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Attributes-based person re-identification via CNNs with coupled clusters loss

Journal of Systems engineering and Electronics 2020年第1期31卷 45-55页

作者： SUN Rui HUANG Qiheng FANGWei ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and *** attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and *** paper proposes an end-to-end framework of deep learning for attribute-based person *** the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN ***,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id *** coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of *** combined features are mapped into the Euclidean *** L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the *** experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

关键词： person re-identification(re-id) convolutions neural network(CNN) attributes coupled clusters loss(CCL)

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OpenPose Based Posture Analysis of Basketball Players 5

OpenPose Based Posture Analysis of Basketball Players

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5th International Conference on Applied Machine Learning, ICAML 2023

作者： Zhou, Xiao Liang Wang, De Yong Li, Xin School of Information Science and Engineering Xinjiang University Xinjiang Urumqi China XinJiang Lianhai INA-INT Information Technology Co. Ltd China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control China

ISBN: (纸本)9798350341416

Human pose estimation has a wide range of applications in the field of computer vision, especially in sports event analysis where its importance is increasingly prominent. This study aims to optimize the basketball player detection model based on OpenPose, improving its performance in real-world scenarios. Firstly, we replace the VGG19 network in Openpose with a lightweight MobileNet structure to increase computational efficiency and reduce model parameters. Secondly, we introduce the Pyramid Spatial Attention (PSA) module into MobileNet to better capture local and global contextual information. Lastly, we adopt the Adaptive Wing Loss as the loss function, making the model more stable and accurate in localization. We pre-train the model on the COCO2017 dataset and conduct experiments on basketball player detection tasks. The experimental data show that, compared to the baseline model, our full model has significant improvements in Average Precision (AP), AP50, and running speed (FPS) metrics. Specifically, the full model achieves an AP of 70.0%, AP50 of 88.0%, and FPS of 41. The research results of this study can provide more accurate pose estimation information for basketball game analysis, contributing to the improvement of game strategy formulation and player performance evaluation. © 2023 IEEE.

关键词： Computational efficiency

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and big data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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Seismic Traveltime Tomography with Label-free Learning

arXiv

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arXiv 2024年

作者： Wang, Feng Yang, Bo Wang, Renfang Qiu, Hong College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310027 China

Deep learning techniques have been used to build velocity models (VMs) for seismic traveltime tomography and have shown encouraging performance in recent years. However, they need to generate labeled samples (i.e., pairs of input and label) to train the deep neural network (NN) with end-to-end learning, and the real labels for field data inversion are usually missing or very expensive. Some traditional tomographic methods can be implemented quickly, but their effectiveness is often limited by prior assumptions. To avoid generating and/or collecting labeled samples, we propose a novel method by integrating deep learning and dictionary learning to enhance the VMs with low resolution by using the traditional tomography-least square method (LSQR). We first design a type of shallow and simple NN to reduce computational cost followed by proposing a two-step strategy to enhance the VMs with low resolution: (1) Warming up. An initial dictionary is trained from the estimation by LSQR through dictionary learning method;(2) Dictionary optimization. The initial dictionary obtained in the warming-up step will be optimized by the NN, and then it will be used to reconstruct high-resolution VMs with the reference slowness and the estimation by LSQR. Furthermore, we design a loss function to minimize traveltime misfit to ensure that NN training is label-free, and the optimized dictionary can be obtained after each epoch of NN training. We demonstrate the effectiveness of the proposed method through the numerical tests on both synthetic and field data. Copyright © 2024, The Authors. All rights reserved.

关键词： Tomography

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Research on resource scheduling method based on Kubernetes

Research on resource scheduling method based on Kubernetes

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Haoyang Song Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling.

关键词： Cloud computing Schedules Clustering algorithms Containers Fish Load management Scheduling Hardware Fish schools Genetic algorithms

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A new Na2BaCa(PO4)2: Eu3+ red-emitting phosphor with excellent thermal stability and high quantum efficiency for WLEDs

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Optik 2023年第1期287卷

作者： Ling, Yi Zhao, Rongli Guo, Xiang Cui, Ruirui Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

A series of new red phosphors Na2BaCa(PO4)2: xEu3+ was synthesized by the high temperature solid phase reaction method. Through employing X-ray diffraction, scanning electron microscope, photoluminescence spectroscopy, quantum efficiency curves and high-precision rapid spectrometers, a systematic investigation was conducted on the microstructure and optical characteristics of the material. The XRD analysis of Na2BaCa(PO4)2: xEu3+ shows the sample to be a pure phase. The spectroscopic results show the presence of a red emission peak originated from 5D0 → 7F2 transition, and the quenching mechanism is attributed to interaction between nearest neighboring ions. Under 423 K, the fluorescence intensity of Na2BaCa(PO4)2: 0.2Eu3+ phosphor can still maintain 94.82 % of the value observed at 298 K, and the thermal activation energy ΔE reaches up to 0.3543 eV. The CCT of the LED packaged with Na2BaCa(PO4)2: 0.2Eu3+ was measured to be 4314 K, with the CIE coordinates (0.3658, 0.3600), which is very close to the point of standard white light source. The quantum efficiency of Na2BaCa(PO4)2: 0.2Eu3+ was measured to be 82.92 %. All results indicate that Na2BaCa(PO4)2: 0.2Eu3+ is suitable for WLED applications. © 2023 Elsevier GmbH

关键词： Thermodynamic stability

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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MFENet: Multi-Feature Extraction Net for Remote Sensing Semantic Segmentation 7

MFENet: Multi-Feature Extraction Net for Remote Sensing Sema...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Zhang, Chao Lu, Xingshun Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net's ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel's information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+"remote sensing image dataset. Experiments show that our network is sufficient against several advanced models. © 2022 IEEE.

关键词： Convolution

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Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 60-65页

作者： Jiayong Pan Wenxin Le Zi’ao Wang Jian Chen College of Engineering China Agricultural UniversityBeijing 100083China Shenzhen Key Laboratory of Intelligent Microsatellite Constellation Shenzhen 518107GuangdongChina Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 200063China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000GuangdongChina Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiang 212013China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310013China

As the function of the decomposition of fungi has been clearly researched in the global carbon cycle,it is obviously of value to explore the decomposition rate of fungal *** study analyzed the relationship between environmental factors and biodiversity step by *** order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time,the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited *** changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the *** illustrate the applicability of the model,Laetiporus conifericola and Hyphoderma setigerum were applied as *** results showed that the higher the degree of population diversity,the greater the decomposition rate,and the higher the decomposition efficiency of the *** rich species diversity is conducive to accelerating the decomposition of litter,lignocellulose,and the circulation of the entire *** on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10℃,16℃,and 22℃ under standardized laboratory conditions,the growth patterns of the five fungi combinations were *** results revealed a general increase in growth rate with increasing temperature,which verifies the accuracy of the ***,it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single *** on the above model,predictions can be made for fungal growth in different environments,and suitable environments for fungal growth can be *** the future,the model can be further optimized,and lignin and cellulose decomposition factors can be added to fit the decomposition of *** application

关键词： Logistic equations fungal decomposition mathematical model

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