• Journal of Environmental Science International
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• v.12 no.12
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• pp.1215-1225
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• 2003

The purpose of this research was to investigate the characteristics of urban ecotopes and to classify ecotopes systematically from them. Total of 15 characteristics for classification of ecotopes were selected, and there were categorized 3 factors, that is abiotic, biotic and anthropological factors. The ecotope types in the study area were classified into 67. The classification of ecotope was made with SPSS for Windows Version 10.0 on the basis of the 15 characteristics. As the results of cluster analysis using the average linkage method between groups, groups of ecotope type were divided into 15 clusters. It was known that there was not a great difference in an affinity as the result of overlapping the maps of ecotope type and land use type. This research suggested characteristics for classification of ecotopes, but there was a limit to Set the objective method for grade classification because of lacking in the basic data, the research of characteristics will be accomplished continuously.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.295-296
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• 2011

식품정보 시스템은 농식품 정보를 DB화하여 분류하고, 다양한 식품정보를 효율적으로 관리하여 식품정보 활용에 대한 효과성을 극대화한 시스템이다. 미국, 일본, EU 에서는 정부 주관부처가 식품정보를 포괄적으로 관리하고 있으며, IT기술(데이터베이스, 메타데이터, 웹 기술, 통합검색 기술 등)을 활용하여 대국민, 산업체, 연구기관 등에 올바른 식품정보를 제공하고 있다. 본 논문은 국내 외 식품정보 시스템에서 활용하고 있는 식품정보와 IT기술에 대한 동향을 분석하여 식품정보를 여러 산업분야에서 손쉽게 활용할 수 있는 방안을 제시하고자 하였고, 이를 통해 식품 안정성, 기능성, 품질정보 등 식품정보를 언제, 어디서나 활용하여 식품산업에 대한 에코 시스템을 구축하는 것이다.

• Journal of Conservation Science
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• v.25 no.4
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• pp.431-438
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• 2009

We have investigated the suitable measuring method and condition on the hardness testing for the earthenwares excavated from Poongnap mud castle in Hanseong Baekje period. The earthenwares which used on hardness testing have been classified according to Mohs hardness and external form and color. The Ultrasonic and Equotip testing method have used to the hardness testing on the surface of the earthenwares and the Rockwell and Micro-vickers testing methods have used to the hardness testing on the cross section of the earthenwares. As the results, the two methods applied to the surface of the earthenwares were very hard on the precise measurement and the measuring values were incompatible with the tendency classified according to Mohs hardness and external form and color. On the testing for the cross section of earthenware, the Rockwell-superficial hardness testing method was more suitable for the soft texture earthenware and highest reproducibility of the measuring value obtained at the test load and indentor are 15kgf and 1/16 “iron ball, respectively. The Micro-Vickers hardness testing method was suitable for the hard texture earthenware and highest reproducibility and accuracy of the measuring value obtained at the test load is 100gf. This results show strong possibility of progress on the classifying and comparing study for hardness of the earthenware and therefore active studies are expected on the field.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.3
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• pp.248-262
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• 2021

Marine planktonic ciliates include two major groups, loricated tintinnids and naked oligotrichs. The study of marine ciliate plankton in Korea began with taxonomic efforts on tintinnids based on the morphology of lorica, a vase-shaped shell. Despite polymorphism in the lorica, it is utilized as a key characteristic in identification of tintinnid species. However, oligotrichs have been studied only recently in Korea due to challenges associated with the observation of ciliary arrangements and the technical development for cell staining. Species diversity and phylogenetic classification of the ciliates have been informed by recent advances in morphological and molecular analyses. Illustrations of the planktonic ciliate in Korea have been published on the basis of taxonomic data of tintinnids and oligotrichs. Planktonic ciliates acting as the major consumers of pico- and nanoplankton as well as the prey of mesozooplankton, has been monitored by spatial and temporal investigations in Korean coastal waters. A practical approach addressing the limitations and potential of marine ciliate studies in Korea is proposed here to improve the data quality of planktonic ciliates, providing an enhanced basis for quality control of ciliate monitoring.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.62-67
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• 1995

In this paper, the features extraction of reflected ultrasonic signals from various type of defects existing in Al metal has been studied by digital signal processing. Since the reflected signals from various type of the defects are ambiguous in features distinction from effects of noise, Wiener filtering using AR (auto-regressive) technique and least-absolute-values norm method has been used in features extraction and comparison of signals. In this experiment, three types of the defect in aluminum specimen have been considered: a flat cut, an angular cut, a circular hole. And the reflected signal have been measured by pulse-echo methods. In the result of digital signal processing of the reflected signal, it has been found that the features extraction method have been effective for classification of the reflected signals from various defects.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.226-239
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• 2023

Purpose To compare the sensitivities of T2-weighted image (T2WI) and susceptibility-weighted imaging (SWI) in detecting cerebral arteriovenous fistula (AVF), cerebral arteriovenous malformation (AVM), and carotid-cavernous sinus fistula (CCF), and to qualitatively evaluate single-echo SWI (s-SWI) and multi-echo SWI (m-SWI) in characterizing vascular lesions. Materials and Methods From January 2016 to December 2021, cerebral angiography-proven lesions were recruited. The sensitivities of T2WI and SWI in detecting vascular lesions were compared using McNemar's test. Qualitative evaluations of s-SWI and m-SWI were categorized to be of poor, average, or good quality and compared using Fisher's exact test. Results A total of 24 patients (mean age: 61 years, 12 female, and 12 male) were enrolled. Twenty patients underwent s-SWI or m-SWI, and four patients underwent both. AVF, AVM, and CCF were diagnosed in 10, 11, and 3 patients, respectively. SWI demonstrated higher sensitivity compared to that of T2WI (82.1% vs. 53.6%, p = 0.013). m-SWI showed better image quality compared to that of s-SWI (good quality, 83.3% vs. 25.0%, p = 0.009). Conclusion SWI demonstrated a higher sensitivity for detecting cerebral arteriovenous shunts compared to that of T2WI. m-SWI exhibited better image quality compared to that of s-SWI in characterizing vascular lesions.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.483-493
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• 2011

Erosion control dams play a primary role in preventing or controlling natural disasters (landslide and debris flow etc.) and also conserve ecosystem in forested watersheds. This study examines structural characteristics of the dams such as the height of ecosystem control and the ecosystem permeability of the erosion control dams under standard drawings and the existing construction works. The objective of this study was to characterize the type classification of erosion control dams as ecosystem. Average permeability was highest on eco-piller dam (63.0%), followed in increasing order by wire rope (13.9%), silt dam (10.9%), multifunctional dam (7.2%), and gravity dam (0.4%). The height of ecosystem control was highest on gravity dam (3.2 m), followed in increasing order by multifunctional dam (1.7 m), wire rope dam (1.2 m), silt dam (0.6 m), and eco-piller dam (0.0 m). Criteria for defining the height of ecosystem control was indefinite. We grouped erosion control dams into three functional types (eco-connection, eco-semi connection, and eco-disconnection) by considering physical and structural characteristics such as the ecosystem permeability and the height of ecosystem control. The type of eco-connection (permeability > 20%) had connection areas from streambed to adjacent riparian areas, and these connection areas serve as ecosystem corridors for fauna and flora. Typical wildlife species includes mammals, reptiles, amphibians, and fishes. The type of eco-semi connection (5% < permeability < 20%) had < 2 m in the eco-barrier height from streambed, however, this type of dams partially serve as wildlife corridors and often provide fish ways. The type of eco-disconnection (permeability < 5%) had > 2 m in the eco-barrier height from streambed, thereby preventing wildlife movement.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.