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

This study explores traditional object detection algorithms and deep neural network-based engineering vehicle detection algorithms. We applied preprocessing algorithms such as image denoising, enhancement, and edge detection to process the images and constructed our own trainable dataset through labeling. Next, we used the YOLOv5 algorithm, which has high detection accuracy and real-time capability, for engineering vehicle detection. To address the issues of missed targets and predicted bounding box misalignment, we introduced the DeepSORT algorithm for target prediction and tracking. This algorithm utilizes Kalman filtering for estimation and updates and employs the Hungarian algorithm to associate data between consecutive frames, thereby achieving engineering vehicle tracking. To tackle the problem of frequent identity switching due to camera motion and non-uniform vehicle movements, we adopted the modified GIoU to calculate the intersection over union between trajectories and detected target bounding boxes, reducing identity jumps during the tracking process. Finally, we designed an engineering vehicle detection and tracking system and applied this algorithm to practical production. © 2023 Owner/Author.

关键词： Deep neural networks

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A Digitally assisted nonlinearity suppression architecture using over-the-air beam cancelation

A Digitally assisted nonlinearity suppression architecture u...

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作者： Mu, Qin Li, Nanxi Chen, Cong Wei, Xiaozheng Wen, Rong Wu, Muqing Li, Xiang Liu, Ying School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China National Key Lab of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two-step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in-band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion. © 2023 John Wiley & Sons Ltd.

关键词： Power amplifiers

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Music emotion prediction based on multidimensional feature fusion 3

Music emotion prediction based on multidimensional feature f...

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3rd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zhou, Xuanye Su, Zhibin Ren, Hui Communication University of China School of Information and Communication Engineering Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (纸本)9781665490788

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets. © 2023 IEEE.

关键词： Forecasting

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Aging State Evaluation of Transformer Oil-paper Insulation based on PDC Asymmetric Current 4

Aging State Evaluation of Transformer Oil-paper Insulation b...

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4th IEEE International Conference on Electrical Materials and Power Equipment, ICEMPE 2023

作者： Zhang, Mingze Lei, Shengjie Liang, Yilin Liu, Heqian Shi, Yunbo Liu, Ji Hrbust Harbin University of Science and Technology Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Hrbust School of Measurement and Communication Engineering Harbin China Harbin University of Science and Technology Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin China Electric Power Research Institute State Grid Heilongjiang Electric Power Company Limited Harbin Harbin China Harbin University of Science and Technology School of Measurement and Communication Engineering Harbin China

ISBN: (纸本)9798350334678

The PDC test is an important means to evaluate the main insulation status of the transformer. When the traditional PDC test current is used to analyze the aging state, it is found that there is an obvious asymmetry between polarization and depolarization current, which will affect the accuracy of the evaluation results. Therefore, this paper has carried out research on the asymmetric PDC variation law of oil-paper insulation. Several groups of oil-paper insulation models with different degrees of aging were prepared in the laboratory environment. Under the polarization voltage, the change law of the transient electric field distribution in the aging model was calculated. According to the traditional interface polarization theory, the influence of insulation aging on the traditional interface polarization behavior is analyzed. Finally, this paper uses the collapsible charge model to explain the causes of PDC current asymmetry. Finally, the quantitative characterization relationship between the amount of decomposing charge and the aging of oil-paper insulation was established. The research results of this paper provide theoretical support for improving the accuracy of insulation state assessment of oil-immersed power equipment based on the PDC testing method. © 2023 IEEE.

关键词： Oil filled transformers

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Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

Network Intrusion Detection Based on Graph Neural Network an...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Zhang, Lina Wang, Yang Wu, Minyue Wang, Yanwei Zheng, Ying Huazhong University of Science and Technology The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Wuhan430074 China Wuhan Institute of Technology School of Mechanical & Electrical Engineering Wuhan430073 China

ISBN: (纸本)9798350337754

Network technology has become an inseparable part of daily life, which is easily attacked by some criminals. Therefore, ensuring the security of network becomes an urgent problem. In the past few years, various deep learning techniques have been put forward for identifying network intrusions. However, the majority of these approaches overlook the non-Euclidean nature of relationships within network intrusion data. In this paper, graph neural network (GNN) is used to learn the relationship between data. The sample balancing module is to address the significant disparity between the number of intrusive samples and normal samples. The results obtained from the experiments indicate that the suggested approach can enhance the effectiveness of the intrusion detection system. © 2023 IEEE.

关键词： Intrusion detection

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Model-Based Hardware-in-the-Loop Closed-Loop control of Brushless Direct Current Motor in Electric Vehicle Applications 2nd

Model-Based Hardware-in-the-Loop Closed-Loop Control of Brus...

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2nd Conference on Flexible Electronics for Electric Vehicles, FlexEV 2021

作者： Ramaraj, Ramachandran Dharmaraj, Ganeshaperumal Balasubramanian, Subathra Kumawat, Renu Department of Instrumentation and Control Engineering School of Electronics and Electrical Technology Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil India Department of Electronics and Communication Engineering School of Electrical Electronics and Communication Engineering Manipal University Rajasthan Jaipur India

ISBN: (纸本)9789811905872

Electric vehicles (EVs) widely use brushless direct current (BLDC) motor with trapezoidal commutation due to its high-power efficiency, ability to operate at high speeds, acceleration, and less maintenance. However, load torque ripples due to trapezoidal commutation and load torque variations due to terrain conditions affect the BLDC motor speed and performance significantly. Furthermore, load torque fluctuations reduce the lifetime of driver components and the battery significantly. Therefore, efficient driver design for BLDC motors that can handle load torque variations and maintains a set-speed for BLDC motors are emerging as an urgent need in the EVs. This investigation presents a hardware-in-the-loop (HIL)-based closed-loop control of BLDC motor for electric vehicle applications. The hardware to be tested which is the proportional integral controller that adjusts the pulse-width modulation applied to the BLDC motor. The motor parameters are obtained from experiments, and their values are used in the simulations. Then the electronic control unit is developed with Arduino and is tested with the MATLAB/Simulink model. Our results demonstrate the ability of HIL to provide a controller design that matches the electric vehicle requirements. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electric vehicles

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Ultrahigh electrochemical performance in mixed-valence Cu_(1.85)Sebased anode for supercapacitors

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Journal of Materiomics 2023年第1期9卷 62-71页

作者： Zhipeng Li Yi Niu Xinrui He Yalin Hu Fu Li Xing Chen Zhirong Wang Jing Jiang Chao Wang School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing100084China Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control College of Safety Science and EngineeringNanjing Tech UniversityNanjing210009China

Mixed-valence is an effective way to achieve high electrochemical performance of anodes for ***,inordinate mixed valence with more structural defects leads to structural *** development of mixed valence electrodes that can maintain a stable structure during the defect formation process is the key to resolving this ***_(2-x)Se with mixed-valence is a potential candidate,the stable monoclinic structure of Cu2Se can be transformed into another stable cubic structure(x>0.15).Herein,Cu_(1.85)Se anode with mixed valence reveals the ultrahigh specific capacity of 247.8 mA·h/g at 2 A/***,the introduction of multi-walled carbon nanotubes(MWCNTs)into Cu1.85Se further improves the specific capacity(435 mA·h/g at 2 A/g).XRD shows that the introduction of MWCNTs can improve the reversibility via chemical interactions and accelerate the electron transfer in the Cu1.85Se/***,the assembled symmetric supercapacitor(SC)device expresses a high energy density of 41.4 W·h/kg,and the capacity remains 83%even after 8000 charge/discharge *** research demonstrates the great potential of developing high specific capacity anode materials for superior performance supercapacitor.

关键词： Supercapacitor Mixed-valence Cu1.85Se/MWCNTs Anode Ultrahigh specific capacity

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