Recently there was an incident that military radars, coastal CCTVs and other surveillance equipment captured a small rubber boat smuggling a group of illegal immigrants into South Korea, but guards on duty failed to notice it until after they reached the shore and fled. After that, the detection of such vessels before it reach to the Korean shore has emerged as an important issue to be solved. In the fields of marine navigation, Automatic Identification System (AIS) is widely equipped in vessels, and the vessels incessantly transmits its position information. In this paper, we propose a method of automatically identifying abnormally behaving vessels with AIS using convolutional autoencoder (CAE). Vessel anomaly detection can be referred to as the process of detecting its trajectory that significantly deviated from the majority of the trajectories. In this method, the normal vessel trajectory is gridded as an image, and CAE are trained with images from historical normal vessel trajectories to reconstruct the input image. Features of normal trajectories are captured into weights in CAE. As a result, images of the trajectories of abnormal behaving vessels are poorly reconstructed and end up with large reconstruction errors. We show how correctly the model detects simulated abnormal trajectories shifted a few pixel from normal trajectories. Since the proposed model identifies abnormally behaving ships using actual AIS data, it is expected to contribute to the strengthening of security level when it is applied to various maritime surveillance systems.

There have been debates on adoption of corporate laws regarding electing independent audit committee members separately and double derivative suit, as well as mandatory cumulative voting to improve corporate governance in Korea. This article examines whether mandatory or market based approaches are desirable to improve corporate governance in Korea. It will explore the ways to strengthen the independence of the board of directors and audit committees. Firstly, this article studies the independence of outside directors in the U.S. and Korea. The independence of the board of directors has been weak in Korea due to controlling shareholders and circular share-holding ownership structure among big business groups, even though there have been attempts to improve this situation. Secondly, this article examines the current role of outside directors, audit committees, and cumulative voting in the U.S and in Korea to find out positive changes in corporate governance Korea. Lastly, his article will discuss the appropriate direction between mandatory and market based soft law approaches on corporate governance in Korea. It will argue that Korea needs to strengthen the monitoring function of the board of directors, independence of outside directors and audit committee.

The long term objective should be to achieve a high degree of self-financing for the WEU. However, initial funding of at least US$5 million will be needed to develop the University through the launching of its initial programs. This will have to come primarily from Korean sources. Korea will need to take a lead in mobilizing the funds required for the ongoing operation of the University until the WEU has progressed to the point where it can command attention and support of international funding sources and significant fee income. The process to establish the University will be initiated as soon as the final decision of the Government of South Korea is confirmed.

본 논문에서는 영화에서 인물의 극적인 등장, 등장 인물 간의 관계를 효과적으로 표현하 고자 할 때 주로 사용하는 영화 촬영 기법 ‘아크 트래킹 샷’을 비주얼 노블 게임에 카메라 연출을 설계 및 구현하여 적용하였다. 비주얼 노블은 소설을 읽듯 이야기 진행에 중 점을 둔 게임의 한 장르로 이미지, 사운드 등 시청각적 요소를 첨가하여 이야기의 몰입을 돕는다. 그러나 기존 비주얼 노블의 카메라는 정적이며 움직임이 극히 제한되어 있어 플레이어의 이야기 몰입도를 높이기에는 어려움이 있다. 이를 극복하고자 이야기의 전달이 중요하다는 공통점을 가진 영화의 촬영 기법 아크 트래킹 샷을 3D 게임 엔진에 가상의 카메라 트래킹 레일을 만들고 카메라에 적용하는 과정을 통해 비주얼 노블의 카메라 연출에 적용하였다. 영화 연출 기법을 비주얼 노블 게임콘텐츠에 적용하면서 기존 비주 얼 노블 연출의 단점을 극복하고 역동적인 카메라 움직임은 플레이어에게 몰입을 유지 시키는 작용을 하며 제작자가 전달하고자 하는 감정과 의미를 더욱 효과적으로 전달할 수 있도록 구현하였다.

Human and material resource planning is one representative example of Operations Research. Resource planning is important not only in civilian settings but also in military ones. In the Air Force, flight scheduling is one of the primary issues that must be addressed by the personnel who are connected to flight missions. However, although the topic is of great importance, relatively few studies have attempted to resolve the problem on a scientific basis. Each flight squadron has its own scheduling officers who manually draw up the flight schedules each day. While mistakes may not occur while drafting schedules, officers may experience difficulties in systematically adjusting to them. To increase efficiency in this context, this study proposes a mathematical model based on a binary variable. This model automatically drafts flight schedules considering pilot’s mission efficiency. Furthermore, it also recommends that schedules be drawn up monthly and updated weekly, rather than being drafted from scratch each day. This will enable easier control when taking the various relevant factors into account. The model incorporates several parameters, such as matching of the main pilots and co-pilots, turn around time, availability of pilots and aircraft, monthly requirements of each flight mission, and maximum/minimum number of sorties that would be flown per week. The optimal solution to this model demonstrated an average improvement of nearly 47% compared with other feasible solutions.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

In this study, we propose a link between L2 rhetorical concepts and ELF as a way of the analysis of the development of a single concept, of an EFL college student’s rhetorical knowledge. Using Vygotskian sociocultural theory as analytical lenses, we examine whether L2 rhetoric can be mastered and internalized as a culturally neutral concept, i.e., the formulaic knowledge of L2 writing the student has learned from the NEST through instruction; and how the student’s L1 rhetorical concept and ELF performance together mediate his L2 concept development in his academic writing. The data consist of a student’s personal narratives, text-based interviews and academic writings. Rather than the mastery of a single variety of English, he produced texts that reflect the flexibility and variability inherent in written ELF. From ELF perspectives, this study offers an opportunity of establishing a new normal, in which rhetorical conventions of texts should be viewed as constructs that are dynamic, emergent, and therefore negotiable and adaptable.

In order to satisfy customers, it is important to identify the quality elements that affect customers’ satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions’ polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

Recently, M/G/1 priority queues with a finite buffer for high-priority customers and an infinite buffer for low-priority customers have applied to the analysis of communication systems with two heterogeneous traffics : delay-sensitive traffic and loss-sensitive traffic. However, these studies are limited to M/G/1 priority queues with finite and infinite buffers under a work-conserving priority discipline such as the nonpreemptive or preemptive resume priority discipline. In many situations, if a service is preempted, then the preempted service should be completely repeated when the server is available for it. This study extends the previous studies to M/G/1 priority queues with finite and infinite buffers under the preemptive repeat-different and preemptive repeat-identical priority disciplines. We derive the loss probability of high-priority customers and the waiting time distributions of high- and low-priority customers. In order to do this, we utilize the delay cycle analysis of finite-buffer M/G/1/K queues, which has been recently developed for the analysis of M/G/1 priority queues with finite and infinite buffers, and combine it with the analysis of the service time structure of a low-priority customer for the preemptive-repeat and preemptive-identical priority disciplines. We also present numerical examples to explore the impact of the size of the finite buffer and the arrival rates and service distributions of both classes on the system performance for various preemptive priority disciplines.