• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.33-38
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• 1997

Criticality experiments with fixed neutron poison plates for water moderated and reflected low enriched(2.35 and 4.31 wt%) UO$_2$fuel rod clusters were evaluated to validate calculation techniques employed in analyzing fuel shipping and storage systems having steel, boral, or cadmium shield. Measurements were obtained for both the 2.35 wt% and the 4.31 wt% enriched rods in square pitched, water flooded lattices. The critical experiments with the 2.35 wt% enriched rods consists of three 20$\chi$ 16 or 20$\chi$ 17 fuel cluster. Critical separation were used in the experiments with the 4.31 wt% enriched fuel rods. In the experiments, the poison plates were placed on both sides of the centrally located fuel cluster. Critical separation between the three sub-critical fuel clusters were then measured for varying plate thicknesses and distances of the plates to the center fuel cluster. Calculations were performed for thirty eight critical configuration using KENO-V. a and MCNP. All of the results were within 1.23% in $\Delta$k when individually compared with the critical value of 1.0. Discrepancies of the code results are probably due to uncertainties in experiments and/or analytical modeling experiments. In general, MCNP predictions were observed to be in best agreement with the experiments.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1125-1139
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• 2006

The cluster analysis of diurnal precipitation patterns is performed by using daily precipitation of 59 stations in South Korea from 1973 to 1996 in four seasons of each year. Four seasons are shifted forward by 15 days compared to the general ones. Number of clusters are 15 in winter, 16 in spring and autumn, and 26 in summer, respectively. One of the classes is the totally dry day in each season, indicating that precipitation is never observed at any station. This is treated separately in this study. Distribution of the days among the clusters is rather uneven with rather low area-mean precipitation occurring most frequently. These 4 (seasons)$\times$2 (wet and dry days) classes represent more than the half (59 %) of all days of the year. On the other hand, even the smallest seasonal clusters show at least $5\sim9$ members in the 24 years (1973-1996) period of classification. The cluster analysis is directly performed for the major $5\sim8$ non-correlated coefficients of the diurnal precipitation patterns obtained by factor analysis In order to consider the spatial correlation. More specifically, hierarchical clustering based on Euclidean distance and Ward's method of agglomeration is applied. The relative variance explained by the clustering is as high as average (63%) with better capability in spring (66%) and winter (69 %), but lower than average in autumn (60%) and summer (59%). Through applying weighted relative variances, i.e. dividing the squared deviations by the cluster averages, we obtain even better values, i.e 78 % in average, compared to the same index without clustering. This means that the highest variance remains in the clusters with more precipitation. Besides all statistics necessary for the validation of the final classification, 4 cluster centers are mapped for each season to illustrate the range of typical extremities, paired according to their area mean precipitation or negative pattern correlation. Possible alternatives of the performed classification and reasons for their rejection are also discussed with inclusion of a wide spectrum of recommended applications.

• Journal of Korean Academy of Nursing
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• v.39 no.5
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• pp.683-692
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• 2009

Purpose: The purpose of this study was to identify cancer-related symptom clusters and to validate the conceptual meanings of the revealed symptom clusters in patients with hepatocellular carcinoma. Methods: This study was a cross-sectional survey and methodological study. Patients with hepatocellular carcinoma (N=194) were recruited from a medical center in Seoul. The 20-item Symptom Checklist was used to assess patients' symptom severity. Selected symptoms were factored using principal-axis factoring with varimax rotation. To validate the revealed symptom clusters, the statistical differences were analyzed by status of patients' performance status, Child-Pugh classification, and mood state among symptom clusters. Results: Fatigue was the most prevalent symptom (97.4%), followed by lack of energy and stomach discomfort. Patients' symptom severity ratings fit a four-factor solution that explained 61.04% of the variance. These four factors were named pain-appetite cluster, fatigue cluster, itching-constipation cluster, and gastrointestinal cluster. The revealed symptom clusters were significantly different for patient performance status (ECOG-PSR), Child-Pugh class, anxiety, and depression. Conclusion: Knowing these symptom clusters may help nurses to understand reasonable mechanisms for the aggregation of symptoms. Efficient symptom management of disease-related and treatment-related symptoms is critical in promoting physical and emotional status in patients with hepatocellular carcinoma.

• Bulletin of the Korean Chemical Society
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• v.32 no.5
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• pp.1547-1553
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• 2011

An effective analytical method of gas chromatography/mass spectrometry (GC/MS) was developed for the rapid determination of essential oils in the crude extract of Acorus species (Acorus gramineus, Acorus tatarinowii, and Acorus calamus). Major phenypropanoids (${\beta}$,${\alpha}$-asarone isomers, euasarone, and methyleugenol) and ${\beta}$-caryophyllene in Acorus species were used as marker compounds and determined for the quality control of herbal medicines. To extract marker compounds, various extraction techniques such as solvent immersion, mechanical shaking, and sonication were compared, and the greatest efficiency was observed with sonication extraction using petroleum ether. The dynamic range of the GC/MS method depended on the specific analyte; acceptable quantification was obtained between 10 and 2000 ${\mu}g/mL$ for ${\beta}$-asarone, 10 and 500 ${\mu}g/mL$ for ${\alpha}$-asarone, 10 and 200 ${\mu}g/mL$ for methyleugenol, and between 5 and 100 ${\mu}g/mL$ for ${\beta}$-caryophyllene. The method was deemed satisfactory by inter- and intra-day validation and exhibited both high accuracy and precision, with a relative standard deviation < 10%. Overall limits of detection were approximately 0.34-0.83 ${\mu}g/mL$, with a standard deviation (${\sigma}$)-to-calibration slope (s) ratio (${\sigma}$/s) of 3. The limit of quantitation in our experiments was approximately 1.13-3.20 ${\mu}g/mL$ at a ${\sigma}$/s of 10. On the basement of method validation, 20 samples of Acorus species collected from markets in Korea were monitored for the quality control. In addition, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed on the analytical data of 20 different Acorus species samples in order to classify samples that were collected from different regions.

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

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.238-243
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• 2019

Recently, the importance of big data continues to be emphasized, and it is applied in various fields based on data mining techniques, which has a great influence on the health care industry. There are many healthcare industries, but only home health care is considered here. However, applying this to real problems does not always give perfect results, which is a problem. Therefore, data mining techniques are used to solve these problems, and the algorithms that affect performance are evaluated. This paper focuses on the effects of healthcare services on patient satisfaction and satisfaction. In order to use the CVParameterSelectin algorithm and the SMOreg algorithm of the classify method of data mining, it was evaluated based on the experiment and the verification of the results. In this paper, we analyzed the services of home health care institutions and the patient satisfaction analysis based on the name, address, service provided by the institution, mood of the patients, etc. In particular, we evaluated the results based on the results of cross validation using these two algorithms. However, the existence of variables that affect the outcome does not give a perfect result. We used the cluster analysis method of weka system to conduct the research of this paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.1
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• pp.89-98
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• 2009

CMG cluster which consists of four CMGs can be used to produce 3-axis torque. There are many issues that we have to investigate and validate when CMG cluster itself is developed. Thus, its ground validation and verification processes are essential. Therefore, CMG simulator which uses a torque sensor to calculate satellite attitude is proposed in this paper. Update and kalman filter are also proposed for gimbal angle problem occurred in development. The first way uses a calculated gimbal angle as a primary and a sensor angle as a scondary to reduce error. Also, the test results of specific CMG steering law as well as attitude control logic are presented as an example.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.259-264
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• 2022

In wireless sensor networks, sensor nodes are often deployed in large numbers in places that are difficult for humans to access. However, the energy of the sensor node is limited. Therefore, one of the most important considerations when designing routing protocols in wireless sensor networks is minimizing the energy consumption of each sensor node. When the energy of a wireless sensor node is exhausted, the node can no longer be used. Various protocols are being designed to minimize energy consumption and maintain long-term network life. Therefore, we proposed KOCED, an optimal cluster K-means algorithm that considers the distances between cluster centers, nodes, and residual energies. I would like to perform a performance evaluation on the KOCED protocol. This is a study for energy efficiency and validation. The purpose of this study is to present performance evaluation factors by comparing the K-means algorithm and the K-medoids algorithm, one of the recently introduced machine learning techniques, with the KOCED protocol.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.135-142
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• 2012

In the present study, a parallel processing method running on a multi-core PC-Cluster is introduced to produce digital surface model (DSM) and digital terrain model (DTM) from huge airborne laser scanning data. A hybrid model using both message passing interface (MPI) and OpenMP was devised by revising a conventional MPI model which utilizes only MPI, and tested on a multi-core PC-Cluster for performance validation. In the results, the hybrid model has not shown better performances in the interpolation process to produce DSM, but the overall performance has turned out to be better by the help of reduced MPI calls. Additionally, scheduling function of OpenMP has revealed its ability to enhance the performance by controlling inequal overloads charged on cores induced by irregular distribution of airborne laser scanning data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.1
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• pp.7-13
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• 2013

Bio Clean Room(BCR) and pharmaceutical product manufacturing facilities require careful assessment of many factors, including HVAC, controls, room finishes, process equipment, room operations, and utilities. Flow of equipment, personnel, and product must also be considered along with system flexibility, redundancy, and maintenance shutdown strategies. It is important to involve designers, operators, commissioning staff, quality control, maintenance, constructors, validation personnel, and the production representative during the conceptual stage of design. Critical variables for room environment and types of controls vary greatly with the clean space's intended purpose. It is particularly important to determine critical parameters with quality assurance to set limits and safety factors for temperature, humidity, room pressure, and other control requirements. In this paper, oxygen cluster ion equipment was utilized in order to enhance the indoor air quality and to prevent the airborne infection of ward in hospital. Moreover, the performance test of the equipment was also performed in order to develop the optimal sterilization system of BCR using the equipment.