• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.599-606
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• 2019

In this study, it was aimed to find the optimal cutting conditions by measuring and analyzing the dimensional accuracy of SNCM 616 alloy steel, which is commonly used in industry, by precision hole machining using Ø25 mm and 8-blade reamer in CNC-HBM to be. As a result of the roundness and dimensional accuracy, it was found that the spindle speed had a significant effect on the dimensional tolerance value. Optimum cutting conditions are spindle speed 25 rpm and feed rate 20 mm / min.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.58-63
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• 2019

In this research, we examine the change of dimensional accuracy in the cutting process while changing cutting conditions such as feed rate and spindle rotational speed with chromium molybdenum steel (SCM415) material and TiCN- and TiAlN-coated end mill tools. According to dimensional accuracy measurement, TiCN-coated tool displays the most accurate dimensional tolerance at ${\varnothing}20mm$ at feed rates of 200 mm/min and 250 mm/min at a spindle rotation speed of 4,000 rpm. The largest dimension of the coating tool was able to make the TiAlN-coated tool suitable when comparing the smallest dimension.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• Current Optics and Photonics
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• v.5 no.2
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• pp.164-172
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• 2021

In this paper a catadioptric laser-irradiation-precision test system is designed, to achieve a high-precision laser-irradiation-accuracy test. In this system, we adopt the method of imaging the entire target surface at a certain distance to realize the measurement of laser-irradiation precision. The method possesses the advantages of convenient operation, high sensitivity, and good stability. To meet the test accuracy requirement of 100 mm/km (0.01%), the coma, field curvature, and distortion over the entire field of view should be eliminated from the optical system's design. Taking into account the whole length of the tube and the influence of stray light on the structure type, a catadioptric system with a hood added near the primary imaging surface is designed. After optimization using the ZEMAX software, the modulation transfer function (MTF) of the designed optical system is 0.6 at 30 lp/mm, the full-field-of-view distortion is better than 0.18%, and the energy concentration in the 10-㎛-radius surrounding circle reaches about 90%. The illumination-accuracy test results show that the measurement accuracy of the radiation hit rate is better than 50 mm when the test distance is 1 km, which is better than the requirement of 100 mm/km for the laser-irradiation-accuracy test.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1645-1651
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• 2015

This paper describes the changes of shape accuracy in workpiece materials depending on the turning clearance angle. The experiments started from choosing three workpiece materials, SM45C(machine structural carbon steel), STS303(stainless steel) and SCM415 (chrome-molybdenum steel). The experiments showed specifically how features of selected materials changed when they were processed with diverse machining depths, 0.1 mm, 0.2 mm and 0.3 mm, with various negative angles, $0.0^{\circ}(-6.0^{\circ})$, $0.3^{\circ}(-6.3^{\circ})$ and $0.9^{\circ}(-6.9^{\circ})$, and called cutting edge inclination starting from a fixed rotational speed, 2,500 rpm, focusing on the feed rate, 0.07 mm/rev and 0.10 mm/rev. The results of the accuracy of processing, cylindricity, deviation from coaxiality, etc. were compared using the graph and table. The accuracy of cylindricity in the order of degree $0.0^{\circ}{\rightarrow}0.3^{\circ}{\rightarrow}0.9^{\circ}$ depending on the workpiece materials showed the best cylindricity when it was $0.9^{\circ}$. In conclusion, the accuracy improved in specific degrees irrespective of the quality of the materials when the bite negative angles increased. This means that workability improved in these experiments. In addition, the processing shape changed depending on depth of the cut and feed rate.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.95-106
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• 2013

The study compares accuracies between humans and computer algorithms in the discrimination of spontaneous smiles from posed smiles. For this purpose, subjects performed two tasks, one was judgment with single pictures and the other was judgment with pair comparison. At the task of judgment with single pictures, in which pictures of smiling facial expression were presented one by one, subjects were required to judge whether smiles in the pictures were spontaneous or posed. In the task for judgment with pair comparison, in which two kinds of smiles from one person were presented simultaneously, subjects were to select spontaneous smile. To calculate the discrimination algorithm accuracy, 8 kinds of facial features were used. To calculate the discriminant function, stepwise linear discriminant analysis (SLDA) was performed by using approximately 50 % of pictures, and the rest of pictures were classified by using the calculated discriminant function. In the task of single pictures, the accuracy rate of SLDA was higher than that of humans. In the analysis of accuracy on pair comparison, the accuracy rate of SLDA was also higher than that of humans. Among the 20 subjects, none of them showed the above accuracy rate of SLDA. The facial feature contributed to SLDA effectively was angle of inner eye corner, which was the degree of the openness of the eyes. According to Ekman's FACS system, this feature corresponds to AU 6. The reason why the humans had low accuracy while classifying two kinds of smiles, it appears that they didn't use the information coming from the eyes enough.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.788-791
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• 2006

The perception of English labial consonants was investigated via experiment where 40 Korean EFL learners identified nonwords with the target labial consonants [p, b, f, v] in 4 different prosodic locations. The results showed that there was a strong positional effect since the accuracy rates of the four target consonants differed by position. Specifically, the average accuracy rate for the target consonants was higher in the stressed intervocalic position and initial onset position than in the unstressed intervocalic position and final coda position. Further, the accuracy rate for [f] is was high in all prosodic locations except the unstressed intervocalic position. This is unexpected in markedness theory given that fricatives are assumed to be more difficult to learn than stops.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.489-498
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• 2006

In this paper, we suggested a vehicle classification algorithm using pattern recognition method. At present, Inductive Loop Detector is rarely used for vehicle classification because of its low accuracy. To improve the accuracy, we suggest a new algorithm for Loop Detector using neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and occupancy-time. The output is classified vehicles. The developed algorithm was assessed at test sites and the recognition rate was 91.3percent. The results verified that the proposed algorithm improves the vehicle classification accuracy compared to the conventional method based on Loop Detector.