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Adjustable patch and feature prior token-based transformer for weakly supervised semantic segmentation

International Journal of Computers and Applications 2024年第7期46卷 445-454页

作者： Li, Linjuan Zhang, Haoxue Xie, Gang Bai, Yanhong School of Electronic and Information Engineering Taiyuan University of Science and Technology Taiyuan China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China

Weakly supervised semantic segmentation is a challenging task, utilizing only low-cost weak supervision to produce pixel-level predictions. Existing transformer-based methods for weakly supervised semantic segmentation have some limitations: (1) Fixing patch size might destroy the structured semantics, which is unfriendly to objects of different scales, and (2) Ignoring the prior features when using multi-head attention mechanisms might lead to inaccurate segmentation localization. To tackle these issues, we proposed an effective transformer framework coupled with the adjustable patch and prior feature tokens, termed as APFPformer, in which an adjustable patch module is developed to split the image according to the area of the salient object for preserving the structured semantics in patches. A prior feature token module is devised to exploit the edge and texture information as prior tokens, ensuring the gain of discriminative representation. Additionally, a single-stage scheme is applied to reduce the computation and rapid segmentation process. Our experiments demonstrate the superiority of our approach over early methods, gaining competitive mean Intersection-over-Union scores of 67.9% on the PASCAL VOC2012 dataset and 40.2% on the MS COCO dataset. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantic Segmentation

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Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Remaining Useful Life Prediction of Multi-Component System C...

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2023 China Automation Congress, CAC 2023

作者： Li, Ke Fan, Xingle Shi, Hui Zhang, Xiaohong Sun, Chaoli Wang, Jianan Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Electronic and Information Engineering Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Economics and Management Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Computer Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method. © 2023 IEEE.

关键词： Forecasting

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NO_(2) gas sensor with high selectivity and fast response based on Pt-loaded nanoporous GaN

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International Journal of Minerals,Metallurgy and Materials 2025年第4期32卷 964-972页

作者： Dan Han Xiaoru Liu Donghui Li Jiexu Shi Yu Wang Yuxuan Wang Hongtao Wang Shengbo Sang Shanxi Key Laboratory of Micro/Nano Sensors&Artificial Intelligence Perception College of Integrated CircuitsTaiyuan University of TechnologyTaiyuan030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030032China

In this work,we realized a room-temperature nitrogen dioxide(NO_(2))gas sensor based on a platinum(Pt)-loaded nanoporous gallium nitride(NP-GaN)sensing material using the thermal reduction method and coreduction with the catalysis of *** gas sensor gained excellent sensitivity to NO_(2) at a concentration range of 200 ppm to 100 ppb,benefiting from the loading of Pt nanoparticles,and exhibited a short response time(22 s)and recovery time(170 s)to 100 ppm of NO_(2) at room temperature with excellent selectivity to NO_(2) compared with other *** phenomenon was attributed to the spillover effect and the synergic electronic interaction with semiconductor materials of Pt,which not only provided more electrons for the adsorption of NO_(2) molecules but also occupied effective sites,causing poor sites for other *** low detection limit of Pt/NP-GaN was 100 ppb,and the gas sensor still had a fast response 70 d after ***,the gas-sensing mechanism of the gas sensor was further elaborated to determine the reason leading to its improved *** significant spillover impact and oxygen dissociation of Pt provided advantages to its synergic electronic interaction with semiconductor materials,leading to the improvement of the gas properties of gas sensors.

关键词： nanoporous gallium nitride platinum nitrogen dioxide gas sensor

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Multi-Path Spatial Detail-Aware Network for Semantic Segmentation

Multi-Path Spatial Detail-Aware Network for Semantic Segment...

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2023 China Automation Congress, CAC 2023

作者： Wang, Quanyi Xie, Xinlin Si, Haoyue Wang, Jianan Fang, Liang Xie, Gang School of Electronic Information Engineering Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

In the existing semantic segmentation, small objects and slender strip objects that are easy to blend into the surrounding background are generally difficult to be accurately identified and segmented. We propose a Multi-Path Spatial Detail-Aware Network (MSDNet) for semantic segmentation of traffic scenes. Specifically, an interactive Dual-branch Parallel Complementary module (DPC) based on the Transformer-CNN framework is firstly proposed to enhance the discriminative representation of similar feature encodings between different objects. Then, a spatial detail assistant module (SDA) with attention mechanism is designed to help the network obtain more detailed spatial detail information. Moreover, a Cross-layer Feature Aggregation module (CFA) is constructed for further aligning and aggregating feature maps of different resolutions in adjacent two layers, which can improve the segmentation performance of small objects. Extensive experimental results show that the proposed method achieves the accuracy of 81.16% mean Intersection over Union (mIoU) on the challenging Cityscapes dataset. © 2023 IEEE.

关键词： Semantic Segmentation

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An Efficient Bidirectional Weighted Global Feature Extraction Algorithm for Steel Surface Defects

An Efficient Bidirectional Weighted Global Feature Extractio...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Taoyuan Xie, Gang Xie, Xinlin Li, Linjuan Zhang, Haoxue Liang, Yu School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China

ISBN: (纸本)9798350303759

Focus on the drawbacks of slow detection speed, and poor detection accuracy for small objects in existing strip steel surface defect detection methods, we propose an efficient bidirectional weighted global feature extraction algorithm for steel surface defects. Firstly, the backbone feature extraction network is consisted of BNCSP-TR(BottleNeckCSP with transformer) blocks to enhance global information and optimize the extraction ability of small object features. Secondly, the enhancement in the network's accuracy for detecting small-sized defects is achieved through the design of BiFPN-Light (lightweight bidirectional weighted feature pyramid network) for multi-scale feature fusion. Finally, a more efficient and concise decoupled detection head called Eff-DcHead is designed to solve the contradiction between classification tasks and regression tasks. Extensive experimental results illustrate that the mAP of our algorithm on NEU-DET reaches 82.4%, which is 4.1% higher than the original algorithm. Simultaneously, the algorithm achieves a detection speed of 92.1 FPS, catering to the demands of industrial environments. © 2023 IEEE.

关键词： Surface defects

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Remaining Useful Life Estimation for Molecular Pumps with Transformer-Based Deep Learning Architecture 5

Remaining Useful Life Estimation for Molecular Pumps with Tr...

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5th International Conference on Artificial intelligence and Computer Engineering, ICAICE 2024

作者： Cheng, Zhixin Bai, Shoujun Anhui Polytechnic University Key Laboratory of Advanced Perception and Intelligence Control of High-End Equipment Wuhu China

ISBN: (纸本)9798331528911

Molecular pumps are critical components in the Experimental advanced Superconducting Tokamak facility, and accurately predicting their Remaining Useful Life is essential for optimizing maintenance planning and experiment scheduling. However, precise Remaining Useful Life prediction remains challenging due to the complex operating environment and inherent data noise. To enhance prediction accuracy and model robustness, this study proposes an innovative C-Transformer hybrid model. This novel approach combines the local feature extraction capabilities of Convolution Neural Networks with the global dependency capturing abilities of Transformers, rendering it more effective in processing time series data. Using molecular pump fault data for verification demonstrated the superior performance of the proposed method in predicting the remaining service life, achieving root mean square error (RMSE) and mean absolute error (MAE) scores of 583 seconds and 501 seconds, respectively. These results significantly outperform other comparative methods. This approach not only provides a reliable Remaining Useful Life prediction solution for molecular pumps in the Experimental advanced Superconducting Tokamak facility's vacuum system but also offers valuable insights for developing predictive maintenance strategies in other complex industrial systems. By implementing this method, it is possible to prevent irreversible damage caused by prolonged overload operation, reduce maintenance costs, and enhance overall operational efficiency and reliability. © 2024 IEEE.

关键词： Predictive maintenance

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Distilled one-shot federated learning for heterogeneous residential load forecasting 3

Distilled one-shot federated learning for heterogeneous resi...

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3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Guan, Chengcheng Xie, Zongchao Ding, Zhinan Xie, Xinlin Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence 66 Waliu RoadShanxi 030024 China Faculty of Science University of Melbourne Grattan Street ParkvilleVIC Australia

ISBN: (纸本)9781510680449

Load prediction is the foundation of power system operation and analysis. It also plays a decisive role in the safety, stability, and economic operation of the smart grid. Traditional load forecasting methods are too difficult to meet the needs of the modern smart grid. As technology evolves, federated learning (FL) offers a new solution for load forecasting. However, in the training of global model, FL multiple iterations are needed to converge, which largely depends on the repeated transmission of model parameter updates between client and server. When the global model structure is complicated or the local data heterogeneity of each client is serious, more iterations are needed to converge the model. Therefore, we designed a load prediction method based on distillated one-shot federated learning to solve these problems. The proposed method distills the training data through a dataset distillation algorithm and uses the distilled data instead of gradients for transmission, greatly reducing communication costs. In addition, we propose a double-weight self-checking mechanism to avoid each client updating the global model of the server on average and improve the accuracy of load forecasting. The results of the British example show that the prediction performance and privacy of distillated one-shot federated learning are better than those of FedAvg and FedDM. Particularly, our proposed method requires only 0.28 MB of communication consumption during the training process, which is much lower than the above two algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Smart power grids

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An End-to-end Campus Fire Detection Algorithm Incorporating Priori Features 6

An End-to-end Campus Fire Detection Algorithm Incorporating ...

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6th International Conference on Robotics, Intelligent control and Artificial intelligence, RICAI 2024

作者： Liang, Yu Xie, Xinlin Liu, Xuan Wang, Jie Shanxi Key Laboratory of Advanced Control and Industrial Intelligence Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798331541699

To address the imbalance in detection speed and accuracy caused by smoke, flame, and background confusion in campus video surveillance scenarios, an end-to-end campus fire detection algorithm incorporating priori features is proposed. First, a feature pyramid fusion module guided by priori features is constructed to cascade the multi-scale feature fusion module. The priori features are individually pre-trained by Convolutional Neural Network (CNN) classification network. The feature complementarity between the two is utilized to improve the feature representation of the detection network. Second, a lightweight set prediction detection head is designed. The head does not require manually designed anchors and post-processing strategies, enabling end-to-end detection while balancing speed and accuracy. Finally, a joint loss function that integrates the losses of categorical and bipartite matching is proposed. Focus on the drawbacks of the slow convergence associated with bipartite matching, both classification information and bipartite bit matching are considered in the detection results. Experimental results on the fire detection dataset demonstrate that the proposed algorithm improved average accuracy by 7.8% while maintaining detection speed, effectively balancing speed and accuracy in fire detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Bipartite Consensus control for Fixed and Switching Multi-Agent Systems via State Feedback, Observer and Output Feedback 6

Bipartite Consensus Control for Fixed and Switching Multi-Ag...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Yao, Yao Zhang, Jie Wang, Jian-An College of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan030024 China

ISBN: (纸本)9798350371253

This paper studies the bipartite consensus problem of high-order linear leader-following multi-agent systems (MASs) under fixed and switching topologies. All followers are divided into two competitive subsets, where the followers in the same subset are cooperative. In different topological environment, we respectively design three different distributed controllers via state feedback, observer and output feedback to achieve the so-called bipartite consensus with the structural balanced condition. Finally, numerical examples are given to demonstrate the analytic results. © 2023 IEEE.

关键词： Multi agent systems

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3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

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Bio-Design and Manufacturing 2024年第2期7卷 137-152页

作者： Xiaoyuan Wang Zixian Liu Qianqian Duan Boye Zhang Yanyan Cao Zhizhong Shen Meng Li Yanfeng Xi Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Department of Pathology Shanxi Provincial Cancer HospitalTaiyuan 030024China General Hospital of Taiyuan Iron and Steel Company Taiyuan 030809China

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted *** address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of *** GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 *** analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the *** with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and ***,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three *** addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude *** evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was *** research therefore acts as a foundation for the application of 3DP-7721 models in drug devel

关键词： 3D bioprinting Hepatoma tumor models Drug screening Antitumor drug development

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