• Knowledge Management Research
• /
• v.22 no.4
• /
• pp.103-118
• /
• 2021

Gross Domestic Product(GDP) is affected by the economic power of each industry. Therefore, using statistical data related to the food and drug industry, we tried to determine the proportion of GDP and analyzed the impact of the food, medical & drug industry on the domestic economy through comparison with other industries. The food, medical & drug industry has a wide range of industries among domestic industries and is closely related to the lives of the people. In addition, human lifespan is increasing, and recently, due to the spread of an infectious disease called COVID-19, the bio sector belonging to the food, medical & drug industry is in the spotlight. Attention is needed to the industry as the competitiveness of the food, medical & drug industry is expected to increase. The Ministry of Food and Drug Safety provides statistics on the food, medical & drug industry, but does not provide a systematic share of GDP. Since it is difficult to determine how influential the industry is compared to other industries, this study attempts to obtain the share of GDP in the food, medical & drug industry and compare it with other industries. In the process of obtaining GDP in the food, medical & drug industry sector, there was a difficulty in that the figures in statistical data were not unified by time point. In order to overcome the limitations, statistical data as a standard are determined. The GDP of the Food, Medical & Drug Industry was estimated using total added value, production, sales, and added value by industry. Compared to other industries, the Food, Medical & Drug Industry's GDP ranked second after the GDP of the manufacturing industry. As a result, it suggests that the food, medical & drug industry has a great influence on the national economic power among domestic industries.

• Journal of Korea Water Resources Association
• /
• v.54 no.11
• /
• pp.903-913
• /
• 2021

In the flood season, the measurement of the river discharge has many restrictions due to reasons such as budget, manpower, safety, convenience in measurement and so on. In particular, when heavy rain events occur due to typhoons, etc., it is difficult to measure the amount of flood due to the above problems. In order to improve this problem, in this study, a method was developed that can measure the river discharge in a flood season simply and safely in a short time with minimal manpower by combining the functions of a drone and a surface velocity doppler radar. To overcome the mechanical limitations of drones caused by weather issues such as wind and rainfall derived from the measurement of the river discharge using the conventional drone, we developed a drone with P56 grade dustproof and waterproof performance, stable flight capability at a wind speed of up to 36 km/h, and a payload weight of up to 10 kg. Further, to eliminate vibration which is the most important constraint factor in the measurement with a surface velocity doppler radar, a damper plate was developed as a device that combines a drone and a surface velocity Doppler radar. The velocity meter DSVM (Dron and Surface Veloctity Meter using doppler radar) that combines the flight equipment with the velocity meter was produced. The error of ±3.5% occurred as a result of measuring the river discharge using DSVM at the point of Geumsan-gun (Hwangpunggyo) located at Bonghwang stream (the first tributary stream of the Geum River). In addition, when calculating the mean velocity from the measured surface velocity, the measurement was performed using ADCP simultaneously to improve accuracy, and the mean velocity conversion factor (0.92) was calculated by comparing the mean velocity. In this study, the discharge measured by combining a drone and a surface velocity meter was compared with the discharge measured using ADCP and floats, so that the application and utility of DSVM was confirmed.

• Journal of Food Hygiene and Safety
• /
• v.37 no.2
• /
• pp.97-106
• /
• 2022

Listeria monocytogenes (L. monocytogenes) is one of gram-positive foodborne pathogens with a very high fatality rate. Unlike most foodborne pathogens, L. monocytogenes is capable of growing at low temperatures, such as in refrigerated foods. Thus, various physical and chemical prevention methods are used in the manufacturing, processing and distribution of food. However, there are limitations to the methods such as possible changes to the food quality and the consumer awareness of synthetic preservatives. Thus, the aim of this study was to evaluate the anti-listeria activity of lactic acid bacteria (LAB) isolated from kimchi and characterize the bacteriocin produced by Lactococcuslactis which is one of isolated strains from kimchi. The analysis on the anti-listeria activity of a total of 36 species (Lactobacillus, Weissella, Lactobacillus, and Lactococcus) isolated from kimchi by the agar overlay method revealed that L. lactis NJ 1-10 and NJ 1-16 had the highest anti-listeria activity. For quantitatively analysis on the anti-listeria activity, NJ 1-10 and NJ 1-16 were co-cultured with L. monocytogenes in Brain Heat Infusion (BHI) broth, respectively. As a result, L. monocytogenes was reduced by 3.0 log CFU/mL in 20 h, lowering the number of bacteria to below the detection limit. Both LAB strains showed anti-listeria activity against 24 serotypes of L. monocytogenes, although the sizes of clear zone was slightly different. No clear zone was observed when the supernatants of both LAB cultures were treated with proteinase-K, indicating that their anti-listerial activities might be due to the production of bacteriocins. Heat stability of the partially purified bacteriocins of NJ 1-10 and NJ 1-16 was relatively stable at 60℃ and 80℃. Yet, their anti-listeria activities were completely lost by 60 min of treatment at 100℃ and 15 min of treatment at 121℃. The analysis on the pH stability showed that their anti-listeria activities were the most stable at pH 4.01, and decreased with the increasing pH value, yet, was not completely lost. Partially purified bacteriocins showed relatively stable anti-listeria activities in acetone, ethanol, and methanol, but their activities were reduced after chloroform treatment, yet was not completely lost. Conclusively, this study revealed that the bacteriocins produced by NJ 1-10 and NJ 1-16 effectively reduced L. monocytogenes, and that they were relatively stable against heat, pH, and organic solvents, therefore implying their potential as a natural antibacterial substance for controlling L. monocytogenes in food.

• Journal of The Korean Association For Science Education
• /
• v.42 no.3
• /
• pp.311-324
• /
• 2022

This study analyzed the types of scientific emoticons made by science-gifted elementary school students and their perceptions on making scientific emoticons. To do this, 71 students from 4th to 6th graders of two gifted science education center in Seoul were selected. Scientific emoticons made by the students were analyzed according to the number and types. Their perceptions on making scientific emoticons were also analyzed through a questionnaire and group interviews. In the analyses for types of text in the scientific emoticons, 'word type' and 'sentence type' were made more than 'question and answer type'. And the majority of students made more 'pun using pronunciation type' and 'mixed type' than other types. They also made more 'graphic type' and 'animation type' than 'text type' in the images of the scientific emoticons. In the analyses for the information of the scientific emoticons, 'positive emotion type' and 'negative emotion type' of scientific emoticons were made evenly. The students made more 'new creation type' than 'partial correction type' and 'entire reconstruction type'. They also used scientific knowledge that preceded the knowledge of science curriculum in their grade level. The scientific knowledge of chemistry was used more than physics, biology, earth science, and combination field. 'Name utilization type' was more than 'characteristic utilization type' and 'principle utilization type'. Students had various positive perceptions in making scientific emoticons such as 'increase of scientific knowledge', 'increase of various higher-order thinking abilities', 'ease of explanation, use, memory, and understanding of scientific knowledge', 'increase of fun, enjoyment, and interest about science and science learning', and 'increase of opportunity to express emotions'. They were also aware of some limitations related to 'difficulties in the process of making scientific emoticons', 'lack of time', and 'limit that it may end just for fun'. Educational implications of these findings are discussed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.6
• /
• pp.249-267
• /
• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Korean Chemical Engineering Research
• /
• v.60 no.2
• /
• pp.243-248
• /
• 2022

As the scope of use of portable and wearable electronic devices is expanding, the limitations of heavy and bulky solid-state batteries are being revealed. Given that, it is urgent to develop a small energy harvesting device that can partially share the role of a battery and the utilization of energy sources that are thrown away in daily life is becoming more important. Contact electrification, which generates electricity based on the coupling of the triboelectric effect and electrical induction when the two material surfaces are in contact and separated, can effectively harvest the physical and mechanical energy sources existing in the surrounding environment without going through a complicated intermediate process. Recently, the interest in the harvest and utilization of wind energy is growing since the wind is an infinitely ecofriendly energy source among the various environmental energy sources that exist in human surroundings. In this study, the optimization of the energy harvesting device for the effective harvest of wind energy based on the contact electrification was analyzed and then, the utilization strategy to maximize the utilization of the generated electricity was investigated. Natural wind based Fluttering TENG (NF-TENG) using fluttering film was developed, and design optimization was conducted. Moreover, the safe high voltage generation system was developed and a plan for application in the field requiring high voltage was proposed by highlighting the unique characteristics of TENG that generates low current and high voltage. In this respect, the result of this study demonstrates that a portable energy harvesting device based on the contact electrification shows great potential as a strategy to harvest wind energy thrown away in daily life and use it widely in fields requiring high voltage.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1505-1514
• /
• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1095-1105
• /
• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.599-614
• /
• 2022

The purpose of this study was to investigate the effect of organizational culture (group culture, development culture, rational culture, hierarchical culture) on job satisfaction based on the mediating effect of organizational trust on workers corporations. In particular, in the relationship between organizational culture and job satisfaction, the mediating effect of organizational trust was demonstrated, which varies by communication control variables. The results of the study of 8,615 workers in the manufacturing, financial, and non-financial industries in HCCP(Human Capital Corporate Panel) are as follows. First of all, the result of research showed that rational culture, group culture, development culture among organizational culture had a positive effect on job satisfaction. But, the hierarchical culture had a negative influence on job satisfaction. Second, rational culture, group culture, development culture among organization culture had a positive effect on organizational trust. But, hierarchical culture had a negative influence on organizational trust. Third, in the relationship between organizational culture and job satisfaction, the partial mediating effect of organizational trust was verified. In other words, although organizational culture directly affects job satisfaction, it indirectly affects job satisfaction through organizational trust. Fourth, it showed a significant moderating effect of communication between organization trust and job satisfaction. In other words, it was found that the group with high organizational trust in the relationship between organizational trust and job satisfaction had higher job satisfaction than the group without it. Finally, in the relationship between organizational culture and job satisfaction, the mediating effect of organizational trust was demonstrated, which varies by communication control variables. In other words, the indirect effect of organizational culture on job satisfaction through organizational trust is higher in the group with high communication capabilities. Through discussion and conclusion, the academic and practical implications, limitations, and research directions of this study were presented.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.939-951
• /
• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.