检索结果-内蒙古大学图书馆

IEEE International Conference on automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

来源：评论

学校读者我要写书评

暂无评论

Double deep Q-learning for anti-saturation attack problem of warship group 1

Double deep Q-learning for anti-saturation attack problem of...

引用

1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Wei Xiong, Gang Tao, Hao Han, Yunjun Wang, Fang Dong, Xisong Shen, Zhen Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan430064 China Shandong University Jinan China

ISBN: (纸本)9781665433372

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios. © 2021 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

引用

Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 600-605页

作者： LIU Min DENG Bin TANG Ying WU Minghu WANG Juan Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System.Hubei University of Technology Wuhan 430068.China Department of Electrical and Computer Engineering.Rowan University GlassboroNJ 08028USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Smart Education Qingdao Academy of Intelligent IndustriesQingdao 266041ShandongChina

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring ***,there is still great challenge because of the limited wireless network *** resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image ***,an adaptively learning background model is improved to discover successive similar image frames from the inputting video ***,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element ***,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or *** evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to *** to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

关键词： wireless visual sensor network(WVSN) temporal redundancy foreground background dynamic texture

来源：评论

学校读者我要写书评

暂无评论

Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

引用

2021 China automation Congress, CAC 2021

作者： Yang, Linyao Xu, Yancai Hou, Jiachen Dai, Yuxin Lv, Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426473

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules. © 2021 IEEE

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification 27th

Learning from the Negativity: Deep Negative Correlation Meta...

引用

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030678319

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Spin-Orbit Torque Driven Memristor in Bulk L10 FePt System for Neuromorphic Computing

SSRN

引用

SSRN 2022年

作者： Tao, Ying Sun, Chao Li, Wendi Wang, Cen Jin, Fang Zhang, Yue Guo, Zhe Zheng, Yuhong Wang, Xiaoguang Dong, Kaifeng School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science & Technology Wuhan430074 China Changxin Memory Technologies Inc. Hefei230601 China

In this study, the memristor driven by spin-orbit torque (SOT) is realized in the bulk L1 0 FePt systems with high perpendicular magnetization anisotropy (PMA). Due to the domain nucleation and expansion driven by current pulses, multi-level Hall resistances can be continuously tuned by the current density, where the memristive states are retained by the domain wall pinning effects. The properties of multi-level resistances for samples with different structures are associated with the magnitude of field-like torque, and the larger efficiency of field-like torque enhances the multiple resistance characteristics. Furthermore, the memristive behaviors in the field-free condition are obtained by utilizing the interlayer exchange coupling. Finally, based on the memristive characteristics of our devices, the three-layer multilayer perceptron (MLP) neural network is built to perform the MNIST handwritten digit recognition task, and the accuracy of weight update can reach up to 88.55%. These results pave the way for the application of SOT-driven memristors in neuromorphic computing. © 2022, The Authors. All rights reserved.

关键词： Memristors

来源：评论

学校读者我要写书评

暂无评论

Blind Spectrum Sensing Based on the Statistical Covariance Matrix and K-Median Clustering Algorithm 6th

Blind Spectrum Sensing Based on the Statistical Covariance M...

引用

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhuang, Jiawei Wang, Yonghua Wan, Pin Zhang, Shunchao Zhang, Yongwei Li, Yi School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Hubei Key Laboratory of Intelligent Wireless Communications South-Central University for Nationalities Wuhan430074 China

ISBN: (纸本)9783030578831

Spectrum sensing is a fundamental function for cognitive radio systems, which can improve spectrum utilization. In this article, a blind spectrum sensing method based on the sample covariance matrix and K-median clustering algorithm is proposed to further improve the sensing performance. Specifically, to obtain a two-dimensional signal feature vector, the received signal matrix is rebuilt into two sub-matrices by the decomposition and reorganization (DAR) method. Moreover, two statistical covariance matrices are constructed by the sub-matrices, respectively. The ratios between the sum of some elements from the sample covariance matrices and the sum of diagonal elements from those matrices are used as a signal feature vector. It is demonstrated that the new signal feature vector and the feature vector based on the improved covariance absolute value method are equivalent. Furthermore, K-median clustering algorithm is trained by signal feature vectors to obtain a classifier. Indeed, this classifier can directly be used to detect whether the PU signal is absent or not. Simulation results report that the proposed algorithm has better sensing performance than some popular sensing algorithms based on random matrix theory or information geometry. © 2020, Springer Nature Switzerland AG.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

An ensemble model based on weighted support vector regression and its application in annealing heating process

引用

Science China(Information Sciences) 2019年第4期62卷 203-205页

作者： Yongyue ZHANG Weihua CAO Yali JIN Min WU School of Information Science and Engineering Central South University School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

Dear editor,In the steel industry, the annealing heating production directly impacts the economic benefits of enterprises; hence, accurately predicting the required gas flow is very important [1]. On account of high equipment complexity, it costs lots of time for maintaining a traditional mechanism model at a good working condition; data-driven model, such

关键词： SVR An ensemble model based on weighted support vector regression and its application in annealing heating process

来源：评论

学校读者我要写书评

暂无评论

Distributed derivative-free learning method for stochastic optimization over a network with sparse activity

arXiv

引用

arXiv 2021年

作者： Li, Wenjie Assaad, Mohamad Zheng, Shiqi Paris Research Center Huawei Technologies France CentraleSupélec France Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

This paper addresses a distributed optimization problem in a communication network where nodes are active sporadically. Each active node applies some learning method to control its action to maximize the global utility function, which is defined as the sum of the local utility functions of active nodes. We deal with stochastic optimization problem with the setting that utility functions are disturbed by some non-additive stochastic process. We consider a more challenging situation where the learning method has to be performed only based on a scalar approximation of the utility function, rather than its closed-form expression, so that the typical gradient descent method cannot be applied. This setting is quite realistic when the network is affected by some stochastic and time-varying process, and that each node cannot have the full knowledge of the network states. We propose a distributed optimization algorithm and prove its almost surely convergence to the optimum. Convergence rate is also derived with an additional assumption that the objective function is strongly concave. Numerical results are also presented to justify our claim. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Spin–Orbit Torque Induced Magnetization Switching of Heusler Alloy Co2fesi for Neuromorphic Computing

SSRN

引用

SSRN 2022年

作者： Zhao, Jiale Zhang, Shuai Li, Wendi Dong, Kaifeng Wang, Ke Guo, Zhe School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical and Electronic Engineering East China University of Technology Nanchang330013 China

Spintronic devices have the advantages of high endurance and energy efficiency for nonvolatile memory and emerging neuromorphic computing applications, especially with the spin-orbit torque (SOT) driven magnetization switching manner. The Co-based Heusler alloy is potentially an ideal material for constructing spintronic devices due to its high spin polarization, high saturation magnetization, high Curie temperature and low damping. Here, we investigate the current induced magnetization switching of Heusler alloy Co 2 FeSi with good perpendicular magnetic anisotropy through SOT. Inspired by the gradual switching process, we further study the memristive behaviors of the device, which can be used as artificial synapse for neuromorphic computing. Both the pulse amplitudes and pulse numbers are exploited to modulate the resistance states. Based on the extracted synaptic characteristics, an artificial neural network is simulated, causing a high accuracy of 91.52%. Our work paves a way for the application of Heusler alloy in magnetic random-access memory and neuromorphic computing. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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：