• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.2
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• pp.49-57
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• 2016

Even if an external forcing that will drive a climate change is given uniformly over the globe, the corresponding climate change and the feedbacks by the climate system differ by region. Thus the detection of global warming signal has been made on a regional scale as well as on a global average against the internal variabilities and other noises involved in the climate change. The purpose of this study is to estimate a timing of unprecedented climate due to global warming and to analyze the regional differences in the estimated results. For this purpose, unlike previous studies that used climate simulation data, we used an observational dataset to estimate a magnitude of internal variability and a future temperature change. We calculated a linear trend in surface temperature using a historical temperature record from 1880 to 2014 and a magnitude of internal variability as the largest temperature displacement from the linear trend. A timing of unprecedented climate was defined as the first year when a predicted minimum temperature exceeds the maximum temperature record in a historical data and remains as such since then. Presumed that the linear trend and the maximum displacement will be maintained in the future, an unprecedented climate over the land would come within 200 years from now in the western area of Africa, the low latitudes including India and the southern part of Arabian Peninsula in Eurasia, the high latitudes including Greenland and the mid-western part of Canada in North America, the low latitudes including Amazon in South America, the areas surrounding the Ross Sea in Antarctica, and parts of East Asia including Korean Peninsula. On the other hand, an unprecedented climate would come later after 400 years in the high latitudes of Eurasia including the northern Europe, the middle and southern parts of North America including the U.S.A. and Mexico. For the ocean, an unprecedented climate would come within 200 years over the Indian Ocean, the middle latitudes of the North Atlantic and the South Atlantic, parts of the Southern Ocean, the Antarctic Ross Sea, and parts of the Arctic Sea. In the meantime, an unprecedented climate would come even after thousands of years over some other regions of ocean including the eastern tropical Pacific and the North Pacific middle latitudes where an internal variability is large. In summary, spatial pattern in timing of unprecedented climate are different for each continent. For the ocean, it is highly affected by large internal variability except for the high-latitude regions with a significant warming trend. As such, a timing of an unprecedented climate would not be uniform over the globe but considerably different by region. Our results suggest that it is necessary to consider an internal variability as well as a regional warming rate when planning a climate change mitigation and adaption policy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.135-142
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• 2001

This study attempts to categorizes the factors of forest fire occurrences based on regional meteorologic data and general forest no characteristics of 18 cities and guns in Kangwon province. lo accomplish this goal, some statistical analyses such as analysis of variance, correspondence analysis and multidimensional scaling were adopted. To reveal the forest fires pattern of study region, a categorization process was conducted by employing the quantification approach which modified and quantified the metric-data of fire occurrence dates. Also, The fire occurrence similarity was compared by using multidimensional scaling for each study region. The major results are summarized as follows: It was found that the meteorological factors emerged as different to each region are average and maximum temperature, minimum dew point temperature and average and maximum wind speed. In the result of correspondence analysis representing relationships between fire causes and study regions, Kangrung is caused by arsonist, Chulwon, Hwachen and Yanggu caused by military factor, Sokcho and Chunchen caused by the debris burning, and Samchuk caused by general man-caused fires, respectively. Finally, the forest fire occurrence pattern of this study regions were divided into five areas such as, group I including Samchuk, Kangryung, Chunchen, Wonju, Hongchen and Hhoingsung, group II including Donghae, Taebaek, Yangyang and Pyongchang, group III including Jungsun, Chulwon and Whachen, group Ⅵ including Gosung, Injae and Yanggu, and group V including Shokcho and Youngwol.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.9-16
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• 2002

Localized muscle fatigue can be identified by a downward shift of the EMG frequency typically represented by a fall in the median frequency The Present experimental study was Performed to investigate the time change of the median frequency and the muscle torque during maximal isometric back extension exercises at different exercise angles (0$^{\circ}$, 12$^{\circ}$, 36$^{\circ}$and 72$^{\circ}$) Twenty heath subjects (mean age : 24.35 $\pm$ 2.70) were Participated in this study Median frequency was extracted from EMG signals by employing the fast Fourier transform. Initial median frequency and the slope of median frequency was not significantly correlated with the muscle torque. Pearson's Product moment correlation was used to quantify the relationship between slopes of median frequency and torque. The results may suggest that the exorcise angle during maximal isometric back extension exercises does not affect the slopes of the median frequency and torque, and y-intercept of the median frequency among exercise angles There was no significant correlation between slopes of median frequency and torque. But there was a moderate correlation between median frequency and torque at each exercise angle. In conclusion, the exercise angle during maximal isometric back extension exercise is not a direct effect on slopes of median frequency and torque. But results showed that the shift of median frequency and torque shift were highly correlated in all subjects.

• Korean Security Journal
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• no.61
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• pp.109-135
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• 2019

The purpose of this study is to investigate the occupational diseases(the number of medical treatment) of fire officials by using time-series analysis. The results of the study are as follows. First, the average rates of the occupational diseases of fire officials were as follows: ① internal diseases were the highest at 9.24% in December, the lowest at 7.76% in February, ② otolaryngologic diseases were the highest at 9.29% in December, the lowest at 6.74% in August, ③ dermatological diseases were the highest at 10.03% in July, the lowest at 7.35% in January and February, ④ surgical diseases were the highest at 10.38% in November, the lowest at 5.62% in February, ⑤ orthopedic diseases were the highest at 9.69% in March, the lowest at 7.52% in November, ⑥ neurosurgical diseases were the highest at 9.33% in April, the lowest at 6.82% in February, ⑦ neurological diseases were the highest at 9.47% in December, the lowest at 7.06% in October, and ⑧ mental health diseases were the highest at 9.93% in December, the lowest at 6.51% in May. Second, the seasonal decomposition of the disease occurrence of fire officials were described by assigning seasonal factor(S), trend factor(T), circulation factor(C) and irregular factor(R): ① internal diseases were 1.075(S) × 189.355(T·C) × 1.174(R) = 238.975(F), ② otolaryngologic diseases were 1.023(S) × 69.605(T·C) × 1.040(R) = 74.000(F), ③ dermatological diseases were 1.002(S) × 73.088(T·C) × 0.874(R) = 64.000(F), ④ surgical diseases were 1.099(S) × 27.229(T·C) × 0.669(R) = 20.000(F), ⑤ orthopedic diseases were 1.115(S) × 73.182(T·C) × 1.213(R) = 99.000(F), ⑥ neurosurgical diseases were 0.993(S) × 27.836(T·C) × 1.303(R) = 36.000(F), ⑦ neurological diseases were 1.029(S) × 62.417(T·C) × 1.152(R) = 74.000(F), and ⑧ mental health diseases were 1.210(S) × 8.781(T·C) × 1.035(R) = 11.000(F).

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.36-49
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• 2019

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.1
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• pp.54-63
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• 2019

As a part of the national marine ecosystem monitoring program, the temporal and spatial variation of sedimentary environment and pollution of organic matters and trace metals from four major tidal flats, i.e., Ganghwa Is., Garolim bay, Jeung Is., Suncheon bay, was investigated for 3 yerars from 2015 to 2017. The mean grain size of the sediment was $5.0-5.3{\varnothing}$ at Ganghwa Is, $4.5-4.8{\varnothing}$ at Garolim bay, $6.1-6.5{\varnothing}$ at Jeung Is, and $8.6-8.7{\varnothing}$ at Suncheon bay. The mean grain size (Mz) tended to decrease from the north (Ganghwa Is.) to the south (Suncheon bay). The ignition loss (IL) was 15.5% in Suncheon bay in 2015, which was relatively high compared to other sites, but gradually decreased over time from 8.3% in 2016 to 7.0% in 2017. In Jeung Is. and Suncheon bay, the concentration of Zn and As exceeded the threshold effect level (TEL) at some stations, but the range of trace metals in the other sites was below the level. In Jeung Is., the Mz and concentration of trace metals except Hg was positively correlated (r= 0.40-0.88, P<0.05). On the other hand, Mz was negatively correlated with trace metals (P<0.05) in Suncheon bay. The geoaccumulation index ($I_{geo}$) to evaluate contamination status of sediments for trace metal was less than 1(not contaminated) for Cu, Zn, Pb, Cd and Hg, and 2-3 (moderately to strongly polluted) for As at several stations in Suncheon bay and Jeung Is.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.31-42
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• 2021

The importance of startups and ventures in the Korean economy is growing. This study measured whether the start-up and venture ecosystem is growing, including the growth of startups and ventures. The startup and venture ecosystem consists of startups and ventures, investors, and government, which are the main actors of the 'ecosystem', and their movements were measured with 25 quantitative indicators. Based on the original data of the time series from 2010 to 2020, the startup and venture ecosystem index was calculated by applying weights through the comprehensive stock index method and AHP. In 2020, the startup and venture ecosystem grew 2.9 times compared to 2010, and the increase in the government index had a significant impact on growth. Also, the individual indicators that make up each index in 2020, the corporate index had the greatest impact on the growth of the number of 100-billion ventures, while the investment index had a recovery amount and the government index had a significant impact. Based on the original data, the startup and venture ecosystem index was analyzed by dividing it into ecosystems (startup ecosystem and venture ecosystem), industry by industry (all industries and manufacturing industry), and region (Korea and Busan). As a result, the growth of the startup ecosystem over the past decade has been slightly larger than that of the venture ecosystem. The manufacturing was lower than that of all industries, and Busan was lower than that of the nation. This study was intended to use it for the establishment and implementation of support policies by developing, measuring, and monitoring the startup and venture ecosystem index. This index has the advantage of being able to research the interrelationships between major actors, and anyone can calculate the index using the results of official statistical surveys. In the future, it is necessary to continuously update this content to understand how economic and social events or policy support have affected the startup and venture ecosystem.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.