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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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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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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Human-to-Robot Handover control of an Autonomous Mobile Robot Based on Hand-Masked Object Pose Estimation

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IEEE Robotics and Automation Letters 2024年第9期9卷 7851-7858页

作者： Huang, Yu-Yun Song, Kai-Tai National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering

This letter presents a human-to-robot handover design for an Autonomous Mobile Robot (AMR). The developed control system enables the AMR to navigate to a specific person and grasp the object that the person wants to handover. This letter proposes a motion planning algorithm for grasping an unseen object held in hand. Through hand detection and segmentation, the hand region is masked and removed from the acquired depth image, which is used to estimate the object pose for grasping. For grasp pose determination, we propose to add the Convolutional Block Attention Module (CBAM) to the Generative Grasping Convolutional Neural Network (GGCNN) model to enhance the recognition rate. For the object-grasp task, the AMR localizes the object in person's hand, and uses the Model Predictive control (MPC)-based controller to simultaneously control the mobile base and manipulator to grasp the object. A laboratory-developed mobile manipulator, equipped with a 6-DoF TM5M-900 is used for experimental verification. The experimental results show an average handover success rate of 81% for five different objects. © 2016 IEEE.

关键词： Collaborative robots in manufacturing mobile manipulation pose estimation task and motion planning

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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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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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IMC Based PID controller for Spherical Tank Liquid Level control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Research on the control Strategy and Stability of a Modular Multilevel Converter in an AC-DC Hybrid Power Grid 4

Research on the Control Strategy and Stability of a Modular ...

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4th Asia-Pacific Conference on Communications Technology and computer Science, ACCTCS 2024

作者： Yang, Ye College of Computer and Control Engineering Northeast Forestry University Heilongjiang Harbin China

ISBN: (数字)9798350359985

ISBN: (纸本)9798350359985

AC and DC hybrid power grids are widely used in the power industry due to the continuous development of energy systems. In this paper, the control strategy and stability analysis of MMC (Modular Multilevel Converter) in AC / DC hybrid power grid are studied. First, the network model of the AC-DC hybrid power grid and the protocol specification of MMC are introduced. Secondly, the fuzzy logic controller is designed and implemented based on the performance evaluation, stability analysis, and dynamic response analysis of the MMC. Further, the experimental results of the MMC are evaluated and analyzed using the experimental platform and experimental scheme. At the end of this paper, a summary of the research results is presented, and conclusions are derived regarding MMC control, which is considered to provide a useful reference for the design of AC and DC hybrid power grid stability and control systems. © 2024 IEEE.

关键词： Fuzzy logic Multilevel converters Protocols Power system dynamics Symbols Power transmission Power system stability

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Path Planning Method for Large-Area Stamp-Based DLP Printing 4

Path Planning Method for Large-Area Stamp-Based DLP Printing

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4th International Conference on Electronic Information engineering and computer Science, EIECS 2024

作者： Gao, Xiaokuo Guo, Yanling Li, Jian Wang, Yangwei College of Computer and Control Engineering Northeast Forestry University Heilongjiang Harbin China

ISBN: (纸本)9798331531409

To address the issues of low efficiency and difficulty in interval printing splicing in large-area stamping-based stereolithography, a print path planning method based on an improved ant colony algorithm is proposed. The approach involves adjusting the distribution of ant parameters, updating the pheromone mechanism, and incorporating a time evaluation function to specifically solve the optimal print path problem. Experimental results show that this method achieves shorter print path lengths compared to the Zigzag algorithm and more effectively reduces the number of interval prints and the difficulty of splicing compared to the original ant colony algorithm, thereby improving print efficiency and quality. © 2024 IEEE.

关键词： Ant colony optimization

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Diffusion based Counterfactual Augmentation for Dual Sentiment Classification 30

Diffusion based Counterfactual Augmentation for Dual Sentime...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Xin, Dancheng Yuan, Jiawei Li, Yang College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9782493814104

State-of-the-art NLP models have demonstrated exceptional performance across various tasks, including sentiment analysis. However, concerns have been raised about their robustness and susceptibility to systematic biases in both training and test data, which may lead to performance challenges when these models encounter out-of-distribution data in real-world applications. Although various data augmentation and adversarial perturbation techniques have shown promise in tackling these issues, prior methods such as word embedding perturbation or synonymous sentence expansion have failed to mitigate the spurious association problem inherent in the original data. Recent counterfactual augmentation methods have attempted to tackle this issue, but they have been limited by rigid rules, resulting in inconsistent context and disrupted semantics. In response to these challenges, we introduce a diffusion-based counterfactual data augmentation (DCA) framework. It utilizes an antonymous paradigm to guide the continuous diffusion model and employs reinforcement learning in combination with contrastive learning to optimize algorithms for generating counterfactual samples with high diversity and quality. Furthermore, we use a dual sentiment classifier to validate the generated antonymous samples and subsequently perform sentiment classification. Our experiments on four benchmark datasets demonstrate that DCA achieves state-of-the-art performance in sentiment classification tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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