• The Korean Journal of Cytopathology
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• v.4 no.1
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• pp.1-8
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• 1993

This study was performed in order to evaluate the accuracy and the usefulness of the fine needle aspiration cytology (FNAC) on the breast lesions, to compare the FNAC findings between fibroadenoma and fibrocystic disease, and to determine the accuracy of cytologic Black's nuclear grading. The subjects in this study were 110 cases of FNAC, later confirmed by biopsy, between January 1988 and December 1991. The results are as follows ; 1 Comparison between the results of the FNAC and the histologic findings revealed that FNAC had a sensitivity of 96.6%, a specificity of 100%, a false negative rate of 3.4% a false positive rate of 0.0%, and an overall diagnostic accuracy of 98.2%. 2 Semi-quantitative evaluation of epithelial celluarity, stroma, and naked nuclei in the smears of aspirate showed high celluarity in 56.7% of the aspirates from fibroadenoma and in 0% of those from fibrocystic disease. Abundant stroma was found in 46.7% of the fibroadenoma and none of fibrocystic disease. Numerous naked nuclei were found in 60% of the fibroadenoma and 4.5% of the fibrocystic disease. The overall diagnostic accuracy was 98% 3. In order to determine the accuracy of Black's nuclear grading of FNAC on breast carcinoma, we retrospectively studied 38 cases of ductal carcinomas diagnosed by FNAC with subsequent histologic confirmation. The concordance rate with histology was 94.7%. These results suggest that FNAC of breast is a diagnostically accurate method, and provide for the preoperative differential diagnosis between fibroadenoma and fibrocystic disease. Our results also suggest that the evaluation of nuclear grading of FNAC can predict clinical outcome and decide the way of management of breast cancer.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1007-1010
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• 2002

The accuracy of high speed feeding type laser cutting M/C is the major factor directly concerned with the accuracy of the processed work, and the feed errors of feed system make the machining errors of work directly on processing. In this point, this study focused on the generative elements in feed errors of laser cutting M/C when operating its laser head. In order to improve the accuracy of this machining center, feed errors are measured by a laser interferometer.

• Transactions of Materials Processing
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• v.23 no.6
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• pp.380-385
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• 2014

An analysis procedure is proposed to improve the prediction accuracy of springback as well as to evaluate the structural stability of the tooling used for fabricating a side sill part from UHSS. The analysis couples the stamping analysis and the subsequent analysis of the tool structural. The deformation and stress results for the tool structure are obtained from the proposed analysis procedure. The results show that the amount of deformation and stresses are so high that the tool structure must be reinforced and the tooling design must consider structural stability. Springback is predicted with CAE in order to compare the prediction accuracy between the given tool geometry and the geometry from the structural analysis. The simulation results with the deformed tool can predict the experimental springback tendency accurately.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1700-1705
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• 2009

In general, short term load forecasting is based on the periodical load pattern during a day or a week. Therefore, the conventional methods do not expose stable performance to every day during a year. Especially for anomalous weather conditions such as typhoons, the methods have a tendency to show the conspicuous accuracy deterioration. Furthermore, the tendency raises the reliability and stability problems of the conventional load forecast. In this study, a new load forecasting method is proposed in order to increase the accuracy of the forecast result in case of anomalous weather conditions such as typhoons. For irregular weather conditions, the sensitivity between temperature and daily load is used to improve the accuracy of the load forecast. The proposed method was tested with the actual load profiles during 14 years, which shows that the suggested scheme considerably improves the accuracy of the load forecast results.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.380-384
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• 1997

For improving the motion accuracy of hydrostatic table, corrective machining algorithm is proposed in this paper. The algorithm consists of three main processes. Reverse analysis is performed firstly to estimate rail profile from measured linear and angular motion error, in the algorithm. For the next step, correctwe machining information is decided as referring to the estimating rail profile. Finally, motion errors on correctively machined rail are analized by using motion error analysls method proposed in the previous paper. These processes can be rtcrated if the analized motion errors are worse than target accuracy. In order to verify the validity of the algorithm theoretically, motion errors by the estimated rail after corrective machining are compared with motion errors by true rail assumed as the measured value. Estimated motion errors show good agreement with assumed values, and it is confirmed that the algorithm IS effective to acquire the corrective machming information to improve the accuracy of hydrostatic table.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.212-221
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• 2003

In order to reduce the lead-time and cost, the technology of rapid prototyping (RP) has been widely used. A new rapid prototyping process, transfer-type variable lamination manufacturing process by using expandable polystyrene foam (VLM-ST), has been developed to reduce building time, apparatus cost and additional post-processing. At the same time, VLM Slicer, the CAD/CAM software for VLM-ST has been developed. In this study, algorithms for accuracy improvement of VLM-ST, which include offset and overrun of a cutting path and generation of a reference shape are developed. Offset algorithm improves cutting accuracy, overrun algorithm enables the VLM-ST process to make a shape of sharp edge and reference shape generation algorithm adds additional shape which makes off-line lamination easier. In addition, proposed algorithms are applied to practical CAD models for verification.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.892-904
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• 2021

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.58-65
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• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.