• Radiation Oncology Journal
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• v.19 no.2
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• pp.118-126
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• 2001

Purpose : In this study, it was investigated whether dose response relation existed or not in local radiotherapy for primary hepatocellular carcinoma. Materials and Methods : From January 1992 to March 2000, 158 patients were included in present study. Exclusion criteria included the presence of extrahepatic metastasis, liver cirrhosis of Child's class C, tumors occupying more than two thirds of the entire liver, and performance status on the ECOG scale of more than 3. Radiotherapy was given to the field including tumor with generous margin using 6, 10-MV X-ray. Mean tumor dose was $48.2{\pm}7.9\;Gy$ in daily 1.8 Gy fractions. Tumor response was based on diagnostic radiologic examinations such as CT scan, MR imaging, hepatic artery angiography at $4\~8$ weeks following completion of treatment. Statistical analysis was done to investigate the existence of dose response relationship of local radiotherapy when it was applied to the treatment of primary hepatocellular carcinoma. Results : An objective response was observed in 106 of 158 patients, giving a response rate of $67.1\%$. Statistical analysis revealed that total dose was the most significant factor in relation to tumor response when local radiotherapy was applied to the treatment of primary hepatocellular carcinoma. Only $29.2\%$ showed objective response in patients treated with dose less than 40 Gy, while $68.6\%\;and\;77.1\%$ showed major response in patients with $40\~50\;Gy$ and more than 50 Gy, respectively. Child-Pugh classification was significant factor in the development of ascites, overt radiation induced liver disease and gastroenteritis. Radiation dose was an important factor for development of radiation induced gastroduodenal ulcer. Conclusion : Present study showed the existence of dose response relationship in local radiotherapy for primary hepatocellular carcinoma. Only radiotherapy dose was a significant factor to predict the objective response. Further study is required to predict the maximal tolerance dose in consideration of liver function and non-irradiated liver volume.

• Journal of Chest Surgery
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• v.31 no.3
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• pp.233-241
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• 1998

To define the risk factors affecting the early major morbidity and mortality after multiple- valve operations, the preoperative, intraoperative and postoperative informations were retrospectively collected on 124 consecutive patients undergoing a multiple-valve operation between October 1985 and July 1996 at the department of Thoracic and Cardiovascular Surgery of Pusan Paik Hospital. The study population consists of 53 men and 71 women whose mean age was 37.9$\pm$11.5(mean$\pm$SD) years. Using the New York Heart Association(NYHA) classification, 41 patients(33.1%) were in functional class II, 60(48.4%) in class III, and 20(16.1%) in class IV preoperatively. Seven patients(5.6%) had undergone previous cardiac operations. Atrial fibrillations were present in 76 patients(61.3%), a history of cerebral embolism in 5(4.0%), and left atrial thrombus in 13(10.5%). The overall early mortality rate and postoperative morbidity was 8.1% and 21.8% respectively. Among the 124 cases of multiple-valve operation, there were 57(46.0%) of combined mitral valve replacement(MVR) and aortic valve replacement(AVR), 48(38.7%) of combined MVR and tricuspid annuloplasty(TVA), 12(9.7%) of combined MVR, AVR and TVA, 3(2.4%) of combined MVR and aortic valvuloplasty, 2(1.6%) of combined MVR and tricuspid valve replacement, and others. The patients were classified according to the postoperative outcomes; Group A(27 cases) included the patients who had early death or major morbidity such as low cardiac output syndrome, mediastinitis, cardiac rupture, ventricular arrhythmia, sepsis, and others; Group B(97 cases) included the patients who had the good postoperative outcomes. The patients were also classified into group of early death and survivor. In comparison of group A and group B, there were significant differences in aortic cross-clamping time(ACT, group A:153.4$\pm$42.4 minutes, group B:134.0$\pm$43.7 minutes, p=0.042), total bypass time(TBT, group A:187.4$\pm$65.5 minutes, group B:158.1$\pm$50.6 minutes, p=0.038), and NYHA functional class(I:33.3%, II:9.7%, III:20%, IV:50%, p=0.004). In comparison of early death(n=10) and survivor(n=114), there were significant differences in age(early death:45.2$\pm$8.7 years, survivor:37.2$\pm$11.6 years, p=0.036), sex(female:12.7%, male:1.9%, p=0.043), ACT(early death:167.1$\pm$38.4 minutes, survivor:135.7$\pm$43.7 minutes, p=0.030), and NYHA functional class(I:0%, II:4.9%, III:1.7%, IV:35%, p=0.001). In conclusion, the early major morbidity and mortality were influenced by the preoperative clinical status and therefore the earlier surgical intervention should be recommended whenever possible. Also, improved methods of myocardial protection and operative techniques may reduce the risk in patients with multiple-valve operation.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Radiation Oncology Journal
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• v.14 no.2
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• pp.95-103
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• 1996

Purpose : A retrospective analysis for patients with oropharyngeal carcinoma who were treated with radiation was performed to assess the results of treatment and patterns of failure, and to identify the factors that might influence survival. materials and methods : From March 1985 through June 1993, 53 patients with oropharyngeal carcinoma were treated with either radiation therapy alone or combination of neoadjuvant chemotherapy and radiation therapy at the Department of Radiation Oncology, Kyungpook National University Hospital. Patients' ages ranged from 31 to 73 years with a median age of 54 years. There were 47 men and 6 women, Forty-two Patients ($79.2\%$) had squamous cell carcinoma, 10 patients ($18.9\%$) had undifferentiated carcinoma and 1 patient ($19\%$) had adenoid cystic carcinoma. There were 2 patients with stage I, 12 patients with stage II, 12 Patients with stage III and 27 patients with stage IV. According to the TNM classification, patients were distributed as follows: T1 7, T2 28, T3 10, T4 7, TX 1, and N0 17, Nl 13, N2 21, N3 2. The primary tumor sites were tonsillar region in 36 patients ($67.9\%$), base of the tongue in 12 patients ($22.6\%$), and soft palate in 5 patients ($9.4\%$). Twenty-five patients were treated with radiation therapy alone and twenty-eight Patients were treated with one to three courses of chemotherapy followed by radiation therapy. Chemotherapeutic regimens used were either CF (cisplatin and 5-fluorouracil) or CVB (cisplatin, vincristine and bleomycin). Radiation therapy was delivered 180-200 cGy daily, five times a week using 6 MV X-ray with or without 8-10 MeV electron beams A tumor dose ranged from 4500 cGy to 7740 cGy with a median dose of 7100 cGy. The follow-up time ranged from 4 months to 99 months with a median of 21 months. Results : Thirty-seven patients ($69.8\%$) achieved a CR (complete response) and PR (partial response) in 16 patients ($30.2\%$) after radiation therapy. The overall survival rates were $47\%$ at 2 years and $42\%$ at 3 years, respectively. The median survival time was 23 months. Overall stage (p=0.02) and response to radiation therapy (p=0.004) were significant prognostic factors for overall survival. The 2-year disease-free survival rate was $45.5\%$. T-stage (p=0.03), N-stage (p=0.04) and overall stage (P=0.04) were significant prognostic factors for disease-free survival. Age, sex, histology, primary site of the tumor, radiation dose, combination of chemotherapy were not significantly associated with disease-free survival. Among evaluable 32 Patients with CR to radiation therapy, 12 patients were considered to have failed Among these, 8 patients failed locoregionally and 4 Patients failed distantly. Conclusion : T-stage, N-stage and overall stage were significant prognostic factors for disease-free survival in the treatment of oropharyngeal cancer Since locoregional failure was the predominant pattern of relapse, potential methods to improve locoregional control with radiation therapy should be attempted. More controlled clinical, trials should be completed before acceptance of chemotherapy as a part of treatment of oropharyngeal carcinoma.

• Radiation Oncology Journal
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• v.20 no.2
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• pp.139-146
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• 2002

Purpose : This study evaluated the treatment outcomes, patterns of failure, and treatment related complications of primary lymphoma patients who received definitive radiation therapy. Materials and Methods : A retrospective analysis was undertaken for 31 patients with primary orbital lymphoma at the Asan Medical Center between February 1991 and April 2001. There were 18 males and 13 females with ages ranging from 3 to 73 years (median, 44 years). The involved sites were 9 conjunctivae, 12 eyelids and 10 other orbits. The histological types were 28 MALT lymphomas (low-grade B-cell lymphoma of mucosa-associated lymphoid tissue type), 1 diffuse large B-cell lymphoma, 1 anaplastic large cell lymphoma and 1 lymphoblastic lymphoma. The Ann Arbor stages were all IE $(100\%)$. Ann Arbor stage III or IV patients were excluded from this study, Bilateral orbital involvement occurred in 6 cases. Radiation therapy was given with one anterior port of high energy electrons $(6\~16\;MeV)$ for the lesions located at the anterior structures like the conjunctivae or eyelids. Lesions with a posterior extension or other orbital lesions were treated with 4 or 6 MeV photons with appropriately arranged portals. In particular, lens blocks composed of lead alloy were used in conjunctival or eyelid lesions. Twelve patients received chemotherapy. The median follow-up period was 53 months. Results : The 5-year overall, cause-specific, and disease-free survival was $91\%,\;96\%,\;and\;80\%$, respectively. The complete response rate 6 months after radiation therapy was $100\%$. Local recurrences were observed in 2 patients at 16 and 18 months after completion of radiation treatment. They were salvaged with additional radiation therapy. Two patients developed distant metastases. A MALT lymphoma patient with a lung relapse was successfully salvaged with radiotherapy, but the other lymphoblastic lymphoma patient with bone marrow relapse expired. There were no severe complications but 5 patients developed radiation-induced cataracts and 2 patients developed dry eye. Conclusion : Most primary orbital lymphomas consisted of MALT lymphomas. Radiation therapy was a successful treatment modality for orbital lymphoma without any severe complications. In cases of local relapses, radiation therapy is also a very successful salvage treatment modality.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.211-226
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• 2023

As we transition into the post-COVID era, economic activities that were stagnant are regaining momentum. In particular, there is a growing trend of technology entrepreneurship driven by the opportunities of digital transformation in the Fourth Industrial Revolution. However, entrepreneurship education content is struggling to keep up with the rapid pace of technological change. This study aims to emphasize the importance of entrepreneurship mentoring as a crucial component of entrepreneurship education content that requires adaptation and advancement due to the increasing demand for technology entrepreneurship. This study redefines startup mentoring, which is differentiated from general mentoring, at the present time when the demand for startups, which increases with the declining employment rate, increases, and the development of quality startup education contents and securing professional startup mentors are required. According to the start-up stage, it is divided into preliminary entrepreneurs and early entrepreneurs, and the effect of entrepreneurship knowledge and self-efficacy among start-up mentoring functions on entrepreneurial will and mentoring satisfaction is improved by empirically researching the effects of start-up mentoring functions in the case of initial entrepreneurs as a moderating effect. To confirm the importance of entrepreneurship mentoring effect for. To this end, among the mentoring functions, entrepreneurship knowledge and self-efficacy were set as independent variables, and entrepreneurial will and mentoring satisfaction were set as dependent variables. The research model was designed and hypotheses were established. In addition, empirical analysis was conducted by conducting a questionnaire survey on trainees who received entrepreneurship mentoring education at ICCE Startup School and Opus Startup School. To summarize the results of the empirical analysis, first, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on entrepreneurial will. Second, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on mentoring satisfaction. Third, it was analyzed that entrepreneurship had no significant moderating effect on entrepreneurial knowledge and entrepreneurial will. Fourth, it was analyzed that entrepreneurship had no significant moderating effect on mentoring satisfaction. Fifth, it was found that entrepreneurship had a significant moderating effect between self-efficacy and will to start a business. As a result of the research analysis, the first implication is that the mentoring function in start-up education is analyzed to produce meaningful results for both the initial entrepreneurs and the prospective entrepreneurs in the will to start a business and satisfaction. . Second, it was analyzed that there was no significant relationship between whether a business was started and the mentoring function and effect. However, it was analyzed that the will to start a business through improvement of self-efficacy through mentoring was significantly related to whether or not to start a business. turned out to be helpful. Many start-up education programs currently conducted in Korea educate both early-stage entrepreneurs and prospective entrepreneurs at the same time for reasons such as convenience. However, through the results of this study, even in small-scale entrepreneurship mentoring, it is suggested that customized mentoring through detailed classification such as whether the mentee has started a business can be a method for successful entrepreneurship and high satisfaction of the mentee.

• Journal of Korean Society of Forest Science
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• v.14 no.1
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• pp.1-20
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• 1972

The trend and achievements of forest genetics research in abroad were investigated through observation tours and reference work and following facts were found to be important aspects which should be adopted in the forest genetics research program in Korea. Because of world wide recognization on the urgency of taking a measure to reserve some areas of the representative forest type on the globe before the extingtion of such forest type as the results of continuous exploitations of the natural forests to meet the timber demand all over the world, it is urgently needed to take a measure to reserve certain areas of natural stand of Pinus koraiensis, Pinus parviflora, Pinus densiflora f. erectra, Abies koreana, Quercus sp., Populus sp., etc. as gene pool to be used for the future program of forest tree improvement. And the genetic studies of those natural forest of economic tree species are also to be performed. 1. Increase of the number of selected tree for breeding purpose. Because of the fact that the number of plus tree at present is too small to carry out selection program for tree improvement, particularly for the formation of source population for recurrent selection of parent trees of the 2nd generation seed orchard it is to be strongly emphasized to increase the number of plus tree by alleviating selection criteria in order to enlarge the population size of plus trees to make the selection program more efficient. 2. Progeny testing More stress should be placed on carrying out progeny testing of selected trees with open pollinated seeds. And particular efforts are to be made for conducting studies on adult/juvenile correlation of important traits with a view to enable to predict adult performances with some traits revealed in juvenile age thus to save time for progeny testing. 3. Genotype-environment interaction Studies on genotype and environment interaction should be conducted in order to elucidate whether the plus trees selected on the good site express their superiority on the poor site or not and how the environment affect the genotype. And the justification of present classification of seed distribution area should be examined. 4. Seed orchard of broad leaf tree species. Due to the difficulty of accurate comparison of growth rate of neighbouring trees of broad leaf tree species in natural stand, it is recommended that for the improvement of broad leaf trees a seedling seed orchard is to be made by roguing the progeny test plantation planted densely with control pollinated seedlings of selected trees. 5. Breeding for insect resistant varieties. In the light of the fact that the resistant characteristics against insect such as pine gall midge (Thiecodiplosis japonensis U. et I.) and pine bark beetle (Myelophilus pinipera L.) are highly correlated with the amount and quality of resin which are known as gene controlled characteristics, breeding for insect resistance should be carried out. 6. Breeding for timber properties. With the tree species for pulp wood in particular, emphasis should be placed upon breeding for high specific gravity of timber. 7. Introduction of Cryptomeria and Japanese Cypress In the light of the fact that the major clones of Cryptomeria are originated from Yoshino source and are being planted up to considerably north and high elevation in Japan, those species should be examined on their cold resistance in Korea by planting them in further northern part of the country.