• Korean Journal of Environment and Ecology
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• v.33 no.2
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• pp.237-251
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• 2019

This study evaluated the ecological value of waterside parks by investigating the animal distribution and ecological feature in 92 waterside parks and analyzed the change of ecological awareness by users and non-users of waterside parks through ecological education and promotion based on the investigation results. The result confirmed inhabitation of various animals including 9 endangered species (Pernis ptilorhynchus orientalis, Accipiter soloensis, Falco subbuteo, Charadrius placidus, Felis bengalensis euptilura, Lutra lutra, Kaloula borealis, Polyphylla laticollis manchurica, and Leptalina unicolor) in waterside parks. Although waterside parks were constructed to be hydrophilic areas for human use, some of them with high natural characteristics are valued as biological habitat. We investigated user status in 5 areas (Daejeon, Sejong, Cheongju, Kongju, and Buyeo) located at Guem river basin to evaluate people's perception of waterside parks and carried out the ecological education and promotion based on the investigation result. The survey of 200 people showed that there were more users of waterside parks than non-users and that people in their 40's showed the highest use rate. The use frequency of waterside parks located in Daejeon and Cheongju was lower than in other areas (Sejong, Kongju, and Buyeo). We considered it was because Daejeon and Cheongju were urban areas and had relatively more leisure areas such as sports facilities and cafe than other areas, and thus the residents had a lower reliance on waterside parks. Moreover, users used waterside parks more frequently when they were nearer to users' residence. It is because most users perceived waterside parks as the leisure sports facility and thus preferred them to be within walking distance. The users' perception of waterside parks as the ecological space "to be preserved" increased after the ecological education and promotion. The change of the perception was higher among users (80%) than non-users (38%). Therefore, ecological education and promotion were potentially more effective to people who user waterside parks and thus had a higher understanding of the characteristics and specification. In conclusion, 1) although waterside parks were constructed for human use, some parts had high ecological value for the distribution of endangered species and outstanding natural beauty, and 2) it is necessary to change the perception of waterside parks from the hydrophilic attribute to the conservation attribute. Such change of perception would contribute to establishing waterside parks that feature both hydrophilic and conservation attributes in the management or upgrading plan of waterside parks in the future.

• Korean Journal of Heritage: History & Science
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• v.41 no.2
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• pp.79-97
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• 2008

Four Wangs' landscape style (四王山水畵風), which had appeared in Beijing in the early 18th century, widely spread to Korea and Japan in the 19th century and became a significant basis for developing new painting styles in both countries. It was first introduced to Korea by Shin Wi (申緯) and Kim Jeong-hee (金正喜) who associated with literary men of the Qing Dynasty. Being influenced by them directly or indirectly, Shin Myeong-yeon (申命淵), Yi Han-cheol (李漢喆), Yu Suk (劉淑), Changv Seung-eop (張承業), An Choog-sik (安中植), and Jo Seok-jin (趙錫晋) attempted to adapt Four Wangs' landscape style and it later became a main Stream painting style of the Korean painting circles. Based on Four Wangs' landscape style, their landscape paintings had something in common in that they captured natural features from a short distance using the Down-Up prospective and placed guardian mountains across mountain streams by making a tall tree in the right or left bottom of the canvas as the starting point. However, recently unveiled court painter An Geon-yeong (1841~1876)'s the Landscape Screen is remarkable in that it is based on Four Wangs' style, which was in fashion in the late 19th century, but shows different aspects from other Four Wangs' style paintings in terms of feature capturing, brush stroke and colors. While most of An Geon-yeong's existing paintings are small ones, this folding screen is a big piece consisting of six-fold landscape paintings. In particular, it shows new aspects by creating a serene and calm atmosphere through the description of various landscape scenes with thin brush strokes using glossy ink, by showing a macroscopic view in some paintings through feature capture using a birds-eye view method, and by giving life to the canvas through smoke and clouds. This painting style is considered to be linked with those of Wang Xue-hao (王學浩, 1754~1832), Tang Yifen (湯貽汾, 1778~1853) and Dai Xi (戴熙, 1801~1860), based on Four Wangs' style in the early 19th century's Jiangnan Circle (江南 畵壇), who tried to express the energy and vitality of real landscapes by going around China's well-known mountains and complementing painting styles with drawing from nature. Therefore, An Geon-yeong's six-fold Landscape Screen is very significant as a rare case proving the introduction and reception of Jiangnan Circle's Four Wangs' landscape style which was different in many aspects from Beijing Circle in the 19th century.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.154-167
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• 2014

The results of this study conducted to identify the substance, regional characteristics or landscaping of Namwon Yonghogugok, which is the only valley of Jiri Mountain, based on Kim Samun's 'Yonghokugok-Gyeongseungannae(龍湖九曲景勝案內)', 'Yongseongji(龍城誌)' and position, meaning of letters carved and projection technique by ArcGIS10.0 on the rocks are as below. The feature landscapes of the canyon of Yonghogugok, which is an incised meander and one of the Eight beautiful scenery of Namwon, ponds, cliffs and rocks generated with metamorphic rocks and granites weathered by rapids torrents. As a result of measuring the GPS coordinates of the letters carved on the rocks, excluding the 3 Gok Hakseoam and the distances based on the origin and destination of the letters carved on the rocks using the API(Application Programming Interface) function of Daum map, the total distance of Yonghogugok was 3.5km and the average distance between the each Gok was 436.5m. It is assumed that Yonghogugok was designated by Sarim(士林) of the Kiho School(畿湖學派) related to Wondong Hyangyak(元洞鄕約) which is the main agent of Yonghojeongsa(龍湖精舍), the forerunner of Yonghoseowon(龍湖書院), between the late Joseon Dynasty and the early Japanese colonial era, in 1927. Its grounds are the existence of Yonghoyeongdang mentioned on 'Yonghojeongsilgi'(龍湖亭實記), records of 'Haeunyugo(荷隱遺稿)', 'Yonghopumje(龍湖品題)' of Bulshindang(佛神堂), 'Yonghojeongsadonggu Gapjachun(龍湖精舍洞口 甲子春)' letters carved on the rocks and 'Yonghogugok-Shipyeong(龍湖九曲十詠)' posted on Mokgandang of Yonghoseowon. Comprehensively considering the numerous poetry society lists carved on the stone wall of Punghodae(風乎臺), the Sixth Gok Yuseondae, its stone mortar, 'Bangjangjeildongcheon(方丈第一洞天)' of Bulshindang and Gyoryongdam(交龍潭), the Yonghoseokmun(龍湖石門) letters carved on the rocks, Yeogungseok adjacent to the First Gok and Fengshui facilities, centered on Yonghoseowon and Yonghojeong, Yonghogugok can be understood as a unique valley culture formed with the thoughts of Confucianism, Buddhism, Taoism and Fengshui. 'Yonghogugok-Gyeongseungannae' provides very useful information to understand the place name, called by locals and landscaping aspects of Yonghogugok in the late Joseon Dynasty. In addition, the meaning of "Nine dragons" and even though 12 chu(湫: pond) of Yonghogugok Yongchudong including Bulyeongchu, Guryongchu, Isuchu, Goieumchu and Daeyachu are mentioned on Yongseongji, a part of them cannot be confirmed now. Various place names and facilities relevant to Guryong adjacent to Yonghogugok are the core of the place identity. In addition, the accurate location identification and the delivery of the landscaping significance of the 12 ponds is expected to provide landscaping attractiveness of Yonghogugok and become very useful contents for landscaping storytelling and a keyword of storyboard.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.1
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• pp.56-76
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• 2024

We for the first time made a successful longest continuous sectional observation in the East Sea by an underwater glider during 95 days from September 18 to December 21 2020 in the Korea along the 106 Line (129.1 °E ~ 131.5 °E at 37.9 °N) of the regular shipboard measurements by the National Institute of Fishery Science (NIFS) and obtained twelve hydrographic sections with high spatiotemporal resolution. The glider was deployed at 129.1 °E in September 18 and conducted 88-days flight from September 19 to December 15 2020, yielding twelve hydrographic sections, and then recovered at 129.2 °E in December 21 after the last 6 days virtual mooring operation. During the total traveled distance of 2550 km, the estimated deviation from the predetermined zonal path had an average RMS distance of 262 m. Based on these high-resolution long-term glider measurements, we conducted a comparative study with the bi-monthly NIFS measurements in terms of spatial and temporal resolutions, and found distinguished features. One is that spatial features of sub-mesoscale such as sub-mesoscale frontal structure and intensified thermocline were detected only in the glider measurements, mainly due to glider's high spatial resolution. The other is the detection of intramonthly variations from the weekly time series of temperature and salinity, which were extracted from glider's continuous sections. Lastly, there were deviations and bias in measurements from both platforms. We argued these deviations in terms of the time scale of variation, the spatial scale of fixed-point observation, and the calibration status of CTD devices of both platforms.

• Monthly Notices of the Royal Astronomical Society
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• v.501 no.4
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• pp.5616-5645
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• 2021

We analyse the large-scale clustering in Fourier space of emission line galaxies (ELG) from the Data Release 16 of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey. The ELG sample contains 173 736 galaxies covering 1170 deg² in the redshift range 0.6 eff = 0.845 we measure D_V(z_eff)/r_drag = 18.33^+0.57_-0.62, with D_V the volume-averaged distance and r_drag the comoving sound horizon at the drag epoch. In combination with the RSD measurement, at z_eff = 0.85 we find fσ₈(z_eff) = 0.289^+0.085_-0.096, with f the growth rate of structure and σ₈ the normalization of the linear power spectrum, D_H(z_eff)/r_drag = 20.0^+2.4_-2.2 and D_M(z_eff)/r_drag = 19.17 ± 0.99 with D_H and D_M the Hubble and comoving angular distances, respectively. These results are in agreement with those obtained in configuration space, thus allowing a consensus measurement of fσ₈(z_eff) = 0.315 ± 0.095, D_H(z_eff)/r_drag = 19.6^+2.2_-2.1 and D_M(z_eff)/r_drag = 19.5 ± 1.0. This measurement is consistent with a flat ΛCDM model with Planck parameters.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Economic and Environmental Geology
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• v.38 no.2 s.171
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• pp.101-111
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• 2005

The objectives of this study we to assess the extent and degree of environmental contamination and to draw general conclusions on the fate of toxic elements derived from mining activities in Korea. 인t abandoned mines with four base-metal mines and four Au-Ag mines were selected and the results of environmental surveys in those areas were discussed. In the base-metal mining areas, the Sambo Pb-Zn-barite, the Shinyemi Pb-Zn-Fe, the Geodo Cu-Fe and the Shiheung Cu-Pb-Zn mine, significant levels of Cd, Cu, Pb and Zn were found in mine dump soils developed over mine waste materials, tailings and slag. Furthermore, agricultural soils, stream sediments and stream water near the mines were severely contaminated by the metals mainly due to the continuing dispersion downstream and downslope from the sites, which was controlled by the feature of geography, prevailing wind directions and the distance from the mine. In e Au-Ag mining areas, the Kubong, the Samkwang, the Keumwang and the Kilkok mines, elevated levels of As, Cd, Cu, Pb and Zn were found in tailings and mine dump soils. These levels may have caused increased concentrations of those elements in stream sediments and waters due to direct dis-charge downstream from tailings and mine dumps. In the Au-Ag mines, As would be the most characteristic contaminant in the nearby environment. Arsenic and heavy metals were found to be mainly associated with sulfide gangue minerals, and mobility of these metals would be enhanced by the effect of oxidation. According to sequential extraction of metals in soils, most heavy metals were identified as non-residual chemical forms, and those are very susceptible to the change of ambient conditions of a nearby environment. As application of pollution index (PI), giving data on multi-element contamination in soils, over 1.0 value of the PI was found in soils sampled at and around the mining areas.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.1
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• pp.53-60
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• 2010

Purpose: We try to calculate EDW-factor easily with the formula applies essential data of EDW-factor and evaluate the validity through a measurement. Materials and Methods: We used the given value of GSTT (Golden Segmented Treatment Table) for the calculation of the EDW-factor. As to the experimental device, 0.6 cc farmer-type ion-chamber, an electrometer and water- phantom were used. A measurement was made at the maximum dose depth of the photon beam energy 6 MV and 15 MV under the condition that SSD (Source to Surface Distance) was 100 cm. The angle of the EDW (Enhanced Dynamic Wedge) which we use in an experiment was 60 degree, 30 degree, 20 degree in the Y1-OUT direction. We used Eclipse planning system (Varian, USA) as RTP system and the EDW-factor was calculated about all fields and EDW direction. In order to show the EDW-factor feature, a measurement was made at the selected field that verify the influence of the dependability about X, Y jaw and off-axis field. Results: When we change the Y1 field, it influence on the EDW-Factor and measured value. But the error between measured values and calculated values was less than 1%. The experimental result indicated the tendency that the error of the result of calculation and measured value becomes smaller as the EDW angle become smaller whether the calculation point (measurement point) and iso-center are same or not. The influence of the field size and energy did not show up. We simulated with the same condition using the RTP system. And we found that it makes no difference between the MU which is calculated manually by applying the EDW-Factor obtained from the commercial program and the value which is calculated by using RTP system. Conclusion: We excluded fitting value from well-known EDW-Factor formula and calculated EDW-factor with the formula applies essential data of EDW-factor only. As a result, there are no significant difference between the measured value and calculated value and it showed errors less than 1%. Also, we implemented the commercial program to calculate EDW-Factor conveniently without measure a factor on each field.

• Journal of Life Science
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• v.13 no.3
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• pp.280-290
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• 2003

The incorporation of RAPD markers into the previous classical and RFLP genetic linkage maps will facilitate the generation of a detailed genetic map by compensating for the lack of one type of marker in the region of interest. The objective of this paper was to present features we observed when we associated RAPD map from an intraspecific cross of a Glycine max$\times$G. max, 'Essex'$\times$PI 437654 with the public RFLP map developed from an interspecific cross of G. max$\times$G. soja. Among 27 linkage groups of RAPD map, eight linkage groups contained probe/enzyme combination RFLP markers, which allowed us the incorporation of RAPD markers into the public RFLP map. Map position rearrangement was observed. In incorporating L.G.C-3 into the public RFLP linkage group a1 and a2, both pSAC3 and pA136 region, and pA170/EcoRV and pB170/HindIII region were in opposite order, respectively. And, pk400 was localized 1.8 cM from pA96-1 and 8.4 cM from pB172 in the public RFLP map, but was localized 9.9 cM from i locus and 18.9 cM from pA85 in our study. A noticeable expansion of the map distances in the intraspecific cross of Essex and PI 437654 was also observed. Map distance between probes pA890 and pK493 in L.G.C-1 was 48.6 cM, but it was only 13.3 cM in the public RFLP map. The distances from the probe pB32-2 to pA670 and from pA670 to pA668 in L.G. C-2 were 50.9 cM and 31.7 cM, but they were 35.9 cM and 13.5 cM in the public RFLP map. The detection of duplicate loci from the same probe that were mapped on the same or/and different linkage group was another feature we observed.