• 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.33-39
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• 2023

Nano-sized materials can be coated on various substrates, and since this material is transparent and conductive, it can be used as a transparent electrode for electronic devices or an electrode for power supply. In this study, CNT and Ag nanowires were repeatedly coated using the silk screen technique, and samples formed up to 5 times were fabricated, and their optical and electrical properties were measured and analyzed. It was confirmed that marks were formed on the surface of the silkscreen-coated sample according to the coating direction, and the trend of transmittance and surface resistance according to the number of times of coating was investigated. As the number of coatings increased, transmittance and surface resistance tended to decrease. In particular, in the case of transmittance, the range of change was large in the samples coated 2 and 5 times. These changes were confirmed by the Ag nanowire coating. In addition, starting from 700 nm, the previous wavelength region increased according to the wavelength, while the above showed a tendency to decrease. The surface resistance was lowered from 9Ω/cm² when coating once to 0.856Ω/cm² when coating five times. It was found that the resistance value was affected by Ag similarly to the permeability. In the future, it is necessary to realize a desired transparent electrode through Ag concentration and coating of Ag nanowires with other methods and fusion with highly transparent CNT to apply to electronic devices.

• 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.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.69-82
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• 2023

High-tech sensibility textile and fashion, in which consumers' emotions and various textile and fashion technologies are converged, is an important industrial group. It is important to develop the ability to apply in practice by gathering the creative by understanding other fields and exchanging ideas through interdisciplinary collaboration in the field of emotional engineering. Through interdisciplinary research and collaboration, talent must be nurtured of individuals who would lead the era of the 4^th Industrial Revolution with the ability to empathize with others as well as the creative convergence-type intellectual ability necessary for the rapidly changing society. To determine content-creation methods, basic research is conducted. Additionally, this study investigates on the current status and educational process of the emotional textile-fashion industry worldwide. To nurture talents in the textile and fashion sensibility science, the basic contents are created to manage the knowledge that delivers sensibility science and the ICT related to this field, as well as in the intensive, PB-style conceptual design based on sensibility. The process from derivation of consumer emotion analysis and product development can be experienced through smart kit practice. Moreover, various methods are developed to set up intellectual property rights generated while developing ICT convergence products as start-ups. The study also covers new knowledge rights to develop emotional textile fashion.

• 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.

• Composites Research
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• v.36 no.6
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• pp.441-447
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• 2023

Recently, as demand for high-strength, lightweight materials has increased, there has been great interest in joining with metals. In the case of mechanical bonding, such as bolting and riveting, chemical bonding using adhesives is attracting attention as stress concentration, cracks, and peeling occur. In this paper, surface treatment was performed to improve the adhesive strength, and the change in adhesive strength was analyzed. For the adhesive strength test were conducted with Carbon Fiber Reinforced Plastic(CFRP), CR340(Steel), and Al6061(Aluminum), and laser and plasma surface treatment were used. After plasma surface treatment, the adhesive strength improved by 7.3% and 39.2% in CFRP-CR340 and CFRP-Al6061, respectively. CR340-Al6061 was improved by 56.2% in laser surface treatment. Surface free energy(SFE) was measured by contact angle after plasma treatment, and it is thought that the adhesion strength was improved by minimizing damage through a chemical reaction mechanism. For laser surface treatment, it is thought that creates a rough bonding surface and improves adhesive strength due to the mechanical interlocking effect. Therefore, surface treatment is effect to improve adhesive strength, and based on this paper, the long-term fatigue test will be conducted to prevent fatigue failure, which is a representative cause of actual structural damage.

• Composites Research
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• v.36 no.6
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• pp.435-440
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• 2023

This study aimed to enhance the thermal conductivity of B₄C/Al composite materials, commonly used in transport/storage containers for spent nuclear fuel, by incorporating both boron carbide (B₄C) and cubic boron nitride(cBN) as reinforcing agents in an aluminum (Al) matrix. The composite materials were successfully manufactured through a stir casting process and practical neutron-absorbing materials were obtained by rolling the fabricated composite ingot. The evaluation of the thermal conductivity of the fabricated composites was carried out because thermal conductivity is critical for neutron absorbing materials. The thermal conductivity measurement results indicated an approximately 3% increase in thermal conductivity under the same volume fraction when compared to composite materials using only B₄C particles. Through neutron absorption cross-sectional area calculations, it was confirmed that the neutron absorption capability decreased to a negligible level. Based on the findings of this study, new design approaches for neutron absorption materials are proposed, contributing to the development of high-performance transport/storage containers.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.