• Asia Marketing Journal
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• v.11 no.3
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• pp.185-203
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• 2009

Yuhan Kimberly, a joint-venture company of Korean Yuhan Company and American Kimberly-Clark, opened a premium diaper market in Korea by launching Pull-Ups which was pants-typed diapers in 1993. Pull-Ups was imported as finished goods from Kimberly-Clark. However, in spite of its huge market share in United States, it failed to land down in Korean market because of wrong positioning strategy which did not consider domestic customers' tastes. In 1996, Yuhan-Kimberly brought out a pants-typed diaper, Huggies-Toddler, to Korean market again. This paved the way for the combination of Kimberly-Clark's production power and Yuhan-Kimberly's marketing power and led to launch new product superior to Pull-Ups. However, this product was unsuccessful in the market because of wrong positioning which did not catch domestic customer's life styles such as cultural, environmental and habitual differences in toilet training, the cost increase coming from IMF crisis in Korea, weak trust within the company, weak trust within the company, and too much higher price than regular diapers. In 2005, Yuhan-Kimberly redeveloped new pants market business strategy. It was organically combined with winning product development plan, winning communication strategy and the market structure change through the pants market creation. Customer's habit, usage and attitude were studied with total 55 times market investigations. Also, all processes from planning to designing were executed in the customer's view by investigating product research, positioning research and advertisement research. Yuhan-Kimberly repositioned new product as a wearing diaper not as a toilet training diaper and launched Huggies Magic Panty as a premium product which had 25% higher price than previous Huggies. Huggies Magic Panty was recognized as a great hit product in domestic diaper market and the sales recorded 37.6 bill won in 2006, 57.2 bill won in 2007, and 90 bill won in 2008 since launching in 2005. The reason of Huggies Magic Panty's success was the repositioning strategy deduced from the precise check of customer's usage habit. It was the winning strategy of Huggies that were market investigation in order to survive in domestic baby goods market where a lot of companies struggled intensively, the exact positioning based on its market investigation and aggressive 360 degree communication strategy to give customers impressions efficiently.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.49-59
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• 2019

The field trial was performed to evaluate the rice growth and yield in different direct seeding methods after Italian Ryegrass Harvest The required time for seed emergence was for 7 ~ 8days in the tested direct seeding methods and there was high in seedling establishment in order of wet hill-seeding with iron-coated seeds > water seeding with iron-coated seeds > wet hill-seeding with soil coverage with pregerminated seeds. The rice plant height was shorter in the tested direct seeding methods than that of machine transplanting until 45day after seeding but there was not significant difference in terms of statistical analysis at 63day after seeding. The growth of tiller number in the rice plant was high in water seeding with iron-coated seeds and wet hill-seeding with soil coverage and low in wet hill-seeding with iron-coated seeds compared to machine transplanting. The yield component in the tested direct seeding methods was not significant difference in terms of statistical analysis. The milled rice yield in the tested direct seeding methods was higher 2 ~ 8% being with 4.94 ~ 5.24t/ha than that of machine transplanting but there was not significant difference in terms of statistical analysis. The percentage of head rice was low in the tested direct seeding methods compared to machine transplanting. The weedy rice was not occurred in the tested rice cultivation methods. In conclusion the direct seeding method would be recommended to be a suitable to in following by Italian ryegrass harvesting in southern area of Korea in terms of reduction in production cost and high income basis for rice growing farmers.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

• Journal of the Korean Institute of Gas
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• v.27 no.3
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• pp.19-26
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• 2023

Hydrogen, a clean energy source free of COx emissions, is poised to replace fossil fuels, with its usage on the rise. Despite its high energy content per unit mass, hydrogen faces limitations in storage and transportation due to its low storage density and challenges in long-term storage. In contrast, ammonia offers a high storage capacity per unit volume and is relatively easy to liquefy, making it an attractive option for storing and transporting large volumes of hydrogen. While NH₃ decomposition is an endothermic reaction, achieving excellent low-temperature catalytic activity is essential for process efficiency and cost-effectiveness. The study examined the effects of different zeolite types (5A, NaY, ZSM5) on NH₃ decomposition activity, considering differences in pore structure, cations, and Si/Al-ratio. Notably, the 5A zeolite facilitated the high dispersion of Ni across the surface, inside pores, and within the structure. Its low Si/Al ratio contributed to abundant acidity, enhancing ammonia adsorption. Additionally, the presence of Na and Ca cations in the support created medium basic sites that improved N₂ desorption rates. As a result, among the prepared catalysts, the 15 wt%Ni/5A catalyst exhibited the highest NH₃ conversion and a high H₂ formation rate of 23.5 mmol/g_cat·min (30,000 mL/g_cat·h, 600 ℃). This performance was attributed to the strong metal-support interaction and the enhancement of N₂ desorption rates through the presence of medium basic sites.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.589-597
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• 2023

Floating debris in large quantities from land during heavy rainfall has adverse social, economic, and environmental impacts, but the monitoring system for the concentration area and amount is insufficient. In this study, we proposed an efficient monitoring method for floating debris entering the river during heavy rainfall in Daecheong Lake, the largest water supply source in the central region, and applied remote sensing techniques to estimate the standing-stock of floating debris. To investigate the status of floating debris in the upper of Daecheong Lake, we used a tracking buoy equipped with a low-orbit satellite communication terminal to identify the movement route and behavior characteristics, and used a drone to estimate the potential concentration area and standing-stock of floating debris. The location tracking buoys moved rapidly during the period when the cumulative rainfall for 3 days increased by more than 200 to 300 mm. In the case of Hotan Bridge, which showed the longest distance, it moved about 72.8 km for one day, and the maximum moving speed at this time was 5.71 km/h. As a result of calculating the standing-stock of floating debris using a drone after heavy rainfall, it was found to be 658.8 to 9,165.4 tons, with the largest amount occurring in the Seokhori area. In this study, we were able to identify the main concentrations of floating debris by using location-tracking buoys and drones. It is believed that remote sensing-based monitoring methods, which are more mobile and quicker than traditional monitoring methods, can contribute to reducing the cost of collecting and processing large amounts of floating debris that flows in during heavy rain periods in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.91-106
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• 2022

Cryptocurrency, stood out the sharp cost rising of Bitcoin has been spotlighted by means of the solution for stagflation because it is decentralized with an existing currency differently. Especially getting into 4th industrial revolution, technologies using block chain and internet of things have been used in the many fields, and the power of influence is also widespread. Nevertheless like a remark of Elon Musk of Tesla CEO, the problems of environmental contamination for cryptocurrency have been pointed out continuously and the most representative of them is an enormous electric usage as the use of fossil fuels. Also the amount generated of carbon dioxide result in the acceleration of global warming mainly based on the climate changes of earth if the existing mining method is continued. On the other hand, review researches have been conducted restrictively as the connection with environmental contamination as the mining of cryptocurrency. In this study, it intended to review problems for environmental contamination as the diversification of ecological system of cryptocurrency concretely. Upon investigation existing prior documents on the putting recent data first, the mining of cryptocurrency has affected on the environmental contamination conflicting with carbon neutrality as increasement of the electric usage and electronic wastes. And POS method without the mining process appeared, but it had a demerit collapsing a decentralization and then we met turning point on appearing various environmental-friendly cryptocurrency. Finally the appearance of cryptocurrency using new renewable energy acted on the opportunity of the usage maximization of energy storage apparatus and the birth of national government intervention. Based on these results, we mention clearly that hereafter cryptocurrency will regress if not go abreast the value of currency as well as environmental approach.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.751-764
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• 2023

Gangwon State is centered on the Taebaek Mountains with very different climate characteristics depending on the region, and localized heavy rainfall is a frequent occurrence. Heavy rain disasters have a short duration and high spatial and temporal variability, causing many casualties and property damage. In the last 10 years (2012~2021), the number of heavy rain disasters in Gangwon State was 28, with an average cost of 45.6 billion won. To reduce heavy rain disasters, it is necessary to establish a disaster management plan at the local level. In particular, the current criteria for heavy rain warnings are uniform and do not consider local characteristics. Therefore, this study aims to propose a heavy rainfall warning criteria that considers the threshold rainfall for the advisory areas located in Gangwon State. As a result of analyzing the representative value of threshold rainfall by advisory area, the Mean value was similar to the criteria for issuing a heavy rain warning, and it was selected as the criteria for a heavy rain warning in this study. The rainfall events of Typhoon Mitag in 2019, Typhoons Maysak and Haishen in 2020, and Typhoon Khanun in 2023 were applied as rainfall events to review the criteria for heavy rainfall warnings, as a result of Hit Rate accuracy verification, this study reflects the actual warning well with 72% in Gangneung Plain and 98% in Wonju. The criteria for heavy rain warnings in this study are the same as the crisis warning stages (Attention, Caution, Alert, and Danger), which are considered to be possible for preemptive rain disaster response. The results of this study are expected to complement the uniform decision-making system for responding to heavy rain disasters in the future and can be used as a basis for heavy rain warnings that consider disaster risk by region.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.697-703
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• 2023

As a part of the global industrial efforts to reduce environmental pollution owing to air pollution, regulations have been established by the International Maritime Organization (IMO). The IMO has implemented various regulations such as EEXI, EEDI, and CII to reduce air pollution emissions from ships. They are also promoting measures to decrease the power consumption in ships, aiming to conserve energy. Most of the power used in ships is consumed by electric motors. Among the motors installed on ships, the engine room blower that takes up a significant load, operates at a constant irrespective of demand. Therefore, energy savings can be expected through frequency control. In this study, we demonstrated the efficacy of energy savings by controlling the frequency of the electric motor of the generator blower that supplies combustion air to the generator's turbocharger. The system was modeled based on the output data of the turboharger outlet temperature in response to the blower frequency inpu. A PI control system was established to control the frequency with the target being the turbocharger outlet temperature. By maintaining the turbocharger design standard outlet temperature and controlling the blower frequency, we achieved an annual energy saving of 15,552kW in power consumption. The effectiveness of energy savings through frequency control of blower fans was verified during the summer (April to September) and winter (March to October) periods. Based on this, we achieved annual fuel cost savings of 6,091 thousand won and reduction of 8.5 tons of carbon dioxide, 2.4 kg of SOx, and 7.8 kg of NOx air pollutants on the training ship.