• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.727-735
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• 2018

Targeting the radiological technologists working in Gyoungsangnam province, this study was performed to obtain the fundamental data to improve the competency and right awareness of the hospital infection management, and to educate infection management of radiological technologists by analyzing the status, awareness, and performance of the hospital infection management. During April 1, 2018 to April 31, 2015, after we sent out a total of 400 questionnaires for the survey to radiological technologists working at the clinic located in Gyoungsangnam province, 320 questionnaires suitable for research were analysis by using SPSS 18.0 statistical analysis software. As the hospital infection management factors, 5 items for hospital infection and 60 items of the awareness and performance for the hospital infection management were used. 60 items of the awareness and performance for the hospital infection management were consisted hand hygiene, personal hygiene and clothing, medical equipment and supplies, cleaning and waste, examination and environment. And as the sociodemographic characteristics, the gender, marriage, age, level of education, working organization, working period, and working department were used. Consequentially, the awareness for the hospital infection management($4.19{\pm}.60$) and the performance($4.22{\pm}.52$) were confirmed as high level. Using these results, the hospital infection management level of the radiological technologists working in Gyoungsangnam province was found to be high in arareness and performance of hospital infection management. There was a significant correlation between the degree of awareness and performance of radiological technologists for hospital infection management. Furthermore, in the multiple regression analysis of cognitive factors on performance, it was found that 66.1% explanatory power had a significant positive influence. In order to improve the awareness of hospital infection management of radiological technologist working in various departments, the infection management education and improvement of hospital work environment are necessary. And also, It is important to participate actively in hospital infection management and preventive education and to play a pivotal role in securing expertise in hospital infection management.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.6
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• pp.521-537
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• 2012

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2011. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) Research trends of thermal and fluid engineering have been surveyed as groups of fluid machinery and fluid flow, thermodynamic cycle, and new and renewable energy. Various topics were presented in the field of fluid machinery and fluid flow. Research issues mainly focused on the rankine cycle in the field of thermodynamic cycle. In the new and renewable energy area, researches were presented on geothermal energy, fuel cell, biogas, reformer, solar water heating system, and metane hydration. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer, nanofluids and industrial heat exchangers. Researches on heat transfer characteristics included heat transfer above liquid helium surface in a cryostat, methane hydrate formation, heat and mass transfer in a liquid desiccant dehumidifier, thermoelectric air-cooling system, heat transfer in multiple slot impinging jet, and heat transfer enhancement by protrusion-in-dimples. In the area of pool boiling and condensing heat transfer, researches on pool boiling of water in low-fin and turbo-B surfaces, pool boiling of R245a, convective boiling two-phase flow in trapezoidal microchannels, condensing of FC-72 on pin-finned surfaces, and natural circulation vertical evaporator were actively performed. In the area of nanofluids, thermal characteristics of heat pipes using water-based MWCNT nanofluids and the thermal conductivity and viscosity were measured. In the area of industrial heat exchangers, researches on fin-tube heat exchangers for waste gas heat recovery and Chevron type plate heat exchanger were implemented. (3) Refrigeration systems with alternative refrigerants such as $CO_2$, hydrocarbons, and mixed refrigerants were studied. Heating performance improvement of heat pump systems were tried applying supplementary components such as a refrigerant heater or a solar collector. The effects of frost growth were studied on the operation characteristic of refrigeration systems and the energy performance of various defrost methods were evaluated. The current situation of the domestic cold storage facilities was analyzed and the future demand was predicted. (4) In building mechanical system fields, a variety of studies were conducted to achieve effective consumption of heat and maximize efficiency of heat in buildings. Various researches were performed to maximize performance of mechanical devices and optimize the operation of HVAC systems. (5) In the fields of architectural environment and energy, diverse purposes of studies were conducted such as indoor environment, building energy, and renewable energy. In particular, renewable energy and building energy-related researches have mainly been studied as reflecting the global interests. In addition, various researches have been performed for reducing cooling load in a building using spot exhaust air, natural ventilation and energy efficiency systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Journal of Technology Innovation
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• v.29 no.3
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• pp.1-31
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• 2021

With the growing importance of technology innovation as a key factor for firms' competitive advantage, 'innovation persistence' became also an important research subject. 'Innovation Persistence' is a concept that indicates whether or not firms' innovation activity or performance continues. However, the data used for innovation studies are carried out as cross-sectional surveys in most countries. For this reason, studies dealing with longitudinal aspect of innovation persistence are rare. In particular, there is almost no research on innovation persistence using Korean innovation survey data. This study reviews the concepts and characteristics of innovation persistence based on extant literature, and perform an empirical analysis on the status and features of Korean firms' technology innovation persistence. Based on the data of the Korean Innovation Survey (KIS) conducted every other year from 2012 to 2018, panel data on 3,379 firms which observed multiple times are constructed. As a result, only part of the firms with persistent innovation were observed (for innovation performance 10~12%, for innovation activity 15~17%), and it was found that the persistence of non-innovation was remarkable(about 52~57%). And it was confirmed that the persistence of innovation activities is stronger than that of innovation performance. Besides, some features by sub-types of innovation appeared. Product innovation showed higher persistence than process innovation, and internal R&D also showed higher persistence than joint/external R&D. As a result of additional logit analysis to identify factors, it was found that radical or gradual product innovation is the most influential factor in persisting innovation in the next period. Since the sample selection bias due to a limitations of raw data might exist in the panel data constructed in this study, it should be noted that faulty generalization of the results are not allowed. Nevertheless, this is the first study to examine the technology innovation persistence targeting Korean firms and is expected to be a starting point for follow-up studies. It is anticipated that advanced research results will be drawn through the establishment of official panel data and improved methodologies.

• Journal of Biosystems Engineering
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• v.12 no.2
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• pp.28-37
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• 1987

Operation of rice whiteners has been depending on operator's experience only and very limitted data are available for operational criteria of rice whiteners in Korea. With developments of new rice varieties and with a tendency of automation of machine operations for precision control, operational criteria depending on physical characteristics of rice grains arc required for an improvement of milled rice recovery and the performance of rice whitening systems. An experimental study was conducted to identify operational criteria of a rice whitening system consisted with an abrasive-aerated whitener developed newly and a frictional-aerated whitener being used commercially. Further, comparisons were made between the performance of the rice whitening system adopted for this study and a commercial system used in small scale milling plants. Results of this study are summarized as follows: 1. Total number of passes necessary for the final white rice in the combined whitening system depended exclusively on the counter pressure level of the frictional whitener successive to the abrasive whitener. 2. The counter pressure required for whitening Japonica type rice variety (Akibare) was higher by about 1.6 times than that for Japonica type (Pung-san), when other conditions were kept at the same. 3. Radial pressure in the whitening chamber of the frictional whitener should be maintained between 1.5 to $2.1kg/cm^2$ for the completion of whitening within 5 to 3 passes regardless of rice varieties. Hence, it was found that the radial pressure in the whitening chamber could be used as an operational criteria to control the counter pressure level. 4. The following regression equation was found between radial pressure($R_p$) in whitening chamber and electric power consumption of the whitening system: $$EPC=-0.545\;R^2_p+1.277\;R_p+0.874[KWH/100kg]$$ 5. The following multiple regression equation was found among radial pressure ($R_p$), counter pressure ($C_p$), and bioyield point ($B_i$), length (L) and width (W) of brown rice. $$R_p/(B_i/W^2)=0.547\{C_p/(B_i/W^2)\}^{0.365}(L/W)^{0.120}(R^2=0.9897)$$ 6. The milled rice recovery and machine efficiency (kg/KWH) from the combined whitening system were higher by about 2.0% point and by 15 to 27% point than those from the conventional system, respectively.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.81-87
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• 2018

Purpose - The perception of the quality of the consumer's distributor's brand(PBs) is generally perceived to be lower than that of the manufacturer's brand(NB), although it is a critical factor in determining the success of PBs. Accordingly, this study examines the characteristics of the convenience store PB products and their correlation with brand trust and purchase intent in the consumers who have had experience purchasing the convenience store PBs to expand the sales variables. Further, this research shows that the marketing strategy is to increase the share of PBs by providing an empirical analysis on the effect of the product attribute factors on the sales volume associated with brand trust, purchase intent, and others. Research design, data, and methodology - The survey period of this study was approximately three weeks from December 1, 2017 to December 21, 2017. The study samples that were taken from 100 random people extracted. The statistical analysis was carried out with multiple regression analysis using the SPSS statistical package. Results - The analysis shows that the brand credibility and purchasing intention were statistically significant differences between the private convenience store private brand products. Specifically, brand trust showed a statistically significant relationship the brand images and quality levels, but the perceived value was not affected statistically. Although the intent of the purchase showed a statistically significant relationship the quality level and the perceived value, the brand image was not statistically significant in its relationship. Conclusions - Overall, it has been established that the perception value does not statistically affect brand trust for convenience store PB products, and that the brand image has no statistically significant effect on the purchase intent. These results are analyzed to be due to the influence of brand in convenience stores themselves rather than brand trust and purchase intentions that affect sales performance, which is the property of private brand food and beverage products, the perceived value of their products. Accordingly, the study found that not only did the marketing performance of the convenience store PB products be improved statistically, but also the cause of the product attributes that were not statistically significant was identified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.618-624
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• 2017

In Seoul and most metropolitan cities, urban trains are delayed due to high congestion during commute times. The delay effect of passengers boarding and disembarking is also significant. In this study, a wide passenger door system was developed as a way to improve the scheduled speed of urban trains by decreasing the passengers' flow time. The door size was defined experimentally to shorten the entrance time. The optimum door size was also determined to improve the stop precision performance of the train while considering the interference effect with peripheral devices. Because the change in door size changes the structural characteristics of the vehicle, the structural stability of a train was analyzed numerically. A prototype of the wide door system was made, and the proposed design was verified using functional and endurance tests. The systematic development process can be used as design data for door size definition and system production when applying a wide door to improve the scheduled speed.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.131-143
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• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.