• Journal of Veterinary Clinics
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• v.33 no.5
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• pp.278-285
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• 2016

Epidemiological studies have shown the association between transportation of live animals and the potential transmission of infectious disease between premises. This finding was also observed in the 2014-2015 foot-and-mouth disease (FMD) outbreak in Korea. Furthermore, slaughterhouses played a key role in the global spread of the FMD virus during the epidemic. In this context, in-depth knowledge of the structure of direct and indirect contact between slaughterhouses is paramount for understanding the dynamics of FMD transmission. But the social network structure of vehicle movements to slaughterhouses in Korea remains unclear. Hence, the aim of this study was to configure a social network topology of vehicle movements between slaughterhouses for a better understanding of how they are potentially connected, and to explore whether FMD outbreaks can be explained by the network properties constructed in the study. We created five monthly directed networks based on the frequency and chronology of on- and off-slaughterhouse vehicle movements. For the monthly network, a node represented a slaughterhouse, and an edge (or link) denoted vehicle movement between two slaughterhouses. Movement data were retrieved from the national Korean Animal Health Integrated System (KAHIS) database, which tracks the routes of individual vehicle movements using a global positioning system (GPS). Electronic registration of livestock movements has been a mandatory requirement since 2013 to ensure traceability of such movements. For each of the five studied networks, the network structures were characterized by small-world properties, with a short mean distance, a high clustering coefficient, and a short diameter. In addition, a strongly connected component was observed in each of the created networks, and this giant component included 94.4% to 100% of all network nodes. The characteristic hub-and-spoke type of structure was not identified. Such a structural vulnerability in the network suggests that once an infectious disease (such as FMD) is introduced in a random slaughterhouse within the cohesive component, it can spread to every other slaughterhouse in the component. From an epidemiological perspective, for disease management, empirically derived small-world networks could inform decision-makers on the higher potential for a large FMD epidemic within the livestock industry, and could provide insights into the rapid-transmission dynamics of the disease across long distances, despite a standstill of animal movements during the epidemic, given a single incursion of infection in any slaughterhouse in the country.

• Journal of Life Science
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• v.18 no.4
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• pp.565-571
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• 2008

Due to rapid development of bioinformatics technologies, various biological data have been produced in silico. So now days complicated and large scale biodata are used to accomplish requirement of researcher. Developing visualization and annotation tool using them is still hot issues although those have been studied for a decade. However, diversity and various requirements of users make us hard to develop general purpose tool. In this paper, I propose a novel system, Genome Viewer and Annotation tool (GenoVA), to annotate and visualize among genomes using known information and multiple relation graph. There are several multiple alignment tools but they lose conserved area for complexity of its constrains. The GenoVA extracts all associated information between all pair genomes by extending pairwise alignment. High frequency conserved area and high BLAST score make a block node of relation graph. To represent multiple relation graph, the system connects among associated block nodes. Also the system shows the known information, COG, gene and hierarchical path of block node. In this case, the system can annotates missed area and unknown gene by navigating the special block node's clustering. I experimented ten bacteria genomes for extracting the feature to visualize and annotate among them. GenoVA also supports simple and rough computational annotation of new genome.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.772-780
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• 2008

K-means is one of the simplest unsupervised learning algorithms that solve the clustering problem. However K-means suffers the basic shortcoming: the number of clusters k has to be known in advance. In this paper, we propose extensions of X-means, which can estimate the number of clusters using Bayesian information criterion(BIC). We introduce two different versions of algorithm: modified X-means(MX-means) and generalized X-means(GX-means), which employ one full covariance matrix for one cluster and so can estimate the number of clusters efficiently without severe over-fitting which X-means suffers due to its spherical cluster assumption. The algorithms start with one cluster and try to split a cluster iteratively to maximize the BIC score. The former uses K-means algorithm to find a set of optimal clusters with current k, which makes it simple and fast. However it generates wrongly estimated centers when the clusters are overlapped. The latter uses EM algorithm to estimate the parameters and generates more stable clusters even when the clusters are overlapped. Experiments with synthetic data show that the purposed methods can provide a robust estimate of the number of clusters and cluster parameters compared to other existing top-down algorithms.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.41-55
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• 2012

On the basis of vegetation data collected by point-centered quarter method in analysis in Baekdudaegan of Gangwon province in the area of Hyangrobong, Odaesan, Seokbyeongsan, Dutasan, Deokhangsan and Hambaeksan, the study was carried out to classify forest communities and to evaluate the successional trends. The classification method of cluster analysis was used to make various disordered forests into several common groups for 1,004 sample points all together. By clustering the forests in the six study areas were classified into 28 forest communities, which were subjected to aggregate 8 representative forest communities on the count of species composition and species diversity. They were Mesophytic mixed forest community, others deciduous forest community, Quercus mongolica (dominant) community, Q. mongolica (pure) community, Pinus densiflora - Q. mongolica community, P. densiflora community, Betula ermanii community and Q. mongolica - Pinus koraiensis community. The ecological outlook from the result indicated that P. densiflora community and P. densiflora - Q. mongolica community, which were located in Seokbyeongsan, Dutasan and Deokhangsan around 1,000m above the sea level showed lower species diversity index. On the contrary Mesophytic mixed forest community, others deciduous forest community which was located in Hyangrobong, Odaesan and Hambaeksan mostly in protected area and national park around 1,500m above the sea level displayed higher species diversity index. As the composition ratio of Q. mongolica within a certain community was decreased, the species diversity was generally increased, assumed that abundance of Q. mongolica might be negatively associated with species diversity in the national deciduous forest.

• 한국노년학
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• v.32 no.4
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• pp.975-991
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• 2012

Using data from 271 elderly couples, this study was to explore the types of group classified by couples' perception of marital relationship(marital intimacy, marital comparison level) and relationship with adult children(triangulation, differentiation between parents and adult children. In addition, this study was to analyze the differences in the demographic variables and psychological maladjustment according to the group types of elderly couples. A cluster analysis result identified four clusters such as chaos, bad, average, and good type. Second, the four clusters were different in the length of marriage, education level, and the type of social activity participation. Third, the four clusters were different in the levels of the psychological maladjustment, indicating that the psychological maladjustment level was the lowest in the cluster of good type but the highest in the cluster of bad type in both relationships with spouses and adult children.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.217-217
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• 2015

The objective of this study was mainly to evaluate the water resources potential of Lake Tana Basin (LTB) by using Soil and Water Assessment Tool (SWAT). From SWAT simulation of LTB, about 5236 km2 area of LTB is gauged watershed and the remaining 9878 km2 area is ungauged watershed. For calibration of model parameters, four gauged stations were considered namely: Gilgel Abay, Gummera, Rib, and Megech. The SWAT-CUP built-in techniques, particle swarm optimization (PSO) and generalized likelihood uncertainty estimation (GLUE) method was used for calibration of model parameters and PSO method were selected for the study based on its performance results in four gauging stations. However the level of sensitivity of flow parameters differ from catchment to catchment, the curve number (CN2) has been found the most sensitive parameters in all gauged catchments. To facilitate the transfer of data from gauged catchments to ungauged catchments, clustering of hydrologic response units (HRUs) were done based on physical similarity measured between gauged and ungauged catchment attributes. From SWAT land use/ soil use/slope reclassification of LTB, a total of 142 HRUs were identified and these HRUs are clustered in to 39 similar hydrologic groups. In order to transfer the optimized model parameters from gauged to ungauged catchments based on these clustered hydrologic groups, this study evaluates three parameter transfer schemes: parameters transfer based on homogeneous regions (PT-I), parameter transfer based on global averaging (PT-II), and parameter transfer by considering Gilgel Abay catchment as a representative catchment (PT-III) since its model performance values are better than the other three gauged catchments. The performance of these parameter transfer approach was evaluated based on values of Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2). The computed NSE values was found to be 0.71, 0.58, and 0.31 for PT-I, PT-II and PT-III respectively and the computed R2 values was found to be 0.93, 0.82, and 0.95 for PT-I, PT-II, and PT-III respectively. Based on the performance evaluation criteria, PT-I were selected for modelling ungauged catchments by transferring optimized model parameters from gauged catchment. From the model result, yearly average stream flow for all homogeneous regions was found 29.54 m3/s, 112.92 m3/s, and 130.10 m3/s for time period (1989 - 2005) for region-I, region-II, and region-III respectively.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.111-128
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• 2019

Subways are eco-friendly public transportation that can transport large numbers of passengers safely and quickly. It is necessary to predict the accurate number of passengers in order to increase public interest in subway. This study groups stations on Lines 1 to 9 of the Seoul Metropolitan Subway using clustering analysis. We propose one final prediction model for all stations and three optimal prediction models for each cluster. We found three groups of stations out of 294 total subway stations. The Group 1 area is industrial and commercial, the Group 2 ares is residential and commercial, and the Group 3 area is residential districts. Various data mining techniques were conducted for each group, as well as driving some influential factors on demand prediction. We use our model to predict the number of passengers for 8 new stations which are part of the 3rd extension plan of Seoul metro line 9 opened in October 2018. The estimated average number of passengers per hour is from 241 to 452 and the estimated maximum number of passengers per hour is from 969 to 1515. We believe our analysis can help improve the efficiency of public transportation policy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.43-56
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• 2012

Wireless sensor networks consist of numerous small-sized nodes equipped with limited computing power and storage as well as energy-limited disposable batteries. In this networks, nodes are deployed in a large given area and communicate with each other in short distances via wireless links. For energy efficient networks, dynamic clustering protocol is an effective technique to achieve prolonged network lifetime, scalability, and load balancing which are known as important requirements. this technique has a characteristic that sensing data which gathered by many nodes are aggregated by cluster head node. In the case of cluster head node is exposed by attacker, there is no guarantee of safe and stable network. Therefore, for secure communications in such a sensor network, it is important to be able to encrypt the messages transmitted by sensor nodes. Especially, cluster based sensor networks that are designed for energy efficient, strongly recommended suitable key management and authentication methods to guarantee optimal stability. To achieve secured network, we propose a key management scheme which is appropriate for hierarchical sensor networks. Proposed scheme is based on polynomial key pool pre-distribution scheme, and sustain a stable network through key authentication process.