中国科学院机构知识库网格系统: 检索

Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks 期刊论文 OAI收割
International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 2, 页码: 279-291
作者: Farid Menacer; Abdelmalek Kadr; Zohir Dibi

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收藏 | 浏览/下载：17/0 | 提交时间：2021/02/22

Nano force sensor carbon nanotube (CNT) finite elements neural network.

A Selective Attention Guided Initiative Semantic Cognition Algorithm for Service Robot 期刊论文 OAI收割
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 5, 页码: 559-569
作者: Huan-Zhao Chen; Guo-Hui Tian; Guo-Liang Liu

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收藏 | 浏览/下载：32/0 | 提交时间：2021/02/23

Service robot cognition computing selective attention semantic knowledge base artificial neural network.

Visual servo control for dynamic hovering of an underwater biomimetic vehicle-manipulator system by neural network 会议论文 OAI收割
Takamatsu, Japan, 2017.8.6—2017.8.9
作者: Rui Wang; Yu Wang; Shuo Wang; Chong Tang; Min Tan

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收藏 | 浏览/下载：31/0 | 提交时间：2018/05/31

Dynamic Hovering Visual Servo Control Undulatory Fin Image Feature Neural Network.

Research on AUV Obstacle Avoidance Based on BP Neural Network 会议论文 OAI收割
2017 The 2nd International Conference on Robotics, Control and Automation (ICRCA 2017), Kitakyushu, Japan, September 15-18, 2017
作者: Dong LY(董凌艳); Xu HL(徐红丽)

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收藏 | 浏览/下载：22/0 | 提交时间：2017/12/21

obstacle avoidance AUV BP neural network.

Model-free adaptive dynamic programming for optimal control of discrete-time affine nonlinear system 会议论文 OAI收割
South Africa, 2014-08
作者: Xia ZP(夏中谱); Dongbin Zhao

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收藏 | 浏览/下载：28/0 | 提交时间：2016/06/16

Model-free Adaptive Dynamic Programming Reinforcement Learning Policy Iteration Multilayer Perceptron Neural Network.

Design of controlling system in multi-function durability testing device for vehicle vacuum booster with brake master cylinder (EI CONFERENCE) 会议论文 OAI收割
2012 International Conference on Mechanical and Electronic Engineering, ICMEE 2012, June 23, 2012 - June 24, 2012, Hefei, China
Hao X.; Zhang R.; Li X.; Wang M.

收藏 | 浏览/下载：37/0 | 提交时间：2013/03/25

The quality of vehicle vacuum booster with brake master cylinder is related to the safe of drivers and automobiles. The testing experiment must be executed strictly before leaving factory based on the national standards.The paper introduces the controlling system of multi-function durability testing device which is designed for doing durable testing experiments and Anti-lock braking system (ABS) performance experiments. The structure and theory of device is presented. The controlling system is illuminated in detail. To test the dynamic property this system was identified by a recursive BP neural network. According to the character of a great deal of sensors and actuators the high precision capabilities and reliability the distributed control mode (DCS) including the computer and PLC by RS-485 bus is utilized. The four channels testing experiments are achieved at the same time. The test data is directly memorized into the computer. The results of general endurance and ABS endurance testing experiments are shown to demonstrate the excellent performance of the testing device. 2012 Springer-Verlag Berlin Heidelberg.

An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection 期刊论文 OAI收割
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 卷号: 41, 期号: 1, 页码: 107-117
作者: Xu, Yanwu; Cao, Xianbin; Qiao, Hong

收藏 | 浏览/下载：23/0 | 提交时间：2015/08/12

Efficient classification false-positive rate (FPR) pedestrian detection performance evaluation radial basis function (RBF) neural network.

Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文 OAI收割
14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states
Dai Y.; Hu J.; Zhao D.; Zhu F.

收藏 | 浏览/下载：29/0 | 提交时间：2013/03/25

Traffic congestion leads to problems like delays decreasing flow rate and higher fuel consumption. Consequently keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.

The costs prediction of AOD furnace based on improved RBF neural network (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
Na T.; Zhang D.-J.; Hui L.

收藏 | 浏览/下载：17/0 | 提交时间：2013/03/25

In order to predict the cost a model of cost prediction was set up based on adaptive hierarchical genetic algorithm and RBF neural network. Hierarchical genetic algorithm could optimize the topology and the parameters simultaneously. Compared with simple genetic algorithm it has more efficiency in not only accelerating and stabilizing the parameters training but also determining the structure of the network. Adaptive crossover and mutation probability could accelerate the speed and avoid prematurity. The model was tested by five samples. The results showed that the prediction model has high prediction accuracy which indicated that it was applicable to predict the cost by the model. 2010 IEEE.

Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文 OAI收割
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Sun J.; Ma D.

收藏 | 浏览/下载：26/0 | 提交时间：2013/03/25

The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly we use frame grabber to capture the 4-bar target image data. Then according to image gray scale we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets along with known target visibility are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.