• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.375-377
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• 2008

The KOMPSAT2 Terminal Polar Station was recently installed at near North Pole, Tromso, and Toulouse. The K2PS consists of one receiving station and two processing sites. The receiving station has been installed at SvalSat ($N78^{\circ}$, $E15^{\circ}$ ), and the two receiving sites have been installed at KSAT (Kongsberg Satellite Service AS), Tromso, Norway ($N69^{\circ}$ ,$E18^{\circ}$ ) and SISA, Toulouse, France ($N43^{\circ}$ ,$E1^{\circ}$ ). The products ofK2PS system can be classified to two categories: Level 1R product and Level 1G product. The Level 1R product is radiometric corrected product with RPC (Rational Polynomial Coefficients) and the Level 1G product is geometric corrected product with POD (Precise Orbit Data) and PAD (Precise Attitude Data) data based on Level 1R product. To meet a SISA (Spot Image SA)'s requirement, K2PS system has high performance product producing capability. This paper describes overall K2PS systems' production generation flow and the mass production test result of K2PS systems.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.1
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• pp.43-56
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• 2009

The Korean navy has made many efforts to apply the concepts of PLM (Product Lifecycle Management) and M&S to its naval design and production. However, most of the efforts that have being applied to some acquisition processes, focused only on the element technologies without information models and data frameworks. This study discusses an information model of naval ships for advanced naval acquisitions. We introduce a naval ship product model, and it refers to the DPD (Distributed Product Description) concept of SBA (Simulation-Based Acquisition). To realize the product model concept, we design a data architecture and develop a Product Model Management System (PMMS) based on a PDM System. It is validated through the case study of building the product model of the battle ship that the PMMS has the applicability to effectively manage the naval ship acquisition data on the basis of a 3D product model.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.234-245
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• 2012

This paper proposes a multi-level product modeling framework for long-term lifecycle products. The framework can help engineers to define product models and relate them to physical instances. The framework is defined in three levels; data, design model, modeling language. The data level represents real-world products, The model level describes design models of real-world products. The modeling language level defines concepts and relationships to describe product design models. The concepts and relationships in the modeling language level enable engineers to express the semantics of product models in an engineering-friendly way. The interactions between these three levels are explained to show how the framework can manage long-term lifecycle product information. A prototype system is provided for further understanding of the framework.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.939-949
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• 2006

With the development of industry, product data management system is becoming more and more important. An expanded view of product definition function that include a bill of material and routing database, current and historical engineering data and specifications and engineering changes order history. PDM (Product Data Management) systems hold and manage such material as product specifications, plans, geometric models, CAD drawings and images. Furthermore, PDM enables companies producing complex products to spread product data in to the entire launch process. Especially, PDTnet XML Schema is an international standard for exchanging of PDM data. But PDTnet XML Schema differs from existing XML Schema in the way that its property of IDREF/IDREFS is irregular. Therefore it is important factor that. what do we use storage devices and storage techniques. We developed storage techniques and application supporting various query types and preserving PDTnet XML Schema using a relational database that guaranteeing the performance nowadays. In this paper, we will show that our storage techniques minimize repeated data and optimize query processing by using application comparison with storage techniques of existing XML Schema data.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.59-76
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• 2008

Studies of recommender systems have focused on improving their performance in terms of error rates between the actual and predicted preference values. Also, many studies have been conducted to investigate the relationships between customer information processing and the characteristics of recommender systems via surveys and web-based experiments. However, the actual impact of recommendation on product pages for customer browsing behavior and decision-making in the commercial environment has not, to the best of our knowledge, been investigated with actual clickstream data. The principal objective of this research is to assess the effects of product recommendation on customer behavior in e-Commerce, using actual clickstream data. For this purpose, we utilized an online bookstore's clickstream data prior to and after the web site renovation of the store. We compared the recommendation effects on customer behavior with the data. From these comparisons, we determined that the relevant recommendations in product pages have positive relationships with the acquisition of customer attention and elaboration. Additionally, the placing of recommended items in shopping cart is positively related to suggesting the relevant recommendations. However, the frequencies at which the recommended items were purchased did not differ prior to and after the renovation of the site.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.189-198
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• 2008

At present, companies write daily work record or use bar-code in order to collect distribution flow data in real time. However, it needs additional works to check the record or read the bar-code with a scanner. In this case, human error could decrease accuracy of data and it would cause problems in reliability. To solve this problem, RFID (Radio Frequency Identification) is introduced in many automatic recognition sector recently. RFID is a technology that identification data is inserted into micro-mini IC chip and recognize, trace, and manage object, animal, or person using wireless frequency. This is being emerged as the core technology in future ubiquitous environment. This study is intended to suggest RFID application method in franchise business. Traceability and visibility of individual product are supplied based on EPCglobal network. It includes DW system which supplies various assessment data about product in supply chain, financial transaction system which is based on product transaction and position information, and RFID middleware which refines and divides product data from RFID tag. With the suggested application methods, individual product's profile data are supplied in real time and it would boost reliability to customer and make effective cooperation with existing operation systems (SCM, CRM, and e-Business) possible.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.6 s.144
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• pp.686-697
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• 2005

The current challenge with which most shipyards are faced is to reduce the design time and the time-to-delivery benefits of new information technologies such as ERP, APS, and SCM system. These technologies should integrate the information that flows in the enterprise wide activities. First of all we should maintain control of ship design and product data such as bill of material, product structure, drawing, 30 modeling and manufacturing information for the integration. The product data management (POM) system copes with these requirements. All the product data is stored in POM database and vaults in a structured manner and all the changes of product data are monitored, controlled and recorded. This study discusses how POM system can be implemented in shipyard. The procedure is surveyed to apply a POM system for the ship design data management. This study also presents several examples in order to make a practical guidance for ship POM implementation. Proposed studies can be useful for shipyards planning to adopt POM systems in enterprise wide manner.