• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.717-729
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• 2005

Peak cooling load of large buildings is generally greater than their peak heating load. Internal and solar heat gains are used fur selection of adquate equipment in large building in cold winter climate like Canada and even Korea. The cost of geothermal heat exchanger to meet the cooling loads can increase the initial cost of ground source heat pump system to the extend less costly conventional system often chosen. Thermal ice storage system has been used for many years in Korea to reduce chiller capacity and shift Peak electrical time and demand. A distribution system designed to take advantage of heat extracted from the ice, and use of geothermal loop (geothermal heat exchanger) to heat as an alternate heat source and sink is well known to provide many benifits. The use of thermal energy storage (TES) reduces the heat pump capacity and peak cooling load needed in large building by as much as 40 to $60\%$ with less mechanical equipment and less space for mechanical room. Additionally TES can reduce the size and cost of the geothermal loop by 1/3 to 1/4 compared to ground coupled heat pump system that is designed to meet the peak heating and cooling load and also can eliminate difficuties of geothermal loop installation such as space requirements and thermal conditions of soil and rock at the urban area.

• Korean Journal of Social Welfare
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• v.59 no.1
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• pp.301-327
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• 2007

The purpose of this study is to examine the timing and the risk factors associated with the adoption of legally-free foster children. The sample of the study was drawn from foster care files of Adoption and Foster Care Analysis and Reporting System(AFCARS) in 32 states between October 1998 (FY 1999) and September 2002(FY 2002). The timing post-TPR to adoption was examined by plotting the Kaplan-Meier cumulative hazard function for adoption and by plotting the KM hazard functions stratified by child's race and child's age at TPR. Cox proportional-hazards regression analysis was used to identify risk factors for adoption of legally-free foster children after TPR. The hazard of adoption was very low immediately after TPR but increased steadily starting at 3 months and then declined after 20 months. The cumulative hazard functions for White non-Hispanic children and Black non-Hispanic children crossed over at 13 months after TPR. Racial minority status, older age, and disability were negatively associated with the hazard of adoption. Physical abuse, sexual abuse had the lower hazard for adoption compared by neglect. Caretaker's inability to cope had the slightly lower hazard for adoption whereas inadequate housing showed the slightly greater hazard for adoption. Characteristics of foster care services turned into be powerful predictors of adoption. Specifically, legally-free children placed in pre-adoptive homes, those who shared the same racial/ethnic background with their foster caretakers, and those who were placed in two-parent families have a greater likelihood of adoption. The findings highlight the importance of foster care service provisions after TPR to facilitate adoption of legally-free foster children. Furthermore, a more substantial resources and targeted support for foster children who experience physical abuse and sexual abuse in need of adoption should be provided for moving the foster children into permanency.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.