• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.17-21
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• 2015

In this paper, we introduce satellite communication for new wireless disaster network to be built on the basis of amateur radio HR (HAM Radio) as a wireless package system, and channel environments of a D2D terminal that tries to connect and communicate with the wireless disaster network. In this disaster network, we propose a LOS component ratio based adaptive channel modeling approach to accurately estimate a variety of channels whose the D2D terminal could have and smoothly transfer to the level of multimedia data based on the Okumura-Hata channel model. As a result of computer simulation, performance of the proposed method was compared with the that of Okumura-Hata model of open area and urban area model and we were confirmed that there is a gain of BER performance from the results of the computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.543-548
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• 2021

The higher the resolution of the image, the higher the satisfaction of the viewers of the image, and the super-resolution imaging has a considerable increase in research value among the fields of computer vision and image processing. In this study, the main features of low-resolution image LR are extracted mainly using deep learning super-resolution models. It learns and reconstructs the extracted features, and focuses on reconstruction-based algorithms that generate high-resolution image HR. In this paper, we investigate SRCNN and VDSR in a super-resolution algorithm model based on reconstruction. The structure and algorithm process of the SRCNN and VDSR model are briefly introduced, and the multi-channel and special form are also examined in the improved performance evaluation function, and understand the performance of each algorithm through experiments. In the experiment, an experiment was performed to compare the results of the SRCNN and VDSR models with the peak signal-to-noise ratio and image structure similarity, so that the results can be easily judged.

• STI Policy Review
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• v.1 no.1
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• pp.1-21
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• 2010

To achieve the "low carbon green growth" vision, the first step is securing core technologies. Therefore, S&T policy direction for green technology development is urgently needed. As of 2008, investment in green technology (GT) development hovered around 10% of the government's total R&D budget. Thus, the Korean government developed a plan to increase that percentage to 15%, by 2013. To develop reasonable investment strategies for green technology development, targeted strategies that reflect technology and market changes by green technology area are needed. However, the overall planning and coordination of national GT development is currently split among, approximately, 10 government ministries. To establish an efficient green technology development system, the so-called "Green Technology R&D Council" should be launched in collaboration with the Presidential Committee on Green Growth and the National Science and Technology Council. Furthermore, to build a solid foundation for commercializing the outcomes of GT development projects and promote GT transfer, the government should undertake two initiatives. First, the government should reinforce GT R&D performance management, by establishing a GT R&D performance management and evaluation system. Second, the government should implement the "customized packaged support for promoting green technology business rights and commercialization" and present "e-marketplace for market-oriented green technologies". Creating a pan-ministerial policy for GT development policy would necessitate restructuring the HR(Human Resources) development system, which is currently separated by technology area. Based upon mid/long-term HR supply and demand forecasts, the government should design differentiated HR development projects, continuously evaluate those projects, and reflect the evaluation results in future policy development. Finally, to create new GT-related industries, the "Green TCS (Testing, Certification, and Standards) System" needs to be implemented. For objective evaluation and diffusion of R&D results by green technology area, a common standardization plan for testing, analysis, and measurement, like the "Green TCS", should be developed and integrated.

• Journal of Hydrogen and New Energy
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• v.32 no.3
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• pp.149-155
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• 2021

Most of the reformer experiments have been conducted only in high-temperature operation conditions above 700℃. However, to design high efficiency solid oxide fuel cell, it is necessary to test actual reaction performance in mid-temperature (550℃) operation areas. In order to study the operation characteristics and performance of commercial reforming catalysts, a reforming performance experiment was conducted on mid-temperature. The catalysts used in this study are Ni-based FCR-4 and Ru-based RuA, RuAL. Experiments were conducted with a Steam-to-carbon ratio of 2.0 to 3.0 under gas hourly space velocity (GHSV) 2,000 to 5,000 hr-1. As a result, RuA and RuAL catalysts showed similar gas composition to the equilibrium regardless of the reforming temperature. However, the FCR-4 catalyst showed a lower hydrogen yield compared to the equilibrium under high GHSV conditions.

• The KSFM Journal of Fluid Machinery
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• v.15 no.5
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• pp.27-31
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• 2012

Turbine inlet temperature is steadily increasing to achieve high specific thrust and efficiency of gas turbine engines. Turbine cooling technology is essential to increase turbine inlet temperature. For this study, a small or medium sized aircraft engine of 10,000 lbf class with the turbine inlet temperature of $1,400^{\circ}C$, the engine overall pressure ratio of 32.2, and the bypass ratio of 5 was set as the baseline model and its performance analysis was performed at the design point. The engine has the performance of 10,013 lbf thrust and the specific fuel consumption of 0.362 lbm/hr/lbf. The thrust and the specific fuel consumption of the baseline model were compared with those of similar class engines. Based on these results, the turbine design requirements were assigned. In addition, the parametric analysis of the engine, related to aerodynamic and cooling design of the high pressure turbine, was performed. Based on the baseline model engine, the influence of turbine inlet temperature, cooling flow ratio, and high pressure turbine efficiency variations on the engine performance was analyzed.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.388-394
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• 2024

Since recommendation systems play a key role in increasing the revenue of companies, various approaches and models have been studied in the past. However, this diversity also leads to a complexity in the types of recommendation systems, which makes it difficult to select a recommendation model. Therefore, this study aims to solve the difficulty of selecting an appropriate recommendation model for recommendation systems by providing a unified criterion for categorizing various recommendation models and comparing their performance in a unified environment. The experiments utilized MovieLens and Coursera datasets, and the performance of linear models(ADMM-SLIM, EASER, LightGCN) and non-linear models(Caser, BERT4Rec) were evaluated using HR@10 and NDCG@10 metrics. This study will provide researchers and practitioners with useful information for selecting the best model based on dataset characteristics and recommendation context.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.100-103
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• 2021

Most companies study why employees resign their jobs to prevent the outflow of excellent human resources. To obtain the data needed for the study, employees are interviewed or surveyed before resignation. However, it is difficult to get accurate results because employees do not want to express their opinions that may be disadvantageous to working in a survey. Meanwhile, according to the data released by the Korea Labor Institute, the greater the difference between the minimum level of education required by companies and the level of employees' academic background, the greater the tendency to resign jobs. Therefore, based on these data, in this study, we would like to predict whether employees will leave the company based on data such as major, education level and company type. We generate four kinds of resignation prediction models using Decision Tree, XGBoost, kNN and SVM, and compared their respective performance. As a result, we could identify various factors that were not covered in previous study. It is expected that the resignation prediction model help companies recognize employees who intend to leave the company in advance.

• Journal of Research and Publication Ethics
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• v.5 no.2
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• pp.1-6
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• 2024

Purpose: PBM is emerging as a major management system for securing corporate productivity and enhancing competitiveness, and various studies are being conducted. The purpose of this study is to analyze research trends published in KCI-listed journals and papers since 1999 to understand the current status of research and provide basic data for more extensive research and development of performance management in the future. Research design, data and methodology: A detailed examination of research trends was conducted through the analysis of abstracts from 154 research papers on PBM. To facilitate a comprehensive analysis of these trends, LDA topic modelling was employed. Results: First, it should be noted that research on PBM is not limited to the area of HRM. Instead, PBM research is expanding to encompass comprehensive personnel systems. Second, the results of topic modeling analysis show that although the initial focus of research was on human resource management, there is now a growing interest in fairness and organizational culture in the entire organization. Conclusions: PBM is becoming a dominant paradigm as it shifts from HR systems to organizational fairness and culture. This suggests that future research should consider both quantitative and qualitative aspects of PBM to improve corporate performance while prioritizing organizational fairness and culture.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.531-536
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• 2003

This study was carried out to develop a vacuum nozzle seeder for large seeds and performance was tested on seed feeding, arranging. and sowing peformance. The results of this study were as follows： The operation of feeding device of the seeder was programmed to operate a period of setting time after sowing 6 rows. The setting time was decided based on a discharged seed by the angular speed of feeding roller. The arranging accuracy of 'tuktozwa', 'hukjong' and 'chambak' was 96.4％, 95.2％ and 89.4％ respectively. The working performance was 75.6sheet/hr which was 3.8 times higher than that of manual work. An average seeding rate of 1 grain was 97.8％.

• Korean Journal of Radiology
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• v.21 no.8
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.