• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.49-54
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• 2014

Background: Hydrogels are a class of polymers that can absorb water or biological fluids and swell to several times their dry volume, dependent on changes in the external environment. In recent years, hydrogels and hydrogel nanocomposites have found a variety of biomedical applications, including drug delivery and cancer treatment. The incorporation of nanoparticulates into a hydrogel matrix can result in unique material characteristics such as enhanced mechanical properties, swelling response, and capability of remote controlled actuation. Materials and Methods: In this work, synthesis of hydrogel nanocomposites containing magnetic nanoparticles are studied. At first, magnetic nanoparticles ($Fe_3O_4$) with an average size 10 nm were prepared. At second approach, thermo and pH-sensitive poly (N-isopropylacrylamide -co-methacrylic acid-co-vinyl pyrrolidone) (NIPAAm-MAA-VP) were prepared. Swelling behavior of co-polymer was studied in buffer solutions with different pH values (pH=5.8, pH=7.4) at $37^{\circ}C$. Magnetic iron oxide nanoparticles ($Fe_3O_4$) and doxorubicin were incorporated into copolymer and drug loading was studied. The release of drug, carried out at different pH and temperatures. Finally, chemical composition, magnetic properties and morphology of doxorubicin-loaded magnetic hydrogel nanocomposites were analyzed by FT- IR, vibrating sample magnetometry (VSM), scanning electron microscopy (SEM). Results: The results indicated that drug loading efficiency was increased by increasing the drug ratio to polymer. Doxorubicin was released more at $40^{\circ}C$ and in acidic pH compared to that $37^{\circ}C$ and basic pH. Conclusions: This study suggested that the poly (NIPAAm-MAA-VP) magnetic hydrogel nanocomposite could be an effective carrier for targeting drug delivery systems of anti-cancer drugs due to its temperature sensitive properties.

• Spatial Information Research
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• v.20 no.6
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• pp.129-137
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• 2012

For construction project in extreme cold region, it is essential to establish basic data on the site such as topographical data from the early stage of construction of planning and designing, and it is needed to frequently perform site investigation when necessary. However, extreme cold regions are characteristic of being at long distance and difficult in approaching, and special regions such as Antarctica, in particular, are hard to conduct site investigation. Although a site investigation may be conducted, those who can visit Antarctica are sufficiently limited so that most of the staff may participate in construction without knowledge of the site and increase the risk of errors in decision making or designing. In order to resolve such problems, the authors in this study identified methods of building wide-area topographical data and bedrock classification data of exposed areas via remote sensing and of building precise topographical data on the construction site. Also, the authors attempted to present methods by which such data can be managed and visualized integrally via three-dimensional GIS technology and all the participants in construction can learn sense of field and conduct necessary analysis as frequent as possible. The areas around the Jangbogo Antarctic Station were selected to be the research area for conducting effective integrational management and three-dimensional visualization of various spatial data such as wide-area digital elevation model, ortho-images, bedrock classification data, local precise digital elevation model, and site images. The results of this study may enable construction firms to analyze local environments for construction whenever they need for construction in extreme cold regions and then support construction work including decision making or designing.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.227-237
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• 2014

The purpose of this paper is to implement a multiple bio-signal central monitoring system based on textile fabrics flexible platform which can obtain and monitor bio signals(heart rate, body temperature, electrocardiography, electromyogram) of workers in special working environments and additional situational information (3-axis acceleration, temperature, humidity, illumination, surrounding image). This system can prevent various accidents that may occur in the remote work environment and provide fast and efficient response by detecting workers' situations in real-time. For it, the textile fabrics flexible platform was made as innerwear or outerwear so that it does not interfere with workers' performance while collecting bio-signal and situational information, and obtained information is sent to the central monitoring system through wireless communication. The central monitoring system is based on wireless medical telemetry service of WMTS (Wireless Medical Telemetry Service); can monitor from 2 to 32 people simultaneously; and was designed so that it can be expanded. Also, in this study, to verify performance of the WMTS communication model, packet transmission rates were compared according to the distance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.56-68
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• 2009

Ongoing and extensive urbanisation, which is frequently accompanied with careless construction works, may threaten important archaeological structures that are still buried in the urban areas. Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) methods are most promising alternatives for resolving buried archaeological structures in urban territories. In this work, three case studies are presented, each of which involves an integrated geophysical survey employing the surface three-dimensional (3D) ERT and GPR techniques, in order to archaeologically characterise the investigated areas. The test field sites are located at the historical centres of two of the most populated cities of the island of Crete, in Greece. The ERT and GPR data were collected along a dense network of parallel profiles. The subsurface resistivity structure was reconstructed by processing the apparent resistivity data with a 3D inversion algorithm. The GPR sections were processed with a systematic way, applying specific filters to the data in order to enhance their information content. Finally, horizontal depth slices representing the 3D variation of the physical properties were created. The GPR and ERT images significantly contributed in reconstructing the complex subsurface properties in these urban areas. Strong GPR reflections and highresistivity anomalies were correlated with possible archaeological structures. Subsequent excavations in specific places at both sites verified the geophysical results. The specific case studies demonstrated the applicability of ERT and GPR techniques during the design and construction stages of urban infrastructure works, indicating areas of archaeological significance and guiding archaeological excavations before construction work.

• Journal of agricultural medicine and community health
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• v.24 no.2
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• pp.331-350
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• 1999

This study was attempted to examine relationship between personality traits and job satisfaction of community health practitioners(CHPs) working in remote rural area in order to suggest some methods to enhance their lob performance and the degrees of job satisfaction. The General Personality Test and the revised version of Job Satisfaction Questionnaire were administered to 200 of 348 CHPs in the Kwangju-Chonnam area and then the percentages, means, standard deviations and Pearson's correlation coefficients of these data were obtained, ANOVA and logistic analysis were used. The results of study were as follows : 1. CHPs without religion were more satisfied with their salary than those with religion. 2. CHPs who hoped for continuous education showed higher scores than the others on necessary job, professional pride and autonomy. Those who chose for independent job showed higher scores than the others on both necessary job and professional pride. Those who hope for long duration showed higher scores than the others on both necessary job and professional pride. Those who were satisfied with the present occupation showed higher scores than the others on pay satisfaction, necessary job, professional pride, interaction, autonomy and demand from organization. 3. Their autonomy scores differed significantly according to work status, both interaction and autonomy scores did so according to the fields of the past job in CHP, and their autonomy scores according to location of clinics. Their interaction scores differed significantly according to the frequency of home visits per mouth, both the degrees of salary satisfaction and professional pride scores did so according to the frequency of counseling education per mouth, and their professional pride scores did so according to total income per year. 4. The levels of their responsibility and self-confidence showed the highest of all personality traits variables. 5. The professional pride score of CHPs showed the highest of all job satisfaction variables. 6. Dominance were mostly correlated with autonomy and responsibility were mostly associated with professional pride. Both emotional stability and self-confidence were mostly related necessary job. In conclusion, religion, location of clinics, clinical experience, opportunity for education, dominance, self-confidence, the duration of services hoped for, satisfaction with the present occupation, the field of past job and administrative affairs were found to be the important factors in the degrees of their job satisfaction. Therefore, the methods to consider these variables will be necessary to develop for enhancing the efficiency of their Job performance and the degrees of job satisfaction.