• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.689-701
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• 2011

The relations between ambient conditions and ice accretion shapes are quantitatively analyzed by employing self-organization maps and analysis of variance. Liquid water contents(LWC), mean volumetric droplet diameter(MVD), ambient temperature and free-stream velocity are chosen as ambient conditions which change ice accretion shapes. The parameters of ice accretion shape are selected as maximum thickness, icing limits, ice heading, and ice accretion area. Qualitative analysis was conducted by employing self-organization maps which show the qualitative relations between ice shapes and ambient conditions. The quantitative results of analysis of variance yield intensity of ambient conditions to the parameters of ice accretion shapes.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.471-480
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• 2010

Cluster analysis is one of the useful methods to find out number of groups and member’s belongings. With the rapid development of computer application in statistics, variety of new methods in clustering analysis were studied such as EM algorism and Self organization maps. The goals of cluster analysis is finding the number of groupings that are meaningful to me. If data are analyzed perfectly with cluster analysis, we can get the same results from discernment analysis.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.47-54
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• 2010

The purpose of this study is finding examine the Thinking Maps and how to use Thinking Maps effectively in Science Education. The result of this study were as follows: First, There are 8 type Maps, Circle Map, Tree Maps, Bubble Map, Double Bubble Map, Flow Map, Multi Flow Map, Brace Map, Bridge Map. Each Maps are useful in the following activities ; Circle Map-Express their thoughts. Tree Map-Activities as like determine the structure, classification, information organization. Bubble Maps-Construction. Double Bubble Map-Comparison of similarities and differences. Flow Map-Set goals, determine the result of changes in time or place. Multi Flow Map-Analysis cause and effect, expectation and reasoning. Brace Map-Analysis whole and part. Bridge Map-Activities need analogies. Second, each element of inquiry has 1~2 appropriate type of Thinking Maps. So student can choose the desired map. Third, the result of analysing of Science Curriculum Subjects, depending on the subject variety maps can be used. Therefore the Thinking Maps can be used for a variety on activities and subject. And student can be selected according to their learning style. So Thinking Maps are effective to improve student's Self-Directed Learning.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.71-79
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• 1998

In this paper, we propose a new mART(modified ART) neural network by combining the winner neuron definition method of SOM(self-organizing map) and the real-time adaptive clustering function of ART(adaptive resonance theory) and construct it in a modular structure, for the purpose of organizing the feature maps of three dimensional targets. Being constructed in a modular structure, the proposed modular mART can effectively prevent the clusters from representing multiple classes and can be trained to organze two dimensional distortion invariant feature maps so as to recognize targets with three dimensional distortion. We also present the recognition result and self-organization perfdormance of the proposed modular mART neural network after carried out some experiments with 14 tank and fighter target models.

• Genomics & Informatics
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• v.4 no.1
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• pp.16-22
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• 2006

The KFDA (Korea Food & Drug Administration) has performed a collaborative toxicogenomics project since 2003. Its aim is to construct a toxicology database of 12 compounds administered to mice at initial phase. We chose 6-MP (6-mercaptopurine) which has been used in the treatment of childhood leukemia. It was administered at low (0.224 mg/kg) and at high (2.24 mg/kg) dose (5 mice per group) intraperitonealy to the postnatal 6 weeks mice, then the serum and liver were collected at the indicated time (6, 24 and 72 h) after scarification. Serum biochemical markers for liver toxicity were measured and histopathologic studies also were carried out. The gene expression profiling was carried out by using Applied Biosystems 1700 Full Genome Expression Mouse. By self-organization maps (SOM), we identified groups with unique gene expression patterns, some of them are supposed to be related to 6-MP induced toxicity, including lipid metabolism abnormality, inflammatory response, oxidative stress, ATP depletion and cell death. The potential toxic effects appearing as gene expression changes are dependent of the time of 6-MP but independent of the dosage of it. This study would contribute to establishment of international database as well as national one about hepatotoxicity.

• Journal of the Korea Institute of Building Construction
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• v.20 no.4
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• pp.347-355
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• 2020

The economic growth rate and the urban population of the Vietnam are steadily increasing. As a result, the size of the Vietnam's construction market for infrastructure development is expected to increase. However, Vietnam is adopting PPP(Public-Private Partnership) to solve this problem because the government lacks the financial and administrative capacity for infrastructure development. PPP is a business that lasts more than 10 years, so risk management is very important because it can be a long term damage in case of business failure. This study proposes a self-organization map (SOM) for analyzing the impact of risk factors and determining the priority of them. SOM is a visualization analysis method that analyzes the inherent correlation through the color pattern of each factor.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.425-435
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• 2002

This paper introduces a macroblock-based rate control algorithm using the neural classifier based in Self Organization feature Maps (SOFM). In contrast to the conventional rate control methods based on the mathematical rate distortion (RD) model and the feedback regression, proposed method can actively adapt to the rapid-varying image characteristics by establishing the global model for bitrate control and by using the SOFM based neural classifier to manage that model. Proposed rate control algorithm has 0.2 dB ~ 0.6 dB better performances than MPEG-4 macroblock-based rate control algorithm by evaluating with the encoded Peak Signal to Noise Ratios while maintaining similar overall computational complexity.

• Toxicological Research
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• v.22 no.2
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• pp.87-93
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• 2006

The KFDA (Korea Food & Drug Administration) has performed a collaborative toxico-genomics project since 2003. Its aim is to construct a toxicologenomic database of 12 hepatotoxic compounds from mice livers. Phenylbutazone which is non-steroidal anti-inflammatory drug was assigned. It was administered at low (0.0238 mg/kg) and at high (0.238 mg/kg) dose (5 mice per group) orally to the postnatal 6 weeks ICR mice, then the serum and liver were collected at the indicated time (6, 24 and 72 h) after administration. Serum biochemical markers for liver toxicity were measured and histopathologic studies also were carried out. The gene expression profiling was carried out by using Applied Biosystems 1700 Full Genome Expression Mouse. The 2-way ANOVA was used to find genes that reflected phenylbutazone-induced acute toxicity or dose-dependant changes. By self-organization maps (SOM), we identified groups with unique gene expression patterns, some of them are supposed to be related to phenylbutazone induced toxicity, including lipid metabolism abnormality, oxidative stress, cell death and cytoskeleton destruction.

• Genomics & Informatics
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• v.2 no.2
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• pp.75-80
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• 2004

In a variety of animal species, the perinatal exposure of experimental animals to the 2,3,7,8-tetrachlorodibenzo­p-dioxin (TCDD) leads to the immune dysfunction, which is more severe and persistent than that caused by adult exposure. We report here the changes of gene expression and the identification of the marker-genes representing the dioxin exposure. The expressions of the transcripts were analyzed using the 11 K oligonucleotide­microarray from the bone marrow cells of male C57BL/6J mice after an intraperitoneal injection of $1{\mu}g$ TCDD/kg body weight at various time intervals: gestational 6.5 day(G6.5), 13.5 day(G13.5), 18.5 day(G18.5), and postnatal 3 (P3W)and 6 week (P6W). The type of self-organizing maps(SOM) representing the specific exposure dioxin could be identified as follows; G6.5D(C14), G13.5D(C0, C5, C10, C18), G18.5D(7): P3W(C2, C21), and P6W(C4, C15, C20). The candidate marker-genes were restricted to the transcripts, which could be consistently expressed greater than $\pm$2-fold in three experiments. The resulting candidates were 85 genes, the characteristics of that were involved in cell physiology and cell functions such as cell proliferation and immune function. We identified the biomarker-genes for dioxin exposure: smc -like 2 from SOM C14 for the dioxin exposure at G6.5D, focal adhesion kinase and 6 other genes from C0, and protein tyrosine phosphatase 4a2 and 3 other genes from C5 for G13.5D, platelet factor 4 from C7 for G18.5D, fos from C2 for P3W.