• Atmosphere
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• v.22 no.3
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• pp.309-319
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• 2012

It is important to characterize and quantify the inherent error in the radar rainfall to make full use of the radar rainfall. This study verified the error structure of the reflectivity and corrected the range dependent error in the CAPPI using a VPR (vertical profile of reflectivity) model. The error of the CAPPI to display the reflectivity data becomes bigger for the range longer than 100 km. This range dependent error, however, is significantly improved by corrected the CAPPI data using the VPR model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.353-364
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• 1998

This paper describes the design processes and evaluation results of a small-sized six-axis force/torque sensor. The new six-axis force/torque sensor including S-type structure has been developed using a parallel plate structure as a basic sensing element. In order tominimize coupling errors, the location of strain gages has been determined based on the finite element analysis and the connections of strain gages have been made such that the bridge circuit with 4 strain gages becomes balanced. Several design modifications result in a similar strain sensitivity for six-axis forces and moments, and the reduced coupling errors of 2.6% FS between each forces and moments. Calibration test results show that the six-axis load cell developed which has light weight of 135g and the maximum capacities of 196 N in forces and 19.6 N.m in moments is estimated to be within 7.1% FS in coupling error.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.385-391
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• 2000

Recently, neural network research for forecasting the consecutive controlling rules of the future is being progressed, using the series data which are different from the traditional statistical analysis methods. In this paper, we suggest the pruning algorithm for the fast and exact weather forecast that excludes the hidden layer of the early optional designed nenral network. There are perform the weather forecast experiments using the 22080 kinds of weather data gathered from 1987 to 1996 for proving the efficiency of this suggested algorithm. Through the experiments, the early optional composed $26{\times}50{\times}1$ nenral network became the most suitable $26{\times}2{\times}1$ structure through the pruning algorithm suggested, in the optimum neural network $26{\times}2{\times}1$, in the case of the error temperature ${\pm}0.5^{\circ}C$, the average was 33.55%, in the case of ${\pm}1^{\circ}C$, the average was 61.57%, they showed more superior than the average 29.31% and 54.47% of the optional designed structure, also. we can reduce the calculation frequency more than maximum 25 times as compared with the optional sturcture neural network in the calculation frequencies.