• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.390-397
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• 2013

The study, utilizing a data envelopment analysis (DEA) which is one of the nonparametric estimation methods, aims to evaluate the management efficiency of chestnut tree cultivators in such provinces in Chungchungnam-do as Cheong-yang, Gong-ju, Bu-yeo and so on. The analysis data of this study is based on inputs and outputs of 20 forestry households surveyed in the 2012 survey titled 'A Study on Current Level and Condition of Chestnut Cultivation and Management', which was conducted from March 2012 to October 2012. The elements of inputs are composed of management cost, harvesting cost, material cost, non-operation expenses and cultivation area, while the element of output is a gross margin only. Then the study analyzes a technical efficiency, a puretechnical efficiency and a scale efficiency using CCR and BCC model among DEA methods. Based on that, it also provides improvement methods for forestry households that turned out to be inefficient. In order to verify the result of DEA analysis, the study additionally compares a result of this efficiency study with that of chestnuts management standard diagnostic table. According to the result, the average value of technical efficiency analyzed was 0.667, proving to be inefficient in general. Given that the average value of pure-technical efficiency was 0.944 and that of scale efficiency was 0.703, it can be inferred that inefficiency exists in the field of scale, not in the field of cultivation techniques. As for forestry households with the efficiency score of 1, it is shown that there were 6 households that recorded 1 in the technical efficiency field and 13 households that recorded 1 in the pure technical efficiency. Meanwhile, there were 6 households that recorded 1 in all of the three aspects. In the comparison with the scores from chestnuts management standard diagnostic table, there were 5 households made a high score of over 80, among which are 3 households with score 1 in the technical efficiency. Also, the results of this study and the chestnuts management standard diagnostic table are proved to have the same result, both of them showing the same households that recorded the highest score and the lowest score. This means the management efficiency evaluation using DEA can be applied to the fieldwork along with the chestnuts management standard diagnostic table.

• Journal of Gastric Cancer
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• v.4 no.4
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• pp.230-234
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• 2004

Purpose: The use of laparoscopic surgery for gastric disease has been gaining popularity. However, there has been the controversy over the indications and the standard techniques of laparoscopic gastric surgery in the early gastric cancer (EGC). The purposes of this study were to compare the clinical outcomes among a hand-assisted laparoscopic distal gastrectomy (HALDG), a laparoscopy-assisted distal gastrectomy (LADG), and an open distal gastrectomy (ODG) and to discuss the role of these procedures in the treatment of EGC. Materials and Methods: Between August 2001 and July 2004, laparoscopic surgery was performed in our institution on 25 patients, LADG (n=7) and HALDG (n=18) with EGC. Analysis was performed on clinical data such as the operative time, the hospital stay, the start of oral intake, and the number of harvested lymph nodes. Patients were categorized into early and late groups by using the date of surgery and were also grouped by surgical procedure. To evaluate the feasibility and efficacy of laparoscopic surgery for EGC, we compared the clinical data with those for ODGs performed during the same period. Results: There was no difference in the number of harvested lymph nodes between the laparoscopic group and the open group, but the operation time in the laparoscopic group was longer than that in the open group (P<0.05). Also, no significant differences in other clinical data were found between the two groups. Comparing the early and the late periods of the series, the number of harvested lymph nodes for a HALDS increased from $22.31\pm4.29\;to\;29.40\pm3.21$ (P<0.05). Conclusion: Our early experience with laparoscopic gastric surgery shows that a wide range of possibilities exist for applying laparoscopic gastric surgery to selected gastric cancer patients. However, the surgical procedure should be standardized, and the outcomes of laparoscopic surgery, in comparison to those of open surgery, need to be confirmed based on a large randomized study. (J Korean Gastric Cancer Assoc 2004;4:230-234)

• Journal of Gastric Cancer
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• v.8 no.2
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• pp.91-96
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• 2008

Purpose: The early detection of gastric cancer and accuracy of preoperative staging has currently been on the increase due to the development of endoscopy and imaging techniques, but there are still many cases of advanced gastric cancer detected at the first diagnosis and there are also many cases of stage IV gastric cancer diagnosed after a postoperative pathological examination. Although the prognosis of stage IV gastric cancer is very poor, this study was performed to determine the value of the use of aggressive treatment determined after a clinical analysis. Materials and Methods: We retrospectively analyzed 150 patients that were diagnosed with stage IV gastric cancer among 1376 patients who underwent a laparotomy for gastric cancer from January 1994 to December 2006. Results: Of the 150 patients with stage IV gastric cancer who underwent a laparotomy, there were 104 men and 46 women. The mean patient age was 57.8 years (age range, 28~93 years). A subtotal gastrectomy or total gastrectomy was performed in 119 patients and 31 patients underwent an explorative laparotomy. The mean survival time of patients that underwent a gastrectomy and patients that did not undergo a gastrectomy was 722 days (range, 14~4,559 days) and 173 days (range, 16~374 days), respectively this result was statistically significant. When patients that underwent a gastrectomy were classified according to the TNM stage, the mean survival time of 33 patients with stage T4 disease was 534 days (range, 17~3,378 days) and the mean survival time of 63 patients with stage N3 disease was 521 days (range, 14~4,190 days), but there was no statistical significance. Chemotherapy was administered to 98 patients and 52 patients did not receive chemotherapy. The mean survival time of patients that received chemotherapy was 792 days (range, 36~4,559 days) and the mean survival time of patients that did not receive chemotherapy was 243 days (range, 14~2,413 days), with statistical significance. Conclusion: If there is no evidence of distant metastasis in stage IV gastric cancer, one can expect improvement of the survival rate by the use of aggressive treatment, including curative gastric resection with radical lymph node dissection and chemotherapy.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.107-108
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• 2008

The spectacular development of AMLCDs, been made possible by a-Si:H technology, still faces two major drawbacks due to the intrinsic structure of a-Si:H, namely a low mobility and most important a shift of the transfer characteristics of the TFTs when submitted to bias stress. This has lead to strong research in the crystallization of a-Si:H films by laser and furnace annealing to produce polycrystalline silicon TFTs. While these devices show improved mobility and stability, they suffer from uniformity over large areas and increased cost. In the last decade we have focused on microcrystalline silicon (${\mu}c$-Si:H) for bottom gate TFTs, which can hopefully meet all the requirements for mass production of large area AMOLED displays [1,2]. In this presentation we will focus on the transfer of a deposition process based on the use of $SiF_4$-Ar-$H_2$ mixtures from a small area research laboratory reactor into an industrial gen 1 AKT reactor. We will first discuss on the optimization of the process conditions leading to fully crystallized films without any amorphous incubation layer, suitable for bottom gate TFTS, as well as on the use of plasma diagnostics to increase the deposition rate up to 0.5 nm/s [3]. The use of silicon nanocrystals appears as an elegant way to circumvent the opposite requirements of a high deposition rate and a fully crystallized interface [4]. The optimized process conditions are transferred to large area substrates in an industrial environment, on which some process adjustment was required to reproduce the material properties achieved in the laboratory scale reactor. For optimized process conditions, the homogeneity of the optical and electronic properties of the ${\mu}c$-Si:H films deposited on $300{\times}400\;mm$ substrates was checked by a set of complementary techniques. Spectroscopic ellipsometry, Raman spectroscopy, dark conductivity, time resolved microwave conductivity and hydrogen evolution measurements allowed demonstrating an excellent homogeneity in the structure and transport properties of the films. On the basis of these results, optimized process conditions were applied to TFTs, for which both bottom gate and top gate structures were studied aiming to achieve characteristics suitable for driving AMOLED displays. Results on the homogeneity of the TFT characteristics over the large area substrates and stability will be presented, as well as their application as a backplane for an AMOLED display.

• Journal of Korean Society of Forest Science
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• v.39 no.1
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• pp.1-34
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• 1978

On July 8, 1977, 432mm of precipitation which is the largest daily storm in Korea fell on the city of Anyang where a nearby suburban community of Seoul. Average storm intensities of 90mm per hour were recorded during the period from 1900~2200 hours on this date. Area of landslides triggered by this storm is about 96 hectares resulting from 1,876 places within about 12,600 hectares of the watershed studied. These hazards injured hundreds of human lives and took 122 human lives. Rail and highway systems were disrupted and about 30 hectares of rice paddies were washed away and hundreds of hectares were inundated. About 500 houses were destroyed. The objectives of this study are (a) to describe the problem areas, identifying critical factors causing the landslide hazards including earth and stone-debris avalanches, (b) suggest measures which might enhance the effectiveness of stabilization measures, and (c) also suggest the landslide and flood damage prevention methods from the point view of the upper-watershed conservation techniques in Anyang hollow-basin.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.4
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• pp.104-117
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• 2017

This study compares the results of landscape image analysis using text mining techniques and factor analysis for Lotte World Tower, which is the first atypical skyscraper building in Korea, and identifies landscape images of the site to determine possibilities of use. Lotte World Tower's landscape image has been extracted from text mining analysis focusing on adjectives such as 'new', 'transformational', 'unusual', 'novelty', 'impressive', and 'unique', and phrases such as in the process of change, people's active elements(caliber, outing, project, night view), media(newspaper, blog), and climate(weather, season). As a result of the factor analysis, factors affecting the landscape image of Lotte World Tower were symbolic, aesthetic, and formative. Identification, which is a morphological feature, has characteristics of scale and visibility but it is not statistically significant in preference. Rather, the psychological factors such as the symbolism with characteristics such as poison and specialty, harmony with the characteristics of the surrounding environment, and beautiful aesthetic characteristics were an influence on the landscape image. The common results of the two research methods show that psychological characteristics such as factors that can represent and represent the city affect the landscape image more greatly than the morphological and physical characteristics such as location and location of the building. In addition, the text mining technique can identify nouns and adjectives corresponding to the images that people see and feel, and confirms the relationship between the derived keywords, so that it can focus the process of forming the landscape image and further the image of the city. It would appear to be a suitable method to complement the limitation of landscape research. This study is meaningful in that it confirms the possibility that big data can be utilized in landscape analysis, which is one research field of landscape architecture, and is significant for understanding the information of a big data base and contribute to enlarging the landscape research area.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.