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5th International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2022

作者： Zhang, Xue Ting Pan, Yan Peng Yang, Li Jie Xue, Chen Chen Zhang, Fu Quan China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819908479

The technology of software-defined changing all areas of life, but the equipment storage system in the field of application is still in its infancy. At present, the traditional equipment warehousing scheduling system is difficult to meet the new needs of rapid scheduling and flexible maintenance of large quantities of equipment. Based on the development and application characteristics of software definition technology, this paper integrates the traditional equipment storage scheduling system and presents the research on the architecture design technology of equipment storage scheduling system under software definition technology. This paper clarifies the key application technologies of the system, realizes the design of new open system architecture based on software-defined technology and the optimization design of system center service platform architecture, and improves the architecture design ability of automation, configuration ability, and easy maintenance of the existing equipment storage scheduling system. It solves the new needs of rapid deployment, intelligent upgrade, flexible configuration, and convenient service of large quantities of equipment warehouse scheduling system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Training-free diffusion model adaptation for variable-sized text-to-image synthesis 23

Training-free diffusion model adaptation for variable-sized ...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Zhiyu Jin Xuli Shen Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University and UniDT Technology

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.

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Feature Map Guided Adapter Network for Object Detection in Low-light Conditions

Feature Map Guided Adapter Network for Object Detection in L...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Cong Pang Wei Zhou Haoyan Li Xiangyu Zhang Xin Lou School of Information Science and Technology ShanghaiTech University Key Laboratory of Intelligent Perception and Human-Machine Collaboration Ministry of Education Shanghai China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Conventional ISP pipelines and image enhancement methods are designed and optimized for human vision, creating a gap between the requirements of computer and human visions. To bridge the requirement gap, we present a co-design framework in which backend computer vision plays a pivotal role in shaping the proceeding image processing algorithm. It features a pre- processing adapter network, responsible for the restoration and enhancement of RAW images from computer vision perspective, especially in challenging environmental conditions. Specifically, we extract feature maps from the backend vision network, utilizing them as constraints for optimizing the preprocessing adapter network. To validate the effectiveness of our proposed framework, we employ object detection in low-light conditions as the computer vision task, with YOLO-v5 as the backbone. Given the considerable noise in low-light images, we compare our results with state-of-the-art denoising algorithms, showcasing the superior performance of our framework.

关键词： Computer vision Circuits and systems Noise reduction Noise Pipelines Object detection Feature extraction

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A Multi-scale Block PatchMatch-based Unified Algorithm for Efficient 6-D Vision processing

A Multi-scale Block PatchMatch-based Unified Algorithm for E...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hongyu Wang Xiangyu Zhang Xin Lou School of Information Science and Technology ShanghaiTech University Key Laboratory of Intelligent Perception and Human-Machine Collaboration Ministry of Education Shanghai China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

6-D vision, which combines stereo and flow estimation to derive 3-D location and motion information, is crucial for comprehensive real-world perception. This paper presents a multi-scale block PatchMatch (MBPM)-based efficient 6-D vision algorithm with a balance tradeoff between computational complexity and accuracy, making it suitable for embedded application scenarios. By adopting a random search strategy, the proposed algorithm effectively reduces the computational burden without compromising accuracy. Additionally, the block-level processing approach further enhances efficiency by processing data in smaller, manageable chunks. Due to the significantly larger search range in flow estimation compared to stereo, we further incorporate direction initialization and multi-scale PatchMatch propagation techniques. These enhancements are crucial for ensuring reliable accuracy while expediting algorithm convergence. Compared with existing 6-D vision algorithms, the proposed method achieves 16.7% higher accuracy in stereo and 11.1% in flow estimation with much lower complexity. Even when faced with larger searching ranges, the proposed method exhibits consistent accuracy, allowing it to effectively handle fast motion scenarios.

关键词： Accuracy Circuits and systems Estimation Search problems Hardware Reliability Computational complexity

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Molecular dynamics simulation of heat transfer during liquid hydrogen boiling on surfaces at the nanoscale

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Nuclear Science and Techniques 2025年第7期36卷 1-12页

作者： Li, Xiao-Jia Chen, He-Ying Sun, Yi-Zheng Fan, Zhi-Chun School of Ocean Information Engineering Jimei University Xiamen361021 China Department of Mechanical Engineering City University of Hong Kong Hong Kong Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen361021 China

Liquid hydrogen, known for its high energy density and eco-friendly properties, has garnered significant attention in the context of sustainable development and clean energy. A comprehensive understanding of its nucleation mechanisms and boiling heat transfer characteristics is crucial. However, current experimental and macroscopic simulation methods offer limited insights. This study employs molecular dynamics simulations to investigate the vaporization nucleation and boiling heat transfer properties of liquid hydrogen at the microscopic scale, with a focus on the effects of hydrogen film thickness, surface temperature, and wettability. The results indicate that hydrogen film thickness plays a critical role in nucleation. Thinner layers disrupt the shape of liquid films, leading to increased errors, whereas a thickness of 7 nm ensures film stability. Different heating methods and temperatures influence nucleation in various ways. Rapid heating results in a higher heat flux, while an increase in temperature under the same heating method accelerates nucleation, resulting in earlier nucleation and enhanced surface heat flow. Surfaces with varying wettability levels exhibit distinct nucleation behaviors. Specifically, an increase in α delays nucleation, causing a shift from the surface to within the liquid film due to stronger solid–liquid interaction forces. This study offers a microscale perspective on the nucleation and boiling processes of liquid hydrogen and provides valuable insights for phase transition studies. © The Author(s), under exclusive licence to China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society 2025.

关键词： Electric heating

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Real-Time Student Behavior Analysis via YOLOv5 with Coordinate Attention 9th

Real-Time Student Behavior Analysis via YOLOv5 with Coordina...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Ma, Shuai Chang, Heyou Yang, Xinru Chen, Quan Han, Jian Key Laboratory of Intelligent Information Processing Nanjing XiaoZhuang University Nanjing China School of Information and Engineering Nanjing XiaoZhuang University Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819629138

The detection and recognition of student behavior play a pivotal role in the context of smart classrooms. However, conventional methods often encounter performance degradation due to challenges such as occlusion, data diversity, and complexity. To effectively addresses these issues, we propose a method that integrates YOLOv5 with coordinate attention mechanism and GhostConv for analyzing students' behavior. Specifically, the coordinate attention module enhances the localization ability of the proposed method by addressing problems related to target scale variations and uneven distribution. Additionally, we employ GhostConv to replace conventional convolutional layers in YOLOv5, reducing parameters while improving computational efficiency. To verify the validity of our method, a self-built challenging dataset for real teaching scenarios and Citypersons dataset are selected for the experiment. In comparison to the original YOLOv5, our method has achieved a 14.8% and 5.7% increase in mean average precision rates, with a 19.0% improvement in frames per second. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Density Estimation-based Effective Sampling Strategy for Neural Rendering

Density Estimation-based Effective Sampling Strategy for Neu...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yunxiang He Xin Lou School of Information Science and Technology ShanghaiTech University Key Laboratory of Intelligent Perception Human-Machine Collaboration Ministry of Education Shanghai China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Expanding upon the foundational Neural Radiance Fields (NeRF) framework, neural rendering techniques have seen wide-ranging applications in 3D reconstruction and rendering. Despite the numerous attempts to accelerate the original NeRF, the ray marching process in many neural rendering algorithms remains a performance bottleneck, constraining their rendering speed. In this work, we introduce an innovative method for effective sampling based on density estimation to alleviate this bottleneck. The proposed method can effectively reduce the number of required occupancy grid access without compromising rendering quality. Evaluation and analysis results validate the effectiveness of the proposed approach, delivering notable improvements in rendering efficiency.

关键词： Three-dimensional displays Accuracy Shape Filtering Noise Estimation Rendering (computer graphics)

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An Ensemble Learning Approach for Wind Power Forecasting 3

An Ensemble Learning Approach for Wind Power Forecasting

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3rd International Conference on Electronic information Engineering and Computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 IEEE.

关键词： Decision trees

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Semantic Segmentation of 3D Liver Image Based on Multi-Path Features Attention Mechanism

Semantic Segmentation of 3D Liver Image Based on Multi-Path ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Jiang, Zhihui Zhang, Xiaolong Deng, He Ren, Hongwei Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Wuhan University of Science and Technology Medical Imaging Research Center Tianyou Hospital Affiliated Wuhan China

ISBN: (纸本)9798350337020

It is challenging to precisely segment the liver from surrounding organs in medical images because of the poor contrast between them. A method of semantic segmentation of 3D liver images based on multi-path features attention mechanism is proposed to address this issue. It integrates three-dimensional spatial information and feature information from several paths in the model to automatically segment the liver area. The model in this paper uses the LiTS dataset for training, testing, and ablation experiments, and compares the results with previous models. The experimental results demonstrate that the model in this paper has reached 0.965 in the DICE similarity coefficient, and has also improved in evaluation indicators such as volume overlap error (VOE) and root mean square symmetric surface distance (RMSD). It also has better segmentation performance when tested on the CHAOS dataset. Cross-validation was carried out on the clinical MRI dataset of a hospital, and the DICE similarity coefficient reached 0.971. The results show that the model has good performance on the multi-modal datasets of CT and MRI. © 2023 IEEE.

关键词： 3D medical image attention mechanism deep learning liver image semantic segmentation

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Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 214-222页

作者： Shuping Liu Yantuan Xian Huafeng Li Zhengtao Yu School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection *** order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

关键词： Dictionary learning Laplacian sparse regularization morphological component analysis(MCA) sparse representation text detection

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