检索结果-内蒙古大学图书馆

1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zha, Zhong Yi Tang, Xue Song Wang, Bo Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China College of Information and Science Donghua University Shanghai China

ISBN: (纸本)9789811576690

This paper presents an improved deep reinforcement learning (DRL) algorithm, namely Advanced Actor Critic (AAC), which is based on Actor Critic (AC) algorithm, to the video game Artificial intelligence (AI) training. The advantage distribution estimator, Normal Constraint (NC) function and exploration based on confidence are introduced to improve the AC algorithm, in order to achieve accurate value estimate, select continuous spatial action correctly, and explorate effectively. The aim is to improve the performance of conventional AC algorithm in a complex environment. A case study of video game StarCraft II mini-game AI training is employed. The results verify that the improved algorithm effectively improves the performance in terms of the convergence rate, maximum reward, average reward in every 100 episode, and time to reach a specific reward, etc. The analysis on how these modifications improve the performance is also given through interpretation of the feature layers in the mini-games. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

ParamCrop: Parametric cubic cropping for video contrastive learning

arXiv

引用

arXiv 2021年

作者： Qing, Zhiwu Huang, Ziyuan Zhang, Shiwei Tang, Mingqian Gao, Changxin Ang, Marcelo H. Jin, Rong Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China ARC National University of Singapore Singapore Alibaba Group China

The central idea of contrastive learning is to discriminate between different instances and force different views from the same instance to share the same representation. To avoid trivial solutions, augmentation plays an important role in generating different views, among which random cropping is shown to be effective for the model to learn a generalized and robust representation. Commonly used random crop operation keeps the distribution of the difference between two views unchanged along the training process. In this work, we show that adaptively controlling the disparity between two augmented views along the training process enhances the quality of the learned representations. Specifically, we present a parametric cubic cropping operation, ParamCrop, for video contrastive learning, which automatically crops a 3D cubic by differentiable 3D affine transformations. ParamCrop is trained simultaneously with the video backbone using an adversarial objective and learns an optimal cropping strategy from the data. The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation. Extensive ablation studies demonstrate the effectiveness of the proposed ParamCrop on multiple contrastive learning frameworks and video backbones. Copyright © 2021, The Authors. All rights reserved.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Memristor-based multilayer neural network with edge learning and weight update

引用

Neurocomputing 2025年 647卷

作者： Jiang, Ningye Xie, Jupeng Jiang, Mingxuan Huang, Haoen Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430074 China

Memristor crossbar array (MCA) is a promising computing-in-memory architecture for accelerating vector-matrix multiplication (VMM). However, existing memristor-based circuits primarily perform forward computations without local learning capabilities. To address the issue, a memristor-based multilayer neural network (MNN) with edge learning is proposed, enabling error backpropagation and one-step weight update. In particular, the overall circuit design of a three-layer MNN is outlined, comprising three operational stages: forward computation, backpropagation, and weight update. Subsequently, the schematics of four adopted circuit blocks are detailed. By integrating the memory and weight update blocks with MCA, the proposed three-layer memristor-based MNN exhibits scalability and universality, allowing it to be extended to the deep neural network (DNN) architecture. Circuit simulations validate the effectiveness of the proposed scheme, while offline-online co-simulations demonstrate its strength in compensating for variations and non-ideal characteristics of physical devices. Comparative results further highlight the advantages of the proposed scheme in realizing full-analog edge learning circuits and performing error backpropagation processes efficiently. © 2025 Elsevier B.V.

关键词： Analog circuit design Computing-in-memory Edge learning Memristor crossbar array (MCA) Multilayer neural network

来源：评论

学校读者我要写书评

暂无评论

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

来源：评论

学校读者我要写书评

暂无评论

Decoupled and memory-reinforced networks: Towards effective feature learning for one-step person search

arXiv

引用

arXiv 2021年

作者： Han, Chuchu Zheng, Zhedong Gao, Changxin Sang, Nong Yang, Yi Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China ReLER University of Technology Sydney Australia Baidu Research China

The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutual interference between the optimization objectives of multiple sub-tasks. The other is the sub-optimal identification feature learning caused by small batch size when end-to-end training. To overcome these problems, we propose a decoupled and memory-reinforced network (DMRNet). Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework. Further, we build a memory-reinforced mechanism to boost the identification feature learning. By queuing the identification features of recently accessed instances into a memory bank, the mechanism augments the similarity pair construction for pairwise metric learning. For better encoding consistency of the stored features, a slow-moving average of the network is applied for extracting these features. In this way, the dual networks reinforce each other and converge to robust solution states. Experimentally, the proposed method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds all the existing one-step methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data control

Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

引用

IEEE Conference on Decision and control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Tight Formulations of Unit Commitment with General Start-Up Ramp Rate Constraints

Tight Formulations of Unit Commitment with General Start-Up ...

引用

Sustainable Power and Energy Conference (iSPEC)

作者： Yijie Gao Xuan Zhang Yaowen Yu Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Hitachi ABB Power Grid San Jose California US

ISBN: (纸本)9781665414401

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations.

关键词： Systematics Computational modeling Conferences Electricity supply industry Computational efficiency Complexity theory Testing

来源：评论

学校读者我要写书评

暂无评论

Semi-global scaled edge-consensus of linear discrete-time multi-agent systems with positive constraint and input saturation

Semi-global scaled edge-consensus of linear discrete-time mu...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Zhipeng Fan Yaping Sun Housheng Su Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

The thesis studies the semi-global scaled edge-consensus of linear discrete-time multi-agent systems under both the directed networks and undirected networks, where the states of each edge are subject to input saturation and positive constraint. By virtue of the MARE-based low-gain feedback method, two control protocols about directed networks and undirected networks are devised respectively to promise the positive scaled edge-consensus with input saturation. Thus we find the sufficient conditions to promise the positive edge states and overcome the input saturation. Finally, two representative examples are presented to prove the results.

关键词： Sufficient conditions Protocols Graph theory Delays Time-varying systems Cybernetics Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Cooperative Stabilization control of the Linear Time-invariant System Based on Distributed Observers

Cooperative Stabilization Control of the Linear Time-invaria...

引用

Chinese Automation Congress (CAC)

作者： Min Li Zhi-Wei Liu Ming Chi Jia-Hao Zhang Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, the cooperative stabilization problem of linear time-invariant (LTI) systems is studied based on a class of distributed Luenberger observers. A given LTI system is estimated by a network of observers. In case of observability configuration or external attack, each observer has to possess different measurement output of the LTI system. Classical distributed control strategies cannot be directly used because they are assumed to obtain identical information. In view of this problem, a class of distributed observers are designed under an undirected graph, which can recover the state of the LTI system by limited local information. By using any single channel of the estimated state, a control input is proposed to stabilize the LTI system. By analysing the stability of an augmented system which involves the LTI system and the estimation error system, some necessary and sufficient conditions are derived for the existence of control gains that guarantee the stabilization of the LTI system. In addition, some algorithms are proposed to specify the control gains. Lastly, some simulation examples are given to verify the effectiveness of the theoretical result.

关键词： Observers Linear systems Channel estimation Stability criteria Power system stability Observability Estimation error

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：