Design and Realization of Intelligent Electricity Processing Task System Supporting for Multiple Time Dimensions

2014 ◽  
Vol 519-520 ◽  
pp. 1364-1367 ◽  
Author(s):  
Feng Yu Wang ◽  
Xue Song Liu ◽  
Xiao Zhen Li ◽  
Li Fang Yang
Keyword(s):  
Processing Technology ◽  
Multiple Time ◽  
Processing Task ◽  
Intelligent Analysis ◽  
Scenario Process ◽  
Task System ◽  
Realization Process

The instant processing technology and intelligent analysis technology are researched in the paper. Then a processing task system is designed combining with the two technologies, whose realization process is described in detail. The system can settle the question that instant processing and intelligent analysis cant dispatch effectively and interchange data in the present technologies of intelligent electricity. It can also assure the timeliness and accuracy of the business scenario process of intelligent electricity with multiple time dimensions.

scholarly journals RBF network based motion trajectory optimization for robot used in agricultural activities

2018 ◽  
pp. 883
Author(s):  
Yanan Sun
Keyword(s):  
Trajectory Optimization ◽  
Learning Algorithm ◽  
Class Imbalance ◽  
Prediction Method ◽  
Optimization Method ◽  
Processing Technology ◽  
Motion Trajectory ◽  
Fruit And Vegetable ◽  
Rbf Network ◽  
Realization Process

At present, the efficiency of the method to track and predict motion trajectory of fruit and vegetable picking robot was low and the realization process was complex. Therefore, a research on motion trajectory optimization of fruit and vegetable picking robot based on RBF network was proposed. After analyzing the reason for data class imbalance of fruit and vegetable picking robot, this paper introduced the processing technology MWMO in RBF network. Then, the MWMO technology was embedded in the tracking and prediction research of motion trajectory optimization of fruit and vegetable picking robot. Moreover, the semi-supervised learning algorithm was used as the framework and integrated the processing technology of data class imbalance of motion trajectory to improve the efficiency of tracking and prediction of fruit and vegetable picking robot. According to the integration result, combined with the idea about the calculation of spatial function and the tracking and prediction of motion trajectory in RBF network, we designed the matching principle of trajectory similarity of time and space and realized the matching between the predicted position and the actual position, so that the tracking and prediction of fruit and vegetable picking robot could be completed. Experimental results show that the average calculation time of proposed method is 2.0S, which is only half of average time of traditional tracking and prediction method. It fully proves that the proposed optimization method can accurately track and predict the motion trajectory of fruit and vegetable picking robot. The prediction efficiency is higher and the time consumptionis shorter.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Neptune: A multielement task system for evaluating human performance

1969 ◽  
Author(s):  
Richard E. McKenzie ◽  
Doyle D. White ◽  
Bryce O. Hartman
Keyword(s):  
Human Performance ◽  
Task System
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