Yeongsang Jeong

Measuring System for Impact Point of Arrow using Mamdani Fuzzy Inference System

The performance of arrow from a manufacturing process depends on arrow`s trajectory(archer`s paradox) and intensity of an impact points. Especially, when conducting a shooting experiment over and over in the same experiment condition, the intensity of impact point is an objective standard to judge the performance of the arrow. However, the analysis method for the impact point is not enough, a previous research of the arrow`s performance has been focused on a skill to optimize a manufacturing variables(feathers of an arrow, barb of an arrow, arrow`s shaft, weight, external diameter, spine). In this paper, We propose measurement system of arrow`s impact point with Mamdani fuzzy inference system and similarity of polygon for automation of impact point`s measurement. Measuring the impact point data of the arrow moving with a high speed(approximately 275km/h) by using line laser and photo diode array, then the measured data are mapped to arrow`s impact point with fuzzy inference and similarity of polygon.

Short-term hourly load forecasting using PSO-based AR model

Load forecasting is essential for effective and stable power system planning and operation. Decision making related to power system operation is influenced by futures electric load patterns. In this paper, particle swarm optimization (PSO) based autoregressive (AR) model is presented for short-term hourly load forecasting. First of all, among several potential input candidates, relevant inputs that have high correlation with prediction models output are selected. According to the number of selected inputs, the order of AR model is fixed. Finally, AR models parameters are optimized using PSO that is a global optimization algorithm. To verify the performance, the proposed method is applied to two kinds of real world hourly load dataset in South Korea. The proposed method shows good prediction accuracy.

A Study on Chaff Echo Detection using AdaBoost Algorithm and Radar Data

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

Identifying chaff echoes in weather radar data using tree-initialized fuzzy rule-based classifier

In order to produce reliable weather forecasts, it is essential to discriminate non-meteorological targets from rain clouds in weather radar data. Identification of chaff echoes, which is one of the main noise sources, is uncertain and imprecise for skilled weather experts because characteristics of them are similar to those of precipitation echoes. This paper uses tree-initialized fuzzy classifier (FC) to identify chaff echoes. Fuzzy models have been widely applied to the domain of uncertainty and vagueness. Classification and regression tree is used to generate an initial crisp model (a set of crisp rules). The number of the rules, corresponding to complexity of the model, is systematically determined by performance criterion. Finally, after transforming the crisp model to the fuzzy one straightforwardly, parameters of the FCs are optimized by genetic algorithms. FCs have more flexible decision boundaries than binary decision trees with rectangular partitioning. In order to evaluate identification performance, the FCs, and comparison methods are applied to many cases where both chaff and non-chaff echoes occurred simultaneously. The results of experiments show that the FCs achieve the best identification performance.

Ensemble of Radial Basis Neural Networks with Chinese Restaurant Process

Ensemble classifier implementation needs several considerations including base classifier selection and decision aggregation. A set of radial basis function networks is one of the most popular method as a base classifier. However, considering that the unsupervised method including clustering is frequently applied in the learning schemes of the radial basis function networks, there is an important issue to solve that the number of cluster must be determined in advance. Most of partitional clustering algorithms including k-means clustering are sensitive to the number of clusters. In this paper, we replace the k-means clustering algorithm in the learning scheme into the Chinese restaurant process, which does not need to determine the number of cluster. With real problems in the radar data analysis, the proposed method shows better results in the experiment.

A Study of Positioning Error Compensation Using Optical-Sensor and Three-Frame

This paper studies about the developed measuring instrument’s degree of precision. Through this research, we could convert archer’s paradox phenomenon to numerical data and make it use as a performance evaluation element. The instrument uses three frames which composed of line-laser and photodiode sensors for reconstructing full-shape of a flying arrow. Moreover, in order to measure arrows’ position precisely which flies about 300km/h, Artificial Neural Network is used for calibration of the measuring instrument. Two grid-plates for calibrating the measuring instrument are installed in first and third frames among three frames. After that, calibration process is performed using converted coordinate data from arrow’s position by placing an arrow at holes in the grid-plate. From the suggested arrow position measurement system and the experiment data using the instrument, the automated system for quality control of arrows or performance experiment could be established.