• Journal of Yeungnam Medical Science
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• v.38 no.2
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• pp.118-126
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• 2021

Background: This study is an analysis of evaluator factors affecting physician-patient interaction (PPI) scores in clinical performance examination (CPX). The purpose of this study was to investigate possible ways to increase the reliability of the CPX evaluation. Methods: The six-item Yeungnam University Scale (YUS), four-item analytic global rating scale (AGRS), and one-item holistic rating scale (HRS) were used to evaluate student performance in PPI. A total of 72 fourth-year students from Yeungnam University College of Medicine in Korea participated in the evaluation with 32 faculty and 16 standardized patient (SP) raters. The study then examined the differences in scores between types of scale, raters (SP vs. faculty), faculty specialty, evaluation experience, and level of fatigue as time passes. Results: There were significant differences between faculty and SP scores in all three scales and a significant correlation among raters' scores. Scores given by raters on items related to their specialty were lower than those given by raters on items out of their specialty. On the YUS and AGRS, there were significant differences based on the faculty's evaluation experience; scores by raters who had three to ten previous evaluation experiences were lower than others' scores. There were also significant differences among SP raters on all scales. The correlation between the YUS and AGRS/HRS declined significantly according to the length of evaluation time. Conclusion: In CPX, PPI score reliability was found to be significantly affected by the evaluator factors as well as the type of scale.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1392-1413
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• 2022

In order to meet the rapid development of the unmanned aerial vehicle (UAV) communication needs, cooperative spectrum sensing (CSS) helps to identify unused spectrum for the primary users (PU). However, multi-UAV mode (MUM) requires the large communication resource in a cognitive UAV network, resulting in a severe decline of spectrum efficiency (SE) and energy efficiency (EE) and increase of energy consumption (EC). On this account, we extend the traditional 2D spectrum space to 3D spectrum space for the UAV network scenario and enable UAVs to proceed with spectrum sensing behaviors in this paper, and propose a novel multi-slot mode (MSM), in which the sensing slot is divided into multiple mini-slots within a UAV. Then, the CSS process is developed into a composite hypothesis testing problem. Furthermore, to improve SE and EE and reduce EC, we use the sequential detection to make a global decision about the PU channel status. Based on this, we also consider a truncation scenario of the sequential detection under the sensing delay constraint, and further derive a closed-form performance expression, in terms of the CSS performance and cooperative efficiency. At last, the simulation results verify that the performance and cooperative efficiency of MSM outperforms that of the traditional MUM in a low EC.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.37-49
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• 2023

In this study, we proposed resource allocation methodologies to improve the productivity of steel bridge manufacturing plants based on the constraint theory which is very popular in the area of manufacturing industries. To this end, after defining the painting process as a bottleneck, three resource allocation methodologies were developed: Operation Specific Resource Allocation (OSRA), Product Specific Resource Allocation (PSRA), and General Resource Allocation (GRA). As a result of experiments for performance evaluation using a simulation model of the steel bridge supply chain, GRA showed the best performance in terms of the Number of Work-In-Process (NWIP) and Waiting Time (WT), in particular, as workload itself and its variability were increased, the performance gap with the specific resource allocation became further deepened. On average, GRA reduced NWIP by 36.2% and WT by 34.6% compared to OSRA, and reduced NWIP by 71.0% and WT by 70.4% compared to PSRA. The reduction of NWIP and WT means alleviating the bottleneck of the painting process, which eventually means that the productivity of the steel bridge manufacturing plant has improved.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.1-15
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• 2023

Purpose - This study investigates whether the inventory levels of Korean manufacturing firms increased or decreased from 2000 to 2019. We also explore the relationship between inventory levels and firm performance. Design/methodology/approach - We use panel data on KOSPI-listed firms in the manufacturing industry. We measure days in inventory as a proxy for inventory levels, and firm performance is measured by return on assets, return on sales, and EBITDA ratio. The panel data regression method is employed in our analysis. Findings - We find that days in inventory of Korean manufacturing firms significantly increased from 2000 to 2019, especially for raw materials and finished goods inventory. In addition, while days in inventory of large- and medium-sized firms were less than those of small-sized firms, the change in days in inventory of large- and medium-sized firms was positively significant over time. Moreover, the increase in days in inventory was more prevalent among industries related to foods, clothes, chemicals, and transportation. Finally, we show that the days in inventory are negatively related to firm performance. Research implications or Originality - While the Korean manufacturing industry has enormously grown over the last 20 years and managing inventory is critical in the manufacturing industry, our findings counter-intuitively show that the days in inventory of the Korean manufacturing industry had been gradually increased. We speculate that the increase in days in inventory is due to the Korean manufacturing firms' heavy reliance on global supply chains.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.355-363
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• 2024

Time synchronization is crucial for ensuring the reliable operation of modern economic and social infrastructures. Techniques such as Global Navigation Satellite System (GNSS)-based methods and Two-Way Satellite Time and Frequency Transfer (TWSTFT) play key roles in precise time comparison and synchronization. TWSTFT, in particular, is recognized for its ability to achieve sub-nanosecond accuracy in time transfer, making it indispensable in fields such as satellite navigation. This paper proposes a comprehensive performance evaluation method for TWSTFT modems, emphasizing pre-validation in controlled environments to mitigate operational challenges. Using the proposed evaluation method, the study presents the standard deviation of RTT according to C/N₀ and compares it with the datasheet of a commercial TWSTFT modem. Through this approach, the aim of this study is to enhance the reliability and accuracy of TWSTFT-based time synchronization across diverse applications.

• Advances in concrete construction
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• v.17 no.5
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• pp.293-310
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• 2024

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is a form of cement-based material that has a compressive strength above 150 MPa, excellent ductility, and superior durability. This composite material demonstrates innovation and has the potential to serve as a viable substitute for concrete constructions that are subjected to harsh environmental conditions. Over many decades, extensive research and progressive efforts have introduced several commercial UHPFRC compositions globally. These compositions have been specifically designed to cater to an increasing variety of applications and meet the rising need for building materials of superior quality. However, the effective manufacturing of UHPFRC relies on the composition of its materials, especially the inclusion of fiber content and the proportions in the mixture, resulting in a more compact and comparatively uniform packing of particles. UHPFRC has notable benefits in comparison to conventional concrete, yet its use is constrained by the dearth of design codes and the prohibitive expenses associated with its implementation. The study demonstrates that UHPFRC presents a viable, long-lasting option for improving sustainable construction. This is attributed to its outstanding strength properties and superior durability in resisting water and chloride ion permeability, freeze-thaw cycles, and carbonation. The analysis found that a rheology-based mixture design technique may be employed in the production of UHPFRC to provide enough flowability. The study also revealed that the use of deformed steel fibers has shown enhanced mechanical qualities in comparison to straight steel fibers. However, obstacles such as higher initial costs, the requirement for highly specialized personnel, and the absence of comprehensive literature on global UHPFRC standards that establish minimum strength criteria and testing requirements can hinder the widespread implication of UHPFRC. Finally, this review attempts to deepen our foundational conception of UHPFRC, encourages additional study and applications, and recommends an in-depth investigation of the mechanical and durability properties of UHPFRC to maximize its practicality.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.1
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• pp.94-131
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• 2007

A review on the papers published in the Korean Journal of Air-Conditioning and Refrigerating Engineering in 2004 and 2005 has been done. Focus has been put on current status of research in the aspect of heating, cooling, air-conditioning, ventilation, sanitation and building environment. The conclusions are as follows. (1) Most of fundamental studies on fluid flow were related with heat transportation of facilities. Drop formation and rivulet flow on solid surfaces were interesting topics related with condensation augmentation. Research on micro environment considering flow, heat, humidity was also interesting for comfortable living environment. It can be extended considering biological aspects. Development of fans and blowers of high performance and low noise were continuing topics. Well developed CFD and flow visualization(PIV, PTV and LDV methods) technologies were widely applied for developing facilities and their systems. (2) The research trends of the previous two yews are surveyed as groups of natural convection, forced convection, electronic cooling, heat transfer enhancement, frosting and defrosting, thermal properties, etc. New research topics introduced include natural convection heat transfer enhancement using nanofluid, supercritical cooling performance or oil miscibility of $CO_2$, enthalpy heat exchanger for heat recovery, heat transfer enhancement in a plate heat exchanger using fluid resonance. (3) The literature for the last two years($2004{\sim}2005$) is reviewed in the areas of heat pump, ice and water storage, cycle analysis and reused energy including geothermal, solar and unused energy). The research on cycle analysis and experiments for $CO_2$ was extensively carried out to replace the Ozone depleting and global warming refrigerants such as HFC and HCFC refrigerants. From the year of 2005, the Gas Engine Heat Pump(GHP) has been paid attention from the viewpoint of the gas cooling application. The heat pipe was focused on the performance improvement by the parametric analysis and the heat recovery applications. The storage systems were studied on the performance enhancement of the storage tank and cost analysis for heating and cooling applications. In the area of unused energy, the hybrid systems were extensively introduced and the life cycle cost analysis(LCCA) for the unused energy systems was also intensively carried out. (4) Recent studies of various refrigeration and air-conditioning systems have focused on the system performance and efficiency enhancement. Heat transfer characteristics during evaporation and condensation are investigated for several tube shapes and of alternative refrigerants including carbon dioxide. Efficiency of various compressors and expansion devices are also dealt with for better modeling and, in particular, performance improvement. Thermoelectric module and cooling systems are analyzed theoretically and experimentally. (5) According to the review of recent studies on ventilation systems, an appropriate ventilation systems including machenical and natural are required to satisfied the level of IAQ. Also, an recent studies on air-conditioning and absorption refrigeration systems, it has mainly focused on distribution and dehumidification of indoor air to improve the performance were carried out. (6) Based on a review of recent studies on indoor environment and building service systems, it is noticed that research issues have mainly focused on optimal thermal comfort, improvement of indoor air Quality and many innovative systems such as air-barrier type perimeter-less system with UFAC, radiant floor heating and cooling system and etc. New approaches are highlighted for improving indoor environmental condition as well as minimizing energy consumption, various activities of building control and operation strategy and energy performance analysis for economic evaluation.