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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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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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The Impact of Grid Harmonics on Commutation Failure 7

The Impact of Grid Harmonics on Commutation Failure

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Ming Shen, Xiaolin Wu, Yucheng Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

Commutation failure is a common fault in Line Commutated Converter High Voltage Direct Current (LCC-HVDC) systems. LCC-HVDC relies on the AC power grid to provide reverse voltage to naturally turn off thyristors. Faults in the AC system on the inverter side can lead to commutation failures. In practical engineering applications, AC network harmonics also affect the commutation of thyristors. This paper further elucidates the impact of AC network harmonics on commutation failure and validates the effect of non-characteristic harmonics of different orders on LCC-HVDC commutation failure using the PSCAD/EMTDC simulation platform. © 2024 IEEE.

关键词： Thyristors

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong 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 Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Virtual Flux-Based Scheme for Passivity Enhancement in MMC-HVDC System 7

Virtual Flux-Based Scheme for Passivity Enhancement in MMC-H...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Liu, Jiayi Zhang, Hengke Zhao, Bo Gu, Fangning Sun, Mingzhe Liu, Yiqi Zhao, Zheng Teng, Shangfu Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350359558

Inherent control delays pose significant challenges to the stability of the Modular multilevel converter-based high voltage direct current (MMC-HVDC) system, inducing the risk of high-frequency resonances (HFR). This paper provides the virtual flux-based passivity enhancement schemes to remove the influence of the time delay terms. The negative damping risks can be eliminated, improving the system's stability and safety. The impedance modeling and simulation results validate the effectiveness of the approach. © 2024 IEEE.

关键词： System stability

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Optimizing Thermo-Electric Generation with Quasi-Z-Source Converter in Energy Harvesting for Wearable Devices 3

Optimizing Thermo-Electric Generation with Quasi-Z-Source Co...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Lotfy, Mostafa Wageh Hussein, Aziza I. Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781643685649

Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capturing energy from different sources, such as thermal energy, and converting it into electrical power. Thermo-Electric Generators (TEGs) are commonly used in many applications such as wearable devices. However Thermo-Electric Generators produce very low output voltages, which is insufficient for most low-power electronic circuits to operate. Therefore, a boost converter is essential to step up the voltage output from the TEG to a usable level. In this paper, a DC-DC converter with a high performance in terms of voltage gain is implemented. The proposed quasi-Z-source (q-ZSC) DC-DC converter not only offers high voltage gain but also several significant advantages. The proposed q-ZSC is capable of both boosting and regulating the voltage output efficiently, even under variable input conditions, making it highly suitable for low-power energy harvesting systems such as Thermo-Electric Generators. The design is simulated using MATLAB Simulink software, focusing on integrated circuits, with a strong emphasis on optimizing the circuit's efficiency. This work underscores critical factors in improving energy harvesting circuits, enhancing their performance and effectiveness in low-power applications for wearable technology. © 2024 The Authors.

关键词： DC generator motors

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Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism 41

Enhance Smart Grid Security With A Coordinated Cyber-Physica...

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第41届中国控制会议

作者： Zhenyong Zhang Xin Che Xuguo Jiao Jingpei Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou University College of Control Science and Engineering Zhejiang University School of Information and Control Engineering Qingdao University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Smart grid,as one of the largest evolutionary critical infrastructures,witnesses the deep integration of cyber technologies and physical ***,recent events show that the vulnerabilities exposed in the cyber side and the physical side can be exploited to cause coupled and severe ***,the adversaries are able to mask the pernicious physical attack using the cyber attack by compromising the digitized data transmitted to the control *** cooperative cyber-physical attack(CCPA) is stealthy to the current defense *** this article,we propose a cyber-physical defensive mechanism(CPDM) to thwart the *** changing the physical value of the line admittance,the model parameters known by the adversary are not useful anymore,which is the basic idea to design the *** implementation of CPDM depends on the deployment of the distributed flexible AC transmission system(D-FACTS).To reduce the infrastructure cost for the CPDM,we investigate the minimum number of required D-FACTS devices to thwart the *** consider both weak constraints on the CCPA and CPDM and practical limitations for the CCPA and *** each case,we propose a method to compute the optimal deployment of D-FACTS devices,that is,where the DFACTS device should be deployed.

关键词： CPA Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism Security

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Enhancing Septic Shock Detection through Interpretable Machine Learning

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computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

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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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Eliminating control Plane Overload via Measurement Task Placement

Eliminating Control Plane Overload via Measurement Task Plac...

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作者： Chen, Xiang Liu, Hongyan Zhang, Dong Huang, Qun Zhou, Haifeng Wu, Chunming Yang, Qiang College of Computer Science and Technology Zhejiang University Hangzhou310007 China College of Computer Science and Big Data Fuzhou University Fuzhou350116 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China College of Control Science and Engineering Zhejiang University Hangzhou310007 China College of Electrical Engineering Zhejiang University Hangzhou310007 China

Recent efforts in network measurement place measurement tasks on programmable switches to measure high-speed traffic. These tasks extract flow data, i.e., events, from packets and send events to the control plane. However, the tasks may generate massive events in a short time. In this context, the links transferring events to the control plane and the control plane servers that handle events may be overloaded, i.e., control plane overload. None of existing solutions can eliminate control plane overload. In this paper, we propose MTP, a framework that eliminates control plane overload via careful measurement task placement. Our key idea is to allocate enough resources for each task during task placement to avoid control plane overload at runtime. For each task, MTP estimates its maximum possible rate of sending events to the control plane. Then its optimization framework addresses the resource restrictions of both switches and the control plane. The experiments on Tofino switches indicate that MTP outperforms existing solutions with higher accuracy in several use cases. © 2022 IEEE.

关键词： Job analysis

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