• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.18-28
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• 2021

This study surveyed how retirees' evaluation of starting food service business affects the effectiveness their new business and quality of life, based on personal factors such as entrepreneurship and business-starting capability and environmental factors by using questionnaires. Bootstrapping was carried out in order to find out factors affecting rapidly changing new business environments and retirees' initial intent to start a business so as to verify basic hypothesis about relation between retirees' evaluation of starting food service business (social, economic, and psychological effects) and the effectiveness of their new business and quality of life and confirm whether the effectiveness of the new business acts as a medium between the evaluation of starting food service business and quality of life. In addition, PLS-MGA was performed in order to verify whether the correlations among test factors can be varied according to the kind of job the target retirees had. Having examined the basic thesis, it was found that social and economic factors had significant positive effect on the effectiveness of the new business, and social and psychological factors had significant positive effect on the quality of life. Having analyzed whether the effectiveness of new business acted as a media between evaluation of starting food service business and quality of life, there was no significant effect as a medium. Having studied whether the kind of job of the retirees controlled or affected the relations among evaluation of starting food service business, effectiveness of new business and quality of life, the results were as follows: in the office job retiree group, the greater economic factor led to increase of effectiveness of new business, while social and psychological factors influenced the quality of life; In the physical labor group, the higher social factor resulted in higher effectiveness of new business, which showed significant positive effect on the quality of life. Having researched about which element is considered to be most important in starting food service business, the most important element was found to be dish/menu, followed by staff management, accounting management, business management, and service education. Having analyzed relation between accomplishment and important consideration for starting food service business, "managers with entrepreneurship," "appropriate distribution of time to prepare for starting business," and "operation of practical field education programs" showed higher importance compared to the degree of satisfaction, so it is needed to more concentrate on the above matters. This study intends to raise retirees' awareness of starting business and help them live better life based on the analysis results, and further suggest detailed mechanism and specific operations of factors affecting retirees' decision making on starting business, such that they can use the information as basic materials to make better choices that can lead to successful business.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.109-114
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• 2021

Cr(VI)-quinoline compound[(C₉H₇NH)₂Cr₂O₇] was synthesized by the reaction between of quinoline and chromium(VI) trioxide, and structure was FT-IR, elemental analysis. The oxidation ability of benzyl alcohol greatly depends upon the dielectric constant of the used organic solvent, where carbon tetrachloride was worst and N,N'-dimethylformamide was best solvent. Noticeably, in N,N'-dimethylformamide solvent, Cr(VI)-quinoline compound oxidized substituted benzyl alcohols. The Hammett reaction constant(ρ)=-0.69(303K). As a resuit, Cr(VI)-quinoline compound was found as efficicent oxidizing agent that converted benzyl alcohol, allyl alcohol, primary alcohol and secondary alcohols to the corresponding aldehydes or ketones. Cr(VI)-quinoline compound was selective oxidizing agent of benzyl alcohol, allyl alcohol and primary alcohol in the presence of secondary alcohol ones.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.295-302
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• 2021

Surface modification of recycled plastic film-based aggregates is demonstrated to enhance the interaction between aggregates and cement paste. It is shown that the oxygen(O₂) atmospheric pressure plasma(APP) treatment leads to a drastic increase in hydrophilicity. In case of the plasma treatment at 100W of RF power, 15/4sccm of O₂/Ar flow rate and 30sec of discharging time, the water contact angle on the aggregates surface decreased from 104.5° to 44.0°. In addition, the contact angle of surface modified aggregates kept in air increased with time elapse. Improvement of hydrophilicity can be explained by the formation of new hydrophilic oxygen functional groups which is identified as C-OH, C-O-C, C=O, -COOH by X-ray photoelectron spectroscopy(XPS) analysis and Fourier-transform infrared spectroscopy(FT-IR). Therefore, it can be concluded that the plasma treatment process is an effective method to improve adhesion of the recycled plastic film-based aggregates and cement paste.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.851-858
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• 2021

Due to the increase in renewable energy and distributed resources, not only traditional data but also various energy-related data are being generated in the new energy industry. In other words, there are various renewable energy facilities and power generation data, system operation data, metering and rate-related data, as well as weather and energy efficiency data necessary for new services and analysis. Energy big data processing technology can systematically analyze and diagnose data generated in the first half of the power production and consumption infrastructure, including distributed resources, systems, and AMI. Through this, it will be a technology that supports the creation of new businesses in convergence between the ICT industry and the energy industry. To this end, research on the data analysis system, such as itemized characteristic analysis of the collected data, correlation sampling, categorization of each feature, and element definition, is needed. In addition, research on data purification technology for data loss and abnormal state processing should be conducted. In addition, it is necessary to develop and structure NIFI, Spark, and HDFS systems so that energy data can be stored and managed in real time. In this study, the overall energy data processing technology and system for various power transactions as described above were proposed.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.397-402
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• 2021

The real-name electric fishing gear system is one of the important policy capable to build 'abundant fishing ground' and to protect marine environment. And, fishing gear automatic-identification system is one of IoT services that can implement above-mentioned policy by using communication such as low power wide area (LPWA) and multi-sensing techniques. Fishing gear automatic -identification system can gather the location data and lost/hold data from electric buoy floated in sea and can provide them to fishermen and monitoring center in land. We have developed the communication modules and electric buoy consisted of fishing gear automatic-identification system. In this paper, we report the test results of communication distance between electric buoy and wireless node installed in fish boat and location error of electric buoy. It is confirmed that line of sight (LOS) distance between electric buoy and wireless node is obtained to be 62 km, which is two times of the desired value, and location error is obtained to be CEP 1 m, which is smaller than the desired value of CEP 5 m. Therefore, it is expected that service area and accuracy of the developed fishing gear automatic-identification system is more extended.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.149-163
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• 2021

The 4th industrial revolution refers to the next-generation industrial revolution led by information and communication technologies such as artificial intelligence (AI), Internet of Things (IoT), robot technology, drones, autonomous driving and virtual reality (VR) and it also has made a significant impact on the development of the advertising industry. However, the world is rapidly changing to a non-contact, non-face-to-face living environment to prevent the spread of COVID 19. Accordingly, the role of the 4th industrial revolution and advertising is changing. Therefore, in this study, text analysis was performed using Big Kinds to examine the 4th industrial revolution and changes in advertising before and after COVID 19. Comparisons were made between 2019 before COVID 19 and 2020 after COVID 19. Main topics and documents were classified through LDA topic model analysis and Word2vec, a deep learning technique. As the result of the study showed that before COVID 19, policies, contents, AI, etc. appeared, but after COVID 19, the field gradually expanded to finance, advertising, and delivery services utilizing data. Further, education appeared as an important issue. In addition, if the use of advertising related to the 4th industrial revolution technology was mainstream before COVID 19, keywords such as participation, cooperation, and daily necessities, were more actively used for education on advanced technology, while talent cultivation appeared prominently. Thus, these research results are meaningful in suggesting a multifaceted strategy that can be applied theoretically and practically, while suggesting the future direction of advertising in the 4th industrial revolution after COVID 19.

• Membrane Journal
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• v.31 no.2
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• pp.101-119
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• 2021

The demand for clean water is virtually present in all modern human societies even as our society has developed increasingly more advanced and sophisticated technologies to improve human life. However, as global climate change begins to show more dramatic effects in many regions in the world, the demand for a cheap, effective way to treat wastewater or to remove harmful bacteria, microbes, viruses, and other solvents detrimental to human health has continued to remain present and remains as important as ever. Well-established synthetic membranes composed of polyaniline (PANI), polyvinylidene fluoride (PVDF), and others have been extensively studied to gather information regarding the characteristics and performance of the membrane, but recent studies have shown that making these synthetic membranes conductive to electrical current by doping the membrane with another material or incorporating conductive materials onto the surface of the membrane, such as allotropes of carbon, have shown to increase the performance of these membranes by allowing the adjustability of pore size, improving antifouling and making the antibacterial property better. In this review, modern electrically conductive membranes are compared to conventional membranes and their performance improvements under electric fields are discussed, as well as their potential in water filtration and wastewater treatment applications.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.