• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.283-303
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• 2016

The concept of a risk society, which was originally suggested by German sociologist Ulrich Beck, is insufficient to reveal how a certain risk materially and discursively unfolds on the ground and how its various dynamics are recognised by diverse actors because of the concept's spatial insensitivity. As an alternative approach, this paper introduces the concept of the riskscape, which was suggested by German geographer Detlef $M{\ddot{u}}ller$-Mahn, and analyses this concept in the context of the East Asian developmental state. It is meaningful that the East Asian developmental state thesis has strongly promoted the role of the state in stimulating national economic development in underdeveloped countries. However, it should also be noted that an active state role in encouraging modernisation and economic growth within a very short time produces consequences of what Beck calls 'manufactured risks', such as nuclear power plants. Therefore, it is essential to analyse the state in comprehending modernisation and the risk society in East Asia. More specifically, using the case of the location policy for nuclear power facilities, this article reveals how dominant social forces acting in and through the state constructed a national riskscape that minimises the gravity of local risks while prioritising the economic value of the national economy over local risks to produce rapid modernisation. Additionally, it is argued that a dominant national riskscape may become weak from competing with different riskscapes that are constructed based on contingency factors (e.g., political democratisation or a natural disaster). Based on these analyses, the article emphasises that interdisciplinary research using the concept of the riskscape is required to better explain the risks in East Asia.

• Research in Plant Disease
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• v.14 no.1
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• pp.71-75
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• 2008

An isolate of Peanut stunt virus (PSV), named as Tr-PSV, was isolated from white clover (Trifolium repens L) showing mosaic symptom. Tr-PSV systemically infected all plants tested in the Nicotiana spp. and induced local lesions on inoculated leaves of Chenopodium amaranticolor. However, Tr-PSV induced typical mosaic symptoms as ER-PSV on Vigna unguiculata 5 to 6 days after inoculation, while Fny-CMV used as a control virus of Cucumovirus produced local lesions on inoculated leaves. In dsRNA analysis, Tr-PSV consisted of four dsRNAs, but satellite RNA was not detected. The cDNA of coat protein gene of Tr-PSV was amplified by RT-PCR using a Cucumovirus-specific single pair primers that designed to amplify a DNA fragment of approximately 950 bp. By restriction mapping analysis using RFLP of the RT-PCR products and by serological properties of gel diffusion test, Tr-PSV belongs to a typical member of PSV subgroup I. This is the first report on the occurrence of PSV in white clover in Korea.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.67-76
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• 2010

This paper proposes a image-based water level measurement method, which adapt to the ruler's surface condition. When the surface of a ruler is deteriorated by mud, drifts, or strong light reflection, the proposed method judges the pollution of ruler by comparing distance between two levels: the first one is the end position of horizontal edge region which keeps the pattern of ruler's marking, and the second one is the position where the sharpest drop occurs in the histogram which is construct using image density based on the axis of image height. If the ruler is polluted, the water level is a position of local valley of the section having a maximum difference between the local peak and valley around the second level. If the ruler is not polluted, the water level is detected as the position having horizontal edges more than 30% of histogram's maximum value around the first level. The detected water level is converted to the actual water level by using the mapping table which is construct based on the making of ruler in the image. The proposed method is compared to the ultrasonic based method to evaluate its accuracy and efficiency on the real situation.

• Geophysics and Geophysical Exploration
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• v.22 no.1
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• pp.37-43
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• 2019

We present gravity anomaly maps based on KIGAM's gravity data measured from 2000 to 2018. Until 2016, we acquired gravity data on about 6,400 points for the purpose of regional mapping covering the whole country with data density of at least one point per $4km{\times}4km$ for reducing the time of the data acquisition. In addition, we have performed local gravity surveys for the purpose of mining development in and around the NMC Moland Mine at Jecheon in 2013 and in the Taebaeksan mineralized zone from 2015 to 2018 with data interval of several hundred meters to 2 km. Meanwhile, we carried out precise gravity explorations with data interval of about 250 m on and around epicenter areas of Gyeongju and Pohang earthquakes of relatively large magnitude which occurred in 2016 and in 2017, respectively. Thus we acquired in total about 9,600 points data as the result. We also used additional data acquired by Pusan National University for some local areas. Finally, gravity data more than 16,000 points except for the repetition and temporal control points were available to calculate free-air, Bouguer, and isostatic gravity anomalies. Therefore, the presented anomaly maps are most advanced in spatial distribution and the number of used data so far in Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1145-1156
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• 2022

In order to effectively secure site suitability for the development of large-scale offshore wind farms, it is essential to minimize the environmental impact of development and analyze the conflicts of benefit between social, ecological, and economic core values. In addition, a preliminary review of site adequacy must be preceded in order not to collide with other used areas in the marine spatial plan. In addition, it is necessary to conduct local meteorological characteristics analysis including wind resources near Jeollanam-do area before project feasibility study. Therefore, wind resource analysis was performed using the observation data of the meteorological mast installed in Wangdeungnyeo near Anmado, Yeonggwang, and the optimal site was selected after excluding geographical constraints related to the location of the offshore wind farm. In addition, the annual energy production was calculated by deriving the optimal wind farm arrangement results suitable for the local wind resources characteristics based on WindSim SW, and it is intended to be used as basic research data for site discovery and selection of suitable sites for future offshore wind farm projects.

• Progress in Medical Physics
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• v.21 no.2
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• pp.153-164
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• 2010

To determine the clinical target volumes considering vascularity and cellularity of tumors, the software was developed for mapping of the analyzed biological clinical target volumes on anatomical images using regional cerebral blood volume (rCBV) maps and apparent diffusion coefficient (ADC) maps. The program provides the functions for integrated registrations using mutual information, affine transform and non-rigid registration. The registration accuracy is evaluated by the calculation of the overlapped ratio of segmented bone regions and average distance difference of contours between reference and registered images. The performance of the developed software was tested using multimodal images of a patient who has the residual tumor of high grade gliomas. Registration accuracy of about 74% and average 2.3 mm distance difference were calculated by the evaluation method of bone segmentation and contour extraction. The registration accuracy can be improved as higher as 4% by the manual adjustment functions. Advanced MR images are analyzed using color maps for rCBV maps and quantitative calculation based on region of interest (ROI) for ADC maps. Then, multi-parameters on the same voxels are plotted on plane and constitute the multi-functional parametric maps of which x and y axis representing rCBV and ADC values. According to the distributions of functional parameters, tumor regions showing the higher vascularity and cellularity are categorized according to the criteria corresponding malignant gliomas. Determined volumes reflecting pathological and physiological characteristics of tumors are marked on anatomical images. By applying the multi-functional images, errors arising from using one type of image would be reduced and local regions representing higher probability as tumor cells would be determined for radiation treatment plan. Biological tumor characteristics can be expressed using image registration and multi-functional parametric maps in the developed software. The software can be considered to delineate clinical target volumes using advanced MR images with anatomical images.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.26 no.3
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• pp.115-120
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• 2016

$CeO_2$ is used as a co-catalyst with $TiO_2$ to improve the catalytic activity of $MnO_x$ and characterization of nano-sized powder is identified with de-NOx efficiency. A comparison between $MnO_x-CeO_2/TiO_2$ and single $CeO_2$ was conducted in terms of microstructural analysis to observe the behavior of $CeO_2$ in the ternary catalyst. The $MnO_x-CeO_2/TiO_2$ catalyst was synthesized by sol-gel method and the average particle size of the single $CeO_2$ is about $285{\mu}m$ due to the low thermal stability, whereas the particle size $MnO_x-CeO_2/TiO_2$ is about 130 nm. The strong interaction between Ce and Ti was identified through the EDS mapping by transmission electron microscopy (TEM). The improvement about 20 % of $de-NO_x$ efficiency is observed in the low-temperature ($150^{\circ}C{\sim}250^{\circ}C$) and vigorous oxygen exchange by well-dispersed $CeO_2$ is the reason of catalytic activity improvement.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1970-1982
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• 1993

This paper describes an efficient array algorithm for parallel computation of vector-radix two-dimensional (2-D) fast discrete cosine transform (VR-FCT), and its VLSI implementation. By mapping the 2-D VR-FCT onto a 2-D array of processing elements (PEs), the butterfly structure of the VR-FCT can be efficiently importanted with high concurrency and local communication geometry. The proposed array algorithm features architectural modularity, regularity and locality, so that it is very suitable for VLSI realization. Also, no transposition memory is required, which is invitable in the conventional row-column decomposition approach. It has the time complexity of O(N+Nnzp-log2N) for (N*N) 2-D DCT, where Nnzd is the number of non-zero digits in canonic-signed digit(CSD) code, By adopting the CSD arithmetic in circuit desine, the number of addition is reduced by about 30%, as compared to the 2`s complement arithmetic. The computational accuracy analysis for finite wordlength processing is presented. From simulation result, it is estimated that (8*8) 2-D DCT (with Nnzp=4) can be computed in about 0.88 sec at 50 MHz clock frequency, resulting in the throughput rate of about 72 Mega pixels per second.