• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1364-1373
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• 2016

Ultra High Definition Television (UHDTV) has attracted much attention as one of next generation broadcasting services. For the commercialization of UHD broadcasting service, standardization groups including the DVB (Digital Video Broadcasting) and the ATSC (Advanced Television Systems Committee) have decided to adopt the Orthogonal Frequency Division Multiplexing (OFDM) for signal transmission. However, when the carrier frequency is not properly synchronized at the receiver, inter-symbol interference (ISI) and inter-carrier interference (ICI) may occur. In order to avoid performance degradation resulting from ISI or ICI, receivers should synchronize the carrier frequency by using preambles and pilot symbols. However, there only few published literature dealing with the frequency offset estimation methods regarding the next generation terrestrial broadcasting. In this respect, this paper proposes a method to estimate timing and fractional frequency offset for the ATSC 3.0 system by using a preamble bootstrap symbol. The proposed detector can detect the fractional frequency offset by adding a complex conjugate product on the conventional estimator where only timing offset can be estimated.

• 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.

• Journal of the Korean Society of Costume
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• v.38
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• pp.193-211
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• 1998

Without a solid marketing system in placed, the fashion industry cannot flourish on out-standing design or technology alone. Even though the significance of collecting and analyzing information, merchandising, and retail distribution is recognized, these functions are not firmly rooted or prevalent in our industry. In contrast, Italy which possesses similar demographic traits such as the lack of natural resources and other physical factors has succeed-ed in globalizing its fashion market by responding swiftly and exercising flexiblity to its constantly changing consumer demand. This in turn has earned Italy the competitive edge in the global fashion arena. Italy's unique management skills and operation know-how, along with successful market strategies come into play in bringing competitiveness to Italy's fashion market. Firstly, smaller companies with ability to adopt swiftly to the ever changing market. Secondly, fashion friendly social environment. Thirdly, niche marketing through highly specialized system and differentiation. Fourthly, timeless innovation through intense corporate competition. Lastly, establishment of foundations to support the industry through diverse networking. The alone building blocks have formed a basis for erecting an unparalleled market with a reputation for excellence in design and quality in the global fashion world. This study has examined how Italy's fashion industry has evolved from an underdeveloped textile business into a cutting edge fashion in-dustry. Italy's unique business processes and practices were studied to come up with a collection and merchandising ideas in a niche market. By selecting this venue we are able to continuously grow and develop in a market with diverse consumer needs. To analyze the Italian fashion market, data from 3 institutions were utilized, namely, CIT-ER which has provided consumer trends and sales analysis, SITA,a data service provided statistics from the textile and apparel businesses, and NBI has also furnished valuable data. Italian consumer preference, buying behavior, consumer profile, retail channels and other related data from the above institutions has formed a backbone for market segmentation and target markets, and as a result, we were able to zero in on the type of consumer, produce, pricing and retail channels for our womenswear. Going forward the direction is to elevate product image and pretige, and create syn-ergy between related industries, and at the same note, in order to develop internationally recognized brands such as Max Mara and Benetton. Certain elements such as the specialization of the fashion industry, alon-g with fashion-related data base and systems support, and most importantly experts with acute fashion sense and capacity to analyze pertinent data are in need. I firmly believe that we can achieve Italy's level in the fashion market with support from the government and unrelenting effort within the industry itself, and hope that this report can prove to be useful.

• Archives of design research
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• v.17 no.3
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• pp.17-26
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• 2004

Those of the growth of e-business, the development of the cultural creative industry, the contents and knowledge industry have expanded and diversified the existing business models and created new ones. They are triggering new interpretations and discussions on business models. Such of changing business environment has paved the way for the expansion of design management within the design industry and opened new windows of opportunity for the traditionally small and non-specialized design business. Until now, the design business has not been distinguished from design industry or design service, and has not been viewed as an independent field. Rather, the design business has merely been part of a process. As such, the lack of dear definition and structured system had been a bottleneck for many design companies trying to achieve capital and social success and establish the foundation for growth and for companies striving to advance the design management in developing design business models or growth strategy in line with the changing environment. Against these sort of backdrops, this paper attempts to dearly define the design business. To this end, the paper tries to developed a design business model framework which classifies design business model into four types - customized, ready-made, provider and contract - according to the business activities between the producer and the end user, and business items into product, additional sonics, knowledge and promotion. The framework will expand the definition of design business and contribute to the expansion of design business activities and the development of diverse business models.

• Journal of the Korean institute of surface engineering
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• v.51 no.4
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• pp.207-213
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• 2018

Not only efficiency of chlorine evolution reaction (CER) but also durability namely service life is very important property in dimensional stable anode for Ballast Water Management System (BWMS) for marine ships. Many researchers have been focused on improving efficiency of CER by controlling composition, phase and surface area for a long time, but the efforts to increase durability was relatively small. In this study, we have investigated the effect of $TiO_2$ protective interlayers on efficiency and durability of DSA electrodes. $TiO_2$ protective interlayers were prepared by thermal oxidation at 500, 600 and $700^{\circ}C$ on Ti substrate. And then the DSA electrodes consisting of $Ti/RuO_2-PdO-TiO_2$ were prepared by thermal decomposition method on $TiO_2$ interlayers. The efficiencies of CER of DSA electrodes without $TiO_2$ interlayer and with $TiO_2$ interlayer grown at 500, 600 and $700^{\circ}C$ were 94.19, 94.45, 84.60 and 76.75% respectively. On the otherhand, durabilities were 30, 55, 90 and 65 hours respectively. In terms of industrial aspect, the performance of DSA is considered high efficiency and durability which can correspond to total production of chlorine. If we considered the performance index of DSA as the product of efficiency and durability, performance indices could be recalculated as 28.26, 50.85, 76.14 and 49.89 respectively. As the thermal oxidation temperature increasing, life time were increased remarkerbly, while efficiency of CER was decreased slightly. As a result, DSA electrode with $TiO_2$ interlayer grown at $600^{\circ}C$ has shown about 2.7 times performace of original DSA electrode without $TiO_2$ interlayer.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• International Commerce and Information Review
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• v.16 no.4
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• pp.409-437
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• 2014

The purpose of this study is to present the specific ways of successful localization by analyzing the success and failures case for localization within the framework of the strategic models through a theoretical background and strategic models of localization. The strategic models of localization are divided by management aspects such as the localization of product and sourcing, the localization of human resources, the localization of marketing, the localization of R&D, harmony with a local community and delegation of authority between headquarters and local subsidiaries. The results, by comparing and analyzing the success and failures case for localization of individual companies operating in India, indicate that in terms of localization of product and sourcing, there are successful companies which procure a components locally and produce a suitable model which local consumers prefer and the failed companies which can not meet local consumers' needs. In case of localization of human resources, most companies recognize the importance of this portion and make use of superior human resource aggressively through a related education. In case of localization of marketing, It is found that the successful companies perform pre-market research & management and build a effective marketing skills & after service network and select local business partner which has a technical skills and carry out a business activities, customer support, complaint handling with their own organization. In terms of localization of R&D, the successful major companies establish and operate R&D center to promote a suitable model for local customers. In part of harmony with a local community, it shows that companies which made a successful localization understand the cultural environment and contribute to the community through CSR. In aspect of delegation of authority between headquarters and local subsidiaries, it is found that most of Korean companies are very weak for this part. there is a tendency to be determined by the head office rather than local subsidiaries. Implication of this thesis is that Korean enterprises in India should carry forward localization of products and components, foster of local human resource who recognize management and system of company and take part in voluntary market strategy decision, wholly owned subsidiary, establishment and operation of R & D center, understanding of local culture and system, corporate social responsibility, autonomy in management.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Korea Science and Art Forum
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• v.31
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• pp.21-32
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• 2017

Local festivals form the regional cultures and atmosphere of communication; they increase the demand of domestic tourism businesses and thus, have an important role in ripple effects (e.g. regional image improvement, tourist influx, job creation, regional contents development, and local product sales) and economic revitalization. IoT (Internet of Thing) technologies have been developed especially, beacon-one of the IoT services has been applied as plenty of types and forms both domestically and internationally. However, notwithstanding expansion of current digital mobile technologies, it still remains as difficult for the individual to track the information about all the local festivals and to fulfill the tourists' needs of enjoying festivals given the weak strategic approaches and advertisement activities. Furthermore, current festival-related mobile applications don't function well as delivering information and have numerous contents issues (e.g. ways of information delivery within the festival places, independent application usage for each festival, one time usage due to one time event). This research, based on the background mentioned above, aims to develop the local festival mobile application and data analysis system applying beacon technology. First of all, three algorithms were developed, namely, 'festival crowding algorithm', 'visitor stats algorithm', and 'customized information algorithm', and then beta test was followed with the developed application and data analysis system. As a result, they could form the database of visitors' types and behaviors, and provide functions and services, such as personalized information, waiting time for festival contents, and 'hot place' function. Besides, in Google Play store, they also got the titles given with more than 13,000 downloads within first three months and as the most exposed application related with festivals; and, thus, got credited with their marketability and excellence. This research follows this order: chapter 2 shows the literature review of local festival related with technology development, beacon service, and festival application. In Chapter 3, design plans and conditions are described of developing local festival mobile application and data analysis system with beacon. Chapter 4 evaluates the results of the beta performance test to verify applicability of the developed application and data analysis system, and lastly, chapter 5 explains the conclusion and suggests the future research.