• Korean Journal of Clinical Laboratory Science
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• v.51 no.1
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• pp.78-85
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• 2019

Membrane proteins are involved in many common diseases, including heart disease and cancer. In various disease states, such as cancer, abnormal signaling pathways that are related to the membrane proteins cause the cells to divide out of control and the expression of membrane proteins can be altered. Membrane proteins have the hydrophobic environment of a lipid bilayer, which makes an analysis of the membrane proteins notoriously difficult. Therefore, this study evaluated the efficacy of two different methods for optimal membrane protein extraction. High-speed centrifuge and reagent-based method with a -/+ filter aided sample preparation (FASP) were compared. As a result, the high-speed centrifuge method is quite effective in analyzing the mitochondrial inner membranes, while the reagent-based method is useful for endoplasmic reticulum membrane analysis. In addition, the function of the membrane proteins extracted from the two methods were analyzed using GeneGo software. GO processes showed that the endoplasmic reticulum-related responses had higher significance in the reagent-based method. An analysis of the process networks showed that one cluster in the high-speed centrifuge method and four clusters in the reagent-based method were visualized. In conclusion, the two methods are useful for the analysis of different subcellular membrane proteins, and are expected to assist in selecting the membrane protein extraction method by considering the target subcellular membrane proteins for study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.79-86
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• 2021

In order to increase the probability of success in big data projects, quantified techniques are required to analyze the root cause of risks from complex causes and establish optimal countermeasures. To this end, this study measures risk factors and relationships through SNA analysis and presents a way to respond to risks based on them. In other words, it derives a dependency network matrix by utilizing the results of correlation analysis between risk groups in the big data projects presented in the preliminary study and performs SNA analysis. In order to derive the dependency network matrix, partial correlation is obtained from the correlation between the risk nodes, and activity dependencies are derived by node by calculating the correlation influence and correlation dependency, thereby producing the causal relationship between the risk nodes and the degree of influence between all nodes in correlation. Recognizing the root cause of risks from networks between risk factors derived through SNA between risk factors enables more optimized and efficient risk management. This study is the first to apply SNA analysis techniques in relation to risk management response, and the results of this study are significant in that it not only optimizes the sequence of risk management for major risks in relation to risk management in IT projects but also presents a new risk analysis technique for risk control.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.127-135
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• 2022

Compared to the continuous increase of domestic nano-satellite development cases, the initial communication success rate is relatively low. In a situation where communication cases of LEO satellites using commercial satellite communication networks are increasing recently. In this situation, the SNIPE project developed by the KASI(Korea Astronomy and Space Science Institute), KARI(Korea Aerospace Research Institute), and Yonsei University apply an Iridium module for communication test to the SNIPE nano-satellites. Therefore, in this paper, the visibility analysis of the iridium module on the SNIPE satellite was analyzed under considering the orbital and communication environment of the iridium satellite constellation and the attitude control mode. In the case of LEO satellites, the communication possibility was limited due to the relatively small iridium communication coverage for high altitude and the high doppler shift considered in the iridium communication network. For this reason, in this paper, it could be simulated that there was a more performance difference according to the difference in relative RAAN(Right Ascension of Ascending Node) angle with the Iridium constellation. Finally, by checking the visibility of communication module under the tumbling situation that occurred during the initial deployment of the nano-satellite, the possibility of using the iridium communication technology was analyzed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.128-130
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• 2021

This study aims to implement a Regression Analysis system for predicting the appropriate production of flatfish. Due to Korea's signing of FTAs with countries around the world and accelerating market opening, Korean flatfish farming businesses are experiencing many difficulties due to the specificity and uncertainty of the environment. In addition, there is a need for a solution to problems such as sluggish consumption and price drop due to the recent surge in imported seafood such as salmon and yellowtail and changes in people's dietary habits. in this study, Using the python module, xlwings, it was used to obtain for the production amount of flatfish and to predict the amount of flatfish to be produced later. was used to predict the amount of flatfish to be produced in the future. Therefore, based on the analysis results of this prediction of flatfish production, the flatfish aquaculture industry will be able to come up with a plan to achieve an appropriate production volume and control supply and demand, which will reduce unnecessary economic loss and promote new value creation based on data. In addition, through the data approach attempted in this study, various analysis techniques such as artificial neural networks and multiple regression analysis can be used in future research in various fields, which will become the foundation of basic data that can effectively analyze and utilize big data in various industries.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• Journal of the Korean GEO-environmental Society
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• v.25 no.6
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• pp.19-24
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• 2024

Urban densification has necessitated the development of subterranean spaces such as subway networks and underground tunnels to facilitate the dispersal and movement of populations. Development of these underground spaces requires excavation from the ground surface, which can induce groundwater flow and potentially lead to ground subsidence and sinkholes, damaging structures. To mitigate these risks, it is essential to model groundwater flow prior to construction, analyze its characteristics, and predict potential groundwater discharge during excavation. In this study, we collected meteorological, topographical, and soil conditions data for the city of ○○, where tunnel construction was planned. Using the Visual MODFLOW program, we modeled the groundwater flow. Excavation sections were set as drainage points to monitor groundwater discharge during the excavation process, and the effectiveness of seepage control measures was assessed. The model was validated by comparing measured groundwater levels with those predicted by the model, yielding a coefficient of determination of 0.87. Our findings indicate that groundwater discharge is most significant at the beginning of the excavation. Additionally, the presence of seepage barriers was found to reduce groundwater discharge by approximately 59%.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Journal of Korean Society of Rural Planning
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• v.30 no.3
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• pp.19-28
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• 2024

This study investigated and analyzed the landscape conservation activity promotion process targeting the 'Alassiasdeuli Community Farming Association Corporation', which is carrying out continuous rural landscape conservation activities led by local residents in the area of Yeohang Mountain, Yeohang-myeon, Haman-gun, Gyeongsangnam-do. Through this, the factors necessary to promote rural landscape conservation activities led by residents were identified, and implications necessary for rural landscape conservation activities led by residents were derived. The first factor that allowed Alassiasdeuli to pursue resident-led rural landscape conservation activities was the fact that an economically stable foundation was established before pursuing conservation activities. Rural landscape conservation activities are carried out based on continuous agricultural activities, and agriculture is closely related to the economic aspect. Accordingly, Alassiasdeuli had a small but regular income from the package business, and was able to carry out various rural landscape conservation activities based on this. Second, within the community, a sense of purpose for rural landscape conservation was shared as a common value. It started with common values that were in line with rural landscape conservation, such as an economic community based on agriculture, indigenous seed conservation, and eco-friendly agriculture, and later, awareness of traditional agriculture and rural landscape conservation was gradually established through members' continued empowerment and related experiences. It has been done. Third, various connections and cooperative relationships were established with residents, related organizations, and administration. We established cooperative relationships by recruiting local organizations and residents as active participants beyond program participation, and exchanged information and expanded the scope of activities by establishing networks with organizations such as the 'Gyeongnam Darang-Non Network'. In addition, through connection with administration, we experienced various projects and ensured the sustainability of activities through support. Fourth, there was a keyman who could organize activities and control the scale of support projects, taking into account the awareness and capabilities of members. In particular, it is thought that this was possible because the Secretary General was based on building a relationship of trust with residents before Alassiasdeuli was formed. Therefore, in order for resident-led rural landscape conservation activities to be continuously carried out, an organization must be formed centered on farmers, and the economic sustainability of the organization, sharing of common values, and trust relationships among members are the basis, and the Sustainable activities can be promoted through various cooperative relationships between residents, organizations, and administration.