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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wang, Hongsheng Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350361643

In view of the existing security products limited use scenarios, deployment and use is not flexible, limited performance and other problems, design and implementation of intelligent video surveillance system based on face recognition technology. The key technology of the system, facial recognition, is deeply studied. The system includes: real-time alarm system, intelligent access control system, intelligent inspection system and so on. Real-time early warning of illegal behaviors can accurately identify the facial features of violations and push them to the relevant departments for early warning. Through intelligent access control, the security protection of the face is realized. The system can collect and mark the information in real time through the monitoring device set in the patrol area, thus greatly improving the efficiency of patrol. Experiments show that this method has high performance in face detection and recognition. It can well solve the safety protection requirements of large venues, theaters, warehouses and other large venues. © 2024 IEEE.

关键词： Face recognition

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Considering Approaches to Enhancing Power System Flexibility Through Distributed Energy Storage 7

Considering Approaches to Enhancing Power System Flexibility...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Ban, Mingfei Xue, Zhiwei College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

In pursuit of the goal of reducing the wastage of renewable energy resources and enhancing the flexibility of the power system, this paper introduces a coordinated optimization scheduling strategy, incorporating distributed energy storage systems as a key component. This strategy comprehensively considers the real-time supply and demand dynamics of the power system, the energy storage status of distributed energy storage stations, and the generation forecasts of renewable energy. Based on these factors, a scheduling model for distributed energy storage systems is constructed. In the proposed scheduling strategy, optimize the allocation of energy resources to meet power demand by utilizing the operational costs of generating units, energy storage station operations, and load as objective functions. The primary aim of the model is to enhance power system flexibility through peak-shifting of renewable energy generation. To validate the effectiveness of the model, utilized MATLAB for its solution and, through practical case studies, verified the model's performance. The case study results demonstrate that the proposed distributed energy storage system strategy effectively reduces wind curtailment rates while simultaneously enhancing the flexibility of power system scheduling. © 2024 IEEE.

关键词： Renewable energy

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Harmonic Current Suppression for Open-End-Wingding PMSM Based on Complex Vector Proportional Integral controller 7

Harmonic Current Suppression for Open-End-Wingding PMSM Base...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Jiang, Chunyang Wu, Yan College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

Because of the existence of zero-sequence circuit, the Open-End-Winding Permanent Magnet Synchronous Motor (OEW-PMSM) with common DC bus generates the zero-sequence current (ZSC) during operation. If the inverters' tube voltage drop and dead time are considered, the harmonics of three-phase current will be further increased, which will affect the operating efficiency and stability of OEW-PMSM. The traditional proportional resonant (PR) controller can't suppress the ZSC and the harmonic components of three-phase current very well. Therefore, a suppression method based on quasi proportional resonant (QPR) controller and complex vector proportional integral (CVPI) controller is proposed, which can suppress the three-phase current harmonics and ZSC by suppressing the harmonics in the d-q axis current and suppressing the ZSC through the zero-axis closed loop controller. The simulation results demonstrate that the proposed method can efficiently suppress the third, fifth and seventh harmonics of current and ZSC. © 2024 IEEE.

关键词： Two term control systems

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Multi-Objective Optimal Planning of Coal-Fired Plants Flexibility Retrofits Considering Multi-Technology Options 7

Multi-Objective Optimal Planning of Coal-Fired Plants Flexib...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Sun, Yaofei Ban, Mingfei College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

Retrofitting coal-fired power plants (CPPs) is a way to accommodate ever-increasing variable renewable energy (VRE) in coal-dominated power systems in countries like China and India. Yet, how to retrofit the CPPS cost-effectively is an important issue to address. This paper proposes a multi-objective two-stage planning approach based on the none-dominated sorting genetic algorithm II (NSGA II). It considers both economic and flexibility requirements for the power system. At the planning stage, optimal technical options considering investment constraints and technical constraints are generated. At the operation stage, a mixed-integer linear programming (MILP) based model considering the detailed start-up and shut-down processes of the CPP is established. Then the optimal solution is selected from the Pareto solution set using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, case studies demonstrate the proposed approaches and show the potential of retrofitting CPPs to enhance power system flexibility. © 2024 IEEE.

关键词： Coal fired power plant

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Lightweight PCB defect detection algorithm and deployment based on ASF-YOLO 7

Lightweight PCB defect detection algorithm and deployment ba...

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7th International Conference on computer Information Science and Application Technology, CISAT 2024

作者： Li, Zhuqi Zhan, Jingbo Qu, Chenpeng Chen, Xiangping Zhang, Lei Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350375107

With the advancement in the precision of printed circuit boards (PCBs), their impact on electronic products has become increasingly significant. PCBs often exhibit various surface defects such as short circuits, open circuits, and burrs, which can have devastating effects on electronic products. To address these defects, this study proposes a lightweight detection model, ASC-YOLO. The dataset used was sourced from a public dataset on Roboflow, comprising 9,669 images. The Adown module enhances the model's ability to recognize defects of different sizes and types while effectively reducing computation and parameter volume. SENetV2 improves the network's learning capacity through multi-branch fully connected layers. Finally, PConv is introduced to reduce redundant computation and memory access, efficiently extracting spatial features. Compared to the original model, the parameters were reduced by 28%, and the accuracy reached 94.8%. Moreover, the model was deployed on mobile terminals, achieving high detection accuracy and efficiency. This advancement makes fully automated, high-precision AI-based detection possible. © 2024 IEEE.

关键词： computer debugging

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Accelerating cardiac DTI using 3D SwinGAN-based multi-directional joint reconstruction 17

Accelerating cardiac DTI using 3D SwinGAN-based multi-direct...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wang, Hexiang Mo, Chong Huang, Jianping Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350387384

Cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive imaging technique that allow us to accurately understand myocardial fiber structure, which is important for analyzing the causes and early diagnosis of cardiovascular diseases. However, it remains challenging to use cDTI in clinical settings due to its scanning time and motion sensitivity. In this paper, we present a multi-directional joint reconstruction method for cardiac magnetic resonance diffusion-weighted images, utilizing a 3D Swin Transformer within the SwinUnet framework, and introduce a generative adversarial network (GAN) to improve the image reconstruction quality and accuracy. Experimental results on diffusion-weighted images of human isolated hearts demonstrate that the proposed method exhibits lower reconstruction errors in both visual and quantitative DTI metrics compared to existing CNN-based 3D Unet image reconstruction techniques. © 2024 IEEE.

关键词： Diffusion tensor imaging

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Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zheng, Guolin Li, Zuoyong Hu, Wenkai Fan, Haoyi Ching, Fum Yew Yu, Zhaochai Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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DMAM-ECG: A Diffusion Model with Self-Attention Module for ECG Signal Denoising

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1278-1296页

作者： Hu, Zheng-Dong Hong, Yang Huang, Jia-Yan Chen, Kai-Hong Zhao, Wan-Qi Grau, Antoni Guerra, Edmundo Wang, Chuan-Sheng Zhang, Fu-Quan New Engineering Industry College Putian University Putian351100 China School of Mathematics and Data Science Minjiang University Fuzhou350108 China The University of Sheffield SheffieldS10 2TN United Kingdom Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Electrocardiogram (ECG) is the most common and non-invasive auxiliary diagnostic technique for heart disease. However, the ECG signal is susceptible to various noises during the collection process, which will impact subsequent analysis and diagnosis. Therefore, ECG signal denoising has become the primary task after signal acquisition. ECG signal denoising aims to remove noise interference from the collected noisy signals for clarify abnormal waveforms in it, and thus diagnose diseases more accurately. In this paper, to obtain high-quality and high-fidelity denoised ECG signals, we propose a novel diffusion model with self-attention module for ECG signal denoising (DMAM-ECG). Specifically, the DMAM-ECG contains two main branches used to extract features of input noise signal observations and potential variables, respectively. Each branch consists of 6 designed Convolution-Instancenormal-LeakyReLU Filter Blocks (CIL-Filter Blocks). Meanwhile, DMAM-ECG introduces the self-Attention Module Fusion Blocks (SAM-Fusion Blocks) to further extract features for the first branch, and then fuse the output features with the second branch. Besides, we use a multi-shot reconstruction strategy to further improve signal denoising. Quantitative and qualitative results on the QT database show that the proposed DMAM-ECG performs better than the existing 5 state-of-the-art ECG signal denoising methods, especially for strong noise interference ECG signal removal. To sum up, our DMAM-ECG introduces self-attention module into the diffusion model to improve the grasp of global information and achieve high-quality information preservation for extremely noise removal of ECG signal. In particular, it uses only 3-shot reconstruction gets much better denoised results than that of the best baseline with 10-shot. The code of the proposed DMAM-ECG is available at https://***/luvletterh/DMAM-ECG. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Signal denoising

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Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

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A transformer-based network for speech recognition

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International Journal of Speech Technology 2023年第2期26卷 531-539页

作者： Tang, Lina College of Computer and Control Engineering Northeast Forestry University Heilongjiang Province Harbin City150040 China

In the field of automatic speech recognition (ASR), the noisy audio data and the ambiguity in recognizing homophone lead to the degradation of model performance. In order to address the mentioned problems, a network called DMRS-transformer, a Transformer-based network, is proposed in this study. The proposed DMRS-Transformer mainly consists of two components except for the traditional Transformer network, which are denoising module and Mandarin recognition supplementary module respectively. The denoising module is used for pruning the trivial features caused by the noisy input audio data. The Mandarin recognition supplementary module, short for MRS module, tends to tackle the problem of recognizing Mandarin speech signals which have several homophones. Empirical evaluations have been conducted on two widely used datasets, which are Aishell-1 and HKUST respectively. The experimental results can validate the effectiveness of the proposed DMRS-Transformer network. Compared with the Transformer baseline, the proposed DMRS-Transformer has 0.8% CER improvement and 1.5% CER improvement in these two datasets respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Speech recognition

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