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8th Workshop on Cognitive Aspects of the Lexicon, CogALex 2024

作者： Xia, Yuhan Zhao, Qingqing Long, Yunfei Xu, Ge Wang, Jia School of Computer Science and Electronic Engineering University of Essex United Kingdom Institute of Linguistics Chinese Academy of Social Sciences China College of Computer and Control Engineering Minjiang University China Department of Intelligent Science Xi’an Jiaotong-Liverpool University China

ISBN: (纸本)9782493814456

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models’ emotional sensitivity. © 2024 ELRA Language Resource Association.

关键词： Classification (of information)

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Analysis of Electric Field Distribution of Insulators YD-JZ 10 kV Oil-Immersed Transformers 2

Analysis of Electric Field Distribution of Insulators YD-JZ ...

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2022 2nd International Conference on Power System and Energy Internet, PoSEI 2022

作者： Feng, Qi Shao, Zhenhua College of Electrical and Electronic Engineering Wenzhou University Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

The electric field distribution of the YD-JZ 10 kV oil-immersed transformer insulator is analyzed by COMSOL simulation, calculation, and actual measurement methods. The result shows that the first shed of the insulator accounts for 18% of the voltage drop. After that, the voltage of each umbrella skirt decreased gradually, and the proportion of voltage drop of the last two umbrella skirts increased again. The electric field of the insulator tip burr is 1.16 × 106 V/m spherical burr is 2.84 × 105 V/m. The simulation result shows that the maximum electric field of spherical air gap defect in an insulator is 1.38 × 105 V/m conical air gap defect is 1.61 × 105 V/m metal particle defect is 1.49 × 105 V/m. The simulated insulator umbrella skirt is covered with water. When the whole insulator umbrella skirt is short-circuited, there may be ground discharge. The measured results and simulation results show that the insulator can work normally if the umbrella skirt is not completely short-circuited. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Defects

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Content-Aware Deep Feature Matching 4th

Content-Aware Deep Feature Matching

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

作者： Wang, Weice Li, Zuoyong Zheng, Xiangpan Lai, Taotao Keikhosrokiani, Pantea College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Physics and Electronic Information Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang Gelugor11800 Malaysia

ISBN: (纸本)9783031200984

Feature matching is a fundamental and critical step for many tasks of computer vision. In recent years, many methods for feature matching have been proposed. However, the image alignment algorithm used in the state-of-the-art matching method is not robust enough, and the number of the features extracted from the images are insufficient, thus the performance obtained by the state-of-the-art matching method is unsatisfactory. To address these issues, firstly, we use a more robust geometric estimation algorithm to align image pairs. Secondly, based on aligned image pairs, we design a content-aware block to more focus on effective features by using a feature extractor and a mask predictor. Experimental results show that our method achieves more superior results, and outperforms several classical and deep learning methods on the HPatches dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Multi-robot Exploration Approach for Larger Maps with Unreliable Communications

A Multi-robot Exploration Approach for Larger Maps with Unre...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Lingxin Luo, Haibo College of Computer and Data Science College of Software FuZhou University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The issues of limited communication distance and task capability are rarely discussed in designing multi-robot cooperative methods. However, these are important problems that cannot be avoided in practical applications. In this paper, simulation experiments are carried out on some task maps that are larger than the team's task ability. The effect of the dispersion of members on the team's task performance is tested, and the existing methods are improved by introducing long-range communication to auxiliary data sharing. At the same time, we use the calculation of Energy-Benefit to improve the task selection method based on the minimum distance to the target. Compared with existing strategies, the improved scheme performs better on communication-constrained large-map exploration tasks. © 2022 IEEE.

关键词： Multipurpose robots

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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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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhongyi Gao, Libin Tong, Yinsheng Lu, Xingjin Li, Rui Xiao, Lei Li, Zuoyong Wenzhou University College of Computer Science and Aritificial Intelligence Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350337488

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases. © 2023 IEEE.

关键词： Brain diseases Classification Diagnostic Multi-scale convolutional neural networks (MsCNN) Resting-state functional magnetic resonance imaging (rs-fMRI)

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Research of interleaved hysteresis control based on soft-switching power amplifier 7

Research of interleaved hysteresis control based on soft-swi...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Qiu, Guangjuan Zhang, Xinyue Wei, Jian Chen, Jingxin Peng, E. College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

This paper delves into the interleaved hysteresis control method based on soft-switching power amplifiers. Firstly, through modeling and analysis, the basic topology and mode transition of the soft-switching power amplifier are clearly defined, revealing the conditions for implementing soft-switching. Secondly, a strategy for interleaved soft-switching hysteresis control is proposed, detailing its working principle, and the frequency range of the interleaved hysteresis loop is obtained through analysis. In order to optimize the design of the filter, this paper also deeply analyzes the design methods and parameter selection of the filter inductance, providing a theoretical foundation for the implementation of the power amplifier. The research results show that the proposed interleaved soft-switching hysteresis control method can reduce switch losses and electromagnetic interference, thereby improving the efficiency and reliability of the power amplifier. This study provides beneficial theoretical support and practical guidance for the application of high-performance motor drive systems. © 2024 IEEE.

关键词： Power amplifiers

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Optimizing Multimodal Emotion Recognition: Evaluating the Impact of Speech, Text, and Visual Modalities

Optimizing Multimodal Emotion Recognition: Evaluating the Im...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

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

ISBN: (纸本)9798331527624

This research aims to explore and optimize multimodal emotion recognition to enhance its performance. Multimodal emotion recognition involves analyzing information from different modalities - speech, vision, and text - to identify and classify emotional states accurately. This study investigates the roles of speech and text modalities and the auxiliary effect of visual modalities in multimodal emotion recognition. Study employs a two-stage multimodal information fusion neural network based on graph and attention mechanisms. The core idea is to achieve an effective fusion of multimodal information through graph convolutional networks and cross-modal attention mechanisms to improve emotion recognition performance. This study uses two datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal Emotion Lines Dataset (MELD), to analyze the impact of speech and text modalities on visual modalities and their potential side effects. Results show that while the visual modality alone is less effective, adding speech or text significantly improves performance. However, introducing the speech modality and the text modality is less beneficial. Experimental outcomes reveal that the speech modality's effect is not as significant as the text modality, and its inclusion does not always lead to positive results, sometimes even degrading overall recognition performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on lightweight algorithm for smoking detection based on improved YOLOv8 5

Research on lightweight algorithm for smoking detection base...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Zhou, Guorui Wang, Xingjian Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350386943

In response to the issues of high computation and large model parameters in current smoking detection algorithms, making them difficult to deploy on edge devices, this paper proposes an improved lightweight YOLOv8 algorithm for real-time detection and accurate recognition of smoking targets. Firstly, the C2f module in the backbone of YOLOv8 is replaced with a lightweight ContextGuided module, which enhances the accuracy of small object detection through the analysis of feature context, and also reduces model complexity. Secondly, a slim-neck structure paradigm is adopted to further optimize the complexity of the neck part without affecting accuracy. Experimental results show that the improved algorithm reduces the number of parameters, computation, and model size by 17.94%, 20.990%, and 15.78%, respectively, with mAP@0.5 and FPS reaching 94.9% and 98. This ensures real-time and reliable detection. © 2024 IEEE.

关键词： ContextGuided lightweight slim-neck YOLOv8

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Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

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

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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