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arXiv 2023年

作者： Niu, Axi Trung, Pham Xuan Zhang, Kang Sun, Jinqiu Zhu, Yu Kweon, In So Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios. © 2023, CC BY.

关键词： Image enhancement

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Sharing Mobilized Energy Storage for Temporal-Spatial Coordination of Transportation and Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第3期13卷 892-903页

作者： Yuhang Ding Xinjiang Chen Guangchun Ruan Gengyin Li Ming Zhou Jiang Dai Jianxiao Wang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China State Grid Hangzhou Lin'an District Power Supply Company Hangzhou China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Power Dispatching and Control Center Guizhou Power Grid Company Guiyang China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

Mobilized energy storage (MES) can provide a variety of services for power systems, including peak shaving, frequency regulation, and congestion alleviation. In this paper, we develop an MES sharing approach based on temporal-spatial network (TSN) toward systemwide temporal-spatial flexibility enhancement, specifically in which the heavy-duty vehicles can exchange batteries at the energy storage stations connected with power grids. To achieve the temporal-spatial coordination of transportation and power systems, we propose a coordinated scheduling model. A decentralized algorithm based on the improved optimality condition decomposition (OCD) algorithm is proposed to address the information asymmetry between transportation and power systems while enhancing computational efficiency. Case studies based on IEEE 30-/118-bus and transportation systems demonstrate that MESs using the proposed approach can significantly improve the utilization of batteries while reducing operating costs by over 40% compared with stationary energy storages (SESs).

关键词： Transportation Power systems Batteries Planning Costs Power grids Uncertainty

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Learning from Multi-Perception Features for Real-Word Image Super-resolution

arXiv

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arXiv 2023年

作者： Niu, Axi Zhang, Kang Pham, Trung X. Wang, Pei Sun, Jinqiu Kweon, In So Zhang, Yanning The School of Computer Science Northwestern Polytechnical University Xi’an710072 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China The School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of The School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other hand, blind-based methods are often limited by their fixed single perception information, which hinders their ability to handle diverse perceptual characteristics. To overcome this limitation, we propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information and a series of newly-designed Cross-Perception Blocks (CPB) to combine this information for effective super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) that improves the model’s learning capability by using newly generated HR and LR images as positive and negative samples for ground truth HR. Experimental results on challenging real-world SR datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in both qualitative and quantitative measures. © 2023, CC BY.

关键词： Feature extraction

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An improved cellular automata model of traffic flow with look-ahead potential1

An improved cellular automata model of traffic flow with loo...

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Chinese Automation Congress (CAC)

作者： Xiao Ma Yu-Hu Liu National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Mathematics Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665426480

This paper presents an improved automata model for traffic flow based upon models for cellular automata and traffic flow. The proposed model is concerned with the headway and multiple velocities. The proposed model enforces the stochastic rules for moving a car on the basis of traffic in front of it. Rules for updating the velocity of car relies upon the location of the car as well as front traffic and their velocities. In particular, by using numerical simulations, the model proposed in this study can reach the steady-state more quickly for random perturbations.

关键词： Analytical models Perturbation methods Electric shock Automata Stochastic processes Numerical simulation Numerical models

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MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model

arXiv

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arXiv 2024年

作者： Ye, Yiwen Chen, Ziyang Zhang, Jianpeng Xie, Yutong Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The College of Computer Science and Technology Zhejiang University Zhejiang China The University of Adelaide Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China The Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China

Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities expands, it becomes increasingly important to generate and strategically position task- and modal-specific priors within the universal model. However, existing universal models often overlook the correlations between different priors, and the optimal placement and frequency of these priors remain underexplored. In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains. MedUniSeg employs multiple modal-specific prompts alongside a universal task prompt to accurately characterize the modalities and tasks. To generate the related priors, we propose the modal map (MMap) and the fusion and selection (FUSE) modules, which transform modal and task prompts into corresponding priors. These modal and task priors are systematically introduced at the start and end of the encoding process. We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets. The results demonstrate that MedUniSeg achieves superior multi-task segmentation performance, attaining a 1.2% improvement in the mean Dice score across the 17 upstream tasks compared to nnUNet baselines, while using less than 1/10 of the parameters. For tasks that underperform during the initial multi-task joint training, we freeze MedUniSeg and introduce new modules to re-learn these tasks. This approach yields an enhanced version, MedUniSeg*, which consistently outperforms MedUniSeg across all tasks. Moreover, MedUniSeg surpasses advanced self-supervised and supervised pre-trained models on six downstream tasks, establishing itself as a high-quality, highly generalizable pre-trained segmentation model. The code and model will be available at https://***/yeerwen/UniSeg. Copyright © 2024, The Authors.

关键词： Image segmentation

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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Group Multi-View Transformer for 3D Shape analysis with Spatial Encoding

arXiv

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arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications

arXiv

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arXiv 2025年

作者： Yang, Songjie Wan, Zihang Xiu, Yue Ning, Boyu Li, Yong Liu, Yuenwei Yuen, Chau National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Wireless Optical Communication Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Hefei230026 China Henan Agricultural Environment Monitoring and Big Data Analysis Engineering Research Center Henan Agricultural University Zhengzhou450046 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Movable antennas offer new potential for wireless communication by introducing degrees of freedom in antenna positioning, which has recently been explored for improving sum rates. In this paper, we aim to fully leverage the capabilities of movable antennas (MAs) by assuming that both the transmitter and receiver can optimize their antenna positions in multi-user multiple-input multiple-output (MU-MIMO) communications. Recognizing that WMMSE beamforming is a highly effective method for maximizing the MU-MIMO sum rate, we modify it to integrate antenna position optimization for MA systems, which we refer to as flexible WMMSE (F-WMMSE) beamforming. Importantly, we reformulate the subproblems within WMMSE to develop regularized sparse optimization frameworks to achieve joint beamforming (antenna coefficient optimization) and element movement (antenna position optimization). We then propose a regularized least squares-based simultaneous orthogonal matching pursuit (RLS-SOMP) algorithm to address the resulting sparse optimization problem. To enhance practical applications, the low-complexity implementation of the proposed framework is developed based on the pre-calculations and matrix inverse lemma. The overall F-WMMSE algorithm converges similarly to WMMSE, and our findings indicate that F-WMMSE achieves a significant sum rate improvement compared to traditional WMMSE, exceeding 20% under appropriate simulation conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Least squares approximations

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