• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Radiation Oncology Journal
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• v.21 no.1
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• pp.82-93
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• 2003

Purpose : To reduce the Irradiation dose to the lungs and heart in the case of chest wail irradiation using an oppositional electron beam, we used an Individualized custom bolus, which was precisely designed to compensate for the differences In chest wall thickness. The benefits were evaluated by comparing the normal tissue complication probablilties (NTCPS) and dose statistics both with and without boluses. Materials and Methods : Boluses were made, and their effects evaluated in ten patients treated using the reverse hockey-stick technique. The electron beam energy was determined so as to administer 80% of the irradiation prescription dose to the deepest lung-chest wall border, which was usually located at the internal mammary lymph node chain. An individualized custom bolus was prepared to compensate for a chest wall thinner than the prescription depth by meticulously measuring the chest wall thickness at 1 emf intervals on the planning CT Images. A second planning CT was obtained overlying the individuailzed custom bolus for each patient's chest wall. 3-D treatment planning was peformed using ADAC-Pinnacle$^{3}$ for all patients with and without bolus. NTCPS based on 'the Lyman-Kutcher' model were analyzed and the mean, maximum, minimum doses, V$_{50}$ and V$_{95}$ for 4he heari and lungs were computed. Results .The average NTCPS in the ipsliateral lung showed a statistically significant reduction (p<0.01), from 80.2${\pm}$3.43% to 47.7${\pm}$4.61%, with the use of the individualized custom boluses. The mean lung irradiation dose to the ipsilateral iung was also significantly reduced by about 430 cGy, Trom 2757 cGy to 2,327 cGy (p<0.01). The V$_{50}$ and V$_{95}$ in the ipsilateral lung markedly decreased from the averages of 54.5 and 17.4% to 45.3 and 11.0%, respectively. The V$_{50}$ and V$_{95}$ In the heart also decreased from the averages of 16.8 and 6.1% to 9.8% and 2.2%, respectively. The NTCP In the contralateral lung and the heart were 0%, even for the cases with no bolus because of the small effective mean radiation volume values of 4.4 and 7.1%, respectively Conclusion : The use of an Individualized custom bolus in the radiotherapy of postrnastectorny chest wall reduced the NTCP of the ipsilateral lung by about 24.5 to 40.5%, which can improve the complication free cure probability of breast cancer patients.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Progress in Medical Physics
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• v.26 no.3
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• pp.160-167
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• 2015

Radiation exposure from medical diagnostic imaging procedures to patients is one of the most significant interests in diagnostic x-ray system. A miniature x-ray intraoral tube was developed for the first time in the world which can be inserted into the mouth for imaging. Dose evaluation should be carried out in order to utilize such an imaging device for clinical use. In this study, dose evaluation of the new x-ray unit was performed by 1) using a custom made in vivo Pig phantom, 2) determining exposure condition for the clinical use, and 3) measuring patient dose of the new system. On the basis of DRLs (Diagnostic Reference Level) recommended by KDFA (Korea Food & Drug Administration), the ESD (Entrance Skin Dose) and DAP (Dose Area Product) measurements for the new x-ray imaging device were designed and measured. The maximum voltage and current of the x-ray tubes used in this study were 55 kVp, and 300 mA. The active area of the detector was $72{\times}72mm$ with pixel size of $48{\mu}m$. To obtain the operating condition of the new system, pig jaw phantom images showing major tooth-associated tissues, such as clown, pulp cavity were acquired at 1 frame/sec. Changing the beam currents 20 to $80{\mu}A$, x-ray images of 50 frames were obtained for one beam current with optimum x-ray exposure setting. Pig jaw phantom images were acquired from two commercial x-ray imaging units and compared to the new x-ray device: CS 2100, Carestream Dental LLC and EXARO, HIOSSEN, Inc. Their exposure conditions were 60 kV, 7 mA, and 60 kV, 2 mA, respectively. Comparing the new x-ray device and conventional x-ray imaging units, images of the new x-ray device around teeth and their neighboring tissues turn out to be better in spite of its small x-ray field size. ESD of the new x-ray device was measured 1.369 mGy on the beam condition for the best image quality, 0.051 mAs, which is much less than DRLs recommended by IAEA (International Atomic Energy Agency) and KDFA, both. Its dose distribution in the x-ray field size was observed to be uniform with standard deviation of 5~10 %. DAP of the new x-ray device was $82.4mGy*cm^2$ less than DRL established by KDFA even though its x-ray field size was small. This study shows that the new x-ray imaging device offers better in image quality and lower radiation dose compared to the conventional intraoral units. In additions, methods and know-how for studies in x-ray features could be accumulated from this work.

• Progress in Medical Physics
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• v.25 no.2
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• pp.100-109
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• 2014

In stereotactic body radiotherapy (SBRT), the accurate location of treatment sites should be guaranteed from the respiratory motions of patients. Lots of studies on this topic have been conducted. In this letter, a new verification method simulating the real respiratory motion of heterogenous treatment regions was proposed to investigate the accuracy of lung SBRT for Volumetric Modulated Arc Therapy. Based on the CT images of lung cancer patients, lung phantoms were fabricated to equip in $QUASAR^{TM}$ respiratory moving phantom using 3D printer. The phantom was bisected in order to measure 2D dose distributions by the insertion of EBT3 film. To ensure the dose calculation accuracy in heterogeneous condition, The homogeneous plastic phantom were also utilized. Two dose algorithms; Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB) were applied in plan dose calculation processes. In order to evaluate the accuracy of treatments under respiratory motion, we analyzed the gamma index between the plan dose and film dose measured under various moving conditions; static and moving target with or without gating. The CT number of GTV region was 78 HU for real patient and 92 HU for the homemade lung phantom. The gamma pass rates with 3%/3 mm criteria between the plan dose calculated by AAA algorithm and the film doses measured in heterogeneous lung phantom under gated and no gated beam delivery with respiratory motion were 88% and 78%. In static case, 95% of gamma pass rate was presented. In the all cases of homogeneous phantom, the gamma pass rates were more than 99%. Applied AcurosXB algorithm, for heterogeneous phantom, more than 98% and for homogeneous phantom, more than 99% of gamma pass rates were achieved. Since the respiratory amplitude was relatively small and the breath pattern had the longer exhale phase than inhale, the gamma pass rates in 3%/3 mm criteria didn't make any significant difference for various motion conditions. In this study, the new phantom model of 4D dose distribution verification using patient-specific lung phantoms moving in real breathing patterns was successfully implemented. It was also evaluated that the model provides the capability to verify dose distributions delivered in the more realistic condition and also the accuracy of dose calculation.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.127-134
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• 2008

Potential economic impact of a hypothetical severe accident at a nuclear power plant(Uljin units 3/4) was estimated by applying the Delphi method, which is based on the expert judgements and opinions, in the process of quantifying uncertain factors. For the purpose of this study, it is assumed that the radioactive plume directs the inland direction. Since the economic risk can be divided into direct costs and indirect effects and more uncertainties are involved in the latter, the direct costs were estimated first and the indirect effects were then estimated by applying a weighting factor to the direct cost. The Delphi method however subjects to risk of distortion or discrimination of variables because of the human behavior pattern. A mathematical approach based on the Bayesian inferences was employed for data processing to improve the Delphi results. For this task, a model for data processing was developed. One-dimensional Monte Carlo Analysis was applied to get a distribution of values of the weighting factor. The mean and median values of the weighting factor for the indirect effects appeared to be 2.59 and 2.08, respectively. These values are higher than the value suggested by OECD/NEA, 1.25. Some factors such as small territory and public attitude sensitive to radiation could affect the judgement of panel. Then the parameters of the model for estimating the direct costs were classified as U- and V-types, and two-dimensional Monte Carlo analysis was applied to quantify the overall economic risk. The resulting median of the overall economic risk was about 3.9% of the gross domestic products(GDP) of Korea in 2006. When the cost of electricity loss, the highest direct cost, was not taken into account, the overall economic risk was reduced to 2.2% of GDP. This assessment can be used as a reference for justifying the radiological emergency planning and preparedness.

• Journal of Biosystems Engineering
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• v.16 no.4
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• pp.384-398
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• 1991

A computer program was developed to select the optimum size of farm machine and analyze its operation costs according to various farming conditions. It was written in FORTRAN 77 and BASIC languages and can be run on any personal computer having Korean Standard Complete Type and Korean Language Code. The program was developed as a user-friendly type so that users can carry out easily the costs analysis for the whole farm work or respective operation in rice production, and for plowing, rotarying and pest controlling in upland. The program can analyze simultaneously three different machines in plowing & rotarying and two machines in transplanting, pest controlling and harvesting operations. The input data are the sizes of arable lands, possible working days and number of laborers during the opimum working period, and custom rates varying depending on regions and individual farming conditions. We can find out the results such as the selected optimum combination farm machines, the overs and shorts of working days relative to the planned working period, capacities of the machines, break-even points by custom rate, fixed costs for a month, and utilization costs in a hectare.

• Journal of the Korean GEO-environmental Society
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• v.15 no.1
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• pp.63-68
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• 2014

In this study, it was found that determined the photocatalytic degradation of antibiotics (lincomycin, LM) with various catalyst composite of titanium dioxide ($TiO_2$), hydroxyapatite (HAP) and germanium (Ge) under UV-A irradiation. At first, various type of complex catalysts were investigated to compare the enhanced photocatalytic potential. It was observed that in order to obtain the removal efficiencies were $TiO_2/HAP/Ge$ > $TiO_2/Ge$ > $TiO_2/HAP$. The composition of $TiO_2/HAP/Ge$ using a statistical approach based on mixture analysis design, one of response surface method was investigated. The independent variables of $TiO_2$ ($X_1$), HAP ($X_2$) and Ge ($X_3$) which consisted of 6 condition in each variables was set up to determine the effects on LM ($Y_1$) and TOC ($Y_2$) degradation. Regression analysis on analysis of variance (ANOVA) showed significant p-value (p < 0.05) and high coefficients for determination value ($R^2$ of $Y_1=99.28%$ and $R^2$ of $Y_2=98.91%$). Contour plot and response curve showed that the effects of $TiO_2/HAP/Ge$ composition for LM degradation under UV-A irradiation. And the estimated optimal composition for TOC removal ($Y_2$) were $X_1=0.6913$, $X_2=0.2313$ and $X_3=0.0756$ by coded value. By comparison with actual applications, the experimental results were found to be in good agreement with the model's predictions, with mean results for LM and TOC removal of 99.2% and 49.3%, respectively.

• The korean journal of orthodontics
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• v.36 no.1 s.114
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• pp.55-62
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• 2006

Orthodontic mini-implants, despite its usefulness as anchorage, have some limits such as loosening. Therefore, various shapes and lengths have been studied. The aim of this study is to analyze the shape and length of mini-implants mechanically. The shapes of mini-implants (1.6 mm, Dual Top, Jeil Medical Co., Seoul, Korea) were cylindrical and taper. The lengths of mini-implants were 6 mm and 8 mm. The tested groups were 5 groups (cylindrical 6 mm, cylindrical 8 mm, taper 6 mm, taper 8 mm and taper 8 mm modified whose thread is reduced from the middle to upper part). All were inserted and removed on the polyurethane foam with the torque measured. During insertion and removal, the taper shape needed higher torque than the cylindrical shape, and the 8 mm length than the 6 mm length (p<0.001). The taper 6mm group showed superior insertion torque (p<0.001) and similar removal torque to the cylindrical 8 mm group. The taper 8 mm modified group with gradually reduced threads, showed continuous high removal torque after the peak. The initial mechanical stability can be provided by the tapered shape and also, affected by length and thread design.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.1
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• pp.20-28
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• 2020

Objectives : The aim of this study was to compare psychosocial characteristics of the functional gastrointestinal disorders FGID group, non-FGID group, and control group and determine factors affecting the QOL of patients with FGID. Methods : 135 patients diagnosed with FGID were selected. 79 adults had no observable symptoms of FGID (control group) and 88 adults showed symptoms of FGID (non-FGID group). Demographic factors were investigated. The Korean-Beck Depression Inventory-II, Korean-Beck Anxiety Inventory, Korean-Childhood Trauma Questionnaire, Multidimensional Scale of Perceived Social Support, Connor-Davidson Resilience Scale, Patient Health Questionnaire-15 and WHO Quality of Life Assessment Instrument Brief Form were used to assess psychosocial factors. A one-way ANOVA was used to compare differences among groups. Pearson correlation test was performed to analyze the correlation of psychosocial factors and QOL of the FGID group. Further, a hierarchical regression analysis was conducted to determine factors affecting the QOL of the FGID group. Results : Between-group differences were not significant in demographic characteristics. Depression (F=48.75, p<0.001), anxiety (F=14.48, p<0.001), somatization (F=24.42, p<0.001) and childhood trauma (F=12.71, p<0.001) were significantly higher in FGID group than in other groups. Social support (F=39.95, p<0.001) and resilience (F=17.51, p<0.001) were significantly lower in FGID group than in other groups. Resilience (β=0.373, p<0.01) was the most important explanatory variable. The explained variance was 47.2%. Conclusions : Significantly more symptoms of depression, anxiety, childhood trauma, and somatization were observed for the FGID group. This group also had less social support, resilience, and quality of life than the non-FGID and control groups. The key factor for quality of life of the FGID group was resilience.