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2021 IEEE International Conference on Power, Intelligent computing and Systems, ICPICS 2021

作者： Zhi, Hui Fang, Xiaotong Zha, Yukun Anhui University Key Laboratory of Computational Intelligence and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665441308

In mobile edge computing (MEC), the task offloading of terminals has become a hot research spot. For terminals, how to effectively utilize limited computing resources and ensure quality of service (QoS) is a key issue. With the rapid improvement of the number of mobile terminals in the cellular network, base station (BS) equipped with MEC server cannot support the large amount of computing tasks of user equipment (UE), which causes high offloading delay. This paper devotes it self to reducing the complexity of the computing offloading in cellular networks. Therefore, an adaptive task offloading scheme is further proposed. The scheme can adaptively select the optimal offloading decision for each terminal according to the distribution of system resources. The simulation results show that the proposed scheme can effectively reduce the total time delay of the system. © 2021 IEEE.

关键词： Adaptive algorithms

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EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation 30

EmoTrans: Emotional Transition-based Model for Emotion Recog...

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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： Jian, Zhongquan Wang, Ante Su, Jinsong Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9782493814104

In an emotional conversation, emotions are causally transmitted among communication participants, constituting a fundamental conversational feature that can facilitate the comprehension of intricate changes in emotional states during the conversation and contribute to neutralizing emotional semantic bias in utterance caused by the absence of modality information. Therefore, emotional transition (ET) plays a crucial role in the task of Emotion Recognition in Conversation (ERC) that has not received sufficient attention in current research. In light of this, an Emotional Transition-based Emotion Recognizer (EmoTrans) is proposed in this paper. Specifically, we concatenate the most recent utterances with their corresponding speakers to construct the model input, known as samples, each with several placeholders to implicitly express the emotions of contextual utterances. Based on these placeholders, two components are developed to make the model sensitive to emotions and effectively capture the ET features in the sample. Furthermore, an ET-based Contrastive Learning (ETCL) is developed to compact the representation space, making the model achieve more robust sample representations. We conducted exhaustive experiments on four widely used datasets and obtained competitive experimental results, especially, new state-of-the-art results obtained on MELD and IEMOCAP, demonstrating the superiority of EmoTrans. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Emotion Recognition

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A Multi-Stage Expensive Constrained Multi-Objective Optimization Algorithm Based on Ensemble Infill Criterion

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Wu, Haofeng Chen, Qingda Chen, Jiaxin Jin, Yaochu Ding, Jinliang Zhang, Xingyi Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

Surrogate-assisted evolutionary algorithms (SAEAs) rely on the infill criterion to select candidate solutions for expensive evaluations. However, in the context of expensive constrained multi-objective optimization problems (ECMOPs) with complex feasible regions, guiding the optimization algorithm towards the constrained Pareto optimal front and achieving a balance between feasibility, convergence, diversity, exploration, and exploitation using a single infill criterion pose significant challenges. We propose an ensemble infill criterion-based multi-stage SAEA (EIC-MSSAEA) to tackle these challenges. Specifically, EIC-MSSAEA comprises three stages. In the first stage, we ignore constraints to facilitate the rapid traversal of infeasible obstacles. In the second stage, only one constraint is activated at a time to increase algorithm diversity. Finally, in the last stage, we activate all constraints to improve overall feasibility. In each stage, EIC-MSSAEA first employs NSGA-III as the underlying baseline solver to explore the search space, in which promising solutions are then selected by an ensemble infill criterion that incorporates multiple base-infill criteria to measure the feasibility, convergence, diversity, and uncertainty of candidate solutions. Experimental results demonstrate the competitiveness of EIC-MSSAEA against state-of-the-art SAEAs for ECMOPs. IEEE

关键词： Linear programming

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COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR POLSAR IMAGE CLASSIFICATION

COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR PO...

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IET International Radar Conference 2023, IRC 2023

作者： Cao, Yice Zhu, Dayi Wu, Zhenhua Chen, Jie Huang, Zhixiang Yang, Lixia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

ISBN: (纸本)9781839539954

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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Determination of Continuous Dielectric Properties of Clothing Materials Using Hybrid Method

Determination of Continuous Dielectric Properties of Clothin...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Chen, Manni Chung, Kwok L. Huang, Zhining Wen, Jiating Li, Yingsong Huizhou University School of Computer Science and Engineering Guangdong516007 China Huizhou University Glorious Sun Guangdong School of Fashion Guangdong516007 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350336740

This paper presents a hybrid method for determination of continuous dielectric properties of clothing materials. The dielectric constant and loss tangent of three types of materials are firstly investigated using open-ended coaxial probe from 0.5 to 20 GHz. The average measured values are input into the CST simulation of two CPW lines to obtain the two-port S-parameters. Finally, more reasonable yet reliable results are obtained by using two retrieval algorithms (TRACE and ABCD) for validation and comparative purposes. © 2023 IEEE.

关键词： Dielectric properties

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AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification 31

AGCL: Aspect Graph Construction and Learning for Aspect-leve...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan Wang, Yancheng Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China College of Management Mahidol University Thailand School of Computing and Data Science Xiamen University Malaysia Malaysia Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9798891761964

Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL's pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model's generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL's superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://***/jian-projects/agcl. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Deep Learning-based AP Selection Method in Software-defined Networks 5

Deep Learning-based AP Selection Method in Software-defined ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Duan, Miaomiao Zhi, Hui Yang, Lixia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In the Software Defined Network (SDN), the terminal has been connected to the network in the process when an access point failure or damage, if the access point is not properly selected, it will lead to a decline in the quality of service applications. To address this problem, most traditional AP selection methods use access point selection based on received signal strength without take into account the channel and channel capacity of the access point access, while this paper uses Deep Neural Networks (DNN)-based access point (AP) selection method and considers the channel and channel capacity as part of the parameters. In the SDN controller, the received signal strength, throughput, number of connected devices, channel, and the channel capacity and access point performance quality are used as the input and label of the DNN, respectively. The analysis and simulation results show that the algorithm can select AP with better performance quality compared with the traditional AP selection method based on received signal strength, and the correct rate is significantly improved by 5% when compared with the AP selection method based on feedforward neural network. Meanwhile, the DNN-based access point selection method in SDN achieves flexible control of network traffic and balances the load of access points in the network. © 2022 IEEE.

关键词： Deep neural networks

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Blockchain-based Transaction Scheme for Massive MIMO Channel Estimation 11

Blockchain-based Transaction Scheme for Massive MIMO Channel...

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11th International Conference on Networks, Communication and computing, ICNCC 2022

作者： Huang, Yu Zhi, Hui Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (纸本)9781450398039

In wireless communication systems, accurate channel estimation is essential to ensure the performance of wireless communication systems. Massive Multiple Input Multiple Output (M-MIMO) systems have a dramatic increase in the number of channels to be estimated due to the large number of antennas, which poses a great challenge to the computing resources of the base station, and one solution is offloading these computing tasks of channel estimation to other computing nodes with more computing resources (such as servers). However, in real life, computing nodes are not willing to participate in channel estimation because of their equipment and operation cost. In order to solve this problem, this paper introduces blockchain technology and designs a blockchain-based transaction scheme for channel estimation (BTS-CE) to solve the problem of computing resources transaction for massive MIMO channel estimation, and designs the specific process of the scheme. In order to solve the problem that computing nodes cannot complete the computation task due to failure or malicious disconnection, a task allocation model is designed to assign a computation task to multiple computation nodes. The security of the transactions is guaranteed through the blockchain. Practical Byzantine Fault Tolerance (PBFT) consensus mechanism cannot be applied to the public chain due to its low performance caused by a large number of nodes participating in consensus, this problem is solved by selecting some active nodes as consensus nodes. Simulation results show that improved PBFT(IPBFT) is lower in transaction delay and higher in throughput than PoW consensus mechanism. © 2022 ACM.

关键词： Blockchain

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Transient Electro-thermal and Bandgap Narrowing Analysis of the bipolar transistor

Transient Electro-thermal and Bandgap Narrowing Analysis of ...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Xiao, Hui Ren, Xingang Yan, Yeqiang Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan23700 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

Given the bandgap narrowing (BGN) effect caused by heavy doping of the bipolar transistor and the self-heating effect (SHE) in the practical work environment, a transient electro-thermal and bandgap narrowing model is proposed in this paper, which is based on the Slotboom model and the modification of the drift-diffusion equation based on semiconductor equations and the heat conduction equation. The transient characteristics of the bipolar transistor in amplification mode are simulated using the differential method, and the results show that the terminal current of the bipolar transistor decreases after considering the SHE and the BGN effect. The model developed and the results obtained in this work can provide some reference significance for the optimization of the amplification characteristics of the bipolar transistor. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Energy gap

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A Novel Broadband EBG Structure For Simultaneous Switching Noise Suppression

A Novel Broadband EBG Structure For Simultaneous Switching N...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Sun, Guoxing Yan, Yeqiang Ren, Xingang Cai, Xueyuan Wu, Bo Ju, Huping Tao, Wei Wang, Gang Zou, Bao Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan23700 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781733509633

In this paper, a compact electromagnetic bandgap (EBG) structure for suppressing simultaneous switching noise (SSN) in high-speed circuits is proposed. At the bandwidth level of 0.35GHz to 20GHz, an ultra-wideband SSN suppression feature with a suppression depth of -40dB is achieved. The basic unit structure of the presented EBG is formed by etching an annular groove in the square patch and connecting the separated patches inside and outside the groove with a bridge adjacent to the end. An improved L-type bridge is used between the EBG units to further improve the EBG performance. Compared with the traditional L-type bridge, the suppression depth of the proposed electromagnetic bandgap structure is improved from -30dB to - 40dB. In addition, the time domain eye simulation results show that the structure has good signal integrity. © 2023 ACES.

关键词： Energy gap

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