• Korea Trade Review
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• v.44 no.3
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• pp.25-42
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• 2019

This research examined the mediation effect of Workforce Agility (WA) on the relationship between environmental uncertainty and operational performance. We manipulated the control variables that are known to be affected by employment flexibility. Employment flexibility is caused by idiosyncratic characteristics of Korean port system. The analysis was tested by Baron & Kenny's method. The result indicates that each path of the proposed model is significant. Furthermore, the mediation effect was checked with the Sobel Test. The research revealed that environment uncertainty poses an indirect effect on operational performance. Both supply/demand uncertainty and technological uncertainty affected operational performance through the mediation effect of WA. Most of the distribution centers located in Busan Newport Distripark are operated in a bimodal labor (human resource) system which includes both permanent employees (workers) and temporary employees (workers). This empirical research provides theoretical and managerial implications by suggesting ways to increase efficiency in distribution center operation through WA enhancement, and to improve the unloading labor system.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.1-9
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• 2022

The study aims to determine the factors and their influence on the income from using public agricultural land of households. Public agricultural land is agricultural land, including land for growing annual crops, perennial crops, and land for aquaculture, leased by commune-level People's Committees with a lease term of not more than 5 years. Secondary data were collected for the 2017-2021 period at state agencies. Primary data were collected from a survey of 150 households renting public agricultural land. The regression model assumed that there were 28 factors belonging to 7 groups. The test results show that 25 factors affect income, and 03 factors do not. The group of COVID-19 pandemic factors has the strongest impact, followed by the groups of agricultural product market factors, land factors, capital factors, production cost factors, labor factors, and climatic factors. The impact rate of COVID-19 pandemic factors is the largest (23.00%); The impact rate of climatic factors is the smallest (6.04%). Proposals to increase income include good implementation of disease prevention and control; increasing the land lease term; accurately forecasting the supply and demand of the agricultural market; raising the level of the household head; ensuring sufficient production capital, and adapting to the climate.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.327-337
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• 2022

The purpose of this study is to determine the direction and significance of variables influencing small and medium enterprises (SMEs) decisions regarding accounting policy in Vietnam. Research data was collected through a survey of 296 subjects, including chief accountants, accountants, managers, and lecturers with practical experience in accounting work at enterprises. With the help of specialized software SPSS, determining the impact of factors on the choice of accounting policy of enterprises is done through a multivariate regression model with control tools Cronbach's alpha determination, EFA factor analysis, and Pearson correlation analysis. Research results show that there are seven factors affecting the choice of accounting policy in Vietnamese SMEs; in which, the factors information technology, legal environment, information demand, manager's awareness, and accounting qualification have a positive impact; and two factors are tax pressure, and financial leverage have a negative impact on accounting policy choice. These results are consistent with most of the previously published studies. However, in contrast to many previous studies, our research shows that accounting's psychological factor does not affect the accounting policy choice. This is consistent with the characteristics of SMEs in Vietnam because the role of accountants is not appreciated in the business.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.745-751
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• 2021

The International Maritime Organization(IMO) has established Emission Control Areas(ECA) in the Baltic Sea, North Sea, and sea areas in the United States since 2012, and encourages the use of clean fuels such as Natural Gas(NG). To keep pace with the increase in international demand for LNG bunkering vessels, research for the localization of key equipment for LNG bunkering must also be performed in Korea. For research and development of core bunkering equipment and systems, in this study, heat transfer analysis and structural analysis were performed by modeling the saddle, which must first be secured structurally by directly receiving the load of the hose. As a result, the suitability of the model was reviewed by analyzing the temperature distribution and stress level through the analysis results of this study.

• Smart Media Journal
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• v.12 no.1
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• pp.92-101
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• 2023

The importance of high-definition video has increased by increase in consumer demand for realistic content. Therefore, this study presented the UHD strategy of terrestrial broadcasters for global competitiveness. First, as a practical strategy, to secure domestic market, it should secure an ecosystem through cooperation among departments, create synergy, and prioritize the UHD field to gain global competitiveness with close cooperation with expertised countries in the field. Also, after securing the domestic market by developing UHDTV focusing on cost-effectiveness on socio-cultural situation of Korea, it should utilize the finance and technology for global market. government should support global competitiveness as a control tower, strengthening global content, composing a long-term roadmap, supporting related materials, and creating and exporting our own UHDTV model

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.108-117
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• 2011

Seoul metropolitan government has been operating traffic signal control system with the name of COSMOS since 2001. COSMOS uses the degrees of saturation and congestion which are calculated by installing loop detectors. At present, inductive loop detector is generally used for detecting vehicles but it is inconvenient and costly for maintenance since it is buried on the road. In addition, the estimated queue length might be influenced in case of error occurred in measuring speed, because it only uses the speed of vehicles passing by the detector. A traffic signal control algorithm which enables smooth traffic flow at intersection is proposed. The proposed algorithm assigns vehicles to the group of each lane and calculates traffic volume and congestion degree using traffic information of each group using VANETs(Vehicular Ad-hoc Networks) inter-vehicle communication. It does not demand additional devices installation such as cameras, sensors or image processing units. In this paper, the algorithm we suggest is verified for AJWT(Average Junction Waiting Time) and TQL(Total Queue Length) under single intersection model based on GLD(Green Light District) Simulator. And the result is better than Random control method and Best first control method. In case real-time control method with VANETs is generalized, this research that suggests the technology of traffic control in signalized intersections using wireless communication will be highly useful.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Phonetics and Speech Sciences
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• v.13 no.1
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• pp.53-64
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• 2021

According to Demand and Capacity Model (DCM), external and internal environments influence the disfluency of children who stutter (CWS). This study investigated the effects of simultaneous changes in motoric and linguistic demands on CWS and children who do not stutter (CWNS). Participants were 4-6 years old CWS and CWNS. A sentence imitation task with changes in speech rate, utterance length, and sentence complexity was used to examine their effects on children's disfluency. When the utterance length changed, CWS showed more disfluency regardless of utterance length and as the speech rate changed, CWS showed more disfluency at fast speech rate than CWNS. When the utterance length and speech rate changed, at fast speech rate, CWS showed more disfluency in both utterances than CWNS. When sentence complexity changed, CWS showed more disfluency than CWNS in complex sentences. Changes in linguistic elements such as speech rate, utterance length, and sentence complexity affect disfluency in CWS, especially when they were exposed to faster, longer, and more complex sentences. This indicates that CWS are vulnerable to fast and complex speech motor control and language processing ability than CWNS. Thus, this study suggests that parents and therapists consider both the speech rate and the utterance length when talking with CWS.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Korea Technology Innovation Society
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• v.17 no.4
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• pp.786-819
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• 2014

Emphasizing the importance of R&D as a source of open innovation, Korean government is developing various programs focused on technology commercialization and is expanding investment on it. In spite of those efforts, technology commercialization is not vitalized yet due to the lack of demand for technology transfer, R&D planning scheme without considering market, immaturity of technology market, and so on. This study aims to suggest the business ecosystem model so that technology commercialization could be facilitated based on business ecosystem perspective. We set the framework for modeling a business ecosystem through reviewing the previous works, and draw several problems to be solved regarding public R&D commercialization in Korea from the perspective of ecosystem. Considering those, this research proposes the business ecosystem model for public R&D commercialization as a reference model for describing, discussing, and developing the technology commercialization strategy. The proposed model consists of 4 domains as follows: R&D, technology market, information distribution channels, and customers. The business ecosystem model shows that technology commercialization could be facilitated to create the market value through close relationship and organic cooperation among its members that form the ecosystem. Public research institutes as a keystone player could control the fate of the ecosystem. In this regard, this paper suggests roles of public research institutes for evolving the business ecosystem.