• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.15-19
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• 2022

The radial distribution systems (RDS) commonly used around the world has the following disadvantages. First, when the DL is operated on a radial system, the line utilization rate is usually kept low. Second, if a fault occurs in the radial DL, a power outage of 3 to 5 minutes is occurring depending on the operator's proficiency and fault situation until the fault section is separated and the normal section is replaced. To solve this problem, Various methods have been proposed at domestic and foreign to solve this problem, and in Korea, research is underway on the advanced system of operating multiple linked DL always. A system that is electrically linked always, and that is built to enable high-speed communication during the protection coordination is named networked distribution system (NDS). Because the load shares the DL, the line utilization rate can be improved, and even if the line faults, the normal section does not need to be cut off, so the normal section does not experience a power outage. However, since it is impossible to predict in which direction the fault current will flow when a failure occurs in the NDS, a communication-based protection coordination is used, but there is no backup protection coordination method in case of communication failure. Therefore, in this paper, we propose a protective cooperation method to apply as a backup method when communication fails in NDS. The new method is to change TCC by location of CB using voltage drop in case of fault.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.43-48
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• 2022

Although the distribution system has been structured as complicated as a mesh in the past, the connection points for each line are always kept open, so that it is operated as a radial distribution system (RDS). For RDS, the line utilization rate is determined according to the maximum load on the line, and the utilization rate is usually kept low. In addition, when a fault occurs in the RDS, a power outage of about 3 to 5 minutes occurs until the fault section is separated, and the healthy section is transferred to another line. To improve the disadvantages of the RDS, research on the construction of a networked distribution system (NDS) that linking multiple lines is in progress. Compared to the RDS, the NDS has advantages such as increased facility utilization, load leveling, self-healing, increased capacity connected to distributed generator, and resolution of terminal voltage drop. However, when a fault occurs in the network distribution system, fault current can flow in from all connected lines, and the direction of fault current varies depending on the fault point, so a high-precision fault current direction determination method and high-speed communication are required. Therefore, in this paper, we propose an accurate fault current direction determination method by comparing the peak value polarity of the fault current in the event of a fault, and a communication-based protection coordination method using this method.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.259-273
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• 2022

Interactions between species in a community are very complex, and they are visualized and analyzed through a food web in simple way. Food web is a network of species connected by trophic links showing energy flow from prey to predator. Various models were developed to characterize the food web in ecosystems. In this study, we classified food web models to static models such as Ecopath and dynamic models such as AQUATOX. We presented characteristics of several different types of food web models in each category, and reviewed their applications used in aquatic ecosystems. Finally, we presented issues to be considered to develop food web models.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.49.1-49.1
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• 2021

We carried out simultaneous monitoring observations of five maser lines, H₂O (22 GHz), SiO 𝝊 =1, 2, J =1-0 (43.1, 42.8 GHz), and SiO 𝝊 =1, J=2-1, J =3-2 (86.2, 129.3 GHz), toward the Mira variable star WX Serpentis with the 21-m antennas of the Korean VLBI Network (KVN) in 2009-2021 (~12 years). Most spectra of the H₂O maser are well separated into two parts of two blue- and one redshifted features within ± 10 km s-1 of the stellar velocity. All detected SiO masers are generally concentrated within ± 5 km s-1 of the stellar velocity, and sometimes appear split into two components. Overall, the profiles of SiO and H₂O masers detected in WX Serpentis illustrate typical characteristics of the Mira variable. In addition, flux variations of both SiO and H₂O masers are well correlated with the optical light curve of the central star, showing a phase lag of ~ 0.1 for SiO masers and ~ 0.2 for H₂O maser. This phenomenon is considered to be the direct effect of propagating shock waves generated by the stellar pulsation, because SiO and H₂O masers are sequentially distributed at different positions with respect to the central star. In addition, we analyzed long-term trends and characteristics of maser velocities, maser ratio, and the velocity extents (the full width at zero power; FWZP). We also investigated a spectral energy distribution (SED) ranging from 1.2 to 240 ㎛ obtained using several infrared data: 2MASS, WISE, IRAS, ISO, COBE DIBRE, RAFGL, and AKARI (IRC and FIS). From the IRAS LRS and ISO SWS spectra of this star, we identified 9.7 and 12 ㎛ silicate emission features consistent with the SE6 spectrum model, corresponding to the typical AGB phase.

• Design Convergence Study
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• v.15 no.2
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• pp.237-250
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• 2016

Along with rapid development, progress of the network technology and digital information technology, human are stepping into the intelligent society of internet. Thereby the quality of living environment and working environment are keep improving. Under the big background of internet era, the timeliness and convenience of smart home system has been improved greatly. While lots of smart products have gradually penetrated into people's daily life. The household appliances are among most popular ones. This paper is intended to compare smart features of household living products among most representative brands in China, Japan and South Korea. The smart features include self-learning, self-adapting, self-coordinating, self-diagnosing, self-inferring, self-organizing, and self adjusting. As result, most smart features of these products showed great similarity. While some features were dominated according to countries such as remote control feature in Korea, energy saving feature in Japan, and one button operation feature in China.

• Journal of Microbiology and Biotechnology
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• v.33 no.4
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• pp.449-462
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• 2023

Previously, we confirmed that Mychonastes sp. 246 methanolic extract (ME) markedly reduced the viability of BxPC-3 human pancreatic cancer cells. However, the underlying mechanism ME remained unclear. Hence, we attempted to elucidate the anticancer effect of ME on BxPC-3 human pancreatic cancer cells. First, we investigated the components of ME and their cytotoxicity in normal cells. Then, we confirmed the G1 phase arrest mediated growth inhibitory effect of ME using a cell counting assay and cell cycle analysis. Moreover, we found that the migration-inhibitory effect of ME using a Transwell migration assay. Through RNA sequencing, Gene Ontology-based network analysis, and western blotting, we explored the intracellular mechanisms of ME in BxPC-3 cells. ME modulated the intracellular energy metabolism-related pathway by altering the mRNA levels of IGFBP3 and PPARGC1A in BxPC-3 cells and reduced PI3K and mTOR phosphorylation by upregulating IGFBP3 and 4E-BP1 expression. Finally, we verified that ME reduced the growth of three-dimensional (3D) pancreatic cancer spheroids. Our study demonstrates that ME suppresses pancreatic cancer proliferation through the IGFBP3-PI3K-mTOR signaling pathway. This is the first study on the anticancer effect of the ME against pancreatic cancer, suggesting therapeutic possibilities and the underlying mechanism of ME action.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.194-198
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• 2021

LoRa is a physical layer technology that is designed to provide a reliable long-range communication with introducing CSS and with introducing a loco parentis tree network. Since a leaf can utilize multiple parents at the same time with a single transmission, PDR increases logarithmically as the number of gateways increases. Because of the ALOHA-like MAC of LoRa, however, the PDR degrades even under the loco parentis tree topology similarly to the single-gateway environment. Our proposed method is aimed to achieve SDMA approach to reuse the same frequency in different areas. For that purpose, it elaborately controls each TxPower of the senders for each message in concurrent transmission to survive the collision at each different gateway. The gain from this so-called capture effect increases the capacity of resource-hungry LPWAN. Compared to a typical collision-free controlled-access scheme, our method outperforms by 10-35% from the perspective of the total count of the consumed time slots. Also, due to the power control mechanism in our method, the energy consumption reduced by 20-40%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.