• 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 the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.45-59
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• 2013

A long-term forecasting method for a new product in early stage of diffusion is proposed. The method includes a constrained non-linear least square estimation with the logistic diffusion model. The constraints would be critical market informations such as market potential, peak point, and take-off. Findings on 20 cases having almost full life cycle are that (i) combining any market information improves the forecasting accuracy, (ii) market potential is the most stable information, and (iii) peak point and take-off information have negative effect in case of overestimation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Korean small business review
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• v.39 no.1
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• pp.59-87
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• 2017

There is growing concern of the international community for the environmental issues including climate change. Scholars have examined various issues regrading Green Supply Chain Management (GSCM) but they mainly have focused on why firms introduce and implement GSCM from the persecutive of manufacturers. In particular, there has been few studies of green supply chain management in Korea. We investigate the effects of long-term relationships on green supply chain management and the relationship between GSCM and environmental performance. We also examine the relationship between long-term relationships and green information sharing, the effects of green information sharing on the GSCM. The data for this study were collected through a questionnaire survey on firms that participated in the Green Partnership Program of Korea Institute of Industrial Technology. Based on the responses of 155 firms the research model is empirically tested through a structural equation model. We found that long-term relationships facilitate GSCM significantly as it was expected. Green purchase and green product design also appears to improve environmental performance. Long-term relationship appears to affect positively green information sharing. Green information sharing does not have a statistically significant effect on environmental performance but show positive effect on GSCM including green purchase and green product design.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.379-388
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• 2012

Objective: This study developed a new shower carrier prototype to reduce caregivers' muscle burden and to increase use convenience by reflecting the needs of domestic long-term care institutions. Background: In the long-term care institutions, one of the ADL(Activities of Daily Life) factors is bathing/showering. Recently, bath/shower-assisting equipment is actively being introduced in care institutions to reduce the caregivers' care cost, but most of the domestic equipment was designed to imitate foreign products and rarely reflected the needs of care institutions. Method: Based on Korean elderly people's body information, the bed size(length: 1,900mm, width: 650mm) was set-up, and a variable headrest with a newly designed headform was developed to provide the comfort for the elderly and convenience for caregivers. To reduce caregivers' muscle burden on transferring and showering activities, a 3-step column lifting module equipped with dual actuators(lowest/highest levels from the ground: 600/1,100mm, Stroke: 500mm) was developed, and the wheelbase parameter(length: 1,250mm, width: 580mm) was defined securing the turn-over safety of the shower carrier. The drivability tests were performed for the prototype and foreign product, and the male and female subject's muscle activities were measured through the tests. Results: The structural stability of the shower carrier prototype was secured by finite element analysis, and the muscle activities of the subjects through the drivability tests largely decreased in the prototype, compared to the foreign product. Conclusion: In this study, a new shower carrier prototype was developed to possibly reduce caregivers' muscle burden and to increase use convenience based on the needs of long-term care institutions. It was expected that the drivability performance of the prototype could be relatively superior to that of the foreign product. Application: The results obtained from the study can be applied for the optimal development of a shower carrier including other equipment to effectively care for the elderly.

• Journal of Information Technology Applications and Management
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• v.24 no.3
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• pp.73-92
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• 2017

Usability is one of the most important concepts regarding software quality. It can be interpreted as the goodness associated with using the software product. This paper distinguishes the goodness of an individual using experience and the goodness of a product for using. This paper proposes a software quality view model which classifies software quality views into two broad categories of end view and means view. End view includes long-term view and short-term view which is classified further into performer's view on software activity and third party's view on software activity. Means view includes intrinsic view and contingency view. The analysis of ISO 25000 Series SQuaRE demonstrates the necessity to decompose product quality model and quality in use model into five models corresponding to the software quality views respectively. The analysis on playability shows that the universal definition of usability may be an illusion. The results provide the theoretical basis to build a comprehensive and consistent body of knowledge regarding software quality, which is consisted with the set of quality models and the theories explaining the relationships among the elements of the models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4191-4211
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• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2139-2151
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• 2014

Embedded system testing, especially long-term reliability testing, of flash memory solutions such as embedded multi-media card, secure digital card and solid-state drive involves strategic decision making related to test sample size to achieve high test coverage. The test sample size is the number of flash memory devices used in a test. Earlier, there were physical limitations on the testing period and the number of test devices that could be used. Hence, decisions regarding the sample size depended on the experience of human testers owing to the absence of well-defined standards. Moreover, a lack of understanding of the importance of the sample size resulted in field defects due to unexpected user scenarios. In worst cases, users finally detected these defects after several years. In this paper, we propose that a large number of potential field defects can be detected if an adequately large test sample size is used to target weak features during long-term reliability testing of flash memory solutions. In general, a larger test sample size yields better results. However, owing to the limited availability of physical resources, there is a limit on the test sample size that can be used. In this paper, we address this problem by proposing a self-adaptive reliability testing scheme to decide the sample size for effective long-term reliability testing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.427-428
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• 2021

정보통신기술의 발달로 전자상거래의 증가와 소비자들의 제품에 대한 경험과 지식의 공유가 활발하게 진행됨에 따라 소비자는 제품을 구매하기 위한 자료수집, 활용을 진행하고 있다. 따라서 기업은 다양한 기능들을 반영한 제품이 치열하게 경쟁하고 있는 현 시장에서 우위를 점하고자 소비자 리뷰를 분석하여 소비자의 정확한 소비자의 요구사항을 분석하여 제품기획 프로세스에 반영하고자 텍스트마이닝(Text Mining) 기술과 딥러닝(Deep Learning) 기술을 통한 연구가 이루어지고 있다. 본 논문의 기초자료가 되는 데이터셋은 포털사이트의 구매사이트와 오픈마켓 사이트의 소비자 리뷰를 웹크롤링하고 자연어처리하여 진행한다. 감성분석은 딥러닝기술 중 CNN(Convolutional Neural Network), LSTM(Long Short Term Memory) 조합의 모델을 구현한다. 이는 딥러닝을 이용한 제품기획 프로세스로 소비자 요구사항 반영, 경제적인 측면, 제품기획 시간단축 등 긍정적인 영향을 미칠 것으로 기대한다.