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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Wang, Bohui Wang, Jingcheng School of Control Science and Engineering Shandong University Jinan250061 China The School of Aerospace Science and Technology Xidian University Xi’an710071 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

The distributed observer design problem holds significant importance in cases in which the output information of a system is decentralized across different subsystems. Each subsystem has a local observer and access to one part of the measurement outputs and information exchanged through communication networks. This paper focuses on the design of distributed observer with jointly connected directed switching networks. The problem presents challenges due to passive switching modes and the open-loop unboundedness that results from local observability. To overcome these challenges, we develop a network transformation mapping method whereby each local observer can classify itself into an independent subgraph based on independent judgment. Next, an observable decomposition and reorganization method is developed for the digraph case to ensure that each subgraph possesses independent dynamic properties. Asymptotic omniscience is then proven using a developed recursive proof method. This paper includes many previous results as special cases, because most are only suitable for undirected switching topologies or fast-switching cases. An adaptive coupling gain design is proposed to simplify the calculation and verification of conditions that guarantee asymptotic omniscience. Finally, simulation results with the power system show the validity of the developed theory. © 2024, CC BY.

关键词： Continuous time systems

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Scalable No-wait Scheduling with Flow-aware Model Conversion in Time-Sensitive Networking

Scalable No-wait Scheduling with Flow-aware Model Conversion...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yanzhou Zhang Qimin Xu Shouliang Wang Yingxiu Chen Lei Xu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665435413

The development of the Industrial Internet of Things (1IoT) has given rise to massive information from the networked controllers, sensors and actuators, leading to the increasing demands for real-time and reliable transmission. Time-Sensitive Networking (TSN) provides the deterministic mechanism guar-antee for these demands, but with an open scheduling problem. For the low-latency and low-jitter traffic, it is hard to schedule in a scalable way, that is, increasing the scheduling speed under the Quality-of-Service (QoS) requirements. Therefore, this paper constructs a no-wait forwarding (NW-TAS) model with a time-aware shaper to eliminate the queuing delay and jitter, and further converts it into a flow-aware model by divisibility theory for scheduling simplification. With the converted model, an interval transformation-based method is proposed to get the analytical expression of feasible scheduling for each flow. Then, a flow-aware NW- TAS scheduling algorithm (FANS) with cyclic interval searching is designed to compress invalid search spaces. The evaluation results show that our method decreases the transmission latency of 1000 flows by more than 23 % while increasing the scheduling speed by 43x than the existing works.

关键词： Fans Schedules Job shop scheduling Scheduling algorithms Quality of service Jitter Delays

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Weakly supervised person search with region siamese networks

arXiv

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arXiv 2021年

作者： Han, Chuchu Su, Kai Yu, Dongdong Yuan, Zehuan Gao, Changxin Sang, Nong Yang, Yi Wang, Changhu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology ByteDance University of Technology Sydney

Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to collect, especially for person identities. A natural question is whether a good person search model can be trained without the need of identity supervision. In this paper, we present a weakly supervised setting where only bounding box annotations are available. Based on this new setting, we provide an effective baseline model termed Region Siamese Networks (R-SiamNets). Towards learning useful representations for recognition in the absence of identity labels, we supervise the R-SiamNet with instance-level consistency loss and cluster-level contrastive loss. For instance-level consistency learning, the R-SiamNet is constrained to extract consistent features from each person region with or without out-of-region context. For cluster-level contrastive learning, we enforce the aggregation of closest instances and the separation of dissimilar ones in feature space. Extensive experiments validate the utility of our weakly supervised method. Our model achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark, which surpasses several fully supervised methods, such as OIM [36] and MGTS [4], by a clear margin. More promising performance can be reached by incorporating extra training data. We hope this work could encourage the future research in this field. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Weakly Supervised Person Search with Region Siamese Networks

Weakly Supervised Person Search with Region Siamese Networks

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International Conference on Computer Vision (ICCV)

作者： Chuchu Han Kai Su Dongdong Yu Zehuan Yuan Changxin Gao Nong Sang Yi Yang Changhu Wang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology ByteDance University of Technology Sydney

ISBN: (纸本)9781665428132

关键词： Training Computer vision Annotations Supervised learning Training data Benchmark testing Feature extraction

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Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

arXiv

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arXiv 2021年

作者： Wu, Yuhang Huang, Tengteng Yao, Haotian Zhang, Chi Shao, Yuanjie Han, Chuchu Gao, Changxin 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 Megvii Technology China

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Closer to pre-trained network transfer better 9

Closer to pre-trained network transfer better

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Chen, Siyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan430074 China

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowdays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and in-spiration for the future work. © 2020 IEEE.

关键词： Deep neural networks

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Locating and Sizing Charging Station in Multi-Period to Promote Electric Vehicles Adoption in Urban Areas

SSRN

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SSRN 2023年

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei The School of Management South-central Minzu University Wuhan430074 China The School of Engineering RMIT University MelbourneVIC3001 Australia The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

The layout of public fast charging stations often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multiperiod locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multiperiod change formula is proposed to reflect the interaction between public fast charging stations and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast charging stations to improve the charging service quality and promote the adoption of EVs. © 2023, The Authors. All rights reserved.

关键词： Location

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Unsupervised Difference Learning for Noisy Rigid image Alignment

arXiv

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arXiv 2022年

作者： Chen, Yu-Xuan Feng, Dagan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method. Copyright © 2022, The Authors. All rights reserved.

关键词： image enhancement

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Data Augmentation for Deep Learning Based a Utomaticmo Dulation Recognition

SSRN

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SSRN 2023年

作者： Ma, Hongbin Feng, Zhixi Yang, Shuyuan Wang, Min Wu, Tao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an710071 China Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xidian University China Institute School of Artificial Intelligence Xidian University TaiBai road 2# Shannxi Province Xi’an China

Data augmentation, as a strategy to expand training datasets, can be utilized to improve the accuracy of Deep Learning(DL)-based models to a certain extent. However, existing data augmentation methods for the DL-based Automatic Modulation Recognition (AMR) task lack a theoretical basis, and the availability of these methods to different types of AMR models has not been evaluated. In this paper, we first model the transformation process from an information source to a modulation signal and analyze the parameters that influence the difference between modulation signals for the theoretical basis of data augmentation in the AMR field. Then we propose nine signal augmentation methods to generate argumentation signals by modifying the influence parameters in the amplitude domain, the time domain, and the frequency domain. Finally, we conduct a series of experiments on the public RML2016.10a and RML2016.10b datasets to evaluate the effectiveness of the proposed methods andanalyze the availability of these methods to Convolutional Neural Network (CNN)-based AMR models, and hybrid AMR models based on CNN and Recurrent Neural Network. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Thymine-Hg2+-Thymine Strategy in Mof-Based Electrochemical Aptamer Sensor for Paes Detection

SSRN

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SSRN 2024年

作者： Liu, Xiaofang Diao, Zhan Liu, Huan Wang, Qun Lei, Jincan Huo, Danqun Hou, Jingzhou Hou, Changjun Key Laboratory for Biorheological Science and Technology Ministry of Education Bioengineering College Chongqing University Chongqing400044 China Chongqing Engineering and Technology Research Center of Intelligent Rehabilitation and Eldercare Chongqing City Management College Chongqing401331 China Key Laboratory of Condiment Supervision Technology for State Market Regulation Chongqing Institute for Food and Drug Control Chongqing401121 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

Phthalate acid esters (PAEs), known food contaminants, can lead to reproductive problems, respiratory diseases, childhood obesity, and neuropsychological disorders. This work aims to develop an electrochemical aptamer sensor for the detection of DAP and its analogs. To achieve specific recognition ability for PAEs, We have introduced the thymidine-Hg2+-thymidine strategy for the first time in an electrochemical aptamer sensor, incorporating metal-organic frameworks (MOFs) as the carrier for the aptamer. Additionally, nanoamplification technology was applied by immobilizing MXene on the electrode to intensify the sensor's current signal. In the presence of PAEs, these molecules specifically bound to the aptamers, resulting in competitive release of Hg2+ and aptamers connected to Hg2+, subsequently reducing current signaling. Under optimal experimental conditions, the designed sensor achieved a linear range from 1.65×10-5 mg/mL to 3×10-2 mg/ml and an extremely low detection limit of 8.94×10-6 mg/mL. Furthermore, it exhibited good reliability and practicality in detecting actual samples. © 2024, The Authors. All rights reserved.

关键词： Esters

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