• International Journal of Precision Engineering and Manufacturing
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• v.4 no.4
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• pp.57-63
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• 2003

Machining information such as form accuracy and surface roughness is an important factor for manufacturing precise parts. To this regard, OMM (On the Machine Measurement) has been researched for last several decades to alternate CMM (Coordinate Measurement Machine) process. In this research, the OMM system with a laser displacement sensor was developed for measuring form accuracy and surface roughness of the machined workpiece on the machine tool. The surface roughness was estimated comparing the sensory signal with the reference data measured from master specimen. Also, form accuracy was determined from the moving averaged raw data. In addition, the geometric error map constructed beforehand using the geometric errors of the machine tool was used to compensate the obtained form accuracy. The overall performance was compared with CMM result, and verified the feasibility of the measurement system.

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

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.81-81
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• 2002

Classification is considered as one of the processes of extracting attributes from satellite imagery and is one of the usual functions in the commercial satellite image processing software. Accuracy of classification plays a key role in deciding the usage of its results. Many tremendous efforts far the higher accuracy have been done in such fields; training area selection, classification algorithm. Our research is one of these effort in different manners. In this research, we conduct classification using multiple satellite image data and evidential approach. We statistically consider the posterior probabilities and certainty in maximum likelihood classification and methodologically Dempster's orthogonal sums. Unfortunately, accuracy for the whole data sets has not assessed yet, but accuracy assessments in training fields and check fields shows accuracy improvement over 10% in overall accuracy and over 0.1 in kappa index.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.588-590
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• 2003

For the normal operations, KOMPSAT-1 orbits are determined using GPS navigation solutions data such as position and velocity vectors. Currently, the accuracy of GPS navigation solution data is generally known as on the order of 10~30 m with the removal of S/A. In this paper, an estimate of the current orbit determination accuracy for the KOMPSAT-1 is given. For the evaluation of orbit determination accuracy, the orbit overlap comparison is used since no independent orbits of comparable accuracy are available for comparison. As a result, It is shown that the orbit accuracy is on the order of 5 m RMS with 4 hrs arc overlap for the 30 hr arc.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.788-797
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• 2011

This paper analyzes the accuracy on the approach and landing of aircraft to an airport through comparison with airbase Precision Approach Radar and aircraft track data of DGPS equipped in aircraft. The proposed analysis result could be a basis for verifying the possibility that DGPS can be utilized in Airbase precision approach and landing. Position identification capability of widely used commercial DGPS is fairly accurate on latitude and longitude, while there is a slight error for being used in an airbase accurate approach and landing of Category I precision when it comes to altitude. Thus, we tested accuracy by analyzing actual flight track data of high performance aircraft to verify the accuracy of the airbase approach and landing using DGPS. Through the research, we developed instrumentation to compare PAR track data with DGPS track data, which can be used in reducing the number of PAR verification Flight utilizing it as a system measuring PAR accuracy at PAR installation phase.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.138-140
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• 2003

The aim of this study is to evaluate the effect of spatial resolutions on the accuracy to landslide susceptibility mapping. For this, landslide locations were identified in the Boun, Korea from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, linearment and land use data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The 15 factors that influence landslide occurrence were extracted and calculated from the spatial database with 5m, 10m, 30m, 100m and 200m spatial resolutions. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability model, likelihood ratio, for the five cases spatial resolutions. The results of the analysis were verified using the landslide location data. In the cases of spatial resolution 5m, 10m and 30m, the verification results was similar, but in the cases of 100m and 200m the results worse than the others. Because the scale of input data was 1:5,000 ? 1:50,000, so the cases of 5m, 10m and 30m have similar accuracy but the cases of 100m and 200m have the lower accuracy. From this, there is an effect of spatial resolutions on accuracy and landslide susceptibility mapping the result is dependent on input map.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.294-304
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• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.