• Economic and Environmental Geology
• /
• v.55 no.5
• /
• pp.551-561
• /
• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Women's Health Nursing
• /
• v.7 no.2
• /
• pp.169-187
• /
• 2001

This study was performed to probe the effect of exercise program on muscle strength, endurance, flexibility, pain, disability level and life satisfaction in female teachers of elementary school who complain of low back pain. For this study, 44 female teachers aged 30-50 years with mechanical low back pain of 6 months' duration, who had the structural normalities in the lumbar spine, were recruited from April 1 to July 10 1999. Twenty three out of them were assigned to the experimental group and twenty one to the control group. The exercise program consisted of education on right postures, the etiology and diagnosis of low back pain, and exercise intervention such as muscle relaxation, elongation and strengthening. With 8 weeks program, the subjects received two sessions of education and six sessions of group exercise in the 1st week, while three sessions of group exercise and four sessions of individual exercise weekly and two sessions of education during the later 7 weeks. The muscle strength and endurance were measured by Cybex 770, the flexibility by flexibility measurement machine, the intensity of pain by Visual Analogue Scale (VAS), the level of disability by Oswestry low back pain disability scale, depression by Beck depression inventory (BDI), and life satisfaction by Life satisfaction index-Z. Study measurements were taken before and after 8 weeks exercise program. Data were analyzed using paired t-test, t-test, and ANCOVA. The results were as follows ; 1. The flexors and extensors peak torque and flexors peak torque per body weight of experimental group were significantly increased at test velocities $30^{\circ}$/sec, $60^{\circ}$/sec compared with those of control group. There was no significant difference in extensors peak torque per flexors peak torque at $30^{\circ}/sec$, $60^{\circ}/sec$ between experimental and control group. 2. The flexors and extensors total work and flexors total work per body weight of experimental group were significantly increased at $120^{\circ}/sec$, compared with those of control group. 3.The flexibility of lumbar spine in experimental group was significantly increased compared with that of control group. The pains in anterior, posterior, left lateral and right lateral bending and in rotation of experimental group were significantly increased compared with those of control group. 4. The Oswestry disability scores of experimental and control group were significantly decreased, and there was no difference in the Oswestry disability score change between experimental and control group. 5. The scores of BDI of experimental group were significantly decreased compared with those of control group. Life satisfaction index-Z scores of experimental group were not changed, but those of control group were significantly decreased. There was no difference in the score change of Life satisfaction index-Z between experimental and control group. 6. ANCOVA analysis for the data variables of inhomogeneous baseline represented that there was no significant difference in extensors peak torque and extensors total work at $120^{\circ}/sec$ and extensor total work per body weight at $120^{\circ}/sec$ change between experimental group and control group. These findings indicate that the exercise program could be effective in increasing the muscle strength, endurance, flexibility and decreasing pain, improving depression in female teachers of elementary school with chronic low back pain. It is suggested that the exercise program could be an essential factor for the effective nursing intervention to the patients suffered from chronic low back pain.

• Childhood Kidney Diseases
• /
• v.6 no.2
• /
• pp.188-197
• /
• 2002

Purpose : Nowadays, drinks, foods and snacks have frequently been intensified with calcium and the insights into the importance of calcium-intake in general has developed in Korea. In this decade, we found the numbers of children who was visited to our hospital for evaluation of hematuria defined with hypercalciuria were increased. So we tried to compare the mean levels of serum calcium, alkaline phosphate, sodium, potassium, chloride, BUN, creatinine, bicarbonate and urinary pH who visited our hospital in 1991, 1992 with in 2000, 2001. Materials and methods : Between January 1991 to December 1992, and between January 2000 to December 2001, each 366 children and 488 children, aged 1 month to 15 years, who presented in our hospital for tonsilectomy and adenoidectomy or for inguinal herniorrhaphy were enrolled in the study, The children in the study were checked the level of serum calcium, alkaline phosphate, sodium, potassium, chloride, BUN, creatinine, bicarbonate and urinary pH with the machine which was corrected the similar levels of practical chemical levels in serum. We compared each mean levels in 1991s' group with in 2001s' group totally and separately through the age and sex. We used t-test to analysis data. Results : The levels of serum calcium, alkaline phosphate, creatinine, sodium, potassium, and urinary pH of 2001s' group were significantly higher than the levels of 1991s' group(P<0.05). The each level was $9.91{\pm}0.50\;mg/dL,\;248.58{\pm}94.98\;U/L,\;0.61{\pm}0.14\;mg/dL,\;138.64{\pm}2.22\;mM/L,\;4.35{\pm}0.40\;mM/L,\;6.18{\pm}0.86$ in 2001s' and $9.13{\pm}0.68;mg/dL,\;198.26{\pm}79.34\;U/L,\;0.433{\pm}0.18\;mg/dL,\;137.86{\pm}2.67\;mM/L,\;4.22{\pm}0.36\;mM/L,\;5.83{\pm}0.95$ in 1991s'. And the levels of serum bicarbonate, $23.64{\pm}2.57\;mM/L$ in 2001s' was significantly lower than the 1991s', $24.60{\pm}2.23\;mM/L$(P<0.05). The similar results were detected each age and sex group. Conclusion : The levels of serum calcium increase in this decade. The results will be used as a basic data for the national health plan in the years to come.

• Journal of Technologic Dentistry
• /
• v.28 no.1
• /
• pp.177-198
• /
• 2006

This study purposed to offer basic data for safety education of the dental laboratory technology after the investigation of how much the students in the dept. of dental laboratory technology are aware of the danger of each instrument, equipment or laboratory procedure that they use during laboratory and how much they demand safety education for this. The objects for this study were 423 students who were in the dept. of dental laboratory technology. In this regard, four colleges which have the dept. of dental laboratory technology were randomly selected to do a questionnaire survey. SPSS 12.0 was used to analyze the collected data. The results were as follows: As for satisfaction with their major, the respondents answered Satisfied (59.1%), Average (35.5%) and Dissatisfied (5.4%). In terms of the production process of a partial denture, they considered casting, polishing the casting body, polishing denture and burn out were most dangerous in order. As for the production process of a full denture, what they regarded as the most dangerous in order was polishing denture, deflasking and wax wash. Regarding the laboratory procedures of porcelain material, casting, trimming casting body, polishing porcelain material and burn out were the most dangerous procedures that they perceived. With regard to materials for use, alcohol, polishing, metal and wire were the most dangerous ones they thought. As for the handling characteristics of each material, small towns showed a higher demand for safety of the handling characteristics of alcohol. In terms of school year and sex, juniors and girls had higher scores in the demand for safety of the handling characteristics of acid. Regarding the handling characteristics of each equipment and instrument, all of small towns, juniors and girls showed the highest demand for safety of the handling characteristics of alcohol lamps. With regard to scores in the demand for safety of other characteristics, all of small towns, juniors and girls had the highest demand for safety of emergency treatment. Concerning the demand for safety education by the completion of safety education, in terms of each material, highest was the demand for safety of acid from the group which completed safety education. In regard to equipments and instruments, when it came to the demand for safety of the handling characteristics of casting machine, the educated group's demand for safety of acid was higher. Regarding other characteristics, the group which was not educated gained higher scores in the demand for safety of emergency treatment. 11. In all areas(materials, machines and others), small towns, girls and juniors showed higher scores in the demand for safety. Based on the above results, it was found that when students conduct the laboratory of dental technology, they would think that many materials, instruments or equipments for use are very dangerous. However, safety education was not fully given to them. Regarding the scores in the damned for safety education, the highest was 4.16 and the lowest was 3.43, which suggests that the scores were generally very high. In this regard, it is necessary to continue delivering a systematic safety education of materials, equipments or instruments used during the laboratory of dental technology. Therefore, through the analysis of each material, instruments or facility used in every laboratory and each process, safety accident types and accident risk factors should be investigated to develop educational materials for this. Moreover, it is required to open safety education as a single course of study or insert safety contents of all materials and machines into the class of dental laboratory instrument or dental materials for the purpose of a systematic and thorough safety education to prevent a safety accident during laboratory.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1723-1735
• /
• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.207-221
• /
• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.979-995
• /
• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Heritage: History & Science
• /
• v.57 no.3
• /
• pp.24-35
• /
• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Biosystems Engineering
• /
• v.16 no.4
• /
• pp.384-398
• /
• 1991

A computer program was developed to select the optimum size of farm machine and analyze its operation costs according to various farming conditions. It was written in FORTRAN 77 and BASIC languages and can be run on any personal computer having Korean Standard Complete Type and Korean Language Code. The program was developed as a user-friendly type so that users can carry out easily the costs analysis for the whole farm work or respective operation in rice production, and for plowing, rotarying and pest controlling in upland. The program can analyze simultaneously three different machines in plowing & rotarying and two machines in transplanting, pest controlling and harvesting operations. The input data are the sizes of arable lands, possible working days and number of laborers during the opimum working period, and custom rates varying depending on regions and individual farming conditions. We can find out the results such as the selected optimum combination farm machines, the overs and shorts of working days relative to the planned working period, capacities of the machines, break-even points by custom rate, fixed costs for a month, and utilization costs in a hectare.

• Journal of Preventive Medicine and Public Health
• /
• v.19 no.1 s.19
• /
• pp.16-30
• /
• 1986

In order to prepare the fundamental data for the improvement of noisy working environments and the effective hearing conservation program on workers exposed to industrial noise, the authors surveyed the working processes and evaluated the noise levels on 56 manufacturing industries in Pusan area from April to July in 1985. The results were summarized as follows : 1. The noise level was the highest in shipbuilding and repairing(95.6 dBA), and followed by steel rolling(94.0 dBA), manufacture of motor vehicles(93.1 dBA), manufacture of fishing nets(92.9 dBA), manufacture of testiles(92.5 dBA), iron and steel foundries(89.3 dBA), manufacture of metal products(89.1 dBA), preserving and processing of marine foods(87.0 dBA), manufacture of rubber products(85.3 dBA), manufacture of plywood(84.9 dBA) and manufacture of paints(84.5 dBA). 2. Among fifty surveyed working processes, the noise level of twenty-one processes (42%) exceeded the threshold limit value for 8 hours per day. 3. As the allowable exposure times by governmental threshold limit values to industrial noise level(dBA), cocking of shipbuilding and repairing and plating(CGL) of steel rolling were the shortest(30 minutes), and followed by assembling(rivet) of manufacture of motor vehicles(1 hour) weaving of manufacture of textiles and shot, machine, pipe laying of shipbuilding and repairing(2 hours). 4. By the result of octave band analysis on noisy working processes in excess of 90 dBA, the sound level was the highest at 2,000 Hz or 4,000 Hz. 5. It was recognized that the measurement of overall sound pressure level was also effective as octave band analysis in evaluating the industrial noise.