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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Performance Design of High-Temperature Chloride Salts as Thermal Energy Storage Material

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Journal of Thermal Science 2024年第2期33卷 479-490页

作者： ZHAO Le WANG Jingyao CUI Liu LI Baorang DU Xiaoze WU Hongwei Key Laboratory of Power Station Energy Transfer Conversion and System(North China Electric Power University) Ministry of EducationBeijing102206China School of Energy and Power Engineering Lanzhou University of TechnologyLanzhou730050China School of Physics Engineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK

The chloride salts have great potential used as high-temperature thermal energy storage(TES) medium for the concentrated solar power *** this study,LiCl,KCl and CaCl_(2) were selected as energy storage materials in order to further broaden the working temperature of ternary chloride salt and improve its energy storage *** new high-temperature energy storage ternary chloride composed of LiCl,KCl,and CaCl_(2) was developed based on the phase diagram generated by *** components of LiCl-KCl-CaCl_(2) with the mass ratio of 37.85%-53.38%-8.77%,30.90%-13.82%-55.28% and 1.78%-18.61%-79.61% were developed,of which the corresponding melting temperature(T_(m)) 340.93,433.57 and 626.85℃,respectively,was obtained *** that T_(m) of the third group of salts was too high,only the first two groups of salts were *** test showed that the actual melting point was only 0.46% and 1.64% different from the melting point predicted by *** thermal properties of the two ternary chloride salts were also *** solid and liquid-specific heat of ternary salts was determined by DSC using sapphire as the standard *** vapor pressure and decomposition temperature of ternary chloride salts were *** results showed that the vapor pressure of salt 1 was almost constant below 650℃ by ***,the TG results showed that the upper working temperature of salt 1 was 650℃ under the air *** addition,the ternary chloride salts after short-term cycling still exhibited excellent thermal properties,which revealed that these good thermal properties make them have broad application prospects in high-temperature thermal energy storage systems.

关键词： chloride salts thermal energy storages thermal properties thermal stability

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A CNN-LSTM Network for Classification of Attention Deficit Hyperactivity Disorder from EEG Data 41

A CNN-LSTM Network for Classification of Attention Deficit H...

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第41届中国控制会议

作者： Yaru Zhang Xiangxin Tan Yue Gu Key Laboratory of Computer Vision and System (Ministry of Education) School of Computer Science and EngineeringTianjin University of Technology Engineering Research Center of Learning-Based Intelligent system Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Attention deficit hyperactivity disorder(ADHD) is one of the most common neuropsychiatric disorders in children,with core symptoms of inattention,impulsivity,and *** a result,early detection and treatment are *** this point,ADHD can be diagnosed using an electroencephalogram(EEG).This study proposes a model for identifying the EEG of children with *** proposed model is made up of three parts:temporal convolutional blocks,spatial convolutional blocks,and Long Short Term Memory(LSTM),and because EEG contains more temporal information than other data,we build a model that can extract more temporal *** study included 24 subjects,including 12 children with ADHD and12 normal *** experimental results show that our proposed model outperforms Support Vector Machine(SVM) and EEGNet,with our model achieving correct classification rates of 92.29%,92.76%,and 90.91% on the three backs,respectively,and 94.25% on the full data,demonstrating the model's feasibility and applicability to the ADHD recognition problem.

关键词： Attention Deficit Hyperactivity Disorder Electroencephalogram Deep learning Classification

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Bi-objective Layout Optimization for Multiple Wind Farms Considering Sequential Fluctuation of Wind Power Using Uniform Design

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CSEE Journal of Power and Energy systems 2022年第6期8卷 1623-1635页

作者： Yinghao Ma Kaigui Xie Yanan Zhao Hejun Yang Dabo Zhang Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei University of TechnologyHefei 230009China State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing UniversityChongqing 400044China School of Computer Science and Technology at University of Science and Technology of China Hefei 230027China

The fluctuation of wind power brings great challenges to the secure,stable,and cost-efficient operation of the power *** of the time-correlation of wind speed and the wake effect of wind turbines,the layout of wind farm has a significant impact on the wind power sequential *** order to reduce the fluctuation of wind power and improve the operation security with lower operating cost,a bi-objective layout optimization model for multiple wind farms considering the sequential fluctuation of wind power is proposed in this *** goal is to determine the optimal installed capacity of wind farms and the location of wind *** proposed model maximizes the energy production and minimizes the fluctuation of wind power *** improve the accuracy of wind speed estimation and hence the power calculation,the timeshifting of wind speed between the wind tower and turbines’locations is also considered.A uniform design based two-stage genetic algorithm is developed for the solution of the proposed *** studies demonstrate the effectiveness of this proposed model.

关键词： Wind farm layout optimization(WFLO) wind power fluctuation bi-objective optimization uniform design

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CRD-CGAN:category-consistent and relativistic constraints for diverse text-to-image generation

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Frontiers of computer Science 2024年第1期18卷 61-75页

作者： Tao HU Chengjiang LONG Chunxia XIAO College of Intelligent Systems Science and Engineering Hubei Minzu UniversityEnshi 445000China School of Computer Science Wuhan UniversityWuhan 430072China Key Laboratory of Performing Art Equipment&System Technology Ministry of Culture and TourismBeijing 100007China Meta Reality Labs BurlingameCA94010USA

Generating photo-realistic images from a text description is a challenging problem in computer *** works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks(GANs).In this paper,we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic *** on those constraints,a category-consistent and relativistic diverse conditional GAN(CRD-CGAN)is proposed to synthesize K photo-realistic images *** use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and ***,we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images,which can improve the performance of basic conditional ***,we introduce a category-consistent loss to alleviate the over-category issues between K synthetic *** evaluate our approach using the Caltech-UCSD Birds-200-2011,Oxford 102 flower and MS COCO 2014 datasets,and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.

关键词： text-to-image diverse conditional GAN relativi-stic category-consistent

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Discrete Geometric Coded Data Layout for Large-scale Object Storage systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 IEEE.

关键词： Efficiency

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Enhancing Ocean Scene Video Captioning with Multimodal Pre-Training and Video-Swin-Transformer 49

Enhancing Ocean Scene Video Captioning with Multimodal Pre-T...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Xinyu Zhao, Meng Shi, Fan Zhang, Meng'en He, Yu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Laboratory of Computer Vision and System of Ministry of Education Tianjin300384 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Beijing100190 China

ISBN: (纸本)9798350331820

With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in interacting and fusing with text features. This paper proposes a multimodal pre-training model that utilizes a Video-Swin-Transformer-based network to encode both video and text data, to achieve better performance in video understanding. The model consists of four modules: video encoder, text encoder, interact encoder, and caption decoder to accomplish the task of ocean scene video captioning. A dataset of ocean scene videos, including various content types such as sea surfaces and shores, is also constructed. The training process is divided into two stages: pre-training and fine-tuning. Pre-training is performed on the Howto100m dataset to allow the model to learn video captions in natural scenes and complete video-language matching tasks. The fine-tuning stage is then performed on the ocean1000 dataset to better understand the events and content in ocean scene videos and generate captions that conform to ocean scene video descriptions. The model achieves satisfying results on both the public dataset YouCook2 and the proprietary dataset Ocean1000, demonstrating its ability in video-text information fusion and interaction. © 2023 IEEE.

关键词： Signal encoding

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Pose-guided feature region-based fusion network for occluded person re-identification

Pose-guided feature region-based fusion network for occluded...

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作者： Xie, Gengsheng Wen, Xianbin Yuan, Liming Wang, Jianchen Guo, Changlun Jia, Yansong Li, Minghao School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

Learning distinguishing features from training datasets while filtering features of occlusions is critical to person retrieval scenarios. Most of the current person re-identification (Re-ID) methods based on classification or deep metric representation learning tend to overlook occlusion issues on the training set. Such representations from obstacles are easily over-fitted and misleading due to being considered as a part of the human body. To alleviate the occlusion problem, we propose a pose-guided feature region-based fusion network (PFRFN), to utilize pose landmarks as guidance to guide local learning for a good property of local feature, and the representation learning risk is evaluated on each part loss separately. Compared with only using global classification loss, concurrently considering local loss and the results of robust pose estimation enable the deep network to learn the representations of the body parts that prominently displayed in the image and gain the discriminative faculties on occluded scenes. Experimental results on multiple datasets, i.e., Market-1501, DukeMTMC, CUHK03, demonstrate the effectiveness of our method in a variety of scenarios. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Classification (of information)

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Foreground Prediction for Image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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