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IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)

作者： Mohammadreza Amel Solouki Jacopo Sini Massimo Violante Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350363128

ISBN: (纸本)9798350363135

Mitigating the risks posed by Random Hardware Failures (RHFs) is crucial to prevent data corruption and control Flow Errors (CFEs) in embedded systems. This paper addresses these concerns through the application of Software-Implemented Hardware Fault Tolerance (SIHFT) methods, emphasizing compatibility with high-level programming languages such as C. Current SIHFT methods, often implemented in low-level Assembly, present challenges in terms of overhead to code size and real-time execution. Our proposed approach focuses on pre-compilation application of SIHFT methods, specifically control Flow Checking (CFC), to identify CFEs within C-language-based code. We conducted a comparative analysis of two established software-based CFE detection methods in C, seamlessly integrating CFC methods into the application behavioral model. Our methodology ensures ISO26262 compliance, crucial for the automotive sector, offering a software-only strategy that aligns with safety and cost considerations.

关键词： Industries Technical requirements Codes Software design Hardware Software Real-time systems Safety Standards Automotive engineering

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Multi-step Image Enhancement for Incinerator Flame Recognition

Multi-step Image Enhancement for Incinerator Flame Recogniti...

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第43届中国控制会议

作者： Lu Ye Zhao Zheng Zhou Ziyu Wang Haojie Zhao Dongzhuo School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to address the issue of low segmentation accuracy in the weak flame region of waste incineration flame images and the potential loss of texture details at the flame edge, this study proposes an algorithm for segmenting incinerator flame images based on multi-step image enhancement. The algorithm consists of several steps. Firstly, a single-scale Retinex algorithm is employed to enhance the details of the noise-reduced image. Subsequently, an adaptive gamma correction method based on the inverse color transform is proposed to enhance the contrast between the image foreground and background, in conjunction with the inverse color transform algorithm. Finally, a 2D-Otsu algorithm is utilized to segment the image and extract the target flame region. Experimental results demonstrate that the proposed incinerator flame image segmentation algorithm based on multi-step image enhancement achieves superior segmentation performance, effectively preserving more detailed information in the weak flame region.

关键词： Image enhancement Retinex Image segmentation 2D-Otsu

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

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

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China International Electrical and Energy Conference (CIEEC)

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

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

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.

关键词： Torque Regulators Simulation Harmonic analysis Permanent magnet motors Vectors Inverters

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

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

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China International Electrical and Energy Conference (CIEEC)

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

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

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.

关键词： Renewable energy sources Maintenance engineering Planning Power systems Mixed integer linear programming Power generation Load modeling

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

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

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International Conference on Signal Processing Proceedings (ICSP)

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

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

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.

关键词： Visualization Three-dimensional displays Sensitivity Signal processing Reconstruction algorithms Optical fiber networks Transformers Generative adversarial networks Image reconstruction Diffusion tensor imaging

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Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion Infilling 25

Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Xu, Leiyang Wang, Qiang Yang, Chenguang Harbin Institute Of Technology Department Of Control Science And Engineering Harbin China University Of Liverpool Department Of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350340266

Motion infilling is a fundamental and challenging research field in human motion modeling and analysis, which aims to generate natural and visually coherent transitions to fill in missing motion frames based on the start and end motion sequences. However, most current methods ignore the spatial structure formed by joints, which may lose some spatial information. This work proposes a novel spatiotemporal graph U-Net that supports flexible inputs for skeleton-based motion infilling. We apply spatiotemporal graph convolutional layers, skeleton pooling layers, and skeleton unpooling layers to extract spatial and temporal features in the motion sequence. At the same time, we use the U-Net structure to integrate the information in the start and end motion sequences. In addition, the generative adversarial mechanism is introduced to ensure the generated skeleton poses are smooth and natural. We conduct experiments on two motion datasets, including one large-scale public dataset and one self-built dataset. The model inputs are joint quaternions or joint coordinates. Experimental results show that our method can improve the performance of skeleton-based motion infilling and achieve state-of-the-art results when using joint coordinates as model input. © 2024 IEEE.

关键词： Generative adversarial networks

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Efficient Augmentation of Human Action Recognition Datasets with Warped Windows 26

Efficient Augmentation of Human Action Recognition Datasets ...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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Jointly Stochastic Fully Symmetric Interpolatory Rules and Local Approximation for Scalable Gaussian Process Regression

SSRN

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SSRN 2024年

作者： Zhang, Hongli Liu, Jinglei School of Computer and Control Engineering Yantai University Yantai264005 China

The main application bottlenecks of large-scale Gaussian process regression lie in the following three points: 1) solving the inverse matrix of n training points results in higher O(n^3) time complexity;2) widely used local approximation methods may have inconsistent prediction result;3) many aggregation methods are not able to effectively differentiate the importance of the experts, which leads to inaccurate prediction results. In order to solve the above problems, this paper proposes a method that combines third-degree stochastic fully symmetric interpolatory rules and local approximation with Tsallis mutual information (TDSFSIRLA). First, TDSFSIRLA introduces a three-degree deterministic fully symmetric interpolatory rules to generate an adaptive dimensional feature mapping and approximate the Gaussian kernel. It not only greatly reduces the number of required orthogonal nodes, but also maintains a higher approximation accuracy. Second, we use the Generalized Robust Bayesian Committee Machine (GRBCM) to approximate local experts consistently. Finally, to address the problem of uneven distribution of expert weights, we introduce Tsallis mutual information to assign different weights for local experts. Experimental results on synthetic and seven real datasets verify that our method can greatly reduce the time complexity and improve the prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Wave Energy Prediction Based on Hybrid Frameworks Integrated with Vmd Quadratic Decomposition

SSRN

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SSRN 2024年

作者： Wang, Jianhui Yang, Ruirong Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai264000 China

Precise prediction of wave energy enables strategic planning for sustainable maritime transportation and optimal siting of power plants, thereby facilitating the transition towards greener energy solutions. However, wave energy exhibits high volatility with time, and intense energy output at specific moments. In this paper, two hybrid frameworks for wave energy prediction combined variational modal decomposition (VMD) were proposed, which integrating with sample entropy (SE), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) network, and attention mechanism. For data preprocessing, raw data was decomposition-reconstruction-decomposition using VMD and SE to reduce data complexity and shorten runtime. For prediction, a CNN-BiLSTM (CBLA) hybrid model based on the attention mechanism was used to predict the processed data. Wave height, wave period, and other factors from three buoy stations in the North Pacific and Gulf of Mexico were used for the experiments. The proposed VMD-SE-VMD-CBLA framework in this paper enables the average wave energy prediction metrics MAPE, R2, MAE, and RMSE to reach 3.7309%, 99.72%, 0.4528kw/m, and 0.9956kw/m at the three stations. The results show that the proposed framework has higher accuracy and improved peak wave energy prediction compared to other benchmark models. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

arXiv

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arXiv 2025年

作者： Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu Dong, Guanghui College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

Accurate and real-time object detection is crucial for anomaly behavior detection, especially in scenarios constrained by hardware limitations, where balancing accuracy and speed is essential for enhancing detection performance. This study proposes a model called HGO-YOLO, which integrates the HGNetv2 architecture into YOLOv8. This combination expands the receptive field and captures a wider range of features while simplifying model complexity through GhostConv. We introduced a lightweight detection head, OptiConvDetect, which utilizes parameter sharing to construct the detection head effectively. Evaluation results show that the proposed algorithm achieves a mAP@0.5 of 87.4% and a recall rate of 81.1%, with a model size of only 4.6 MB and a frame rate of 56 FPS on the CPU. HGO-YOLO not only improves accuracy by 3.0% but also reduces computational load by 51.69% (from 8.9 GFLOPs to 4.3 GFLOPs), while increasing the frame rate by a factor of 1.7. Additionally, real-time tests were conducted on Raspberry Pi4 and NVIDIA platforms. These results indicate that the HGO-YOLO model demonstrates superior performance in anomaly behavior detection. © 2025, CC BY.

关键词： Object detection

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