• Women's Health Nursing
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• v.20 no.1
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• pp.48-61
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• 2014

Purpose: This study was done to explore the essence and meaning of the experience of 'aging', as a process of 40's and 50's women in Korea by applying the Parse's Human Becoming theory (2002). Methods: Data was collected from February to April, 2013, using the phenomenological research method. Data was collected through in-depth informal interview and analyzed following Colaizzi method. After IRB permission and informed consent from the participants, all interviews were recorded with MP3 recorder and transcribed for analysis. Results: Data analysis revealed 112 of meanings, 33 key subject words, 8 subject phrases, and 4 categories. The main themes were elaborated as 'going down' ('Being changed of body and mind', 'Being considered on my identity'), 'going up' ('Being expanded of productive role', 'Being transcendent multi-dimensionally'), 'pausing' ('Becoming more thoughtful about family', 'Looking back'), 'going forward again' ('Age is just a number, 'Contemplating of life and death'). Experiences in aging among women in 40's and 50's enlightened with Parse's theory of Human Becoming in terms of 'going down', 'going up', 'pausing', 'going forward again' appeared simultaneously, rather than consecutively. Conclusion: Women in 40's and 50's require holistic nursing intervention with physical, psychological, socio-economical, and spiritual aspects, rather than focusing on problematic physical symptom relief and prevention of further conditions. It is recommended to develop various nursing intervention considering on different environment, type of experience, and level of human becoming, individually.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.640-647
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• 2013

Building Information Modelling (BIM) has become the new international benchmark for efficiency in design, construction and maintenance of buildings. It is the platform that brings about collaboration between project stakeholders and improvement of project outcomes. With all its potentials, not much of the impact of BIM technologies has been felt in the Nigerian construction industry. This research aimed at assessing the readiness of the Nigerian building design firms to adopt BIM technologies. The research was exploratory in nature. A field survey was conducted with the use of structured questionnaire, self administered to a sample of building design consultancy firms (architectural, structural, M&E, quantity surveying, and multi-disciplinary design firms) within Abuja and Kaduna. The questionnaire sought the perception of the responding firms on the factors affecting BIM adoption in the Nigerian construction industry, and their level of readiness to adopt BIM technologies in their practices based on the four categories of readiness-management, people, process and technology. 42.26% response rate was achieved and used for analysis. ANOVA and DUNCAN post-hoc tests were used to establish the differences between the responses of the groups of firms, while means and standard deviations were obtained to establish the important factors affecting BIM adoption in Nigeria. The survey revealed that all the groups of Nigerian design firms are appreciably ready for the adoption of BIM technologies in their practice, with slight variations in their respective levels of readiness. 'Lack of awareness of BIM technology among professionals' and clients and 'lack of knowledgeable and experienced partners' were identified as the most important barriers of BIM adoption in Nigeria; while the most significant drivers are 'availability of well trained professionals' and 'cooperation and commitment of professional bodies to the adoption'. Education and training of building design professionals and cooperation of all stakeholders in the design and construction supply chain were recommended as part of measures to ensure successful adoption of BIM in the Nigerian construction industry.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.141-149
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• 2008

Recently we frequently analyze multivariate data with quite large number of variables. In such data sets, virtually duplicated variables may exist simultaneously even though they are conceptually distinguishable. Duplicate variables may cause problems such as the distortion of principal axes in principal component analysis and factor analysis and the distortion of the distances between observations, i.e. the input for cluster analysis. Also in supervised learning or regression analysis, duplicated explanatory variables often cause the instability of fitted models. Since real data analyses are aimed often at multiple purposes, it is necessary to reduce the number of variables to a parsimonious level. The aim of this paper is to propose a practical algorithm for selection of a subset of variables from a given set of p input variables, by the criterion of minimum trace of partial variances of unselected variables unexplained by selected variables. The usefulness of proposed method is demonstrated in visualizing the relationship between selected and unselected variables, in building a predictive model with very large number of independent variables, and in reducing the number of variables and purging/merging categories in categorical data.

• Journal of Korea Water Resources Association
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• v.38 no.5 s.154
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• pp.379-390
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• 2005

This study is to trace the change of stream shape using the past series of aortal photographs and topographic maps, and to compare the land use changes of inland along the stream. For the Gyeongan first & second class of local stream, aerial photographs of 1966, 1981 and 2000 were selected and ortho photographs were made with interior orientation and exterior orientation, respectively. In addition, topographic maps of 1914 - 1915 were used to compare with stream of 1966, 1981 and 2000. As apparent changes of the stream, the consolidated reaches of stream with levee construction were straightened and their stream width widened. Especially the stream width of inlet part of Paldang lake was widened almost twice because of the rise of water level by dam construction in 1974. The land use maps (1966, 1981, 2000) of riparian areas were also made, respectively and classified into 6 categories (water, forest, agricultural land, urban area, road, sandbar) by digitizing, The main changes of land use were agricultural land, urban area, road and sandbar.

• Tunnel and Underground Space
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• v.17 no.2 s.67
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• pp.128-138
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• 2007

At low in-situ stress, the continuity and distribution of natural fractures in rock mass predominantly control the failure processes. However at high in-situ stress, the failure process are affected and eventually dominated by stress-induced fractures preferentially growing parallel to the excavation boundary. This fracturing is often observed in brittle type of failure such as slabbing or spatting. Recent studies on the stress- or excavation-induced damage of rock revealed its importance especially in a highly stressed regime. In order to evaluate the brittle failure around a deep underground opening, physical model experiments were carried out. For the experiments a new tue triaxial testing system was made. According to visual observation and acoustic emission detection, brittle failure grades were classified under three categories. The test results indicate that where higher horizontal stress, acting perpendicular $(S_{H2})$ and parallel $(S_{H1})$ to the axis of the tunnel respectively, were applied, the failure grade at a constant vertical stress level (Sy) was lowered. The failure initiation stress was also increased with the increasing $S_{H1}\;and\;S_{H2}$. From the multi-variable regression on failure initiation stress and true triaxial stress conditions, $f(S_v,\;S_{H1},\;S_{H2})$ was proposed.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.10-23
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• 2019

Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.481-494
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• 2018

This study investigated elementary students' understanding of Earth revolution and its accompanied apparent motion of constellation in terms of spatial reasoning. We designed a set of multi-tiered constructed response items in which students described their own idea about the reason of consecutive movement of constellations for three months and drew a diagram about relative locations of the Sun, the Earth, and the constellations. Sixty-five sixth grade students from four elementary schools participated in the tests both before and after science classes on the relative movement of Earth and Moon. Their answers to the items were categorized inductively in terms of transforming frames of reference which are observed on the Earth and designed from the Space-based perspective. We analyzed those categories by the levels of spatial reasoning and depicted the change of students' levels between pre/post-tests so that we could get an idea on the preliminary developmental pathway of students' understanding of this topic. The lower anchor description was that constellations move around the Earth with geocentric perspective. Intermediate level descriptions were planar understanding of Earth movement, intuitive idea on constellation movement along with the Earth. Students with intermediate levels did not reach understanding of the apparent motion of constellations. As the upper anchor description students understood the apparent motion of constellations according to the Earth revolution and could transform their frames of reference between Earth-based view and Space-based view. The features as the case of evolutionary learning progressions and critical points of students' development for this topic were discussed.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.