• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.817-825
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• 2013

The UHDTV system, which provides realistic service with ultra-high definite video and multi-channel audio, has been studied as a next generation broadcasting service. Since the conventional digital terrestrial transmission system is not capable to cover the increased transmission data rate of the UHDTV service, there are great necessity of researches about increase of data rate. Accordingly, the researches has been studied to increase the transmission data rate of the DVB-T2 system using dual-polarized MIMO technique and high order modulation. In order to optimize the MIMO DVB-T2 system where irregular LDPC codes are used, it is necessary to study the design of the bit mapper that matches the LDPC code and QAM symbols in MIMO channel. However, the research related to the design of the bit mapper has been limited to the SISO system. Therefore, this paper defines a new parameter that indicates the VND distribution of MIMO DVB-T2 system and performs the performance analysis according to the parameter which will be helpful for designing a MIMO bit mapper.

• Journal of Korea Technology Innovation Society
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• v.18 no.4
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• pp.590-620
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• 2015

Although the investment scale and the qualitative performance of national research and development project in Korea have been increased, the practical use of the performance is still insignificant. Thus it becomes more important to understand and analyze factors that affect commercialization of national R&D project. Most of prior literatures have done with qualitative research rather than data-based analysis; however, mostly focusing on influential factors in between R&D inputs and outputs and it remains as limitation. The important key to avoid the limitation in this study is using data-based analysis of factors (such as research performance, types of research institution, scale of the government fund, project structure, competency in the researcher, and technical field) that affect commercialization with the case of the Competitiveness Enhancement in Material and Component Industries. As a result, patents performance, scale of the government fund, and technical field turned out to be influential factors of commercialization. On the other hand, research performance, types of research institution, project structure, and competency in the researcher did not show statistically significant results. To increase commercialization in project scheme, process, and assessment of national R&D project, including the Competitiveness Enhancement in Material and Component Industries project, it is required to design scientific research with better understanding of causal relationship.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.477-483
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• 2021

In a military maintenance system, the standard maintenance man-hour of weapon systems is a tool to estimate the maintenance capabilities of maintenance units, provide standards for determining the maintenance needs and workload, and provide basic data for establishing a maintenance plan. The standard maintenance man-hours of major weapon systems have already been derived and used, but the standard maintenance man-hour in a wartime maintenance environment has not been computed. Therefore, the standard wartime maintenance man-hours need to be derived and This study proposes a process and method of computing the maintenance man-hours. In addition, this work suggests the criteria of collecting and screening data that is necessary for estimating the standard maintenance man-hours and introduces a methodology for analyzing the characteristics of maintenance man-hour distribution in the process. The proposed process first designs a model that reflects the wartime maintenance environment, selects statistical techniques, collects maintenance data, analyzes the descriptive statistics, estimates the distribution, and finally presents representative values of maintenance man-hour. Based on the proposed method, the standard wartime maintenance man-hours of the four weapon systems were calculated, and the distribution of the maintenance man-hours was analyzed to follow a lognormal distribution, and the method presented reliable results.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.3-12
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• 2022

In general construction project planning ratio of manpower and quantity of outputs produced, such as the construction estimate standard, is used as the criterion for labor productivity. This method is highly effective in construction projects with repetitive work, however, there is a limit to apply in large-scale projects with high complexity. This is because the influence of non-work time caused by various work interruption factors that act complexly on the productivity of the project is greater than the average labor productivity derived from the performance data of the project. Therefore, this study proposes a productivity measurement method that can evaluate the characteristics of construction works and the cause of non-working time. To this end, first, detailed work processes and their non-work factors for each work type are defined, and the Adv-FMR technique is developed for quantitatively measuring them. Next, based on the concept of obtainable productivity, methods for comparative productivity analysis by work type, evaluating non-work factors, and deriving productivity improvement methods are proposed. Finally, a case study is conducted to validate that the analysis results based on Adv-FMR data can support the decision-making of construction managers on productivity management.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.97-130
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• 2024

The current debate in the academic community is on the criteria for predatory journals. Researchers are perplexed about what constitutes a predatory journal. The purpose of this study is to investigate how South Korean researchers discover and evaluate predatory journals. In order to achieve this, we collected 2,484 statements, comprising posts and comments, from Korean researcher communities, namely the Biological Research Information Center (BRIC), Hibrain.net, Phdkim.net, and the Scholarly Ecosystem Against Fake Publication Environment (SAFE). We divided the data into three primary categories-journals, publishers, and researchers-for the topic analysis. For each statement, we assigned 11 in-depth subtopic tags based on these categories. Six main points of contention emerged from the combinations of these sub-topic tags: (1) researchers' confusion about predatory journals and discussions about research performance; (2)(3) researchers' positive and negative perceptions of predatory journals; (4) researchers' evaluation criteria for journal quality and problems associated with the quality of Korean journals; (5) changes in publishing brought about by the introduction of open access (OA) and associated issues; and (6) discussions on broader issues within the academic ecosystem. By using a qualitative approach to examine how South Korean researchers view predatory journals, this study aims to advance basic knowledge of the discourse around them in the communities of domestic researchers.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Journal of the Korea Concrete Institute
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• v.26 no.4
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• pp.491-497
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• 2014

The corrosion of steel reinforcement in reinforced concrete bridge decks significantly affects the degradation of the capacity. Due to the advantageous characteristics such as high tensile strength and non-corrosive property, fiber reinforced polymer (FRP) has been gathering much interest from designers and engineers for possible usage as a alternative reinforcement for a steel reinforcing bar. However, its application has not been widespread, because there data for short- and long-term performance data of FRP reinforced concrete members are insufficient. In this paper, seven full-scale decks with dimensions of $4000{\times}3000{\times}240mm$ were prepared and tested to failure in the laboratory. The test parameter was the bottom reinforcement ratio in transverse direction. The decks were subjected to various levels of concentrated cyclic load with a contact area of $577{\times}231mm$ to simulate the vehicle loading of DB-24 truck wheel loads acting on the center span of the deck. It was observed that the glass FRP (GFRP) reinforced deck on a restraint girder is strongly effected to the level of the applied load rather than the bottom reinforcement ratio. The study results showed that the maximum load less than 58% of the maximum static load can be applied to the deck to resist a fatigue load of 2 million cycles. The fatigue life of the GFRP decks from this study showed the lower and higher fatigue performance than that of ordinary steel and CFRP rebar reinforced concrete deck. respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.312-321
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• 2018

The Korean DR market proposes suppression of peak demand under reliability crisis caused a natural disaster or unexpected power plant accidents as well as saving power plant construction costs and expanding amount of reserve as utility's perspective. End-user is notified a DR event signal DR execution before one hour, and executes DR based on requested amount of load reduction. This paper proposes a DR energy management algorithm that can be scheduled the optimal operations of chiller system and ESS in the next day considering the TOU tariff and DR scheme. In this DR algorithm is divided into two scheduling's; day-ahead operation scheduling with temperature forecasting error and operation rescheduling on DR operation. In day-ahead operation scheduling, the operations of DR resources are scheduled based on the finite number of ambient temperature scenarios, which have been generated based on the historical ambient temperature data. As well as, the uncertainties in DR event including requested amount of load reduction and specified DR duration are also considered as scenarios. Also, operation rescheduling on DR operation day is proposed to ensure thermal comfort and the benefit of a COB owner. The proposed method minimizes the expected energy cost by a mixed integer linear programming (MILP).

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.28-39
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• 2011

While the broadband multimedia technologies have been developing, the commercial market of digital contents has also been widely spreading. Most of all, digital cartoon market like internet cartoon has been rapidly large so video cartooning continuously has been researched because of lack and variety of cartoon. Until now, video cartooning system has been focused in non-photorealistic rendering and word balloon. But the meaningful frame extraction must take priority for cartooning system when applying in service. In this paper, we propose new automatic frame extraction method for video cartooning system. At frist, we separate video and audio from movie and extract features parameter like MFCC and ZCR from audio data. Audio signal is classified to speech, music and speech+music comparing with already trained audio data using GMM distributor. So we can set speech area. In the video case, we extract frame using general scene change detection method like histogram method and extract meaningful frames in the cartoon using face detection among the already extracted frames. After that, first of all existent face within speech area image transition frame extract automatically. Suitable frame about movie cartooning automatically extract that extraction image transition frame at continuable period of time domain.