• Asian-Australasian Journal of Animal Sciences
• /
• v.18 no.8
• /
• pp.1088-1097
• /
• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.1144-1149
• /
• 2003

It is very important to measure circular motion accuracy of NC lathes it affects accuracy, performance, interchange ability and quality of machine parts machined by the NC lathes in industries. So, in this study, measuring units system to measure circular motion accuracy two axes circular motion accuracy of NC lathes was composed of two optical linear scales installed on the z and x-axes of work coordinate system on NC lathe and a computer inserted with PC counter card enables to obtain measuring data. Here, ATC(Automatic Tool Changer) and moving part of linear scales are fixed with magnet bases in order to measure circular motion accuracy of the ATC of NC lathe. And next, computer software was developed in order to measure the circular motion accuracy of NC lathe under resolution of 0.1 $\mu\textrm{m}$ using two linear scales, and also computer softwares were developed so that measuring data could be modeled on plots and be analyzed numerically,

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.363-366
• /
• 2007

Road Design is being reached to the working design to produce drawings, calculate construction quantity and cost, through the basic design that contained feasibility study and all impact assessment. In general, to plan the route we use topographic map. The vertical positional accuracy is 30cm and horizontal positional accuracy is 35cm in 1:1,000 scale topographic map. In LiDAR, vertical positional accuracy is 15cm and horizontal positional accuracy is 30cm. So if we use LiDAR on road design, more accurate earth-volumn will be calculated when we plan the route. In this paper we try to find the method to use the LiDAR data on road design by drawing the profile and cross sectional view and comparing the earth-volumn to the road that working design is in process adopting the topographic map and LiDAR data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2382-2386
• /
• 2003

The Global Positioning System(GPS) is a satellite based precise positioning system avaliable worldwide. The GPS have many error sources. The earth's ionosphere and atmosphere cause delays in the GPS signal that translate into position errors. Some errors can be factored out using mathematics and modeling. The configuration of the satellites in the sky can magnify other errors. The problem of accuracy on GPS measurement data can be meaningful. In this study, we propose the method for GPS positioning accuracy improvement. The FUZZY set theory on PDOP(Position Dilution of Precision) and SNR(Signal to Noise Ratio) provide improved for measured positioning data. The accuracy of positioning has been improved by selecting data from original using the FUZZY set theory on PDOP and SNR.

• 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.

• Korean Journal of Geomatics
• /
• v.3 no.2
• /
• pp.107-114
• /
• 2004

The National Geographic Information Institute (NGII) in korea, through the National Geographic Information System (NGIS) Program, has prepared to generate and disseminate digital elevation data for Korea. This is a pilot research to propose a policy for production, maintenance, and supply of Korea Digital Elevation Data(KDED). Customer demands for accuracy and resolution of DEM was surveyed through a questionnaire. In order to investigate the quality, the technical efficiency and the production cost, a tentative DEM in a small test site was generated based on digital topographic maps (original paper map scale 1:5,000), analytical plotter, and LIDAR. The Accuracy standard for KDED was derived based on source data generation methods. As a result of this research, a uniformly spaced grid model was recommended for KDED. Its preferable grid space is 5m in urban areas and its vicinity, and 10m in field and mountainous area. LIDAR has been valuated as a proper KDED generation method fulfilling customers' demands for the accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.253-259
• /
• 2002

The rigid body characteristics (value of mass, Position of center of mass, moments and products of inertia) of mechanical systems can be identified from FRF data or vibration spectra of rigid body motion. Therefore the accuracy of rigid body characteristics is connected directly with the accuracy of measured data for rigid body motions. In this paper, a method of improving accuracy of measurement of rigid body motion is presented. Applying rigid body theory, ail translational and rotational displacements at a tentative point on the rigid body are calculated using the measured translational displacements for several points and transfer matrix. Then the estimated displacements for the identical points are calculated using the 6 displacements of the tentative Point and transfer matrix. By using correlation coefficient between measured and estimated displacements, we can detect the existence of errors that are contained in a certain measured displacement. Consequently, the improved rigid body motion with respect to a tentative point can be obtained by eliminating the contaminated data.

• Korean Journal of Remote Sensing
• /
• v.17 no.2
• /
• pp.121-129
• /
• 2001

A DEM(digital elevation model) is generated from a SPOT panchromatic stereo-pair using automated algorithms over a 8 km$\times$10 km region around Mokpo city. The aims are to continue the accuracy assessment over diverse conditions and to examine the applicability of a SPOT DEM for coastal disaster monitoring. The accuracy is assessed with respect to three reference data sets: 10 global positioning system records, 19 leveling data, and 1:50,000 topography map. The planimetric error is 10.6m r.m.s. and the elevation erroer ranges from 12.4m to 14.4m r.m.s.. The DEM accuracy of the flat Mokpo region is consistent with that over a mountainous area, which supports the robustness of the algorithms. It was found that coordinate transformation errors are significant at a few meters when using the data from leveling and topographic maps. The error budget is greater than the requirements for coastal disaster monitoring. Exploiting that a sub-scene is used, the affine transformation improves the accuracy by 50% during the camera modeling.

• ETRI Journal
• /
• v.44 no.3
• /
• pp.438-449
• /
• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• The Korean Journal of Cytopathology
• /
• v.19 no.2
• /
• pp.65-71
• /
• 2008

In Korea, the quality control(QC) program forcytopathology was introduced in 1995. The program consists of a checklist for the cytolopathology departments, analysis data on all the participating institutions' QC data, including the annual data on cytologic examinations, the distribution of the gynecological cytologic diagnoses, as based on The Bethesda System 2001, and the data on cytologic-histolgical correlation of the gynecological field, and an evaluation for diagnostic accuracy. The diagnostic accuracy program has been performed 3 times per year with using gynecological, body fluid and fine needle aspiration cytologic slides. We report here on the institutional QC data and the evaluation for diagnostic accuracy since 2004, and also on the new strategy for quality control and assurance in the cytologic field. The diagnostic accuracy results of both the participating institutions and the QC committee were as follows; Category 0 and A: about 94%, Category B: 4-5%, Category C: less than 2%. As a whole, the cytologic daignostic accuracy is relatively satisfactory. In 2008, on site evaluation for pathology and cytology laboratories, as based on the "Quality Assurance Program for Pathology Services" is now going on, and a new method using virtual slides or image files for determining the diagnostic accuracy will be performed in November 2008.