• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.210-218
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• 2000

Since the SSC(soluble solid contents) and titratable acidity of fruit are highly concerned to the taste, the need for measuring them by non-destructive technology such as NIR(Visual and Near-infrared) spectroscopy is increasing. Specially, in order to grade the quality of each fruit with a sorter at sorting and packing facilities, technologies for online measurement satisfying the tolerance in terms of accuracy and speed should be developed. Many researches have been done to develop devices to measure the internal qualities of fruit such as SSC, titratable acidity, firmness, etc. with the VIS(Visual)/NIR(Near Infrared) reflectance spectra. The distributions of the SSC, titratable acidity, firmness, etc. are different with respect to the position and depth of fruit, and generally the VIS/NIR light can interact with fruit in a few millimeters of pathlength, and it is very difficult to measure the qualities of inner flesh of fruit. Therefore, to measure the average concentrations of each quality factor such as SSC and titratable acidity with the reflectance-type NIR devices, the spectra of fruit at several positions should be measured. Recently, the interest about the transmittance-type VIS/NIR devices is increasing. NIR light can penetrate through the fruit about 1/10-1/1,000,000 %. Therefore, very intensive light source and very sensitive sensor should be adopted to measure the transmitted light spectra of intact fruit. The ultimate purpose of this study was to develop a device to measure the transmitted light spectra of intact fruit such as apple, pear, peach, etc. With the transmittance-type VIS/NIR device, the feasibility of measurement of the SSC and titratable acidity in intact fruit cultivated in Korea was tested. The results are summarized as follows; A simple measurement device which can measure the transmitted light spectra of intact fruit was constructed with sample holder, two 500W-tungsten halogen lamps, a real-time spectrometer having a very sensitive CCD array sensor and optical fiber probe. With the device, it was possible to measure the transmitted light spectra of intact fruit such as apple, pear and peach. Main factors affecting the intensity of transmitted light spectra were the size of sample, the radiation intensity of light source and the integration time of the detector. Sample holder should be designed so that direct light leakage to the probe could be protected. Preprocessing method to the raw spectrum data significantly influenced the performance of the nondestructive measurement of SSC and titratable acidity of intact fruit. Representative results of PLS models in predicting the SSC of peach were SEP of 0.558 Brix% and R2 of 0.819, and those in predicting titratable acidity were SEP of 0.056% and R2 of 0.655.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.531-543
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• 2014

Research on activity-based travel demand forecasting and activity-travel simulator has received an international attention for the last two decades. Ways to develop the activity-based simulator may be manifold. It is obvious that importing an existing simulator that has been proven internationally likely reduces the development cost and the risk of failure. By definition of the activity-based approach, however, the details of an activity-based simulator inevitably relies on particular social, economic and cultural characteristics of the society where the simulator is developed. When importing such a simulator from overseas, the researcher should be aware of the importance of tuning the system for the society to which the imported system is applied. There are many potential works on this, including for example the tuning of data structure that is likely different form of the original system. The authors are yet aware of certain research on those. The current paper aims to report the result of transforming the input data for applying the existing activity-travel simulator to Seoul. The paper first introduces FEATHERS that was developed in Belgium having Albatross which is the core of system. FEATHERS Seoul that is under development and modified version of the original FEATHERS is briefly described and the related problems are discussed. The paper then explored to resolve and to alleviate such problems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.105-112
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• 2018

To determine the position of receiver, the GPS (Global Positioning System) uses position information of satellites and pseudo ranges based on signals. These are reflected by surrounding structures and multipath errors occur. This paper proposes a method for multipath error reduction using digital images to enhance the accuracy. The goal of the study is to calculate the shielding environment of receiver using image processing and apply it to GPS positioning. The proposed method, firstly, performs a preprocessing to reduce the effect of noise on images. Next, it uses hough transform to detect the outline of building roofs and determines mask angles and permissible azimuth range. Then, it classifies the satellites according to the condition using the image processing results. Finally, base on point positioning, it computes the receiver position by applying a weight model that assigns different weights to the classified satellites. We confirmed that the RMSE (Root Mean Square Error) was reduced by 2.29m in the horizontal direction and by 15.62m in the vertical direction. This paper showed the potential for the hybrid of GPS positioning and image processing technology.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.49-57
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• 2013

A novel approach for the feature selection is proposed, which is the important preprocessing task of on-line document classification. In previous researches, the features based on information from their single population for feature selection task have been selected. In this paper, a mixed feature set is constructed by selecting features from multi-population as well as single population based on various information. The mixed feature set consists of two feature sets: the original feature set that is made up of words on documents and the transformed feature set that is made up of features generated by LSA. The hybrid feature selection method using both filter and wrapper method is used to obtain optimal features set from the mixed feature set. We performed classification experiments using the obtained optimal feature sets. As a result of the experiments, our expectation that our approach makes better performance of classification is verified, which is over 90% accuracy. In particular, it is confirmed that our approach has over 90% recall and precision that have a low deviation between categories.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.29-37
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• 2015

In this paper, we propose a robust pre-processing module for camera-based Optical Music Score Recognition (OMR) on mobile device. The captured images likely suffer for recognition from many distortions such as illumination, blur, low resolution, etc. Especially, the complex background music sheets recognition are difficult. Through any symbol recognition system, the staff line height and staff line space are used many times and have a big impact on recognition module. A robust and accurate staff line height and staff line space are essential. Some staff line height and staff line space are proposed for binary image. But in case of complex background music sheet image, the binarization results from common binarization algorithm are not satisfactory. It can cause incorrect staff line height and staff line space estimation. We propose a robust staff line height and staff line space estimation by using run-length encoding technique on edge image. Proposed method is composed of two steps, first step, we conducted the staff line height and staff line space estimation based on edge image using by Sobel operator on image blocks. Each column of edge image is encoded by run-length encoding algorithm Second step, we detect the staff line using by Stable Path algorithm and removal the staff line using by adaptive Line Track Height algorithm which is to track the staff lines positions. The result has shown that robust and accurate estimation is possible even in complex background cases.

• Atmosphere
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• v.28 no.2
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• pp.113-121
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• 2018

In order to monitor greenhouse gases including $CO_2$, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface $CO_2$ flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface $CO_2$ flux, additional observations would be required. In this study, a system that assimilates aircraft $CO_2$ measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft $CO_2$ measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft $CO_2$ measurement data. This system could also be used to assimilate other aircraft $CO_2$ measurement data in CarbonTracker.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.183-191
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• 2014

This paper describes the development of the observational data transmission algorithm for high-speed network in radio astronomy. For the preprocessing of VLBI data observed by radio telescope, data transmission algorithm uses the VDIF specification, VDIFCP, and UDP protocol by transferring VLBI data stored in a massive storage server with one-to-one correspondence between the server and the RVDB of Daejeon correlator. A transmission method is proposed, which reads the recorded data in Mark5B VSI format and trnasmits 2048 Mbps VLBI data by software through UDP packet transmission, while RVDB system is waiting for the transmitting data from the server. In order to check the effectiveness of the proposed method, the data transmission between the massive storage server and RVDB is conducted and the transmitted data is correlated by Daejeon correlator for the accurate comparison concerning the data before and after. The transmitted data is shown to be completely the same as the original data without any data transmission loss. Henceforth, the developed data transmission algorithm in this research is expected to be applied effectively as e-VLBI for KaVA network.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.