• Korean Journal of Environmental Biology
• /
• v.26 no.4
• /
• pp.311-322
• /
• 2008

The objectives of the study were to analyze chemical water quality and physical habitat characteristics in the urban streams (Miho and Gap streams) along with evaluations of fish community structures and ecosystem health, throughout fish composition and guild analyses during 2006$\sim$2007. Concentrations of BOD and COD averaged 3.5 and 5.7 mg L$^{-1}$, in the urban streams, while TN and TP averaged 5.1 mg L$^{-1}$ and 274 ${\mu}g$ L$^{-1}$, indicating an eutrophic state. Especially, organic pollution and eutrophication were most intense in the downstream reach of both streams. Total number of fish was 34 species in the both streams, and the most abundant species was Zacco platypus (32$\sim$42% of the total). In both streams, the relative abundance of sensitive species was low (23%) and tolerant and omnivores were high (45%, 52%), indicating an typical tolerance and trophic guilds of urban streams in Korea. According to multi-metric models of Stream Ecosystem Health Assessments (SEHA), model values were 19 and 24 in Miho Stream and Gap Stream, respectively. Habitat analysis showed that QHEI (Qulatitative Habitat Evaluation Index) values were 123 and 135 in the two streams, respectively. The minimum values in the SEHA and QHEI were observed in the both downstreams, and this was mainly attributed to chemical pollutions, as shown in the water quality parameters. The model values of SEHA were strongly correlated with conductivity (r=-0.530, p=0.016), BOD (r=-0.578, p< 0.01), COD (r=-0.603, p< 0.01), and nutrients (TN, TP: r>0.40, p<0.05). This model applied in this study seems to be a useful tool, which could reflect the chemical water quality in the urban streams. Overall, this study suggests that consistent ecological monitoring is required in the urban streams for the conservations along with ecological restorations in the degradated downstrems.

• Restorative Dentistry and Endodontics
• /
• v.36 no.6
• /
• pp.498-505
• /
• 2011

Objectives: The purpose of this in vivo study was to investigate the microbial diversity in symptomatic and asymptomatic canals with primary endodontic infections by using GS FLX Titanium pyrosequencing. Materials and Methods: Sequencing was performed on 6 teeth (symptomatic, n = 3; asymptomatic, n = 3) with primary endodontic infections. Amplicons from hypervariable region of the small-subunit ribosomal RNA gene were generated by polymerized chain reaction (PCR), and sequenced by means of the GS FLX Titanium pyrosequencing. Results: On average, 10,639 and 45,455 16S rRNA sequences for asymptomatic and symptomatic teeth were obtained, respectively. Based on Ribosomal Database Project Classifier analysis, pyrosequencing identified the 141 bacterial genera in 13 phyla. The vast majority of sequences belonged to one of the seven phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, Spirochetes, and Synergistetes. In genus level, Pyramidobacter, Streptococcus, and Leptotrichia constituted about 50% of microbial profile in asymptomatic teeth, whereas Neisseria, Propionibacterium, and Tessaracoccus were frequently found in symptomatic teeth (69%). Grouping the sequences in operational taxonomic units (3%) yielded 450 and 1,997 species level phylotypes in asymptomatic and symptomatic teeth, respectively. The total bacteria counts were significantly higher in symptomatic teeth than that of asymptomatic teeth (p < 0.05). Conclusions: GS FLX Titanium pyrosequencing could reveal a previously unidentified high bacterial diversity in primary endodontic infections.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.4
• /
• pp.111-124
• /
• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.111-125
• /
• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Journal of Korea Port Economic Association
• /
• v.25 no.1
• /
• pp.15-28
• /
• 2009

The purpose of this paper is to show the empirical extraction way of benchmarking ports for overcoming the shortcoming which the traditional DEA method has by using 20 Korean ports in 2003 for 2 inputs (birthing capacity, cargo handling capacity) and 2 outputs(Export and Import Quantity, Number of Ship Calls). Because DEA method has produced the limited set of efficient units which are reference to inefficient units respective of their differences in efficiency scores, it is necessary to adopt the more feasible benchmarking information according to the path analysis(tier or stratification). The core empirical results of this paper are as follows. Benchmarking ports against inefficient ports according to the tier analysis are that Masan Port(Janghang$\rightarrow$Jeju$\rightarrow$Seogoipo$\rightarrow$Yeosu), Jinhae Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando), Pohang&DonghaePort(Janghang$\rightarrow$Samcheonpo$\rightarrow$Pyungtag$\rightarrow$Samcheog), and Sogcho Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando). The policy implication to the Korean seaports and planners is that Korean seaports should introduce the new methods like Tier analysis of this paper for evaluating the port performance and enhancing the efficiency in short term, mid term, and long term according to the tier 3 stage, the tier 2 stage, and the tier 1 stage with original DEA stage.

• Archives of design research
• /
• v.18 no.3 s.61
• /
• pp.85-94
• /
• 2005

Most of product designers use 3D CAD system as a inevitable design tool nowadays and many new products are developed through a concurrent engineering process. However, it is very difficult for novice designers to get the sense of reality from modeling objects shown in the computer screens. Such a intangibility problem comes from the lack of haptic interactions and contextual information about the real space because designers tend to do 3D modeling works only in a virtual space of 3D CAD system. To address this problem, this research investigate the possibility of a interactive quantified structure simulation for product design using AR(augmented reality) which can register a 3D CAD modeling object on the real space. We built a quantified structure simulation system based on AR and conducted a series of experiments to measure how accurately human perceive and adjust the size of virtual objects under varied experimental conditions in the AR environment. The experiment participants adjusted a virtual cube to a reference real cube within 1.3% relative error(5.3% relative StDev). The results gave the strong evidence that the participants can perceive the size of a virtual object very accurately. Furthermore, we found that it is easier to perceive the size of a virtual object in the condition of presenting plenty of real reference objects than few reference objects, and using LCD panel than HMD. We tried to apply the simulation system to identify preference characteristics for the appearance design of a home-service robot as a case study which explores the potential application of the system. There were significant variances in participants' preferred characteristics about robot appearance and that was supposed to come from the lack of typicality of robot image. Then, several characteristic groups were segmented by duster analysis. On the other hand, it was interesting finding that participants have significantly different preference characteristics between robot with arm and armless robot and there was a very strong correlation between the height of robot and arm length as a human body.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.47-60
• /
• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.1
• /
• pp.301-311
• /
• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

• Korean Journal of Ecology and Environment
• /
• v.35 no.3 s.99
• /
• pp.152-159
• /
• 2002

The seasonal variations of nitrifying bacterial population sampled from 3 sites in Moon-Chon reservoir were analyzed by in situ hybridization with fluorescently labeled rRNA-targeted oligonucleotide probes from August 2000 until July 2001. In addition, physico-chemical parameters such as temperature, pH, chi-a and DOC were measured to determine correlations between those factors and the size of nitrifying bacterial populations. Total bacterial numbers varied in the range of $0.8{\sim}1.5{\times}10^6\;cells/ml$ independent of sites and had the maximal values in March at all 3 stations. The ratio of eubacteria to total bacteria ranged from 44.9% to 79.5%, and the ratio of each nitrifying bacteria to eubacterial numbers reached only $1.0{\sim}7.4%$. The variations of ammonia-oxidizing bacteria ranged from $1.1{\times}10^4$ to $3.0{\times}10^4\;cells/ml$ without noticeable peak values whereas those of nitrite-oxidizing bacteria varied in $1.3{\sim}5.7{\times}10^4\;cells/ml$ with the increasing tendency in winter regardless of the sites. Moreover it was observed that the numbers of nitrite-oxidizing bacteria were higher than those of ammonia-oxidizing bacteria. Total bacterial numbers correlated with water temperature (r = 0.355, p<0.05) and DOC (r = 0.58G, p<0.01) positively whereas nitrite-oxidizing bacteria correlated with temperature (r = -0.416, p<0.05) and pH (r = -0.568, p = 0.001) negatively. In addition, DOC represented good correlations with eubacterial numbers (r = 0.448, p<0.01). These results indicate that temperature, DOC and pH might be one of the main factors affecting variations of bacterial populations in the aquatic ecosystem. It was also suggested that FISH method is a useful tool for detection of slow growing nitrifying bacteria.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.41 no.5
• /
• pp.581-589
• /
• 2021

With the recent increase in the introduction of micro-electric vehicles in Korea, interest in micro-electric vehicle user satisfaction is increasing to revitalize related markets. In this paper, a basic study was conducted on the development of public services using micro-electric vehicle based on the constituent factors of user satisfaction. The survey includes: ① 'Analytic Hierarchy Process (AHP) for selecting the priority factors of user satisfaction of micro-electric vehicles', ② 'A survey of micro-electric vehicles image' to collect data in advance for providing users' preferences and transportation services for micro-electric vehicles, ③ In order to investigate the user satisfaction level of users who actually operated micro-electric vehicles, the order of 'user satisfaction survey of micro-electric vehicle drivers' was conducted. In the Analytic Hierarchy Process (AHP) analysis, it was found that users regarded as important in the order of 'user utilization data', 'vehicle movement data', and 'charging service data'. In the micro-electric vehicle image survey, users perceived micro-electric vehicles more positively in terms of "safety", 'durability', 'Ride comfort', 'design', 'MOOE (Maintenance and other operating expense)', and 'environment-friendly' when comparing micro-electric vehicles with electric motorcycles. In the survey on the user satisfaction of micro-electric vehicle drivers, the use of micro-electric vehicle did not directly affect work performance efficiency, and there was an experience of being disadvantaged on the road due to the size of the micro-electric vehicle, and driving in a cluster of micro-electric vehicle for outdoor advertisements. The city's public relations effect was great, but it was concerned about safety. In the future, based on the results of this study, we plan to build a user satisfaction structural equation model, preemptively discover feedback R&D for micro-electric vehicle utilization services in the public field, and actively seek to discover new public mobility support services.