• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.1-13
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• 2018

This study examines the relationship between the organizational general characteristics (industry, size, location, development stage, and company age) and success factors of Korean venture firms using secondary data. Among the industries with the highest sales figures in 2016 are food / fiber / (non) metals, and the smallest category was software development. The sectors with the highest net profit were computer / semiconductor / electronic components, and the smallest category was telecommunication equipment / broadcasting equipment. The industries with the largest sales growth rate are IT / broadcasting services and software development. The industries with the highest net profit margin of sales are energy / medical / precision, and the smallest is telecommunication equipment / broadcasting equipment. In terms of the number of employees, venture firms with more than 100 employees have the largest sales and net profit, with employees between 1 and 9 have the smallest. However, these results are predictable. In general, the number of employees is highly correlated with sales and net profit. Rather, the sales growth rate and the net profit margin of sales may be meaningful. In particular, with employees between 50 ~ 99, the growth rate of sales and the net profit margin of sales were high. In terms of location, Seoul / Incheon / Gyeonggi were the regions with the highest sales and Daejeon / Sejong / Chungcheong / Gangwon were the least regions. Gwangju / Jeolla / Jeju and Seoul / Incheon / Gyeonggi were almost similar in the areas with the largest net profit. However, Daejeon / Sejong / Chungcheong / Gangwon had the lowest net profit. Unusually, the areas with the highest sales growth rate and the highest net profit margin of sales were Gwangju / Jeolla / Jeju, and the smallest areas were Busan / Jeonnam / Ulsan In the relationship between the stage of development and the performance of the company, the sales of maturity and decline stages were the highest and establishing stage was the lowest. Net profit was also the highest in mature stage and the smallest in establishing stage. The sales growth rate shows a typical pattern in the order of establishing stage, early growth stage, high growth stage, maturity stage, and decline stage. In terms of business performance, sales and net profit are the highest with 21 years or more of company age, and the smallest is less than 3 years. In addition, the sales growth rate was the highest in three years or less, and the net profit margin of sales was the highest in 4 to 10 years. This study can present lots of useful implications by suggesting integrated research model and examining the success factors of Korean venture firms and presenting the application methods of secondary data in analyzing the current status of venture industry in Korea.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.59-66
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• 2018

In this paper, we propose a system to detect various damage automatically inflicted on road pavement by collecting and analyzing data from acceleration and camera sensors in real time. The proposed system sends the collected images, acceleration signals, and GPS coordinates to the road manager and the database in the remote server, shortly after detecting the damage to the road pavement. Our study makes three key contributions. The proposed system 1) enables road managers to maintain road conditions quickly, accurately, and conveniently; 2) allows road mangers to take care of various kinds of damage to the road pavement at the initial stage; and finally 3) even makes it possible to track the damage, which suggests that the integration of a high-level decision support function becomes affordable. We tested the sensitivity and precision of the proposed system against real-time data obtained from the vehicles driving on the highway at an average speed of 100 km/h. With ten iterations, the proposed system achieved an average sensitivity of 74% and an average precision of 84% in road pavement damage detection, which is comparable with the best competing schemes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1051-1057
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• 2011

By the recently developed GMP(Glass Molding Press) process, aspheric glass lenses are widely used in many optical applications such as digital cameras, optical data storages and electrical devices etc. The GMP process can economically produce complex shaped glass lenses with high precision and good repeatability. This study deals the optimization of molding conditions for aspheric glass lenses in progressive GMP process through Design Of Experiment(Taguchi method). Tree main factors for molding conditions were selected based on pressure, temperature and cooling time at 1st cooling stage. From the analysis of experiments which were preformed with 3-cavity glass mold, it was revealed that the cooling time was the most sensitive parameter for form accuracy(PV) in progressive GMP process.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.111-116
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• 2001

Waste office paper, photocopied or laser printed, has recently increased along with office automatization. In waste office paper, toner ink is used as the printing medium in place of conventional oil ink. Since toner ink cannot be saponificated by alkali and be decolored by bleaching, different from the case of oil ink, toner remains on regenerated paper as black specks. Although cascade recycling of waste office paper is compelled at present, the demand for low-grade paper is limited. From such circumstances, a new separation process for waste office paper is demanded to achieve parallel recycling. At the first stage of separation studies, the sharpness of separation is evaluated using small separators to obtain fundamental data. In a lab-scale separator, the sample amount of one feed is generally a few grams. However, the sample amount used for brightness, ERIC, and image analysis that are generally used to evaluate the efficiency of deinking are not small for lab-scale tests of these analyses. This paper describes an investigation of a sheet preparation method by a small amount of sample under 0.5g and compares the precision of toner content determination of spectroscopic analysis and image analysis from the viewpoint of separation evaluation. The easiness and convenience of the operation using only general-purpose equipments has also been set as a principle purpose. From the viewpoint of an analysis that yields high precision with a small amount of sample in short time, the method calculating the absorption coefficient from diffuse reflectance in the visible region is the most rational method in this study.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.52-57
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• 1999

Funtional metal prototypes are often required in numerous industrial applications. These components are typically needed in the early stage of a project to determine form, fit and function. Recent R/P(Rapid Prototyping) part are made of soft materials such as plastics, wax, paper, these master models cannot be employed durable test in real harsh working environment. Parts by direct metal rapid tooling method, such as laser sintering, by now are hard to get net shape, pores of the green parts of powder casting method must be infiltrated to get proper strength as tool, and new type of 3D direct tooling system combining fabrication welding arc and cutting process is reported by song etc. But a system which can build directly 3D parts of high performance functional material as metal part would need long period of system development, massive investment and other serious obstacles, such as patent. In this paper, through the rapid tooling process as silicon rubber molding using R/P master model, and fabricate wax pattern in that silicon rubber mold using vacuum casting method, then we tranlsated the wax patterns to numerous metal prototypes by new investment casting process combined conventional investment casting with rapid pototyping & rapid tooling process. with this wax-injection-mold-free investment casting, we developed new investment casting process of fabricating numerous functional metal prototypes from one master model, combined 3-D CAD, R/P and conventional investment casting and tried to expect net shape measuring total dimension shrinkage from R/P part to metal part.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.636-649
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• 2005

Field-associated Terms itself have field Information. So, they determine field of document just like when human being perceives field. In case of Korean, we organized and experimented them by collecting approximately IS,999 document banks that are classified into 180 fields. We obtained high precision of extraction that 88,782 single field-associated terms are contracted into 8,405 ones thus recording compression rate as approximately 9$\%$ and recall as above 0.77 (average 0.85), precision as above 0.90 (average 0.94). By applying established field-associated terms to initial determination for document classification and comparing it with filed determination by human being, we got correct answers above approximately 90$\%$. We can use results of research as fundamental research for initial stage and apply it document retrieval between multilingual environment thus utilizing it as fundamental research for multilingual information retrieval.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.2
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• pp.101-104
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• 2018

Purpose Alpha fetoprotein(AFP) is a fetal serum protein that increases in germ cell tumors derived from liver cancer or egg yolk. AFP test has been used for screening of liver cancer, determination of tumor stage, determination of therapeutic effect, and fetal congenital malformations. The purpose of this study was to compare the results of the four kits, identify the advantages and disadvantages of each kit, and select the appropriate kits for our laboratory. Materials and Methods Blood samples were obtained from 89 patients attending the Seoul national university hospital. Experiments were carried out in accordance with manufacturer's instructions of four companies(A, B, C, D). The precision, recovery, linearity, and sensitivity test were performed for each kit. Results In case of the precision within the measurement, the CV value of the C kit was less than 5% at the low, middle, and high concentrations. The A, B and D kit's the CV value was less than 5% at the concentrations except the low concentration. The recovery rates of the A, B, C, and D kits were $100{\pm}15%$, $100{\pm}30%$, $100{\pm}16%$ and $100{\pm}14%$, respectively. All kits showed good linearity. Sensitivity was measured as 0.5 IU/mL for A, 0.4 IU/mL for B, 0.98 IU/mL for C, and 0.3 IU/mL for D. Conclusion The CV values of the four kits were within 10%, and the correlation coefficients were close to 1 for $R^2=0.978$, $R^2=0.992$ and $R^2=0.8957$. As a result, they are clinically available. Therefore, each laboratory should select the appropriate kit for their experiment's environment.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.131-140
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• 2020

In training a classification model using machine learning, the acquisition of training data is a very important stage, because the amount and quality of the training data greatly influence the model performance. However, when the cost of obtaining data is so high that it is difficult to build ideal training data, the number of samples for each class may be acquired very differently, and a serious data-imbalance problem can occur. If such a problem occurs in the training data, all classes are not trained equally, and classes containing relatively few data will have significantly lower recall values. Additionally, the reliability of evaluation indices such as accuracy and precision will be reduced. Therefore, this study sought to overcome the problem of data imbalance in two stages. First, we introduced weighted accuracy and weighted precision as new evaluation indices that can take into account a data-imbalance ratio by modifying conventional measures of accuracy and precision. Next, oversampling was performed to balance weighted precision and recall among classes. We verified the algorithm by applying it to the problem of facies classification. As a result, the imbalance between majority and minority classes was greatly mitigated, and the boundaries between classes could be more clearly identified.