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3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Liu, Zhichen Luo, Jianping Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen University China

ISBN: (纸本)9781510668355

In recent years, there are more and more researches in the field of video compression based on deep learning. Most of these researches maintain the same framework, including motion estimation and compensation modules. We propose a different method, which does not need an explicit motion estimation module. Instead, video compression is achieved by extracting and compressing the residuals between two adjacent frames. The residual is extracted by a 3D pyramid network. Based on the robust representation ability of deep features in various applications, the whole coding and compression process is transferred from pixel space to feature space to improve compression efficiency. The transformation process is realized by two networks: feature extraction and frame reconstruction. Our video compression framework has a simple structure and a smaller number of parameters than most subsequent proposed models. The experiment result shows that the performance of our PSNR model is better than H.264, and is comparable to H.265 in some cases, while our MS-SSIM model is better than H.265 in all cases. © 2023 SPIE.

关键词： Image compression

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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Construction and Characteristic Analysis of Two-Layer Complex Network Model

Construction and Characteristic Analysis of Two-Layer Comple...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Tang, Chunyang Ye, Zhonglin Zhao, Haixing Xiao, Yuzhi Bai, Libing College of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

ISBN: (纸本)9789819708543

To characterize the homogeneity and heterogeneity between nodes or connecting edges, we considered the multi-relationship attributes of nodes and constructed six dual-layer network models. Firstly, based on node homogeneity, we constructed three homogeneous two-layer network models (GEE,R), (GWW,R) and (GBB,R) using the same generation algorithm for intra-layer connections and random connections for inter-layer connections. Secondly, based on node heterogeneity, we constructed three heterogeneous two-layer network models (GEW,R), (GBE,R) and (GBW,R) using different generation algorithms for intra-layer connections and random connections for inter-layer connections. Then, we derived the average degree analytical expressions and node degree distribution analytical expressions for the six two-layer networks. Finally, according to the differences in inter-layer connection probabilities, we depict the experiments with node degree distribution. Experiments have shown that the node degree distribution of (GEW,R) networks exhibits a bimodal characteristic, while the node degree distribution of (GBE,R) networks presents both Power-law and unimodal mixed characteristics. Overall, this study provides insights into the characteristics of different two-level network models and their application in modeling complex systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Joint Trajectory Design and Resource Allocation Strategy for Underwater AUV-Aided MEC System 35

Joint Trajectory Design and Resource Allocation Strategy for...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Song, Chaohua Zhao, Yisheng Li, Tengteng Hu, Zhiyi Liu, Peng Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9798350362244

Sensor nodes (SNs) in underwater wireless sensor network (UWSN) may generate massive amounts of data and have computing task requirements. In this paper, mobile edge computing (MEC) is considered in UWSN. A resource allocation strategy in autonomous underwater vehicle (AUV)-aided MEC system is investigated to minimize the total system energy consumption. Specifically, the MEC servers are deployed at both AUV and buoy floating on the sea. By adopting magnetic induction (MI) communication technology, the AUV collects computing tasks from different SNs. Partial tasks are computed at the AUV, while the rest of tasks are offloaded to the buoy. The trajectory of the AUV, the transmitting power of SNs and the AUV, and the offloading ratio coefficient are jointly optimized. The suboptimal solution to the formulated optimization problem is obtained by using a social learning particle swarm optimization (SL-PSO) algorithm. Simulation results show that the proposed strategy has a faster convergence speed and better global search capability compared with other baseline schemes. © 2024 IEEE.

关键词： Energy utilization

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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

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Science China(information Sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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Distributed Secondary Control of DC Microgrids with Operation Loss Reduction 43

Distributed Secondary Control of DC Microgrids with Operatio...

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43rd Chinese Control Conference, CCC 2024

作者： Qiu, Maoxiang Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581581

In DC microgrids, current sharing and voltage regulation are two main objectives for real-time control, while the operation loss reduction is typically achieved by steady-state optimization. However, the ratio of current sharing is generally determined by the result of the centralized optimization in the classical hierarchical control framework of DC microgrids. Such an issue brings insufficient capability in respond to the changes of photovoltaic power and stored energy in the solar-powered energy systems. To handle this, this work proposes a distributed control scheme for single-bus DC microgrids. Under the proposed controller, the objectives of current sharing, bus voltage regulation and the total operation loss (converter loss and line loss) reduction can be guaranteed simultaneously. Moreover, the constraints on line currents and bus voltage are tackled as well. Finally, simulations verify the performance of the proposed control scheme. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Microgrids

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Improved LYT-Net Image Enhancement Model for Underwater Robotics Vision 13

Improved LYT-Net Image Enhancement Model for Underwater Robo...

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

作者： Li, Wenjie Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Güttin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

—Although underwater robots can replace humans to explore the ocean which is rich in resources but fraught with unknown risks, there are phenomena such as monotonous colors, complex backgrounds and uneven illumination in the underwater environment, which makes it difficult for underwater robots relying on the optical vision system to obtain clear images and is not conducive to subsequent recognition and grasping tasks. In this paper, relevant preparatory work was carried out for underwater image processing applicable to underwater robots, and the underwater imaging model as well as other underwater image processing models were analyzed. Subsequently, a method combining the underwater physical model and the dark channel image was proposed to improve LYT-Net, enabling it to be applicable to mobile embedded devices such as underwater robots. After training and testing on the real datasets LSUI and UIEB, our model achieved good results on important indicators such as PSNR and SSIM. Meanwhile, it met the theoretical requirements of mobile devices in terms of FLOPs, Params and single-frame processing time, which verified its robustness and real-time processing ability in various underwater environments. © 2025 IEEE.

关键词： Underwater imaging

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding 3

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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3rd International Conference on Electronics and information technology, EIT 2024

作者： Huang, Guimin Huang, Zhiqing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350369151

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance. © 2024 IEEE.

关键词： Graph embeddings

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SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction 31

SURE: Mutually Visible Objects and Self-generated Candidate ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Feng, Yuxuan Chen, Qian Wu, Qianyou Guo, Xin Wang, Suge School of Computer and Information Technology Shanxi University China Key Laboratory of Computational Intelligence and Chinese Information Processing China

ISBN: (纸本)9798891761964

Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.3% in terms of strict F1 scores on SciERC, ACE05 and ACE04. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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