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2024 China Automation Congress, CAC 2024

作者： Gao, Xudong Wen, Xinyu Dong, Yaling Wang, Zhihao School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350368604

To address composite disturbances in the system, this paper proposes a disturbance rejection method. The considered disturbances include both regular and irregular components. First, by extracting the characteristics of the frequency components, a frequency compensation observer(FCO) is designed to fully utilize the disturbance characteristics, processing only the regular disturbances. Next, the remaining irregular components and the errors of the regular disturbances are estimated and compensated using a nonlinear extended state observer (NESO). In the proposed method, the multi-loops are independent of each other, which reduces the computational complexity to a large extend. The two types of observers form a nested structure, handling disturbances more targetedly, making the method more suitable for addressing composite disturbances in the ***, the effectiveness of the proposed method is verified through simulations. © 2024 IEEE.

关键词： Disturbance rejection

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User story clustering in agile development:a framework and an empirical study

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Frontiers of Computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Towards Adversarial Robustness in Blind Image Quality Assessment with Soft Thresholding Norm

Towards Adversarial Robustness in Blind Image Quality Assess...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Desen Wang, Lei School of Communication and Information Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781665410205

In this study, we address the issue of adversarial robustness within the context of Blind Image Quality Assessment (BIQA), an area of heightened importance due to the inherent susceptibility of Deep Neural Networks (DNNs) to adversarial assaults. Current approaches primarily rely on adversarial training, which, despite its efficacy, imposes a significant computational burden. Our research proposes an alternative strategy known as the Soft Thresholding Norm (ST Norm). This approach counters the 'feature shift' phenomenon, identified by a substantial Euclidean Distance Statistics (EDS) between original and adversarial features, through the imposition of sparse constraints on potential features following batch normalization. This novel method offers several advantages: it reduces the Lipschitz constant yielding smoother models, seamlessly integrates with existing models, and boasts inherent denoising capabilities, thereby effectively mitigating the impact of adversarial perturbations. Results suggest that our approach achieves robustness comparable to adversarial training but with significantly less computational overhead. Moreover, it consistently outperforms other adversarial defense strategies on BIQA datasets, highlighting its practical effectiveness in enhancing adversarial robustness. This study underscores the potential of the Soft Thresholding Norm within the realm of IQA tasks, positioning it as a resource-efficient alternative to traditional adversarial training methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of SO2 Emission Concentrations at FGD Outlets in Thermal Power Plants on GRU-XGBoost 4

Prediction of SO2 Emission Concentrations at FGD Outlets in ...

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4th International Conference on Electrical engineering and Control science, IC2ECS 2024

作者： Zuo, Taiying Jiang, Guanjie Chen, Xuebo Li, Congya University of Science and Technology Liaoning School of Electronic and Information Engineering Anshan China

ISBN: (纸本)9798331510107

As environmental regulations become increasingly stringent, desulfurization systems in thermal power plants play a crucial role in reducing SO2 emission mass concentrations. Traditional methods for predicting SO2 concentrations often face limitations in accuracy and adaptability when dealing with complex non-linear dynamic characteristics. To address this, we propose a hybrid model that combines the GRU (Gated Recurrent Unit) neural network and XGBoost to predict the mass concentration of SO2 emissions at the outlet. This method dynamically adjusts the weights of the combined model using an adaptive allocation approach, significantly improving prediction accuracy. Experimental results show that, compared to a single LSTM model, GRU model, XGBoost model, and a GRU-XGBoost model with mean weighting, the proposed GRU-XGBoost model with adaptive allocation achieves predictions that closely match the actual SO2 emission mass concentrations at the FGD outlet. This enhanced accuracy is vital for conserving resources and improving operational efficiency in power plants. © 2024 IEEE.

关键词： Recurrent neural networks

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Research on D2D Communication Resource Allocation Algorithm Based on Graph Coloring-SGADIA 4

Research on D2D Communication Resource Allocation Algorithm ...

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4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Wang, Yijun Guo, Jing Xv, Dongyv Meng, Lingfeng School of Electronic and Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

Device-to-device (D2D) communication enhances system throughput and alleviates the load on the network core by utilizing the spectrum resources allocated to cellular users, and has become one of the key technologies of 6G. Aiming at the problem of serious user interference, this study introduces a new D2D resource allocation algorithm that combines graph coloring technology and a soft-greedy asynchronous distributed interference avoidance method. First, the interference graph between D2D users is constructed, the priority of D2D users is calculated, and D2D users are grouped with the help of graph coloring;then Different groups of D2D users and cellular users are mapped into a bipartite graph, which is solved using the KM algorithm;Finally, the initial channel allocation approach is refined through the application of a soft greedy distributed asynchronous interference mitigation algorithm. Based on simulation findings, the method presented in this study can mitigate the effects of channel sharing among D2D users, enhance spectrum efficiency, and significantly reduce the system's overall interference. © 2024 IEEE.

关键词： Graph algorithms

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MTPANet: a multi-scale transformer with position-aware attention for remote sensing image segmentation

MTPANet: a multi-scale transformer with position-aware atten...

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2024 International Conference on Advanced Image Processing technology, AIPT 2024

作者： Han, Yueyi Zhan, Weida Guo, Jinxin Xing, Jian School of Electronic and Information Engineering Changchun University of Science and Technology Jilin China

ISBN: (纸本)9781510682542

Remote sensing images exhibit the characteristics of large intra-class variances and small inter-class variances, making remote sensing image segmentation a distinctive semantic segmentation task. Currently, the mainstream approach for remote sensing image segmentation is to combine CNN and transformer to enhance the network's feature extraction capability. However, simply concatenating CNN and transformer modules does not effectively leverage the information extraction advantages of each module. In this paper, we propose a U-shaped architecture that utilizes a multi-scale transformer with position-aware attention for remote sensing image segmentation (MTPANet). We specifically design a novel dual-branch self-attention module to extract features. The key feature of this module is the separate incorporation of position-aware attention and multi-scale components into both the CNN and transformer, followed by their fusion. This approach enhances the network's global attention capability and local perception ability, effectively addressing the inter-class and intra-class variability issues. Lastly, we validate the effectiveness and robustness of MTPANet on publicly available datasets, achieving precise segmentation of remote sensing images. © 2024 SPIE.

关键词： Semantic Segmentation

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Research on Image Fusion Method Based on Nonsubsampled Contourlet Transform 4

Research on Image Fusion Method Based on Nonsubsampled Conto...

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4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Zhao, Pengfei Liu, Peng Zhao, Haili Zhao, Zhi School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

In modern military operations, as camouflage technology advances, soldiers, weaponry, and other military materials have become highly similar to their surroundings, making it difficult for traditional optical target recognition techniques to effectively identify camouflaged targets. Polarization imaging technology captures the polarization characteristics of light reflected from object surfaces, enhancing contrast between the object and the background, providing additional image information, and helping to distinguish optically similar materials or surface features, particularly excelling in complex environments. This paper proposes a multi-scale, multi-directional fusion algorithm based on NSCT, incorporating the local similarity preservation of LSP features, adaptive weight allocation, and an improved PCNN algorithm to handle details, nonlinear information, noise, and directionality issues in images. Experimental results show that this algorithm outperforms traditional fusion methods in evaluation metrics such as EN, AG, SNR, and SF, with both subjective and objective assessments demonstrating significant advantages. The proposed algorithm offers a new approach to image fusion and camouflage target recognition, demonstrating significant potential for military applications. © 2024 IEEE.

关键词： Image fusion

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Robust Tracking Based on Improved YOLOv5s and Dynamic Neighborhood Target Association 7

Robust Tracking Based on Improved YOLOv5s and Dynamic Neighb...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Wei, Zifu Yang, Jian Peng, Chen Zhao, Guangyao School of Electronic Science and Engineering Hunan University of Information Technology Changsha China

ISBN: (纸本)9798331529314

In order to enhance the video single-target tracking task's ability to cope with complex scenarios such as, rotation, rapid scale change, background interference, and occlusion, this paper proposes a tracking method based on improved YOLO detection and dynamic neighboring region target association. First, the YOLOv5s model is improved by introducing different attention mechanisms such as CBAM, CA, and PA, and retraining to select the optimal model so as to enhance the target detection effect. Second, adaptive tracking search region adjustment is performed within the neighborhood of the tracked target, and an IOU-based similarity measure and filtering mechanism is used to reduce the number of detected target boxes, thus reducing the number of false matches and computation. Finally, the appearance features of the retained target boxes are extracted by a reidentification network, and the final tracking target box is determined by performing cosine matching followed by IOU matching to enhance the tracking stability in complex scenes. The experimental results show that the algorithm of this paper achieves an average success rate of 0.816 and an average accuracy rate of 0.946 on 30 vehicle video sequences, and has good robustness in a variety of complex scenarios. © 2024 IEEE.

关键词： Footage counters

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Underwater Polarization Imaging Method Based on Frequency Domain Processing Combined with Global Estimation 4

Underwater Polarization Imaging Method Based on Frequency Do...

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4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Zhaolst, Zhi Zhao, Haili Zhao, Pengfei School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

This paper proposes an underwater polarization imaging method that combines frequency domain processing with global estimation to improve scattered light suppression. By preprocessing orthogonal polarization images with histogram stretching and high-pass filtering, the method reduces low-frequency scattered light and accurately estimates the degree of polarization. A low-pass filter and optimal threshold factor in the frequency domain further refine this estimation. Enhanced Metric Evaluation (EME) is used to optimize the de-scattering process, leading to significant improvements in image contrast, sharpness, and clarity compared to traditional methods. Experimental results show that the contrast, sharpness, and EME of the restored images were improved by 80.68%, 30.89%, and 66.22% for one type of target, and by 34.32%, 34.48%, and 104.76% for another type, respectively, compared to Schechner's method. The data indicate that this method effectively suppresses scattered light in different water quality environments, significantly enhancing the contrast and clarity of the images, with a noticeable improvement over traditional methods. © 2024 IEEE.

关键词： Frequency domain analysis

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Spatial-Temporal Network Model for Action Recognition Based on Keyframe Extraction 4

Spatial-Temporal Network Model for Action Recognition Based ...

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4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Zhao, Dongjie Zhao, Haili Yao, Kai Meng, Jiahe Liang, Meihui School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

This paper proposes a spatiotemporal network model TransTC3D for behavior recognition. The proposed SIC is used to sample video key frames, and then the key frames are sent to the TransTC3D network to complete behavior recognition. Based on the fine-grained feature extraction capability of the C3D model, the TransTC3D network adds a temporal attention mechanism (SVAM) and a channel attention mechanism (SCAM) to the seventh and twelfth layers respectively. The high-dimensional fine-grained feature information is extracted through the improved C3D network to obtain fine-grained spatial and temporal feature information, which is then sent to the Transformer for long-term sequence modeling to finally achieve behavior recognition. It has been verified that SIC sampling combined with the TransTC3D model has achieved an effect of 90.23% on the UCF101 dataset. © 2024 IEEE.

关键词： Action Recognition component Keyframes Spatial-temporal model

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