• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.452-452
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• 1998

1. 세계 항공시장의 동향 ○ 항공운송산업의 규제완화 및 항공자유화 추세의 확산 -78년 미국의 규제완화 이후 유럽, 대양주, 아시아 등으로 파급 ○ 미국의 항공자유의 정책(Open skies Policy)은 EU의 역대 완전자유화와 함께 세계항공시장의 자유화 촉진 ○ 항공사간 전략적 제휴의 확산 -British Connection, Star Alliance, Southern Comfort, Dutch Treat 등 4대 범세계적 제휴가 결성 또는 추진 중. 2. 우리나라 항공운송산업의 현황 ○ 세계 10위권 항공운송국가로 성장 -화물 세계5위, 여객 11위(97 ICAO통계) -김포공항은 97년 중 36.5백만명 처리로 세계9위 차지 ○ IMF체계의 영향과 경영수지 변화 추이 -98년 내국인 출국자는 감소하였으나, 외국인 관광객은 증가(10.8%) * 98. 1∼8전년대비 수송수요 국제선 17%, 국내선 26% 감소 -97년 양 항공사 적자규모 약 4,000억원으로 대폭 증가, 98상반기 회복추세. 3. 21세기 국제항공정책 방향 ○ 적극적인 항공시장 개방정책 추진과 역내 주요도시간 셔틀화 ○ 세계 주요 항공사와의 전략적 제휴를 지원하여 세계항공시장에서의 경쟁력 제고 * KAL-이, AAR-AA -인천국제공항의 HUB화 추진(민간 경영기법 도입, 항공개방정책 등) ○ 항공사가 최적의 노선망과 경제성을 갖도록 하는 적정 성장방안 강구 ○ 선진국 수준의 획기적 항공안정 체계 확립

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.86-88
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• 2022

LoRa has been evaluated as a promising technology for Internet of things networks. Theoretically, the technology is robust for long-range communication by using chirp spreading spectrum, provides several orthogonal logical channels in a physical channel, exploits spatial reusability by introducing a star-of-stars topology. A part of recent studies indicates the quasi-orthogonality, or the imperfect orthogonality, between logical channels. The channel elements, however, are both spreading factor and bandwidth. Nevertheless, Most of the researches only treat the spreading factor. This study presents the quasi-orthogonality between all of the logical channels built by variations of the both factors. Furthermore, it shows the performance varied by the logical channel interference.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.9
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• pp.29-36
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• 2010

Consider an RFID network configured as a star such that a single reader is surrounded by a crowd of tags. In the RFID network, prior to attaining the information stored at a tag, the reader must cognize the tags while arbitrating a collision among tags' responses. For this purpose, we present a tag cognizance scheme based on framed and slotted ALOHA, which statically provides a number of slots in each frame for the tags to respond. For the evaluation of the cognizance performance, we choose the cognizance completion probability and the expected cognizance completion time as key performance measures. Then, we present a method to numerically calculate the performance measures. Especially, for small numbers of tags, we derive them in a closed form. Next, we formulate a problem to find an optimal time structure which either maximizes the cognizance completion probability under a constraint on the cognizance time or minimizes the expected cognizance completion time. By solving the problem, we finally obtain an optimal number of slots per frame for the tags to respond. From numerical results, we confirm that there exist a finite optimal number of slots for the tags to respond. Also, we observe that the optimal number of slots maximizing the cognizance completion probability tends to approach to the optimal number of slots minimizing the expected cognizance completion time as the constraint on the cognizance time becomes loose.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.145-151
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• 2020

'Hansik', or Korean cuisine is one of Korea national brands. To understand the domestic consumer awareness of Korean cuisine, data was gathered under the keyword search, 'Hansik.' Textom 3.5 was used to gather data from blogs, news media found on Naver from November 1, 2018, to October 31, 2019. The results from frequency and TF-IDF analysis indicate that the 'buffet' had the largest proportion in terms of consumer awareness to Hansik. Also, broadcasting contents starring star chefs had a great influence. The Hansik awareness did not remain in the domains of its traditionality, but also branched into extents into areas such as fusional and gourmet cuisine. UCINET6 and NetDraw were used to conduct CONCOR analysis. Four cluster formations have been found; various food cultural cluster, high-end restaurant cluster referring to aired restaurants on media, Hansik brand cluster, and Hansik buffet cluster. This study proposes presenting a various menu of Hansik which use a multiple number of ingredients. Also, a promotion that introduces fine Hansik and a development of marketing views and media contents about the convenient HMRs make the associated imagery of Hansik to be strengthen.

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.107-119
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• 2020

The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.21-29
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• 2015

For higher reliability against component failures in railroad trains with many electronic sensors and actuators, a distributed control system with which all electronic components are connected via a network is being considered. This paper compares and analyzes various topologies of Ethernet network for a railroad train in the aspects of (1) failure recovery, (2) the number of ports per device, (3) the number of cable connections between vehicles, and (4) performance. Especially, the unique characteristic of a train system that the number of vehicles changes is considered through analysis. Various combinations of in- and inter-vehicle topologies are considered. In addition, we introduce a hybrid of star and daisy-chain topology for inter-vehicle connection when the maximum number of inter-vehicle connections is limited to reduce possible failures of inter-vehicle connections. Simulation results show performance comparison between different topology combinations; the hybrid topology is shown to enhance delay performance even with a highly limited number of inter-vehicle connections.

• The Korean Journal of Air & Space Law and Policy
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• v.25 no.2
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• pp.139-162
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• 2010

In terms of the international air transport, the open skies policy implies freedom in the sky or opening the sky. In the normative respect, the open skies policy is a kind of open-door policy which gives various forms of traffic right to other countries, but on the other hand it is a policy of free competition in the international air transport. Since the Airline Deregulation Act of 1978, the United States has signed an open skies agreement with many countries, starting with the Netherlands, so that competitive large airlines can compete in the international air transport market where there exist a lot of business opportunities. South Korea now has an open skies agreement with more than 20 countries. The frequent flyer program (FFP) is part of a broad-based marketing alliance which has been used as an airfare strategy since the U.S. government's airline deregulation. The membership-based program is an incentive plan that provides mileage points to customers for using airline services and rewards customer loyalty in tangible forms based on their accumulated points. In its early stages, the frequent flyer program was focused on marketing efforts to attract customers, but now in the environment of intense competition among airlines, the program is used as an important strategic marketing tool for enhancing business performance. Therefore, airline companies agree that they need to identify customer needs in order to secure loyal customers more effectively. The outcomes from an airline's frequent flyer program can have a variety of effects on international competition. First, the airline can obtain a more dominant position in the air flight market by expanding its air route networks. Second, the availability of flight products for customers can be improved with an increase in flight frequency. Third, the airline can preferentially expand into new markets and thus gain advantages over its competitors. However, there are few empirical studies on the airline frequent flyer program. Accordingly, this study aims to explore the effects of the program on international competition, after reviewing the types of strategic alliance between airlines. Making strategic airline alliances is a worldwide trend resulting from the open skies policy. South Korea also needs to be making open skies agreements more realistic to promote the growth and competition of domestic airlines. The present study is about the performance of the airline frequent flyer program and international competition under the open skies policy. With a sample of five global alliance groups (Star, Oneworld, Wings, Qualiflyer and Skyteam), the study was attempted as an empirical study of the effects that the resource structures and levels of information technology held by airlines in each group have on the type of alliance, and one-way analysis of variance and regression analysis were used to test hypotheses. The findings of this study suggest that both large airline companies and small/medium-size airlines in an alliance group with global networks and organizations are able to achieve high performance and secure international competitiveness. Airline passengers earn mileage points by using non-flight services through an alliance network with hotels, car-rental services, duty-free shops, travel agents and more and show high interests in and preferences for related service benefits. Therefore, Korean airline companies should develop more aggressive marketing programs based on multilateral alliances with other services including hotels, as well as with other airlines.