• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.107-118
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• 2016

The advent of 5G mobile communications, which is expected in 2020, will provide many services such as Internet of Things (IoT) and vehicle-to-infra/vehicle/nomadic (V2X) communication. There are many requirements to realizing these services: reduced latency, high data rate and reliability, and real-time service. In particular, a high level of reliability and delay sensitivity with an increased data rate are very important for M2M, IoT, and Factory 4.0. Around the world, 5G standardization organizations have considered these services and grouped them to finally derive the technical requirements and service scenarios. The first scenario is broadcast services that use a high data rate for multiple cases of sporting events or emergencies. The second scenario is as support for e-Health, car reliability, etc.; the third scenario is related to VR games with delay sensitivity and real-time techniques. Recently, these groups have been forming agreements on the requirements for such scenarios and the target level. Various techniques are being studied to satisfy such requirements and are being discussed in the context of software-defined networking (SDN) as the next-generation network architecture. SDN is being used to standardize ONF and basically refers to a structure that separates signals for the control plane from the packets for the data plane. One of the best examples for low latency and high reliability is an intelligent traffic system (ITS) using V2X. Because a car passes a small cell of the 5G network very rapidly, the messages to be delivered in the event of an emergency have to be transported in a very short time. This is a typical example requiring high delay sensitivity. 5G has to support a high reliability and delay sensitivity requirements for V2X in the field of traffic control. For these reasons, V2X is a major application of critical delay. V2X (vehicle-to-infra/vehicle/nomadic) represents all types of communication methods applicable to road and vehicles. It refers to a connected or networked vehicle. V2X can be divided into three kinds of communications. First is the communication between a vehicle and infrastructure (vehicle-to-infrastructure; V2I). Second is the communication between a vehicle and another vehicle (vehicle-to-vehicle; V2V). Third is the communication between a vehicle and mobile equipment (vehicle-to-nomadic devices; V2N). This will be added in the future in various fields. Because the SDN structure is under consideration as the next-generation network architecture, the SDN architecture is significant. However, the centralized architecture of SDN can be considered as an unfavorable structure for delay-sensitive services because a centralized architecture is needed to communicate with many nodes and provide processing power. Therefore, in the case of emergency V2X communications, delay-related control functions require a tree supporting structure. For such a scenario, the architecture of the network processing the vehicle information is a major variable affecting delay. Because it is difficult to meet the desired level of delay sensitivity with a typical fully centralized SDN structure, research on the optimal size of an SDN for processing information is needed. This study examined the SDN architecture considering the V2X emergency delay requirements of a 5G network in the worst-case scenario and performed a system-level simulation on the speed of the car, radius, and cell tier to derive a range of cells for information transfer in SDN network. In the simulation, because 5G provides a sufficiently high data rate, the information for neighboring vehicle support to the car was assumed to be without errors. Furthermore, the 5G small cell was assumed to have a cell radius of 50-100 m, and the maximum speed of the vehicle was considered to be 30-200 km/h in order to examine the network architecture to minimize the delay.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.111-122
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• 2017

This study analyzes the current state of entrepreneurship education and start-up foundations by country in order to find ways to improve the domestic entrepreneurial environment and to promote the recognition of desirable entrepreneurship practices. It also investigates the relationship between entrepreneurship, entrepreneurial will, and the level of opportunity-based entrepreneurships, by using data from the 2016 Global Entrepreneurship Trend Report (GETR). First, the results show the urgent need for the expansion of entrepreneurship education in Korea. In the GETR category of 'experience of entrepreneurship education in elementary, middle and high schools', Korea was ranked very low (19th place), among the 20 countries. In the 'college' and 'lifelong entrepreneurship education' categories, it procured a mid-level ranking (15th). While entrepreneurship education for all ages is being promoted globally, entrepreneurship education for middle-aged individuals in Korea is relatively weak. This implies that the expansion of entrepreneurship education to lifelong education and education for employees and retirees is required. Second, the individual's entrepreneurial intention in Korea was 3.8 points, implying a mid-level ranking (15th), and it ranked the lowest in terms of opportunity-based entrepreneurship (20th). In comparison to China (4.55) and the United States (4.01), the entrepreneurial intention of Koreans was found to be low. The level of opportunity-driven entrepreneurship was also found to be very low, compared to China (4.35), Japan (4.04) and the United States (4.59). In general, the proportion of the level in opportunity-driven entrepreneurship, increases from the factor-driven and efficiency-driven, to the innovation-driven type. In Korea, the percentage of entrepreneurial ventures centered around involuntary entrepreneurship and small businesses is high. It is also interpreted that opportunity-based entrepreneurships are low in number because of this high proportion of involuntary start-up and small businesses. Last, the entrepreneurial intention in all types (factor-driven, efficiency-driven, and innovation-driven) was exceptionally high. It has been confirmed that exposure to all entrepreneurship education (elementary, junior high, university, and lifelong education) in innovation-driven countries, greatly increases entrepreneurial intention. In the case of Korea, which is an innovation-driven country, qualitative improvement based on quantitative expansion of entrepreneurship education is expected to be a major driving force for individuals' entrepreneurial intention to obtain a mid-level ranking (15th).

• Progress in Medical Physics
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• v.23 no.2
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• pp.81-90
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• 2012

The effect of setup uncertainties on CTV dose and the correlation between setup uncertainties and setup margin were evaluated by Monte Carlo based numerical simulation. Patient specific information of IMRT treatment plan for rectal cancer designed on the VARIAN Eclipse planning system was utilized for the Monte Carlo simulation program including the planned dose distribution and tumor volume information of a rectal cancer patient. The simulation program was developed for the purpose of the study on Linux environment using open source packages, GNU C++ and ROOT data analysis framework. All misalignments of patient setup were assumed to follow the central limit theorem. Thus systematic and random errors were generated according to the gaussian statistics with a given standard deviation as simulation input parameter. After the setup error simulations, the change of dose in CTV volume was analyzed with the simulation result. In order to verify the conventional margin recipe, the correlation between setup error and setup margin was compared with the margin formula developed on three dimensional conformal radiation therapy. The simulation was performed total 2,000 times for each simulation input of systematic and random errors independently. The size of standard deviation for generating patient setup errors was changed from 1 mm to 10 mm with 1 mm step. In case for the systematic error the minimum dose on CTV $D_{min}^{stat{\cdot}}$ was decreased from 100.4 to 72.50% and the mean dose $\bar{D}_{syst{\cdot}}$ was decreased from 100.45% to 97.88%. However the standard deviation of dose distribution in CTV volume was increased from 0.02% to 3.33%. The effect of random error gave the same result of a reduction of mean and minimum dose to CTV volume. It was found that the minimum dose on CTV volume $D_{min}^{rand{\cdot}}$ was reduced from 100.45% to 94.80% and the mean dose to CTV $\bar{D}_{rand{\cdot}}$ was decreased from 100.46% to 97.87%. Like systematic error, the standard deviation of CTV dose ${\Delta}D_{rand}$ was increased from 0.01% to 0.63%. After calculating a size of margin for each systematic and random error the "population ratio" was introduced and applied to verify margin recipe. It was found that the conventional margin formula satisfy margin object on IMRT treatment for rectal cancer. It is considered that the developed Monte-carlo based simulation program might be useful to study for patient setup error and dose coverage in CTV volume due to variations of margin size and setup error.

• 한국지구물리탐사학회:학술대회논문집
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• 1999.08a
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• pp.137-155
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• 1999

Recently, Televiewer(Borehole Acoustic Scanner(Televiewer)) has come to be widely used specially for the general engineering construction design. The Televiewer tool using a focussed acoustic beam is to detect the amplitude and traveltime of each reflected acoustic signal at the wall, resulting in the amplitude- and traveltime image respectively. Fractures can be well detected, because they easily scatter the acoustic energy due to the highly narrow beam. In addition, the drilling work will rough the borehole wall so that the acoustic energy can be scattered simply due to the roughness of the wall. Thus, the amplitude level can be directed associated with the elastic properties(impedance) and the hardness of the rock as well. Meanwhile, the traveltime image provides an information about the borehole shape and can be converted to a high precision 3D caliper log(max. 288 arms). In this paper, based on the high resolution of Televiewer images, general evaluation methods are illustrated to derive very reliable rock parameters.

• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.471-481
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• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.

• 한국사회정책
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• v.23 no.2
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• pp.49-81
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• 2016

This study agrees with the idea that a situation centered perspective provides a useful contribution in understanding women's attitude on organizations. Women's occupational experiences are less related to their "femaleness" than to the structural constraints inherent in the occupational positions women fill. So characteristics of the organizational situation including gender composition and hierarchical status may "shape and define" women's experience on the job. The present study examined the managerial level's gender gap and "glass ceiling" of the corporation. According to Kanter, if the ratio of women to men in organizations begins to shift, as affirmative action and new hiring and promotion policies promised, forms of relationships and corporate culture should also change. However, the mere presence of women on workplace may not, in itself, result in women-friendly work condition. This study analyzes "Korean Women Manger Panel survey(2010 3rd. wave)" to examine how much gender gap of the managerial level persists and when the glass ceiling effect emerges. Using t-test and ANOVA, various aspects of the gender gap within managerial level were verified. The most significant finding is the glass ceiling effect starts from very low level of management. Policy implications from the statistical analysis of the Panel survey are: 1) We need to increase the absolute number of the women managers for securing middle level women leadership pipe line. 2) We need to confront the fact that the glass ceiling starts from the very low managerial level, and to explore more realistic way to break up the vicious circle for the tokenism. and 3) We need to looking beyond numbers in approaching women's matter at work. At the cultural and institutional level, work-family programs and policies, women's ratings of their competence, and family-friendly organization's climate should be considered.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.343-352
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• 2007

The present study aims to investigate the design standard for acoustic criteria of Korean traditional music which could be used for the design of Korean traditional music halls. In order to do this, subjective listening tests were undertaken to musicians using auralized sounds which were convolved with the impulse response of traditional instruments recorded in an anechoic chamber. 94 pairs of sound were made which have different value of acoustic parameters including RT, BR, Brilliance, G, C80, ITDG, IACC. A paired comparison method(PCM) was used to analyze the results from the subjective listening tests. The results show that the preference of acoustic criteria for the Korean traditional music is far different from those of western music. As a result, specific range of acoustic criteria were suggested for the appropriate acoustic conditions of Korean traditional music. Also, a guideline of the acoustic design of halls for performing the Korean traditional music was suggested which could be used as a basic reference in the future works.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.65-69
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• 2017

Purpose KSNMT(Korea Society of Nuclear Medicine Technology) stepping first step in 1997, has published first journal related with nuclear medicine technology in 1985. With classifying In Vivo Session Dissertation reported in the entire journal, trend of the Dissertation will be studied. Materials and Methods Dissertations which published from 1985 to first half of 2016 in the journal are classified with presentation form and with scanner, And all the data is organized with Excel program. Through the data, the number of dissertations published in each year, the number of dissertation published in details, and keyword distributions in each period are analyzed. Results The number of In-vivo section dissertations was 1151 and the number of In-vivo section dissertations that have common subject with In-vitro section was 28. The number of In-vivo section dissertation in 1980s was 46, in 1990s was 149, in 2000 was 467 and from 2010 to the first half of 2016 was 517. The number of dissertation with original articles was 571, with abstract was 529, with symposium was 31, with special lecture was 25, with review was 11, with interesting image was 7, with poster was 3 and with case report was 2. With symposium and special lecture excluded, which count 56, the number of dissertation with PET was 319, with Planar was 302, with SPECT was 172, with radiopharmaceutical was 113, with guard and safety management 103, with BMD was 28, etc. was 86. The number of dissertation about oncology was 201, about scanner was 179, about cardiovascular and circulatory system was 102, about safe environment was 82, about musculoskeletal system was 76, about nervous nuclear medicine was 66, about quality assurance was 61, about genitourinary system was 56, about endocrine system was 49, about digestive system was 44, about Therapy, about industrial safety was 24, about molecular imaging was 15, infection and inflammation was 9, about respiratory system was 8 and etc. was 108. The mostly used keyword through 1999 to 2005 was PET and through 2006 to 2016 was PET/CT. Conclusion To encourage various dissertations to be submitted, Korea Society of Nuclear Medicine should analyze date about not only about dissertations that are already published, but also about various research materials. Moreover, Korea Society of Nuclear Medicine also have to provide technical support such as sharing big data from homepage and systematical support to its member to publish dissertation that has high impact factor. It is important each individual researcher to have continuing effort as well as each organization cooperation.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.