• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.483-490
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• 2022

Floating LiDAR is a system that provides a new paradigm for wind condition observation, which is essential when creating an offshore wind farm. As it can save time and money, minimize environmental impact, and even reduce backlash from local communities, it is emerging as the industry standard. However, the design and verification of a stable platform is very important, as disturbance factors caused by fluctuations of the buoy affect the reliability of observation data. In Korea, due to the nation's late entry into the technology, a number of foreign equipment manufacturers are dominating the domestic market. The west coast of Korea is a shallow sea environment with a very large tidal difference, so strong currents repeatedly appear depending on the region, and waves of strong energy that differ by season are formed. This paper conducted a study examining buoys suitable for LiDAR operation in the waters of Korea, which have such complex environmental characteristics. In this paper, we will introduce examples of optimized design and verification of ship-type buoys, which were applied first, and derive important concepts that will serve as the basis for the development of various platforms in the future.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.1
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• pp.1-10
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• 2006

Previous studies on zooplankton in Asan Bay were mostly based on samples collected seasonally with three months intervals. Present study was aimed to know the temporal variation of meso-zooplankton distribution using the data collected monthly. Relationships between zooplankton abundances and environmental factors such as seawater temperatures, salinities and chlorophyll-a contents were also studied. Seawater temperature showed typical pattern of seasonal variation found in temperate waters. The fluctuations of environmental factors ranged relatively wider In the inner part of the bay than those in outer part of the bay. Salinity was very low right after the summer rainy period due to the sporadic outflow of freshwater from the adjacent artificial lakes. Sudden changes in salinity seemed to have significant impact on zooplankton assemblages. Chlorophyll-a contents were increased in general when compared with previous reports probably due to the recent human exploitations in the coastal zone, which might enhance the nutrients level . The timing and duration of spring bloom showed geographical differences. In the inner part of the bay it began earlier (February) and last longer (three months) while in the outer part of the bay it began late (April) and last just one month. Zooplankton abundance, especially most abundant taxon Acartia hongi, showed weak but significant positive correlation with chlorophyll-a contents. The difference in temporal variation found with two different sampling intervals indicated the necessity of shorter time interval samplings.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Design & Manufacturing
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• v.17 no.3
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• pp.21-27
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• 2023

The mold industry in Japan, an advanced country in the mold industry, is also at a point of great change. The main causes are the Ukraine crisis and the collapse of the global supply chain (parts supply chain) caused by COVID-19. In addition, the prices of overseas products are rising sharply due to rapid exchange rate fluctuations (decrease in the value of the yen). Until now, Japan's monotsukuri industry has been actively pursuing overseas expansion, riding the trend of globalization. However, the trend began to rapidly reverse, and now the monotsukuri industry that had expanded overseas is showing a tendency to return to Japan. Another factor of change is the change in the automobile industry, which is the most demanded product in the mold industry. As the automobile industry evolves from gasoline cars to electric cars, the number of parts that make up a car will drastically decrease. This trend is expected to increase the demand for small-scale production of a variety of products in the mold industry, and furthermore, it is expected that short delivery times will be required in parts development. As in Korea, the production population working in the mold industry is rapidly decreasing in Japan as well. Even if you add up the total population working in manufacturing in Japan, it only accounts for about 15%. Even in Japan, it is judged that it will be difficult to sustain the monotsukuri industry with this small production population. Therefore, since improvement in production efficiency cannot be expected with the same manual dexterity as before, the mold industry is also demanding the development of mold technology at a different level than before to increase productivity. In this paper, I would like to introduce new Japanese mold technology collected through attending the Intermold exhibition. This is an example of applying a dedicated pin (Gastos) to a mold to prevent an increase in internal pressure during plastic injection molding, and a deep drawing press molding technology with an inherent hydraulic function.

• Industry Promotion Research
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• v.9 no.2
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• pp.13-22
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• 2024

The purpose of this study is to derive passenger trends and change rates for each airline and identify directionality and synchronization phenomenon. Data by each airlines was collected from the National Statistics Forum of Statistics Korea, and we used a total of 156 monthly data from January 2011 to December 2023. In this study, the rate of change was calculated for domestic Full Service Carriers (Korean Air, Asiana Airlines) and Low Cost Carriers (Jeju Air, Jin Air, T'way, foreign airlines). As a result of the analysis, the correlation was found to be high for KOREA in that order: Asiana, Korean Air, Jeju Air, T'way, Jin Air, foreign airlines. The rate of increase was highest in that order: T'way, Jin Air, Jeju Air, foreign airlines, Asiana, Korean Air. In the Scatter analysis, Asiana and Korean Air showed a very strong synchronization with KOREA. In addition, Jeju Air, T'way, Jin Air and foreign airlines also showed the same direction toward KOREA to a certain degree. In the Box-Box Plot analysis, it was determined that each airline experienced a number of unusual sudden fluctuations due to the outbreak of COVID-19. Passengers have a wider range of choices due to the emergence of Low Cost Carriers, and as a result, expectations for airline service are increasing. Airlines will need to make appropriate environmental improvements to satisfy these needs for corporate development.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.33-48
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• 2024

This study aims to establish the scope and statistics of the K-address information industry in Korea, estimating its size and prospects and estimates the economic effects through K-address information industry based on Input-Output analysis. Considering the characteristics and sectoral structure of the K-address information industry, the study delineates the scope and specific sectors, constructing sectoral statistics linked to the KSIC and the Bank of Korea's industrial classification. The study estimates the sectoral industry size, taking into account potential markets. Furthermore, it analyzes the economic impact of each sector within the K-address information industry. To figure out the economic effects, the study conducts Input-Output analysis by setting the K-address information industry as an exogenous sector in the input-output table. The results indicate that the overall size of the K-address information industry is estimated to grow from 406.1 billion KRW in 2021 to 3.65 trillion KRW in 2030. The economic effects of the K-address information industry vary by sector, emphasizing the importance of synergies and integration with related sectors, particularly those with significant inducement effects in high value-added manufacturing and service sectors. Furthermore, the industry's sensitivity to economic fluctuations is evident through the input-output analysis of inter-industry chain effects.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.32-38
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• 2024

In the case of apartment building, it is difficult to conduct non-destructive testing due to the actual presence of people and the dust and noise generated during the core test, so inspections are performed each time in the common area and underground parking lot, and the tests are conducted on the finishing material rather than on the concrete surface due to low-cost orders. As the process progresses, poor inspection is inevitable. In addition, the proposed formulas for strength estimation have large fluctuations depending on the differences in test conditions and environments, and even if they show the same measured value, the deviation between each proposed formula is large, making it difficult to accurately estimate strength, making it difficult to use. Accordingly, we would like to select finishing materials mainly used in apartment complexes and compare and evaluate the compressive strength of concrete according to the type of finishing material by using non-destructive testing methods directly on the finishing materials without removing the finishing materials. The reliability evaluation results of the estimated compressive strength of concrete using the ultrasonic velocity method according to the type of finishing material are as follows. The error rate between the estimated compressive strength and compressive strength derived through the ultrasonic velocity method shows a wide range of variation, ranging from 21.83% to 58.89%. The effect of the presence or absence of finishing materials on the estimated compressive strength was found to be insignificant. Accordingly, it is necessary to select more types of finishing materials and study ultrasonic velocity methods according to the presence or absence of finishing materials, and to study estimation techniques that can increase reliability.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.2
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• pp.142-151
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• 2024

Recently, domestic fishing production of Japanese horse mackerel has been continuously decreasing. To achieve sustainable fishing of this species, it is essential to acquire its target strength (TS) for accurate biomass estimation and to study its ecological characteristics. To date, there has been no TS research using a broadband echosounder targeting Japanese horse mackerel. In this study, for the first time, we synchronized an underwater camera with a broadband frequency (nominal center frequency of 200 kHz, range: 160-260 kHz) to measure the TS according to the body size (16.8-35.5 cm) and swimming angle of the species. The relationship between Japanese horse mackerel length and body weight showed a general tendency for body weight to increase as length increased. The pattern of the frequency spectra (average values) by body length exhibited a similar trend regardless of body length, with no significant fluctuations in frequency observed. The lowest TS value was observed at 243 kHz while the highest TS values were recorded at 180 and 257.5 kHz. The frequency spectra for the swimming angles appeared to be flat at angles of -5, 0, 30, 60, 75, and 80° while detecting more general trends of frequency spectra for swimming angle proved challenging. The results of this study can serve as fundamental data for Japanese horse mackerel biomass estimation and ecological research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.93-105
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• 2024

Car-sharing is a representative model of the sharing economy, and it is a service that rents or uses a car for the necessary time without owning a car. This industry is growing due to various factors such as technological advances, increasing awareness of environmental protection, and increasing demand for solving traffic congestion problems in cities. Accordingly, there is a need for a strategic approach for companies providing car-sharing services to respond quickly to market changes in order to expand market share and differentiate services. Accordingly, this study conducted a case study on open innovation activities between Gongcar and existing rental car companies, focusing on the research question "What effects do open innovation activities between car-sharing companies and existing rental car companies cause?" As a result of the study, it was confirmed that Gongcar have (1) the ability to actively respond to market fluctuations by establishing a flexible vehicle supply chain based on demand, (2) have significantly reduced growth capital expenditure (Growth Capex), and both cafe and rental car companies have (3) performed successful open innovation by improving key KPI indicators and recording financial performance. This study reveals how open innovation acts as a key business growth engine in the car-sharing industry, and its significance is found in that it empirically confirmed the successful implementation conditions of open innovation based on resource dependence theory.