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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Zhao, Haixia Chang, Xia Gao, Yuelin Ningxia Key Laboratory of Intelligent Information and Big Data Processing School of Math and Information Science North Minzu University Yinchuan China

ISBN: (纸本)9789819924424

Deep learning can extract image features automatically and then fuse them under the constraint of loss function by training multi-layer and deep neural networks, which is more intelligent, and has been successfully applied to the field of infrared and visible image fusion. This paper gives an overview of infrared and visible image fusion methods, followed by a detailed analysis of the deep learning based infrared and visible image fusion framework and loss function, and points out the existing problems of infrared and visible image fusion methods and the development prospects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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CKE-Former: A clinical knowledge-enhanced transformer for disease classification in telemedicine

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KNOWLEDGE-BASED SYSTEMS 2025年 316卷

作者： Peng, Qi Cai, Yi Liu, Jiankun Chen, Xing Wang, Zefeng Xie, Jiayuan Li, Qing South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China Meizhou Peoples Hosp Data Ctr Meizhou Peoples R China Univ Sydney Fac Engn Sydney Australia Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Recently, disease classification in telemedicine scenario has become an important research topic, as it enables consultations without the need for face-to-face interactions between doctors and patients, which is especially beneficial for individuals in remote regions. Existing disease classification methods rely on specialized medical data to diagnose diseases, such as clinical records, radiology images, biological signals, etc. However, in telemedicine scenarios, only the patient's self-description and image taken with a mobile phone are available. To deal with it, we first construct a corresponding dataset from the online consultation system of a large public hospital, which consists of patient self-description texts, images, and doctor's diagnosis results. We propose a clinical knowledge-enhanced transformer (CKE-Former). Specifically, we first construct a multi-modal medical knowledge base (MMedKB), then we propose a medical concept normalization module and a multi-modal retrieval mechanism into our model, accessing specialized medical information to achieve disease classification. Experimental results show that our model is competitive compared with existing methods, which provides a solid benchmark for future research.

关键词： Telemedicine Disease classification Multi-modal knowledge Transformer

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Experimental and Numerical Study on the Transient Flow Behavior in Gasoline Refueling System

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Frontiers in Heat and Mass Transfer 2024年第1期22卷 107-127页

作者： Chenlin Zhu Yan Zhao Zhitao Jiang Jiafeng Xie Lifang Zeng Lijuan Qian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China School of Aeronautics and Astronautics Zhejiang UniversityHangzhou310027China Ocean College Zhejiang UniversityZhoushan316021China

Efficient and secure refueling within the vehicle refueling systems exhibits a close correlation with the issues concerning fuel backflow and gasoline *** paper investigates the transient flow behavior in fuel hose refilling and simplified tank fuel replenishment using the volume of fluid *** numerical simulation is validated with the simplified hose refilling experiment and the evaporation simulation of Stefan *** effects of injection flow rate and injection directions have been discussed in the fuel hose refilling *** both the experiment and simulation,the pressure at the end of the refueling pipe in the lower located nozzle case is 30%higher than that in the upper located nozzle case at a high flow rate,and the backflow phenomenon occurs at the lower filling *** fluid will directly flush into the first pipe elbow,changing the flow pattern from bubble flow to slug flow,which results in low-frequency and high-amplitude flowpressure *** refueling system,consisting of a refueling pipe,fuel tank and a vapor return line,is analyzed,in which hexane evaporation is *** the early refueling period,a higher refueling rate will lead to more obvious splashing,which leads to a higher average mass of hexane vapor and pressure in the *** optimized fuel tank designs are *** lower fuel tank filling port exhibits significantly lower vapor hexane in the fuel tank compared to the other design,resulting in a reduction of 200 Pa in the peak pressure in the tank,which contributes to a substantial reduction of gasoline loss during tank filling.

关键词： Gas-liquid flow flow pattern gasoline evaporation optimized fuel tank

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution 43

DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framew...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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Spatial-Semantic Collaborative Cropping for User Generated Content 38

Spatial-Semantic Collaborative Cropping for User Generated C...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Su, Yukun Cao, Yiwen Deng, Jingliang Rao, Fengyun Wu, Qingyao South China Univ Technol Sch Software & Engn Guangzhou Peoples R China Tencent Inc WeChat Shenzhen Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Shenzhen Peoples R China

ISBN: (纸本)1577358872

A large amount of User Generated Content (UGC) is uploaded to the Internet daily and displayed to people world-widely through the client side (e.g., mobile and PC). This requires the cropping algorithms to produce the aesthetic thumbnail within a specific aspect ratio on different devices. However, existing image cropping works mainly focus on landmark or landscape images, which fail to model the relations among the multi-objects with the complex background in UGC. Besides, previous methods merely consider the aesthetics of the cropped images while ignoring the content integrity, which is crucial for UGC cropping. In this paper, we propose a Spatial-Semantic Collaborative cropping network (S2CNet) for arbitrary user generated content accompanied by a new cropping benchmark. Specifically, we first mine the visual genes of the potential objects. Then, the suggested adaptive attention graph recasts this task as a procedure of information association over visual nodes. The underlying spatial and semantic relations are ultimately centralized to the crop candidate through differentiable message passing, which helps our network efficiently to preserve both the aesthetics and the content integrity. Extensive experiments on the proposed UGCrop5K and other public datasets demonstrate the superiority of our approach over state-of-the-art counterparts.

关键词： Message passing

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CMNet: Component-Aware Matching Network for Few-Shot Point Cloud Classification

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9241-9251页

作者： Lai, Lvlong Chen, Jian Lin, Guosheng Wu, Qingyao South China Univ Technol Sch Software Engn Guangzhou 510640 Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510640 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Pazhou Lab Guangzhou 510280 Peoples R China

Few-shot point cloud classification is currently an under-explored problem which aims to learn a point cloud classifier for novel categories given a few annotated training data. Most existing methods achieve classification by matching a query point cloud to the most similar support category at the global representation level. However, due to the complicated structure of the point cloud and scarce available training data, the global representations of the point clouds and categories are of low quality, limiting the matching accuracy. Therefore, in this paper, we propose the Component-Aware Matching Network (CMNet) that matches the point clouds at the component level in addition to the global level. Specifically, we construct a component set for each query point cloud and support category and develop a metric to measure the similarity between two component sets. The final prediction is the weighted sum of the global and component matching probabilities. Besides, we carefully devise a component matching pretraining scheme for CMNet to enhance its ability to extract component features, further improving its performance. To evaluate the effectiveness of our design, we conduct comprehensive experiments on three benchmarks, namely ModelNet-FS, ShapeNet-FS and Shrec-FS. As a result, CMNet consistently outperforms the existing methods with significant margins in all the experiments of the three benchmarks and sets new state-of-the-art performance.

关键词： Few-shot metric learning meta learning point cloud classification component matching Few-shot metric learning meta learning point cloud classification component matching

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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Fine-grained Personalized Hierarchical Federated Learning towards Heterogeneous Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 16349-16362页

作者： Zhang, Puning Shao, Shuman Wu, Chao Yang, Zhigang Zhang, Ruoying Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing400707 China China Merchants Testing Vehicle Technology Research Institute Co. Ltd Chongqing404100 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China China Academy of Information and Communications Beijing100083 China

High mobility and non-independent and identically distributed (non-iid) data in the Internet of Vehicles (IoV) pose challenges for the convergence speed and accuracy of federated learning model training. Traditional federated learning approaches fail to account for the unique characteristics of local vehicle data, limiting the model performances. To address this, we propose a personalized hierarchical federated learning method tailored for heterogeneous IoV. Our approach customizes models for each vehicle, enhancing their performance on local data. We introduce a network-aware client selection method that dynamically chooses vehicle clients based on their spatiotemporal distribution and changing channel conditions. Additionally, our fine-grained personalized learning method leverages frequency domain analysis to extract and retain both fundamental and personalized knowledge from local model parameters. This allows us to build highly accurate personalized models suited to each vehicle's data characteristics. Experimental results show that our method improves model accuracy by up to 10.31% and reduces training time by up to 24.93% compared to the latest personalized federated learning algorithms. © 2025 IEEE.

关键词： Federated learning

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