• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.349-355
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• 2023

This study attempted to examine the effect of Korean and Japanese college students' perception of welfare for the elderly, dementia perception, and dementia attitude on dementia policy. The main results are as follows. As a result of analyzing the impact of Korean-Japanese college students on their perception and attitude of dementia policy, it was found that Korean-Japanese college students' perception of welfare for the elderly and dementia attitude had a positive (+) effect on dementia policy perception. As a result, I would like to present the following implications. Korea and Japan have developed social insurance systems for the elderly in a low birth rate and aging society, and are developing and implementing support services suitable for the culture of both countries. In the perception of dementia policies of college students in Korea and Japan, the influence of long-term care insurance for the elderly in Korea and nursing care insurance in Japan has led to changes in the perception of dementia among college students. However, it has been shown that dementia awareness does not affect dementia policy awareness, indicating that college students lack dementia awareness. Dementia awareness can increase the demand for the development of various dementia-related services or dementia policies, but low dementia awareness can lead to misunderstanding or negative perceptions of dementia. This can also affect the perception of dementia policies, and services and policies such as social support, prevention, and treatment related to dementia may not be sufficiently developed. In order to compensate for these problems in the future, efforts should be made to improve awareness through the provision of various information such as the government and society to help improve and understand dementia awareness for college students.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.493-499
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• 2023

Trade credit refers to a transaction where a product supplier allows an distributor to defer payment for a certain period of time for the purchase cost of the products. This practice is generally permitted as a means of differentiation between competing companies. Such trade credit is commonly granted based on the volume of transactions, aiming to increase customer orders. From the perspective of the distributor, trade credit allows for a deferred payment period for the purchase cost, leading to cost savings in inventory investment. These cost savings in inventory investment can be a factor in reducing selling prices with the aim of increasing customer demand. In this study, we analyze a model that determines the optimal selling price and order quantity from the perspective of the distributor, assuming that the supplier allows a deferred payment period dependent on the transaction volume. We assume that the final customer's annual demand exhibits an exponential decrease with respect to the distributor's selling price, using a constant price elasticity function. To analyze the problem, we assume that the product deteriorates at a constant rate over time and aim to establish an inventory model for the intermediate distributor. We also want to analyze the impact of deterioration on the inventory policies of the intermediate distributor.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• 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.

• Korean small business review
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• v.42 no.3
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• pp.173-193
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• 2020

This study analyzes the effects of KOSDAQ-listed firms' convertible bonds, which have recently increased rapidly in number and size. Although KOSDAQ companies are called mid-size companies, KOSDAQ companies belong to SMEs. Furthermore, convertible bonds have traditionally been a critical capital raising tool for SMEs in the US and Europe. In Korea, KOSDAQ companies actively employ convertible bonds. Convertible bonds provide investment incentives for hesitant investors, allowing companies to raise capital at low interest rates. This study analyzes whether capital raising through issuance of convertible bonds by KOSDAQ companies affects their financial constraints. Financial constraints result from incomplete capital markets, which are embedded in most companies and countries.. In particular, financial constraints have a significant impact on the growth and survival of SMEs. The seminal study FHP(1988) is the most important and effective study of firm's financial constraints. We find that FHP's financial constraint measures show that convertible bond issuance would mitigate the financial constraints of KOSDAQ companies. However, the significance of the evidence is not strong.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.41-57
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• 2023

The drybulk shipping market has high freight rate volatility in the chartering market and various and complex factors affecting the market. In the unstable economic situation caused by the COVID-19 pandemic in 2020, the BDI plunged due to a decrease in trade volume, but turned from the end of 2020 and maintained a booming period until the end of 2022. The main reason for the market change is the decrease in the available fleet that can actually be operated for cargo transport due to port congestion by the COVID-19 pandemic, regardless of the fleet and trade volume volatility that have affected the drybulk shipping market in the past. A decrease in the actual usable fleet due to vessel waiting at port by congestion led to freight increase, and the freight increase in charting market led to an increase in second-hand ship and new-building ship price in long-term equilibrium relationship. In the past, the drybulk shipping market was determined by the volatility of fleet and trade volume. but, in the future, available fleet volume volatility by pandemics, environmental regulations and climate will be the important factors affecting BDI. To response to the IMO carbon emission reduction in 2023, it is expected that ship speed will be slowed down and more ships are expected to be needed to transport the same trade volume. This slowdown is expected to have an impact on drybulk shipping market, such as a increase in freight and second-hand ship and new-building ship price due to a decrease in available fleet volume.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.395-413
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• 2023

This research studied the effect of customer experience characteristics in offline home appliance stores on perceived value and revisit intention. Among the offline distribution of home appliances with more than 100 stores nationwide, two home appliance retailers (HiMart, E-Land), three hypermarkets (E-Mart, Homeplus, Lotte Hi-Mart), and two home appliance stores (LG Best Shop, Samsung Digital Plaza) were selected, and a survey was conducted on men and women in their 20s or older in Seoul, Gyeonggi, and Incheon who had visited and purchased the home appliance store within the last 6 months. As a result of the survey, a statistical analysis was conducted on a total of 330 samples using the PLS (Partial Least Squares) structural equation model and SPSS statistical package. Through this study, the following research results can be obtained. First, educational experience, deviant experience, and aesthetic experience had a positive (+) effect on the functional value. However, entertainment experience did not affect functional value. Second, educational experience, deviant experience, and aesthetic experience all had a positive (+) effect on emotional value. Third, both functional and sensory values had a positive (+) effect on the revisit intention. Fourth, it was confirmed that brand loyalty had no moderating effect between functional value and sensory value revisit intention. The results of this study show the structural relationship between customer experience characteristics, perceived value (functional value, sensory value), and revisit intention. This result provides guidelines on what activities home appliance offline stores should do at a time when online channels threaten the survival of offline channels.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.91-100
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• 2023

Reverberation time (T60) is a typical acoustic parameter that provides information about reverberation. Since the impacts of reverberation vary depending on the frequency bands even in the same space, frequency-dependent (FD) T60, which offers detailed insights into the acoustic environments, can be useful. However, most conventional blind T60 estimation methods, which estimate the T60 from speech signals, focus on fullband T60 estimation, and a few blind FDT60 estimation methods commonly show poor performance in the low-frequency bands. This paper introduces a modified approach based on Attentive pooling based Weighted Sum of Spectral Decay Rates (AWSSDR), previously proposed for blind T60 estimation, by extending its target from fullband T60 to FDT60. The experimental results show that the proposed method outperforms conventional blind FDT60 estimation methods on the acoustic characterization of environments (ACE) challenge evaluation dataset. Notably, it consistently exhibits excellent estimation performance in all frequency bands. This demonstrates that the mechanism of the AWSSDR method is valuable for blind FDT60 estimation because it reflects the FD variations in the impact of reverberation, aggregating information about FDT60 from the speech signal by processing the spectral decay rates associated with the physical properties of reverberation in each frequency band.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.109-118
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• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.