• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2496-2498
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• 2001

광 디스크 channel상에서 RSPC의 error correction capability를 확률적인 계산 방법으로 계산하는 데는 많은 어려움이 있다. 그 이유는 바로 광 디스크 channel이 burst error channel이기 때문인데, 이 burst error를 어떻게 다루는 가에 따라 그 error correction capability는 사뭇 달라진다. 이 논문에서는 Sony의 dust error distribution[1] 아래에서 4-state Morkov Chain[2]로 modeling하고 그 결과를 가지고 burst error를 channel의 특성과 ECC format의 특성에 맞게 제어할 수 있는 확률적인 계산방법을 소개하고 그것을 simulation하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1201-1206
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• 2014

Retail stores are mostly relying their product management on handwriting. This handwriting management is limited and poor at managing the status of stocking and releasing. Also, to confirm real-time product status, additional statistic system is required. POS system developed to overcome foresaid problems is operating only in big and special stores because of expensive price and limited space. Therefore, new automated management system which has low installation price needs to be developed and adopted instead of handwriting management system. In this paper, we developed real-time product management system which can manage the products in retail stores by utilizing NFC function in mobile device.

• Korean Journal of Computational Design and Engineering
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• v.16 no.1
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• pp.1-10
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• 2011

In this paper, we propose an AR-based tangible interaction using a finger fixture for virtual evaluation of digital handheld products. To realize tangible interaction between a user and a product in a computer-vision based AR environment, we uses two types of tangible objects: a product-type object and a finger fixture. The product-type object is used to acquire the position and orientation of the product, and the finger fixture is used to recognize the position of a finger tip. The two objects are fabricated by RP technology and AR markers are attached to them. The finger fixture is designed to satisfy various requirements with an ultimate goal that the user holding the finger fixture in his or her index finger can create HMI events by touching specified regions (buttons or sliders) of the product-type object with the finger tip. By assessing the accuracy of the proposed interaction, we have found that it can be applied to a wide variety of digital handheld products whose button size is not less than 6 mm. After performing the design evaluation of several handheld products using the proposed AR-based tangible interaction, we received highly encouraging feedback from users since the proposed interaction is intuitive and tangible enough to provide a feeling like manipulating products with human hands.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.175-180
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• 2013

To compete global marketplace, many firms are adopting product families to increase variety, satisfy customer requirements, shorten lead-times and reduce costs. The key to a successful product family is the platform from which it is derived. When designing product families, it is important to resolve the tradeoff between product commonality and distinctiveness. To measure the commonality within a product family, it is necessary to develop the commonality index. It utilizes different parameters such as the number of common components, their cost, their manufacturing processes and so on. This paper present a new product family assessment index using the benchmarking method and considering environmental performance called green product family index(GPFI). Through the comparison analysis between some previous developed commonality indices and GPFI, we verify the suitability and effectiveness. Eventually, the proposed a new green product family assessment index can be helpful to design and develop the environmentally conscious product families.

• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.283-299
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• 2011

In this paper, we draw upon Task-Technology Fit theory to investigate the fit attributes which impacted on customer preference over three virtual shopping channels which included TV home shopping, Internet shopping, and broadband applications, i.e. IPTV. The fit attributes also reflected the product category contingency, which is classified based on the degree of quality assessing difficulty on the web, such as quasi-commodity, look and feel goods, and look and feel with variable quality goods. Using the collected survey data, we employed stepwise regression analysis to validate the fit attributes in the context of performing virtual shopping task via those three distinctive media technologies. Furthermore, through ANOVA test with Duncan statistics, we reported comparative intensity of the valid fit attributes across the product categories and distinct media technologies. The results validated four critical fit attributes and significant distinctions among product categories and three virtual shopping channels. The findings provide practical insights in distribution channel design exploiting digital convergence technologies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.125-130
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• 2014

The competitiveness in today's global market forces many companies to develop families of products to provide enough variety for the marketplace. The challenge when designing a product family is in resolving the tradeoff between product commonality and distinctiveness. Simultaneously it is necessary to consider environmental performance to design a product family as well as to shorten lead-times, improve quality and reduce costs. This paper proposes a metaheuristic algorithm based redesign methodology for green product family considering environmental performance. The proposed method uses a genetic algorithm as metaheuristic algorithm and green product family index (GPFI) to support green product family design. In addition, it provides the redesign methodology such as product family level and component level. A case study used table lamps as an product family's example shows the verification and effectiveness of the proposed method.

• Journal of National Security and Military Science
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• s.3
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• pp.197-242
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• 2005

Active catalog is a kind of digital content that enables consumers to test the functions and features of products from their PCs as if they were using it in real life, by simulating the actions and responses of the product. This new type of interactive digital content can be used extensively to make sales personnel training manuals, sales tools, user manuals and user trouble shooting documents. With active catalogs, companies will be able to compare different designs, show actions according to different functions, and evaluate user reaction to new products without having to produce a single physical prototype or mock-up. At the same time, consumers will be able to understand and 'operate' the product and make well-informed purchase decisions. In this paper, we present a visual event-driven modeling tool, PlayMo, for creating active catalogs, analyze the advantages of using PlayMo, describe the event-driven method used by PlayMo and also introduce two enhanced characteristics of the Event Flow Chart with which the events in PlayMo are structured. Interactive digital content by using the PlayMo3D makes easy, simple and effective design for e-learning, e-catalogue, e-marketing/sales, e-prototyping, customer support, etc. Through its application-ready 3D function visualization solution, engineers and designers can rapidly turn a CAD design model into a 3D interactive virtual product, and the effective function prototyping job can be also completed within a minute.

• ETRI Journal
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• v.31 no.2
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• pp.215-224
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• 2009

Analyzing the diffusion of a convergence product is a new and challenging research field. It is very difficult to find research dealing with this issue due to the inherent complexity and lack of data. In analyzing the diffusion of a convergence product, we should simultaneously take into account its relationship with related single-function products because of their similarities in terms of technology and functionality. In this study, we empirically analyze the diffusion of the convergence mobile phones in South Korea and find that the convergence products can affect the diffusion of MP3 players and digital cameras positively or negatively. This research may be significant for business strategies in technology management and product development.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.