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IAENG International Journal of Computer Science 2024年第1期51卷 32-38页

作者： Song, Xiuzheng Zhang, Jun Lu, Jingjing Rao, Sanping College of Science Nanchang Institute of Technology Jiangxi Nanchang330099 China College of Science Nanchang Institute of Technology Jiangxi Nanchang330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Jiangxi Nanchang330099 China College of Science Nanchang Institute of Technology Jiangxi Nanchang330099 China

—Blind image deconvolution has garnered significant attention in the realm of image processing. Among existing blind deconvolution models, the adaptive weighted total variation-based model can better recover local image features. However, this model may give rise to undesirable staircase effects. In contrast, when considering non-blind deconvolution, a model that uses adaptive Euler’s elastica (AEE) regularization can reduce the appearance of staircase effects and better maintain image local features. Inspired by the successful application of the AEE regularization in non-blind deconvolution, we propose an AEE model for blind image deconvolution. This novel model can preserve image features in smooth regions and restore local characteristics of the image. In addition, an efficient alternating direction method of multipliers is designed to address this non-convex model. Experimental results illustrate the superiority of the proposed method compared with other related blind deconvolution methods. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Image processing

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3-D Printed Cylindrical Luneburg Lens Antenna Based on Concentric Dielectric Shells with Same Thickness 11

3-D Printed Cylindrical Luneburg Lens Antenna Based on Conce...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Zhang, Ning Lu, Kai Yang, Nan Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China

ISBN: (纸本)9798350326277

A millimeter-wave dielectric cylindrical Luneburg Lens antenna is proposed, which is comprised of multiple concentric dielectric shells of the same thickness. The dielectric-constant distribution of the cylinder is controlled by the volume fraction of air between dielectric shells. The measured gain of the proposed antenna varies from 9.86 to 11.80 dBi from 30 GHz to 40 GHz. A low side-lobe level of -18.35 dB is obtained from measured radiation patterns at 35 GHz. © 2023 IEEE.

关键词： antenna equivalent media theory Luneburg lens millimeter wave

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Application of Trellis-Coded Modulation in Long-Haul Optical Fiber Transmissions 21

Application of Trellis-Coded Modulation in Long-Haul Optical...

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21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Chen, Yifan Yi, Xingwen Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Guangzhou510006 China

In this paper, we explore the application of trellis-coded modulation (TCM) in long-haul optical fiber transmissions. We discuss the benefits of TCM in linear transmission as well as degradation in the nonlinear regio... 详细信息

ISBN: (纸本)9798350343502

关键词： Light transmission

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Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1885-1905页

作者： Baofeng Ji Ying Wang Weixing Wang Shahid Mumtaz Charalampos Tsimenidis School of Information Engineering Henan University of Science and TechnologyLuoyang471000China Research and Innovation Center of Intelligent System Longmen LaboratoryLuoyang471000China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE) Luoyang471000China Sustainable Digital Communications and Energy Systems(DIGCOM)Group Nottingham Trent UniversityNottinghamUK

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency *** study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system ***,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference ***,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ***,the analytical expression for the outage probability of the system in Rayleigh channels is derived and *** study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through *** proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link *** simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

关键词： Unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) energy harvesting computational offloading outage probability

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Scientific Reading Comprehension with Sentences Selection and Ranking 12th

Scientific Reading Comprehension with Sentences Selection a...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Chen, Jialei Wang, Weihua Shao, Shuai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446986

Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： information retrieval

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Optimization algorithm for global k-means algorithm 13

Optimization algorithm for global k-means algorithm

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Sen Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes. © 2025 IEEE.

关键词： Optimization algorithms

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Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis

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Building Simulation 2024年第10期17卷 1709-1730页

作者： Chenglong Xiong Guannan Li Ying Yan Hanyuan Zhang Chengliang Xu Liang Chen School of Urban Construction Wuhan University of Science and TechnologyWuhan 430065China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Key Laboratory of Intelligent Buildings Technology School of Information and Electrical EngineeringShandong Jianzhu UniversityJinan 250101China Key Laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University) Ministry of Education of ChinaChongqing UniversityChongqing 400044 China State Key Laboratory of Green Building in Western China Xi’an University of Architecture&TechnologyXi’an 710055China

Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major ***,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further ***,the interpretability performance of DL models under various information scenarios needs further *** address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information ***,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN *** R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the *** results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information *** the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s ***,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative *** study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpr

关键词： air handling unit(AHU) fault diagnosis convolutional neural network(CNN) layer-wise relevance propagation(LRP) interpretation and explanation various information scenarios

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A Holistic Evaluation of Piano Sound Quality 10th

A Holistic Evaluation of Piano Sound Quality

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10th Conference on Sound and Music technology, CSMT 2023

作者： Zhou, Monan Wu, Shangda Ji, Shaohua Li, Zijin Li, Wei Central Conservatory of Music Beijing China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnaires based on a piano sound quality dataset. The method selects the optimal piano classification models by comparing the fine-tuning results of different pre-training models of Convolutional Neural Networks (CNN). To improve the interpretability of the models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The results reveal that musically trained individuals are better able to distinguish between the sound quality differences of different pianos. The best fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3% as the piano classifier. However, the dataset is limited, and the audio is sliced to increase its quantity, resulting in a lack of diversity and balance, so we use focal loss to reduce the impact of data imbalance. To optimize the method, the dataset will be expanded, or few-shot learning techniques will be employed in future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Cooperative Selection and Communications in 6G Ultra-mMTC IoV Networks 4

Cooperative Selection and Communications in 6G Ultra-mMTC Io...

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4th International Symposium on Electrical, Electronics and information Engineering, ISEEIE 2024

作者： Huang, He Huang, Wei College of Communication Technology Chengdu China School of Computer Science and Information Engineering Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350355772

Although Cooperative Communications (CC) already exists in 4G/5G, performance improvement is just not enough. As 5G standard has gradually established recently, CC has been one of most critical communication technologies which plays a founding role in 6G IoV networks. Therefore, it is important to apply collaborative technology to achieve Internet of Everything (IoV) and improve communication quality significantly in 6G IoV ultra-mMTC networks. In this study, we review that collaborative ideology has been regarded as foundation theory which widely exists in future multiple hybrid scenarios, such as, Cognitive Internet of Things (C-IoT) networks, UAVs (Unmanned Aerial Vehicles) communications, air-space-ground integrated networks, underwater acoustic communication and so on. Besides, we discuss key communication technologies that are closely combined with unique communication scenes, and elaborate critical problems that shall be solved in 6G ultra-mMTC IoV networks. Furthermore, we discuss extreme low complexity and fast ultra-massive relays selection algorithm in all sorts of future 6G scenarios. At last, it is concluded that for arbitrary two communication points (source/destination devices, sensors, relays, IoT nodes and so on), ideology of nodes selection of collaborative communication is theory of foundation to realize IoV and optimize communication transmission performance in future networks. © 2024 IEEE.

关键词： Cooperative communication

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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

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IEEE Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 IEEE.

关键词： Computational complexity

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