• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.648-654
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• 2018

Currently, the Defense Agency for Technology and Quality (DTaQ), which is responsible for the quality assurance of military supplies, divides munitions into four categories, in order to conduct its governmental quality assurance activities, including product examination, process review and system audit. However, these 4 categories may differ depending on the related organizations' (e.g. Defense Acquisition Program Administration, munitions manufacturing) military requirements. Therefore, in this study, appropriate classification criteria for munitions are suggested for the sake of the efficient procurement, production and quality assurance of military supplies. We investigated the item classification system of the Public Procurement Service, which is a similar organization to the DTaQ. We also compared the appropriate classification criteria with those of related organizations and identified the current status of munitions classification data according to the current standard. In addition, application samples are presented using the proposed quality assurance classification criteria. Finally, the classification criteria of military supplies proposed in this paper will contribute to improving the efficiency of government quality assurance activities.

• International Journal of Contents
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• v.10 no.1
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• pp.54-61
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• 2014

In this paper, we propose a pioneering concept of DPD(Disaster Prevention Design) to realize a securable society in the future. Features of danger in the future society are expected to be diverse, abrupt occurring, large scale, and complicated ways. Due to increment of dangers with their features of uncertainty, interactivity, complexity, and accumulation, human-oriented design concept naturally participates in activities to prevent our society against disasters effectively. We presented DPD is an essential design activity in order to cope with dangers expected in the future societies as well as realize securable environments. DPD is also an integrated design aids including preemptive protections, rapid preparing, recovery, and interactive cooperation. We also expect these activities of DPD is effective for generation of new values in the market, satisfaction of social needs, expansion of design industry, and a novel chance for development in the future society. Throughout this paper, we submit various aspects of DPD concepts including definition, classification, scope, necessity, strategy, influencing elements, process, and its principle. We expect these concepts will be the seed and/or basement of DPD research for the future works. For the direction of study for DPD in the future, we emphasize alarm system for preemptive protection rather than recovery strategy for the damage occurred. We also need to research about progressive prevention techniques and convergence with other areas of design. In order to transfer the concept of product design from facility-oriented mechanism to human-oriented one, we should develop new kinds of city basis facilities, public-sense design concepts referred to social weak-party, e-Learning content design preparing disasters, and virtual simulation design etc. On the other hand, we have to establish laws and regulations to force central and/or provincial governments to have these DPD strategies applying their regional properties. Modern design activities are expanding to UI(user interface) content design area overcoming the conventional design concept of product and/or service. In addition, designers are recognized as art directors or life stylists who will change the human life and create the social value. DPD can be divided into prevention design, preparedness design, response design, and recovery design. Five strategies for successful DPD are Precaution-oriented, Human-oriented, Sense-oriented, Legislation, and Environment Friendly Strategies.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.2
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• pp.507-516
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• 2004

Alzheimer's disease(AD) is a geriatric dementia that is widespread in old age. In the near future AD will be the commom disease in public health service. Although a variety of oriental presciptions in study POD(Polygala tenuifolia extracted from dichlorometan) have been traditionally utilized for the treatment of AD, their pharmacological effects and action mechanisms have not yet fully elucidated. It has been widely believed that AP peptide divided from APP causes apoptotic neurotoxicity in AD brain. However, recent evidence suggests that CT105, carboxy terminal 105 aminoacids peptide fragment of APP, may be an important factor causing neurotoxicity in AD. SK-N-SH cells expressed with CT105 exhibited remarkable apoptotic cell damage. Based on morphological observations by phase contrast microscope and NO formation in the culture media, the CT105-induced cell death was significantly inhibited by POD. In addition, AD is one of brain degeneration disease. So We studied on herbal medicine that have a relation of brain degeneration. From old times, In Oriental Medicine, PO water extract has been used for disease in relation to brain degeneration. We were examined by ROS formation, neurite outgrowth assay and DPPH scravage assay. Additionally, we investigated the association between the CT105 and neurite degeneration caused by CT105-induced apoptotic response in neurone cells. We studied on the regeneratory and inhibitory effects of anti-Alzheimer disease in pCT105-induced neuroblastoma cell lines by POD. Findings from our experiments have shown that POD inhibits the synthesis or activities of CT105, which has neurotoxityies and apoptotic activities in cell line. In addition, treatment of POD(>50 ㎍/㎖ for 12 hours) partially prevented CT(105)-induced cytotoxicity in SK-N-SH cell lines, and were inhibited by the treatment with its. POD(>50 ㎍/㎖ for 12 hours) repaired CT105-induced neurite outgrowth when SK-N-SH cell lines was transfected with CT105. As the result of this study, In POD group, the apoptosis in the nervous system is inhibited, the repair against the degerneration of Neuroblastoma cells by CT105 expression is promoted. Decrease of memory induced by injection of scopolamin into rat was also attenuted by POD, based on passive avoidance test. Taken together, POD exhibited inhibition of CT105-induced apoptotic cell death. POD was found to reduce the activity of AchE and induced about the CA1 in rat hippocampus. Base on these findings, POD may be beneficial for the treatment of AD.

• Journal of Korean Library and Information Science Society
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• v.37 no.4
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• pp.351-370
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• 2006

The goal of this study was to evaluate vision, mission, strategies, and action plans from the long-term development plan for the B University's library, and specificity, measurability, achievability, relevance, and time-frame were measured and evaluated for each with SMART method. The results obtained through this study are as below : Firstly, SMART evaluation result for the B University's library was 3.80 for the vision, 3.97 for the mission, 3.74 for strategies, and 3.64 for action plans. Secondly, specificity of the long-term development plan for the B University's library was 4.06, measurability was 3.72, achievability was 3.68, relevance was 3.90, and time-frame was 3.58. Thirdly, the overall evaluation of the long-term development plan for the B University's library showed that among components from the development plan, the mission was the most superior while action plans had problems. Fourthly, in SMART evaluation factors, specificity was the most superior while time-frame had problems such that it should be supplemented in the future.

• Culinary science and hospitality research
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• v.22 no.7
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• pp.58-71
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• 2016

This study aimed to gauge the public awareness of the cultural value and superiority of Korean traditional sweets. Furthermore, it evaluated the obstacles that the Korean traditional sweet industry faces in the modern society, and a method to popularize it. It also analyzed the awareness of Korean traditional sweets. Both male and female study subjects showed the highest awareness of yakgwa and the lowest of chasugwa. Female respondents showed significantly (p<0.05~p<0.001) higher awareness of Osaekdasik, Hukimjadasik, Bellflower-junggwa, and Genseng-junggwa than male respondents. Maejakgwa, Osaekdasik, Rice-dasik, and Hukimjada- sik showed significant difference in awareness by the area survey respondents originated. Contrarily, Walnut-gangjung had significantly (p<0.001) higher awareness in rural areas than in more urban areas (e.g., large cities and small and medium-sized cities). Both male and female respondents answered (OR indicated) that the obstacles facing the Korean traditional sweet industry are uncommon products and expensive price. Both male and female respondents said that they had Korean traditional sweets less because it was harder to purchase than western sweets (due to limited access to these sweets), less delicious, and too expensive. Both male and female respondenst suggested that the urgent tasks to popularize the Korean traditional sweets were diversification in shape and ingredient, developing various new flavors, and cheaper products. Both male and female respondents responded that product diversification and strengthened marketing were urgent tasks to industrialize Korean traditional sweets. Therefore, it was believed that failure in generalization was the urgent problem of the Korean traditional sweet industry, and that Korean traditional sweets were harder to purchase because of lower accessibility than western sweets. To popularize Korean traditional sweets, it may be necessary to develop sweets in various shapes and ingredients, flavors suiTable to modern people, become cheaper in price, and have fancier (OR better) packaging.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.1
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• pp.38-47
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• 2016

Objectives: The objective of this study was to determine the exposure levels of forklift operators to diesel engine exhaust(DEE) using black carbon(BC), elemental carbon(EC), and nitrogen dioxide($NO_2$) as indicators. Methods: A total of eight forklift operators in six collection companies were assessed over a period of two months from July to September 2015. BC was measured using a real-time monitor and respirable EC samples were analyzed using the NIOSH method 5040. $NO_2$ samples were collected using a passive badge-type sampler. Results: The geometric mean of BC, EC and $NO_2$ were $3.1-19.1{\mu}g/m^3$, $2.1-23.8{\mu}g/m^3$, and 12.5-166.6 ppb at all companies. When forklifts were operating both outside and inside, BC concentrations increased 2.0-5.6 times. The highest increase was observed when forklifts were operating indoors. The increase in BC concentrations varied by company(company A: 2.0 times, B: 3.2 times, C: 5.6 times, D: 2.1 times, E: 5.1 times, F: 2.6 times). The geometric mean of BC, EC, and $NO_2$ for the forklift operators was $9.6{\mu}g/m^3$, $7.9{\mu}g/m^3$, and 48.9 ppb, respectively. The geometric mean of BC, EC, and $NO_2$ for manufacturing workers was $9.3{\mu}g/m^3$, $0.9{\mu}g/m^3$, and 85.2 ppb, respectively. The mean BC and EC exposure levels for the forklift operators were slightly higher than those for manufacturing workers, but $NO_2$ levels for manufacturing workers were higher than those for the forklift operators(p>0.05). Multiple regression analysis revealed that diesel exhaust emissions standard, forklift weight and forklift manufacturer were the most influential factors in determining worker exposure. Conclusions: In the DEE work environment, workers who perform tasks within the workplace as well as inside forklifts as operators are likely to be exposed to a lack of ventilation. Further study of forklift operators' exposure to DEE indicators should be conducted to include a wider range of occupational and environmental situations, such as collection procedures, seasonal situations, types of fuel used, and number of forklifts.

• Journal of the Economic Geographical Society of Korea
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• v.7 no.1
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• pp.45-63
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• 2004

The main purpose of this article is to suggest policy implications for building innovative cluster in Korea by investigating the operating system and role of the Kanagawa Science Park (KSP) located in Kanagawa prefecture, Japan as a central network agent. The KSP established mainly by private and government partnership has played a critical role for building innovative clusters as a way in which increase national competitiveness. But they also provide variety of real service from R&D to commercialization for local firms by facilitating and coordinating networks among regional economic actors such as firms, universities and public research institutes. The regional policy as a way in which increase national competitiveness in Korea is also the establishment of innovative clusters based on regional and industrial characteristics. However, the main problem with building the innovative cluster is the reduction of policy effectiveness due to duplicated supporting and coordinating institutes and institutions established by the each central administration and local governments, aimed at facilitating networks among regional economic actors. In this context, the article suggests that there is a need to build a regional central network agent by designing an organic operating system for the effective management of each network agent in accordance with the process from R&D to commercialization, i.e. an encompassed supporting system, on the basis of benchmarking the KSP operating system in Japan.