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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Data-Driven Modeling for Pneumatic Muscle Using Koopman-Based Kalman Filter

Data-Driven Modeling for Pneumatic Muscle Using Koopman-Base...

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IEEE International Conference on Development and Learning, ICDL

作者： Yu Caol Mengshi Zhang Bo Yang Jian Huang Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper presents a data-driven modeling approach for enhancing the understanding of pneumatic muscles (PMs) through Koopman kalman filter (KKF). The proposed method involves the construction of a comprehensive pneumatic muscle motion data-set, obtained by capturing extensive input-state-output data through the application of random inputs. Subsequently, the states of pneumatic muscle are lifted through a kernel-based technique. The nonlinear pneumatic muscle model is then transformed into a linear counterpart using the extended dynamic mode decomposition (EDMD). Then, a kalman filter is further used to improve the model accuracy. The effectiveness of this approach is demonstrated through simulation analysis, highlighting its capacity to yield superior pneumatic muscle modeling and state estimation results when compared to conventional Koopman operator methodologies.

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Thermal Safety Analysis and Dynamic control Strategy for Mode Switching Process of Reversible Solid Oxide Cell System

Thermal Safety Analysis and Dynamic Control Strategy for Mod...

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第42届中国控制会议

作者： Wei Zhao Guoqiang Liu Zexin Li Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology

Reversible solid oxide cells(rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both *** used in dynamic operation with intermittent electrical power sources,rSOC system switches from the fuel cell(SOFC) to electrolyzer(SOEC) and vice versa depending on load and grid *** can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the *** the present work,a system-level dynamic model of r SOC is established and validated against experimental ***,detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed,and a temperature management controller based on Model Predict control method(MPC) is *** flow and temperature at the stack inlet are controlled by air flow and bypass valves,avoiding problems associated with temperature overshoot during transient *** simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety,which indicates the competitiveness of the controller.

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3-D temperature field reconstruction of lithium-ion battery pack using real-time distributed moving horizon estimation

3-D temperature field reconstruction of lithium-ion battery ...

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第35届中国控制与决策会议

作者： Jiayu Yan Yiming Wan School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand Key Laboratory of Image Processing and Intelligent ControlMinistry of Education

As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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MoLo: Motion-Augmented Long-Short Contrastive Learning for Few-Shot Action Recognition

MoLo: Motion-Augmented Long-Short Contrastive Learning for F...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Zhiwu Qing Changxin Gao Yingya Zhang Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception; ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

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A semantic segmentation method for UAV image based on improved BiseNetv2 5

A semantic segmentation method for UAV image based on improv...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Guo, Fengjuan Han, Sen Zhao, Jiahang Han, Jiale Xin, Jing Shaanxi Key Laboratory of Integrated and Intelligent Navigation The 20th Research Institute of China Electronics Technology Corporation Shaanxi Xi’an710068 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510668621

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for "Human", the IoU of TBiseNetv2 is about 4.3% higher than that of the popular UNetFormer. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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HyRSM++: Hybrid relation guided temporal set matching for few-shot action recognition

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Pattern Recognition 2024年 147卷

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Meta AI United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. However, existing attempts still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method consistently outperforms existing methods and achieves state-of-the-art performance under various few-shot settings. The source code is available at https://***/alibaba-mmai-research/HyRSMPlusPlus. © 2023 Elsevier Ltd

关键词： Benchmarking

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A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

A Recipe for Scaling up Text-to-Video Generation with Text-f...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Hangjie Yuan Zhiwu Qing Biao Gong Yingya Zhang Yujun Shen Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Zhejiang University Ant Group

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10m vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at here.

关键词： Training Video on demand Scalability Pipelines Text to image Performance gain Market research

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Selection of Optimal Focusing Evaluation Function Based on Sparse Microscopic images

Selection of Optimal Focusing Evaluation Function Based on S...

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2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Lv, Meini Gan, Hui Liu, Xin Chen, Jia Yang, Qiuhui Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China

The auto focusing process is affected by the amount of image content, resulting in the focus curve not meeting the characteristics of the ideal focus curve. In this paper, aiming at the problem that the traditional autofocus cannot find the focal plane successfully in the case of microscopic sparse content, the qualitative analysis and quantitative analysis of 12 traditional focus evaluation functions are carried out. The experimental results show that the F Tenengrad function is suitable for the optimal focus evaluation function of sparse microscopic images. © Published under licence by IOP Publishing Ltd.

关键词： Focusing

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Ensemble prediction modeling of flotation recovery based on machine learning

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International Journal of Mining Science and Technology 2024年第12期34卷 1727-1740页

作者： Guichun He Mengfei Liu Hongyu Zhao Kaiqi Huang Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources Ganzhou 341000China School of Resources and Environmental Engineering Jiangxi University of Science and TechnologyGanzhou 341000China Hefei GoldStart Intelligent Control Technical Co. Ltd.Hefei 230088China School of Mechanical and Electrical Engineering Jiangxi University of Science and TechnologyGanzhou 341000China

With the rise of artificial intelligence(AI)in mineral processing,predicting the flotation indexes has attracted significant research ***,current prediction models suffer from low accuracy and high prediction ***,this paper utilizes a two-step ***,the outliers are pro-cessed using the box chart method and filtering ***,the decision tree(DT),support vector regression(SVR),random forest(RF),and the bagging,boosting,and stacking integration algorithms are employed to construct a flotation recovery prediction *** experiments compared the prediction accuracy of six modeling methods on flotation recovery and delved into the impact of diverse base model combinations on the stacking model’s prediction *** addition,field data have veri-fied the model’s *** study demonstrates that the stacking ensemble approaches,which uses ten variables to predict flotation recovery,yields a more favorable prediction effect than the bagging ensemble approach and single models,achieving MAE,RMSE,R2,and MRE scores of 0.929,1.370,0.843,and 1.229%,*** hit rates,within an error range of±2%and±4%,are 82.4%and 94.6%.Consequently,the prediction effect is relatively precise and offers significant value in the context of actual production.

关键词： Machine learning Stacking Bagging Flotation recovery rate Filtering algorithm

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