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Robotics, Intelligent control and Artificial intelligence (RICAI), International Conference on

作者： Jingjing Liang Xinlin Xie Tihao Cui Haoxue Zhang Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Change detection in remote sensing images is a challenging task, particularly when identifying small and complex objects such as buildings. The task is often hindered by issues such as high fragmentation rates, blurred edges, and elevated false and missed detection rates. These issues arise due to the inherent complexity of the scenes and the problem of class imbalance. To address these challenges, we propose a superpixel edge refinement and differential feature enhancement for Transformer-based change detection networks. First, a region-enhanced Transformer encoder is introduced, incorporating a superpixel edge refinement module. This module leverages pixel-level features extracted by the Transformer to generate superpixel feature embeddings, thereby enhancing local and edge information. Second, a differential feature enhancement module is designed to emphasize true change regions while suppressing pseudo-change areas. This is achieved through a dual-branch structure comprising feature enhancement and change extraction, further augmented by a saliency module. Finally, a Dice loss function is employed as an auxiliary loss term, complementing the cross-entropy loss in the construction of an edge-optimized loss function. The resulting loss function aims to balance overall detection accuracy with edge refinement. The proposed algorithm was evaluated on the widely used datasets LEVIR-CD and DSIFN-CD. Compared to existing algorithms, the proposed method effectively improved edge detail and overall consistency while maintaining detection accuracy. Additionally, false detections and missed detections were reduced.

关键词： Measurement Accuracy Change detection algorithms Image edge detection Buildings Feature extraction Transformers Object recognition Remote sensing Robots

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Transformer-Based Differential Superpixel Selection Network for Remote Sensing Images Change Detection

Transformer-Based Differential Superpixel Selection Network ...

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Artificial intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Tihao Cui Xinlin Xie Jingjing Liang Haoxue Zhang Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In remote sensing images change detection, the results of Transformer often face the low precision and edge fitting problems, which are caused by the highly complex background of building categories and the imbalance of sample ratio. To address these challenges, we propose a Transformer-based differential superpixel selection network. First, a differential superpixel selection module is proposed which introduces difference selecting to refine the edge feature representation of the object region by using the boundary information of the selected superpixel. Second, we propose a multi-scale feature-aware enhancement module, which fuses multi-scale feature differences with the features extracted by the hierarchical Transformer encoder. This module enhances difference awareness while suppressing interference from complex backgrounds. Finally, we construct a composite loss function that combines the focal loss function with the binary cross-entropy loss function. The composite loss function can pay more attention to hardly classifying samples and suppress the influence of easily classifying samples. Our proposed algorithm is tested on the widely used LEVIR-CD and DSIFN-CD datasets. The results show that it effectively improved detection accuracy and edge fitting compared to existing methods.

关键词： Accuracy Sensitivity Image edge detection Fitting Buildings Interference Transformers Feature extraction Manufacturing Remote sensing

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A novel self-attention weight transformer for air pollution smoke detection 5

A novel self-attention weight transformer for air pollution ...

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5th International Conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Zhou, Chengxu Fan, Xiaojie Jiang, Yanlin Liu, Yuchen Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing100083 China Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing102400 China Beijing Laboratory of Smart Environmental Protection Beijing100124 China Beijing Artificial Intelligence Institute Beijing100000 China School of Electronic & Information Engineering Liaoning University of Technology Liaoning Anshan114051 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Liaoning Dalian116024 China

ISBN: (纸本)9781510683006

There is a necessary combustion facility called the flare stack to ensure production safety in every petrochemical plant, smelter, and refinery all over the world. Because of the incomplete combustion of flare gas, the flare stack discharges a large amount of smoke into the atmosphere and make it dirty. The air pollution is becoming more and more serious caused worldwide concern. Hence, there is in desperate need of an efficient and available smoke detection method to protect the atmosphere. To this end, we present a novel self-attention weight transformer (SAWT) that can detect accurately smoke, then guarantee full combustion of the flare stack. First, we are concerned about the weight relationship between channels and draw on the self-attention mechanism in the MobileViT block structure to adaptively adjust channel-wise feature weight. Second, we leverage short connections to thoroughly learn and fuse local and global features. Results of experiments on a real smoke dataset reveal that the proposed SAWT achieves superior performance to the popular deep CNNs and state-of-the-art smoke detection algorithms. © 2024 SPIE.

关键词： Flare stacks

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Edge-guided image estimation for array Gm-APD Lidar in smoky environment using multi-scale depth images 6

Edge-guided image estimation for array Gm-APD Lidar in smoky...

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6th Optics Young Scientist Summit, OYSS 2023

作者： Zhang, Yinbo Li, Sining Sun, Jianfeng Jiang, Peng Zhang, Hailong Liu, Zhihui Zhu, Yuebing National Key Laboratory of Science and Technology on Tunable Laser Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Henan Zhengzhou450000 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China Sichuan Jiuzhou Electrical Group Company Limited Sichuan Mianyang621000 China

ISBN: (纸本)9781510672642

Limited statistical frame number and strong backscatter interference from smoke result in a photon-starved regime, severely limiting the depth imaging capability of array Gm-APD lidar in smoky environment. Here, we propose a depth image estimation algorithm that can significantly improve the integrity of targets in dense smoke environments when signal photons are starved. At the signal level, the algorithm improves the accuracy of extracting signal photons by constructing multi-scale superpixels. At the image level, using edge information of depth images at different scales to guide and fill the original scale depth image achieves efficient noise reduction and improves target integrity. It has been successfully demonstrated under different attenuation lengths and statistical frame numbers. Compared with other state-of-the-art methods, the proposed algorithm has maximum target recovery and structural similarity, especially for the attenuation length (AL) is 3.6 and statistical frame number is 1500 (imaging time is 75ms). This study is of great significance for the fast depth imaging of dynamic targets by array Gm-APD lidar in dense smoke environments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical radar

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V-SWIR-IF: Visible and Short-Wave Infrared Image Fusion 4

V-SWIR-IF: Visible and Short-Wave Infrared Image Fusion

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4th International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2023

作者： Fang, Hao Su, Gaomin Xu, Guanjie Cheng, Chuantong Brain Machine Fusion Intelligence Institute Suzhou China Xi'an Jiaotong-Liverpool University School of Advanced Technology Jiangsu Suzhou China Institute of Semiconductors Chinese Academy of Sciences State Key Laboratory on Integrated Optoelectronics Beijing China University of Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Beijing China

ISBN: (纸本)9798350306323

This paper presents an algorithm for the fusion of visible light and short-wave infrared (SWIR) images. The algorithm is structured around three core processes: image registration, fusion, and color reconstruction. To register multimodal images, an intensity-based image registration method is employed, which utilizes Mattes mutual information as metrics to guide the 1+1 evolutionary algorithm optimizer. This step produces a registered SWIR image. The image fusion step entails separating the visible image into three images by separating the RGB channels. The registered SWIR image is then fused with each of the three images individually using the Discrete Wavelet Transform (DWT). Finally, the RGB channels of the three fused images are reconstructed to complete the fused image's color information. Experimental results on our dataset of visible and SWIR images show that the proposed algorithm achieves good performance in terms of image fusion, with a fast fusion speed of approximately 1 seconds per pair on a common household computer. © 2023 IEEE.

关键词： Image fusion

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Eigenvector of Gram-Matrix based Open-Fault Diagnosis Method for Two-level Three-Phase Inverter

Eigenvector of Gram-Matrix based Open-Fault Diagnosis Method...

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International Conference on Power electronics and Motion control (IPEMC)

作者： Yang Zhou Jin Zhao Yang Liu Lijun Sun Ministry of Education School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China Electronic Product Reliability and Environmental Testing Research Institute Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

This paper studies the power switch open-circuit(OC) fault in two-level three-phase (2L3P) voltage source inverter (VSI). According to the topological symmetry of the inverter, when it works in healthy conditions, its phase currents are standard sine waves with only 120-degree phase difference, and the similarity between phase currents is high. When OC fault occurs, the current waveforms of both normal and fault phases will be distorted, the similarity will be changed. Therefore, this paper introduces the Gram matrix in image processing to measure the similarity and proposes a fast and computationally efficient diagnostic method based on the eigenvector of Gram-Matrix. Physical experiment shows that both single-switch fault and multiple-switch fault on the same leg can be effectively detected and located.

关键词： Legged locomotion Fault diagnosis Voltage source inverters Switches Computational efficiency Maintenance Velocity measurement

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3D bioprinting of a dermal scaffold for full-thickness skin tissue regeneration

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Bio-Design and Manufacturing 2025年第1期8卷 68-84,I0039-I0041页

作者： Lu Han Zixian Liu Meng Li Zhizhong Shen Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China General Hospital of TISCO Taiyuan 030809China

Dermal substitutes have provided a template for the regeneration and reconstruction of the ***,the healed skin tissue often exhibits abnormal morphology and functionality,including scarring and *** this study,a composite bioink composed of methacrylated gelatin(GelMA)and chitosan oligosaccharide(COS)was proposed for printing a dermal scaffold using digital light processing(DLP)*** GelMA/COS bioink exhibited suitable porosity,swelling,degradation rate,and mechanical *** inclusion of COS demonstrated antibacterial effects against both Gram positive and Gram-negative bacteria,while simultaneously fostering the proliferation of human dermal fibroblasts(HDFs).Additionally,the application of COS could effectively reduce the expression levels of fibrosis-related genes,such as collagen I,collagen III,and fibronectin *** three-dimensionally printed cell-laden dermal scaffold exhibited excellent shape fidelity and high cellular viability,facilitating the extension of HDFs along the scaffold and the simultaneous secretion of extracellular matrix ***,the HDF-laden dermal scaffold transplanted into full-thickness skin defect sites in nude mice was shown to accelerate wound closure,reduce inflammation,and improve wound ***,the DLP-printed dermal scaffold provides an appealing approach for effectively treating full-thickness skin defects in clinical settings.

关键词： 3D printing Dermal scaffold Photo-cross-linking Skin tissue regeneration

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Single-Loop Deadbeat Predictive control Framework for Uncertain SPMSM Drives With Disturbance Observation

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IEEE Transactions on Transportation Electrification 2025年

作者： Jiang, Fuxi Wang, Siqi Guo, Yuhao Cheng, Zhe Ye, Jie Cheng, Shanmei Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Optical and Electronic Information Wuhan430074 China

To mitigate the detrimental effects of parameter perturbations and external load torque on the accuracy of speed and current tracking control in surface-mounted permanent magnet synchronous motor (SPMSM) drive systems, while avoiding the complexities inherent in traditional speed-current series control architectures, this paper presents a single-loop deadbeat predictive control (SDPC) framework that incorporates online disturbance observation. The control input voltage vector is directly computed using an SDPC controller in conjunction with a set of discrete-time disturbance observers (DDOBs). This proposed methodology offers two notable advantages over existing research: (i) it substantially reduces the number of adjustable controller parameters, requiring only the tuning of the observer bandwidth and the speed scaling gain, which enhances its practicality for engineering applications;and (ii) the disturbance estimation and compensation mechanism utilizing DDOBs significantly improves the robustness of the SPMSM system. Furthermore, the SDPC framework ensures that the system maintains satisfactory transient and steady-state tracking performance in response to reference signals. Experimental comparisons validate both the effectiveness and superiority of the proposed method. © 2015 IEEE.

关键词： Permanent magnets

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Efficient Infrared Image Generation technology Based on Deep Learning

Efficient Infrared Image Generation Technology Based on Deep...

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Academic International Symposium on optoelectronics and Microelectronics technology (AISOMT)

作者： Xiang Zhang Chao Yue Jian Wang Yong Zhang Zhen Yang Jianlong Zhang Junliang Yan Xi'an Institute of Modern Control Technology Xi'an China Department of Opto-Electronic Information Science and Technology National Key Laboratory of Laser Space Information Harbin Institute of Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

In existing infrared image generation technologies, challenges such as insufficient scene realism and low image generation efficiency are commonly encountered. To address these issues, this paper proposes an efficient infrared image generation technique based on deep learning, aimed at enhancing both the realism and efficiency of infrared images. This technique is built on an improved CycleGAN architecture, utilizing a U-Net structure as the generator and a convolutional neural network (CNN) as the discriminator. By introducing a diverse set of loss functions, we enhance the adversarial training of the generation network, allowing the generated infrared images to better resemble real scenes in terms of structure and detail. Furthermore, to improve image generation efficiency, we optimized the system's data transmission and display processing methods, achieving an image generation speed of up to 200 frames per second. The proposed technique not only effectively addresses multiple issues inherent in traditional infrared image generation but also provides robust technical support for the practical applications of infrared simulation systems, showcasing a broad range of application prospects

关键词： Deep learning Training Image synthesis Generators Microelectronics Convolutional neural networks Data communication

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A path planning method for large-aperture non-spherical surface detection profilometer 3

A path planning method for large-aperture non-spherical surf...

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3rd International Conference on Laser, Optics, and optoelectronic technology, LOPET 2023

作者： Wang, Tingyu Wang, Zhiyi Wang, Jianli Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Jilin Provincial Key Laboratory of Intelligent Wavefront Sensing and Control Changchun130033 China

ISBN: (纸本)9781510667624

To enhance the accuracy, efficiency, and safety of large-aperture non-spherical optical components, we have proposed a novel strategy for path planning and detection, which uses a non-contact optical probe suitable for spindle-rotating contourimeters. Our strategy leverages the contourimeter's working principle and the structural characteristics of the surface, transforming the three-dimensional collision problem into a two-dimensional detection space. To accomplish this, we first decompose the cross-section of the workpiece into a quadtree space and establish oriented bounding boxes for each sub-node. We then use the separation axis principle to ensure motion safety between the positions of the test points. Next, we utilize the ant colony optimization algorithm to plan the detection path and generate an interference-free scanning route that ensures safety during the detection process. Overall, our path-planning strategy significantly improves detection efficiency, while ensuring that the detection process is conducted safely. By combining these methods, we created a robust and effective approach to detecting large-aperture non-spherical optical components. © 2023 SPIE.

关键词： Motion planning

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