• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.131-142
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• 2021

This research is based on the design of pictures in picture books, and the spatial representation of illustrations in the picture books contains the significantly important objective. Various texts, pictures, spaces, etc. in a picture must have the operator's various editing skills so that spatial arrangement is made smoothly.In this paper, the characteristics of spatial expression of design in picture books are derived by analyzing several examples of paintings and studies on classic picture books. First, the fusion of picture and text, that is, that both picture and text convey spatial information together as elements of the screen. Second, as a characteristic of the coherence of the space design in picture books, the story and content must be smoothly connected when reading the book. Third, when expressing a space, the creator should utilize the strengths and weaknesses of each other between abstract and conceived spatial expressions as needed. Fourth, as a symbolic feature of picture book spatial expression, it can be seen that many symbolic expression techniques are applied to the spatial expression of picture books according to the semiotic principle, which greatly improves the cognitive efficiency of reading picture books. The fifth characteristic is that the spatial expression of an excellent picture book has excellent interesting element, rich design means, and interestingly conveys screen contents and screen format to readers. In this research, it is thought that designers and artists should guide the creation within a spatial framework as designing picture books, thus greatly improving the efficiency of the creation process, while also provide a reader-centered visual Interesting experience.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.202-210
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• 2022

Recently with the advance in digital dentistry, the fabrication of dentures using computer-aided design and computer-aided manufacturing (CAD-CAM) is on the rise. The denture designed through a CAD software can be produced in a 3-dimensional manufacturing process. This process includes a subtractive processing method such as milling and an additive processing method such as 3D printing and in which it can be applied efficiently in more complex structures. In this case, complete dentures were fabricated using Stereolithography (SLA)-based 3D printing to shorten the production time and interval of visits in patient with physical disabilities due to cerebral infarction. For definitive impression, the existing interim denture was digitally replicated and used as an individual tray. The definitive impression obtained with polyvinyl siloxane impression material was including information about the inclination and length of the maxillary anterior teeth, vertical dimension, and centric relation. In addition, facial scan data with interim denture was obtained so that it can be used as a reference in determination of the occlusal plane and in arrangement of artificial teeth during laboratory work. Artificial teeth were arranged through a CAD program, and a gingival festooning was performed. The definitive dentures were printed by SLA-based 3D printer using a FDA-approved liquid photocurable resin. The denture showed adequate retention, support, and stability, and results were satisfied functionally and aesthetically.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.35-40
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• 2022

In the agricultural environment, pesticide control requires a high risk of work and a high labor force for farmers. The effectiveness of pesticide control using unmanned aerial vehicles varies according to climate, land type, and characteristics of unmanned aerial vehicles. Therefore, an effective method for pesticide control by unmanned aerial vehicles considering the spraying conditions and environmental conditions is required. In this paper, we propose an efficient pesticide control system based on agricultural unmanned aerial vehicles considering the application conditions and environmental information for each crop. The effectiveness of the proposed model was demonstrated by measuring the drop uniformity of pesticides according to the change in altitude and speed after attaching the sensory paper and measuring the penetration rate of the drug inside the canopy according to the change in crop growth conditions. Experiment result, the closer the height of the UAV is to the ground, the more evenly the crops are sprayed, but for safety reasons, 2m more is suitable, and on average a speed of 2m/s is most suitable for control. The proposed control system is expected to help develop intelligent services based on the use of various unmanned aerial vehicles in agricultural environments.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.99-111
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• 2021

The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.3
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• pp.231-241
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• 2022

Objectives: The purpose of this study is to evaluate the input status of exposure-related information in the working environment monitoring database (WEMD) and special health examination database (SHED) for the construction of a national exposure surveillance system. Methods: The industrial and process code input status of WEMD and SHED for 21 carcinogens from 2014 to 2016 was compared. Data from workers who performed both work environment monitoring and special health examinations in 2019 and 2020 were extracted and the actual status of input of industrial and process codes was analyzed. We also investigated the cause of input errors through a focus group interview with 12 data input specialists. Results: As a result of analyzing WMED and SHED for 21 carcinogens, the five-digit industrial code matching rate was low at 53.5% and the process code matching rate was 19% or less. Among the data that simultaneously conducted work environment monitoring and special health examination in 2019 and 2020, the process code matching rate was very low at 18.1% and 5.2%, respectively. The main causes of exposure-related data input errors were the difference between the WEMD and SHED process code input systems from 2020, the number of standard process and job codes being too large, and the inefficiency of the standard code search system. Conclusions: In order to use WEMD and SHED as a national surveillance system, it is necessary to simplify the number of standard code input codes and improve the search system efficiency.