检索结果-内蒙古大学图书馆

Chinese Automation Congress (CAC)

作者： Wu, Guoliang Shen, Zhen Shang, Xiuqin Wu, Huaiyu Xiong, Gang Yang, Jing Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 102202 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China

ISBN: (纸本)9781728140940

In order to solve the printing failure caused by the projection part of the light source in the 3D printing process, this paper proposes a system adjustment scheme. By analyzing the polymerization state of the photosensitive resin, detecting the intensity of ultraviolet light, extracting the outline of the picture, and comparing the similarity of the picture, the optical detection system can find out the cause of the printing failure and provide us with advice on how to adjust the system to ensure that the system can continue to be normal. In the following, the authors proposed the detection process of the system, and simulated the experiment to confirm whether the ultraviolet light irradiated on the photosensitive resin is the same as the image in the slice file.

关键词： DLP self-detection light intensity detection contour extraction picture similarity detection

来源：评论

学校读者我要写书评

暂无评论

Forest Representation Learning with Multiscale Contour Feature Learning for Leaf Cultivar Classification

Forest Representation Learning with Multiscale Contour Featu...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Wenbo Zheng Chao Gou Lan Yan School of Software Engineering Xi'an Jiaotong University Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781728118680

Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. However, deep neural networks based leaf cultivar classification model can not deeply mine the contour features of leaf images. Besides, the deep neural networks are very data-hungry due to the large complexity of the models, which means that the model's performance can decrease significantly when the size of the training data decreases. In this paper, we propose the leaf cultivar classification model based on forest representation learning and multiscale contour feature learning. Firstly, we build the multiscale contour feature learning to extract the contour features of leaf images. Then, the original input features are transformed by the data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs the data of dimensionality reduction on the features. Finally, we use the improved deep forest algorithm and autoencoder by forest to build forest representation learning for classification. The experimental results show that the proposed algorithm has higher performance than the original deep forest (gcForest) algorithm and other existing start-of-art algorithms, and has higher performance and efficiency than other deep learning algorithms.

关键词： Leaf Cultivar Classification Deep Learning Deep Forest AutoEncoder by Forest Data Augmentation contour Forest Learning plant species foliage Botanists Taxonomy dimensionality reduction algorithms cultivars HIGH-PERFORMANCE

来源：评论

学校读者我要写书评

暂无评论

From AR to AI: Augmentation Technology for intelligent Surgery and Medical Treatments

引用

IFAC-PapersOnLine 2020年第5期53卷 792-796页

作者： Mei Zhang Zhicheng Zhang Xiao Wang Hui Yu Yifan Xia Kanran Tan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Qingdao Academy of Intelligent Industries Qingdao 266109 China The Department of the 7th Medical Center of PLA General Hospital Beijing 100700 China The Department of Computer Science Purdue University West Lafayette Indiana 47906 USA The School of Creative Technologies University of Portsmouth UK The Department of Computer Science Johns Hopkins University Baltimore Maryland USA

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more information beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly determine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly deformable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency.

关键词： Potential impact of automation open problems Augmented reality Medical treatment Parallel theory 3D technology

来源：评论

学校读者我要写书评

暂无评论

A Novel Geometric Transportation Approach for Multiple Mobile Manipulators in Unknown Environments

引用

IEEE systems JOURNAL 2018年第2期12卷 1447-1455页

作者： Cao, Zhiqiang Gu, Nong Jiao, Jile Nahavandi, Saeid Zhou, Chao Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia

In this paper, a geometric transportation approach is proposed for multiple mobile manipulators transporting a large object in unknown environments. The candidate interval of system width is first determined based on constraints from the object, mobile manipulators as well as the obstacles. Then, a passageway-based decision is made to facilitate the selection of the optimized system width and moving direction. On this basis, each mobile manipulator adjusts its position and posture with a form of system consistency to maintain a flexible marching while avoiding the obstacles. The main advantage of the proposed approach is that it leads to smooth changes in system configuration. The effectiveness of the proposed approach is verified by simulation experiments.

关键词： Geometric transportation approach multiple mobile manipulators system consistency unknown environments

来源：评论

学校读者我要写书评

暂无评论

Two-timescale voltage control in distribution grids using deep reinforcement learning

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

引用

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2019年第1期2019卷 1页

作者： Zhang, Shunchao Wang, Yonghua Li, Jiangfan Wan, Pin Zhang, Yongwei Li, Nan Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Hubei Peoples R China

To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

关键词： Cooperative spectrum sensing Information geometry Decomposition and recombination Fuzzy c-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A statistical learning approach to reactive power control in distribution systems

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in complex Electromagnetic Environment

引用

complexITY 2019年第1期2019卷

作者： Wang, Yonghua Zhang, Shunchao Zhang, Yongwei Wan, Pin Li, Jiangfan Li, Nan Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Hubei Peoples R China

In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Analyzing and optimizing yield formation of tomato introgression lines using plant model

引用

EUPHYTICA 2021年第6期217卷 100-100页

作者： Kang, Mengzhen Wang, Xiujuan Qi, Rui Jia, Zhi-Qi de Reffye, Philippe Huang, San-Wen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100949 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Amadeus 485 Route Pin Montard F-06410 Biot France Coll Hort Henan Agr Univ Zhengzhou 450002 Peoples R China Univ Montpellier CNRS AMAP CIRADINRAIRD F-34000 Montpellier France Chinese Acad Agr Sci Agr Genomes Inst Shenzhen Shenzhen 518124 Peoples R China

Generally, the relation between quantitative trait loci (QTLs) and yield is empirical, and their roles in source-sink dynamics are unclear. A tomato introgression line (IL) population (S. pennellii ILs) was applied to analyze the effect of chromosome segment from wild cultivar on numerous yield-related phenotypes, including plant yield, the weight of vegetative part, the number and weight of individual fruits. A functional-structural plant model was applied to analyze the difference in yield formation of tomato ILs. Measurements on organ biomass were performed at four stages during the growth period of plants. Source and sink parameters were estimated from the experimental measurements of different organs for each IL, discovering how the final yield is linked to the fruit number, size and expansion process. The correlation and distribution of source-sink parameters for ILs were analyzed. The sink parameters were optimized to find a better combination of ILs to improve the yield using Particle Swarm Optimisation (PSO) algorithm. Optimization results indicate a potential yield increase of 35% for the control M82. This model-assisted analysis provides a promising approach to deeper insight in phenotypic data.

关键词： Greenlab.model Yield formation Parameter estimation Tomato introgression line Optimization

来源：评论

学校读者我要写书评

暂无评论

Differential-Evolution-Based Generative Adversarial Networks for Edge Detection

Differential-Evolution-Based Generative Adversarial Networks...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

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

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Since objects in natural scenarios possess various scales and aspect ratios, learning the rich edge information is very critical for vision-based tasks. Conventional generative adversarial networks (GANs) based methods for edge detection don't perform so well due to model collapse. In order to capture rich edge information and avoid model collapse as much as possible, we consider the learning of GANs as an evolutionary optimization and propose a novel method termed as differential-evolution-based generative adversarial networks (DEGAN) for richer edge detection. In particular, built upon GANs structure, we introduce an improved differential evolutionary algorithm to refine the input of generator, with fitness function evaluated by the discriminator. Experimental results on the well-known BSDS500 and NYUD benchmarks indicate our proposed DEGAN can achieve state-of-the-art performance while retaining a fast speed and validate its simplicity, effectiveness, and efficiency. The high quality of our results on edge detection with proposed DEGAN may promise to make other vision-based tasks work better.

关键词： Image edge detection Generators Gallium nitride Training Generative adversarial networks Evolutionary computation Evolution (biology)

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：