• Journal of Korean Society for Geospatial Information Science
• /
• v.15 no.4
• /
• pp.111-116
• /
• 2007

This study is aimed to analyze the accuracy of depth information of reservoir using the robot-ship equipped with GPS and echosounder. The accuracy of depth measurements by sounding data was analyzed according to change of robot-ship's speed in the water. The field experiment results showed that as robot-ship's speeds were slow, accuracy of sounding data were increased. Until Robot-ship's speed was up to 5 km/hr, the accuracy of sounding data were included reliable section of normal distribution.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.1
• /
• pp.277-290
• /
• 2019

The demand for indoor positioning is increasing day by day. However, the widely used positioning methods today cannot satisfy the requirements of the indoor environment in terms of the positioning accuracy and deployment cost. In the existing research domain, the localization algorithm based on three-dimensional space is less accurate, and its robustness is not high. Visible light communication technology (VLC) combines lighting and positioning to reduce the cost of equipment deployment and improve the positioning accuracy. Further, it has become a popular research topic for telecommunication and positioning in the indoor environment. This paper proposes a surface centroid TOA localization algorithm based on the VLC system. The algorithm uses the multiple solutions estimated by the trilateration method to form the intersecting planes of the spheres. Then, it centers the centroid of the surface area as the position of the unknown node. Simulation results show that compared with the traditional TOA positioning algorithm, the average positioning error of the surface centroid TOA algorithm is reduced by 0.3243 cm and the positioning accuracy is improved by 45%. Therefore, the proposed algorithm has better positioning accuracy than the traditional TOA positioning algorithm, and has certain application value.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2003.04a
• /
• pp.301-308
• /
• 2003

As the results of this study, the proposed method of this study which is increased to accuracy of DEM by classification of terrain is better than accuracy of DEM which is automatically generated by digital photogrammetry workstation system(DPWS). And, the edge detection method which is proposed by this study is established to extraction of geo-spatial information in ortho image.

• Journal of Animal Science and Technology
• /
• v.65 no.4
• /
• pp.720-734
• /
• 2023

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the M_e, the higher the accuracy of EBV.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.185-194
• /
• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.05a
• /
• pp.546-550
• /
• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method(LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.2
• /
• pp.149-153
• /
• 2017

Objective: This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information. Methods: After collecting phenotypic and genomic information for the total number of piglets born by Landrace pigs, selection accuracy between genomic breeding value estimates using genomic information and breeding value estimates of best linear unbiased prediction (BLUP) using conventional pedigree information were compared. Results: Genetic standard deviation (${\sigma}_a$) for the total number of piglets born was 0.91. Since the total number of piglets born for candidate pigs was unknown, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy of the breeding value was 0.216. Assuming that the replacement rate of sows per year is 100% and generation interval is 1 year, genetic gain per year is 0.346 head when genomic information is used. It is 0.128 when BLUP is used. Conclusion: Genetic gain estimated from single step best linear unbiased prediction (ssBLUP) method is by 2.7 times higher than that the one estimated from BLUP method, i.e., 270% more improvement in efficiency.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.4_2
• /
• pp.669-678
• /
• 2023

Recently, spatial information has been applied to various fields and its usability is increasing day by day. In particular, in the field of civil engineering and construction, BIM based on spatial information is being applied to all construction industries and related research has been conducted. BIM is a technology that utilizes spatial information from the design phase and aids in the construction and maintenance of buildings, including the management of their attributes. However, to apply BIM technology to existing buildings, it takes a lot of time and money to produce models based on design drawings along with current surveying. In this study, quantitative and qualitative analysis was conducted to determine the applicability of the acquired data and the applicability of BIM by generating data and analyzing the accuracy using UAV images and ground lidar, which are representative spatial information acquisition methods. Quantitative analysis revealed that TLS (Terrestrial Laser Scanner) showed reliable accuracy in both planar and elevation measurements, whereas unmanned aerial images exhibited lower accuracy in elevation measurements, resulting in reduced reliability. Qualitative analysis indicated that neither TLS nor unmanned aerial images alone provided perfect completeness. However, the combination of both spatial information sources, tailored to specific needs, resulted in the most comprehensive completeness. Therefore, it is concluded that the appropriate utilization of spatial information acquired through unmanned aerial images and TLS holds the potential for application in the fields of BIM and reverse engineering.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1521-1530
• /
• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.1-13
• /
• 2019

The advent of a fourth industrial revolution, built on advances in digital technology, has coincided with studies using various unmanned aerial vehicles (UAVs) being performed worldwide. However, the accuracy of different sensors and their suitability for particular research studies are factors that need to be carefully evaluated. In this study, we evaluated UAV photogrammetry using smart technology. To assess the performance of digital photogrammetry, the accuracy of common procedures for generating orthomosaic images and digital surface models (DSMs) using terrestrial laser scanning (TLS) techniques was measured. Two different type of non-surveying camera(Smartphone camera, fisheye camera) were attached to UAV platform. For fisheye camera, lens distortion was corrected by considering characteristics of lens. Accuracy of orthoimage and DSM generated were comparatively analyzed using aerial and TLS data. Accuracy comparison analysis proceeded as follows. First, we used Ortho mosaic image to compare the check point with a certain area. In addition, vertical errors of camera DSM were compared and analyzed based on TLS. In this study, we propose and evaluate the feasibility of UAV photogrammetry which can acquire 3 - D spatial information at low cost in a construction site.