• Korean Journal of Legislative Studies
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• v.27 no.3
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• pp.69-106
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• 2021

This paper aims to explain why severe political conflicts and confrontation between the ruling and opposition forces have been continuously caused, focusing on the institutional combination of presidentialism and the two-party system with strong party disciplines, after democratization in South Korea. And this also presents the US as a case in which presidentialism and a two-party system with weak party disciplines were combined once, and the Chile as another case in which presidentialism and a multi-party system with strong party disciplines is combined, respectively, and further analyzes how the chance of consensual politics could be raised in both the countries. In addition, this study suggests a practical implication that, in South Korea also, the political reforms for changes in party system such as the decentralization or democratization in party organizations to enhance the autonomy of individual legislators, or the introduction of runoff system in presidential elections or proportional representation system in parliamentary elections to product a multi-party system, are required for a high chance of consensual politics.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.215-228
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• 2023

The State Space Representation (SSR) method provides individual corrections for each Global Navigation Satellite System (GNSS) error components. This method can lead to less bandwidth for transmission and allows selective use of each correction. Precise Point Positioning (PPP) - Real-Time Kinematic (RTK) is one of the carrier-based precise positioning techniques using SSR correction. This technique enables high-precision positioning with a fast convergence time by providing atmospheric correction as well as satellite orbit and clock correction. Currently, the positioning service that supports PPP-RTK technology is the Quazi-Zenith Satellite System Centimeter Level Augmentation System (QZSS CLAS) in Japan. A system that provides correction for each GNSS error component, such as QZSS CLAS, requires monitoring of each error component to provide reliable correction and integrity information to the user. In this study, we conducted an analysis of the performance of residual error bounding for each error component. To assess this performance, we utilized the correction and quality indicators provided by QZSS CLAS. Performance analyses included the range domain, dispersive part, non-dispersive part, and satellite orbit/clock part. The residual root mean square (RMS) of CLAS correction for the range domain approximated 0.0369 m, and the residual RMS for both dispersive and non-dispersive components is around 0.0363 m. It has also been confirmed that the residual errors are properly bounded by the integrity parameters. However, the satellite orbit and clock part have a larger residual of about 0.6508 m, and it was confirmed that this residual was not bounded by the integrity parameters. Users who rely solely on satellite orbit and clock correction, particularly maritime users, thus should exercise caution when utilizing QZSS CLAS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.17-31
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• 2023

An activity-based model requires detailed population information to model individual travel behavior in a disaggregated manner. The recent innovative approach developed deep generative models with novel regularization terms that improves fidelity and diversity for population synthesis. Since the method relies on measuring the distance between distribution boundaries of the sample data and the generated sample, it is crucial to obtain well-defined continuous representation from the discretized dataset. Therefore, we propose an improved entity embedding models to enhance the performance of the regularization terms, which indirectly supports the synthesis in terms of feasible and diverse populations. Our results show a 28.87% improvement in the F1 score compared to the baseline method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.2
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• pp.188-202
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• 1997

The seasonal variability of the sea surface winds over the last Sea of Korea (Japan Sea) is investigated by means of empirical orthogonal function (EOF) analysis. The combined representation of fields of three climatic variables by empirical orthogonal functions is discussed. The eigenvectors are derived from daily sea level pressure, wind speed and 10-day mean sea surface temperature (SST) during 15 years $(1978\~1992)$. The spatial patterns of the mean pressure are characterized by the high pressure in the western part and the low pressure in the eastern part. The spatial distribution of the standard deviation (SD) of pressure are characterized by max SD of 6.6 mb near the Vladivostok, and minima along the coast of the Japan. In Vladivostok, the maxima of SD of SST and south-north wind (WV) were also occurred. The representation of fields of individual meteorological variables by EOF shows that the first mode of the west-east wind (WU) explain over $47.3\%$ of the variance and the second mode of WU represents $30\%$. Especially, the first mode of the WV explain $70.9\%$ of the variance and their time series coefficients show 1-cpy, 0.5-cpy frequency spectrum. The spatial distribution of the first mode eigenvectors of SST are characterized by maximum near Vladivostok. The combined representation of fields of several variables (pressure, wind, SST) reveals that the first mode magnitudes of the variance of the combined eigenvectors (WU-PR) are increased. By means of this result, the 1-year peak and the 6-months peak are remarkable. In the three combined patterns (wind, pressure, SST), the second mode of the eigenvector (wind) is affected by the SST. Their time coefficients of the first mode show noticeable 1-year peak. The spectral analysis of the second mode shows broad seasonal signal with the period of 4-months and a significant peak of variability at 3-month period.

• Journal of Popular Narrative
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• v.27 no.2
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• pp.215-262
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• 2021

This thesis focuses on Wandeuki and Elegant Lies - novels written by Kim Ryeo-reong and adapted into the film by Director Lee Han; this thesis analyzes the process of storytelling being transformed as the media is converted. Also, this thesis discusses cultural-political implications of transmedia storytelling where different narrative responses coexist concerning post-IMF family disorganization and "individualization." First of all, this thesis critically reviews existing discourses on the concept of transmedia storytelling and refers to 'transfictionality' the narratological concept of Marie-Laure Ryan in order to look into media conversion storytelling that starts from original novels. The novels Wandeuki and Elegant Lies show two aspects of "individualization" that adopts existential conditions of family disorganization. Wandeuki deviates from patriarchal family romance through self-discovery and exhibits loose family bond, which is something similar to companionship of close individuals. Elegant Lies shows individualization of pain by portraying a teenager who found herself completely isolated, while showing that it is impossible for the people left behind to mourn. On the other hand, director Lee Han's films and show stories in which family members, who are confronting family dissolution, rediscover and restore their families against family dissolution. The film promotes the expansion of family community through multicultural identity, and the film completes condolence of the people left behind by having the remaining families survive as survivors of suicide. The storyworld of the novels puts emphasis on 'self-discovery' of individual adolescents, while the storyworld of the movies puts emphasis on 'rediscovery of family'. Through transformation of storytelling - especially the redesigning of narrative structures called "modification" - transmedia storytelling shows that the relationship between media-converted texts is far from "faithful representation," but rather, shows conflicting themes and perspectives. With a reference point of 'the emergence of character' transmedia storytelling, which is predicated on the original work but aims to free itself from the original work by transforming storytelling through media conversion, opens up polyphonic storyworld by creating heterogeneous voices. In the post IMF-era, where uncertainty mounts over family dissolution and individualization, polyphonic storyworld created by transmedia storytelling provides an opportunity to experience disparate desires over individual freedom/risk and complacency toward community. We can call this the cultural-political implication of transmedia storytelling based on transferring, transcednding, and transforming.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.34-42
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• 2006

As art analytic method to uncover interesting patterns hidden under a large volume of data, data mining research has been actively done so far in various fields. However, current state-of-the-arts in data mining research have several challenging problems such as being too ad-hoc. The existing techniques are mostly the ones designed for individual problems, so there is no unifying theory applicable for more general data mining problems. In this paper, we address the problem of classification, which is one of significant data mining tasks. Specifically, our objective is to evaluate radial basis function (RBF) model for classification tasks and investigate its usefulness. For evaluation, we analyze the popular Monk's problems which are well-known datasets in data mining research. First, we develop RBF models by using the representational capacity based learning algorithm, and then perform a comparative assessment of the results with other models generated by the existing techniques. Through a variety of experiments, it is empirically shown that the RBF model has not only the superior performance on the Monk's problems but also its modeling process can be controlled in a systematic way, so the RBF model with RC-based algorithm might be a good candidate to handle the current ad-hoc problem.

• Proceedings of the Korean DIstribution Association Conference
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• 2007.08a
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• pp.3-26
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• 2007

Recently, both scholars and marketers have asserted the importance of boundary spanning behaviors, such as external representation, being vocal advocates to outsiders of the organization's image, goods, and services, internal influence, taking individual initiative in communications to the firm and co-workers to improve service delivery by the organization, co-workers, and oneself, and service delivery, serving customers in a conscientious, responsive, flexible, and courteous manner. However, there is lack of study dealing with bourdary spanning behaviors bewteen organizational dyads, in which boundary spanning behaviors are expected to have important roles. The objectives of this paper is to investigate these important concerns with prior research by developing a theoretical model predicting how distinct buyer's boundary spanning behaviors occur. To be concrete, this paper develops a seller characteristics-based model of the attitudinal antecedents of three conceptually distinct forms of boundary spanning behaviors, and tests the hypothesized differential effects of seller characteristics on the three forms of boundary spanning behaviors, and investigates the extent to which these relationships are mediated by relationship satisfaction and organizational commitment. For the purpose of empirical testing, 420 respondents of leading automobile dealers, dining franchisees, industrial material retailers in Korea were surveyed and the analysis utilizing structural equation model indicated that communication quality, fairness, and marketing program dynamism had positive effects on buyer's boundary spanning behaviors via relationship satisfaction and organizational commitment. In addition, boundary spanning behaviors occurred more in contractural and corporate distribution channel than in conventional distribution channel.