• Journal of Korean Academy of Nursing
• /
• v.49 no.4
• /
• pp.472-485
• /
• 2019

Purpose: This study aimed to develop and evaluate the effectiveness of an adapted health literacy self-management intervention for elderly cancer patients undergoing chemotherapy. Methods: The intervention in this study was systematically developed through the six stages of Intervention Mapping Protocol and was based on Fransen et al's causal pathway model. A quasi-experimental trial was conducted on a total of 52 elderly patients (26 in an experimental group and 26 in a control group) undergoing chemotherapy in Korea. The intervention consisted of seven sessions over 5 weeks. The experimental tool for this study was an adapted health literacy self-management intervention, which was designed to promote a reduction in the symptom experience and distress of elderly cancer patients through the promotion of self-management behavior. To develop efficient educational materials, the participants' health literacy was measured. To educate participants, clear communication and the teach-back method were used. In addition, for the improvement of self-efficacy, four sources were utilized. For the promotion of self-management behavior, five self-management skills were strengthened. Data were collected before and after the intervention from June 4 to September 14, 2018. The data were analyzed with SPSS/WIN 21.0. Results: Following the intervention, self-management knowledge and behavior and, self-efficacy significantly improved in experimental group. Symptom experience and distress decreased in the experimental group compared to the control group. Conclusion: The self-management intervention presented in this study was found to be effective in increasing self-management knowledge and behavior and, self-efficacy, and ultimately in reducing symptom experience and distress for elderly patients undergoing chemotherapy.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.5
• /
• pp.607-613
• /
• 2019

Objective: Intramuscular fat (IMF) content plays an important role in meat quality. Identification of single nucleotide polymorphisms (SNPs) and genes related to pig IMF, especially using pig populations with high IMF content variation, can help to establish novel molecular breeding tools for optimizing IMF in pork and unveil the mechanisms that underlie fat metabolism. Methods: We collected muscle samples of 453 Chinese Lulai black pigs, measured IMF content by Soxhlet petroleum-ether extraction method, and genotyped genome-wide SNPs using GeneSeek Genomic Profiler Porcine HD BeadChip. Then a genome-wide association study was performed using a linear mixed model implemented in the GEMMA software. Results: A total of 43 SNPs were identified to be significantly associated with IMF content by the cutoff p<0.001. Among these significant SNPs, the greatest number of SNPs (n = 19) were detected on Chr.9, and two linkage disequilibrium blocks were formed among them. Additionally, 17 significant SNPs are mapped to previously reported quantitative trait loci (QTLs) of IMF and confirmed previous QTLs studies. Forty-two annotated genes centering these significant SNPs were obtained from Ensembl database. Overrepresentation test of pathways and gene ontology (GO) terms revealed some enriched reactome pathways and GO terms, which mainly involved regulation of basic material transport, energy metabolic process and signaling pathway. Conclusion: These findings improve our understanding of the genetic architecture of IMF content in pork and facilitate the follow-up study of fine-mapping genes that influence fat deposition in muscle.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.8
• /
• pp.1005-1012
• /
• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.463-470
• /
• 2021

Due to natural and human factors, dunhuang frescoes in China have gradually degenerated. China has conducted digital transformation of frescoes since 1990. However, it requires expensive research costs. Most of the visitors passively accept transformation contents and lack subjective participation. The paper focuses on produces a prototype of Dunhuang frescoes on the CoSpaces EDU. It was implemented as a metaverse through procedures such as transforming the cave into 3D, mapping images to the cave model and developing CoBlocks. The research puts forward a more specific methodology without expensive costs of development. The paper makes it easier to realize the immersive and interactive virtual Dunhuang frescoes world, to improve the tourism contents and educational effect. This research carries on the statistics to the product result which develops according to the user experience of 100 different ages, has obtained the good feedback. The research results need to be improved is to strengthen the processing of graphic details. Therefore, Optimization and improvement of the result will be carried out in the future research.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.613-626
• /
• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.248-248
• /
• 2023

In recent years, the number of landslides in Korea has been increasing due to extreme weather events such as localized heavy rainfall and typhoons. Landslides often occur with debris flows, land subsidence, and earthquakes. They cause significant damage to life and property. 64% of Korea's land area is made up of mountains, the government wanted to predict landslides to reduce damage. In response, the Korea Forest Service has established a 'Landslide Information System' to predict the likelihood of landslides. This system selects a total of 13 landslide factors based on past landslide events. Using the LR technique (Logistic Regression) to predict the possibility of a landslide occurrence and the accuracy is known to be 0.75. However, most of the data used for learning in the current system is on landslides that occurred from 2005 to 2011, and it does not reflect recent typhoons or heavy rain. Therefore, in this study, we will apply a total of six machine learning techniques (KNN, LR, SVM, XGB, RF, GNB) to predict the occurrence of landslides based on the data of Inje, Gangwon-do, which was recently produced by the National Institute of Forest. To predict the occurrence of landslides, it is necessary to process converting landslide events and factors data into a suitable form for machine learning techniques through ArcGIS and Python. In addition, there is a large difference in the number of data between areas where landslides occurred or not. Therefore, the prediction was performed after correcting the unbalanced data using Tomek Links and Near Miss techniques. Moreover, to control unbalanced data, a model that reflects soil properties will use to remove absolute safe areas.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.54-54
• /
• 2023

하천유역조사는 관련 법률의 규정에 의해 물관리정책의 수립에 필요한 기초정보를 제공하는 것을 목적으로 기본현황, 이수, 치수 환경생태 등 유역관리에 필요한 주요 조사항목을 대상으로 수행되고 있다. 조사방법 중 원격탐사자료 활용한 조사는 드론 모니터링 영상 및 위성영상자료를 이용해 댐·제방과 같은 치수 시설물의 안전관리, 수질 모니터링, 하천지형조사, 하상변동조사 등에 활용되고 있다. 최근에는 일반 RGB 영상뿐만 아니라 수백개의 분광밴드를 포함한 초분광영상을 이용한 하천조사 연구가 이루어지고 있다. 초분광영상은 분광해상도가 높아 다항목 조사에 활용할 수 있다는 장점이 있지만, 많은 양의 분광정보를 포함하고 있기 때문에 초기 수집 자료의 용량이 너무 크고, 분석을 위한 전처리 과정이 까다롭다는 단점이 있다. 반면, 10개 이하 밴드의 분광정보를 수집하는 다분광영상은 2개 밴드를 이용해 정규식생지수(NDVI)를 즉각적으로 모니터링할 수 있고, 작물의 생육현황 등을 분석할 수 있어 농업 및 산림분야에서 널리 활용되고 있다. 초분광영상을 이용한 수심산정 연구는 최적 밴드비 탐색 기법(OBRA)을 활용해 측정수심과 상관관계가 높은 밴드비를 이용해 수심맵을 구축하는 방식이 활용되어왔다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정기법을 다분광영상에 적용하여 분광밴드수가 축소된(경량화된) 자료를 활용한 수심산정 가능성을 확인하기 위해 동일한 현장에서 초분광과 다분광 두가지 영상을 촬영하였으며, 각각 수심맵을 구축해 하천분야에서 다분광영상의 활용도를 평가하였다. 또한, 기존의 OBRA의 한계를 개선하기 위해 가우시안 혼합 모델(GMM; Gaussian Mixture Model)을 활용해 영상을 군집화하여 수심산정 정확도를 개선하였다.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.17 no.1
• /
• pp.36-48
• /
• 2021

As the material aging of nuclear power plants has been progressing in domestic and overseas, crack growth becomes one of the most important issues. In this respect, the crack growth assessment has been considered an essential part of structural integrity. The crack growth assessment for nuclear power plants has been generally performed based on ASME B&PV Code, Sec. XI but the idealization of crack shape and the conservative solutions of stress intensity factor (SIF) are used. Although finite element analysis (FEA) based on iterative crack growth analysis is considered as an alternative method to simulate crack growth, there are yet no guidelines to model the crack-tip spider-web mesh for such analysis. In this study, effects of various meshing factors on FE SIF calculation are systematically examined. Based on FEA results, proper criteria for spider-web mesh in crack-tip are suggested. The validation of SIF calculation method through mapping initial stress field is investigated to consider initial residual stress on crack growth. The iterative crack-tip modeling program to simulate crack growth is developed using the proposed criteria for spider-web mesh design. The SIF results from the developed program are validated by comparing with those from technical reports of other institutes.

• Journal of Korea Spatial Information System Society
• /
• v.3 no.1 s.5
• /
• pp.117-126
• /
• 2001

We have developed the Mobile Multi Sensor System(MMSS) for the data construction of 4S application and for basic technology acquisition of mobile mapping system in Korea. Using this MMSS, we will collect the information of road and road facilities for DB creation and also construct the Digital Elevation Model(DEM) as ancillary data in urban area. The MMSS consist of the integrated navigation sensor, DGPS & IMU, and digital CCD camera set. In the S/W aspect, we developed the post-processing components for extracting the 3D coordinate information (Spatial Information) and the client program for the MMSS user group. In this paper, we will overview the MMSS constitution and post-processing program, and introduce the utilization plan of MMSS.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.4
• /
• pp.355-362
• /
• 2017

Recently, UAV(Unmanned Aerial Vehicle) have been utilized in various fields, including surveys, mapping, and spatial analysis, depending on the increase in demand for spatial information and UAV is receiving a lot of attention due to rapid data acquisition and economic viability. In this study, the applicability of UAV image images was analyzed for urban disaster prevention. UAV images were acquired for the study area and digital surface model and ortho image were generated through data processing. Also, the process using PPK(Post Processed Kinematic) GNSS method is compared with existing method. Through the research, it was able to effectively deploy urban disaster prevention information about the target area, and displayed the effectiveness of the methods for efficient comparison with existing unmanned aerial photogrammetry. If the PPK technique is applied to thethe disaster prevention field, it is expected that the work flow in the field of rapid data acquisition and disaster prevention data construction can be greatly improved.