• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.133-141
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• 2024

Considering fluid-structure-soil interactions, a finite-element model for a liquid-storage tank is presented and the nonlinear earthquake response analysis is formulated. The tank structure is modeled considering shell elements with geometric and material nonlinearities. The fluid is represented by acoustic elements and combined with the structure using interface elements. To consider the soil-structure interactions, the near- and far-field regions of soil are modeled with solid elements and perfectly matched discrete layers, respectively. This approach is applied to the seismic fragility analysis of a 200,000 kL liquid-storage tank. The fragility curve is observed to be influenced by the amplification and filtering of rock outcrop motions at the site when the soil-structure interactions are considered.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.139-148
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• 2016

The importance of technology commercialization is being emphasized more and more, but high technology excellence does not always lead to a successful business. In addition, companies that invest heavily in basic research and technology development are also not always able to guarantee high profits. This is due to the technical problems when present in step research and development levels are difficult to commercialize. And the market will not work rationally is also the cause of the problem. Therefore, to effectively utilize the resources necessary to ensure such partners to build alliances and cooperation with external funding or manpower is crucial to commercialize the research results of basic science successfully. In previous studies, it has been made many studies in accordance with the approach to the technology (e.g. Research and Development, Management of Technology, Technology Innovation, etc.). But the study of technology commercialization point of view is not being done much. This study should explores the available business required, or realized in the process of researching the basic science and trying to understand the imperfections of the market through the property of technology (tacit knowledge, objectified value of technology and the information asymmetry between innovation subjects, etc.). In addition, this paper, we try to focus on a strategic approach to the role of International Science Business Belt (ISBB) with success in science and technology business as appropriate countermeasures about breaking the 'Valley of Death.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of Gifted/Talented Education
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• v.21 no.1
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• pp.107-122
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• 2011

The purpose of this study is to explore the perception of the observation and nomination system for the identification of the gifted and to find out the importance and practice level perceived by the gifted education teachers using the concept mapping approach. For this, twelve gifted education teachers brainstormed and gathered ideas for the ideal ways of observation and nomination system and the gathered statements were solicited. Multidimensional scaling and hierarchical cluster analysis were also used. In addition, 112 gifted education teachers rated the importance of and the practice level for the suggested ideas of observation and nomination system. Results were as follows: First, 36 statements were solicited and as a result of concept mapping the suggested observation and nomination system were categorized as 'attainment of professionality', 'attainment of administrative support', 'attainment of fairness', and 'considering points for recommendation.' Second, there were significant differences between the perceived importance levels and the practice levels. Based on the results, imlications of the study were discussed in depth.

• Journal of Engineering Education Research
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• v.11 no.3
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• pp.5-11
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• 2008

Far greater than ever before is the present industrial demand for skilled professionals in innovative product design and development. Yet there is an apparent lack of a sufficient curricular provision for training design professionals in almost all engineering schools. The present study is to propose a systematic procedure for developing a strategy for building an innovative product design curriculum and demonstrate its application. The procedure consists of three major steps: strategic element derivation, task formulation, and task execution roadmap construction. The proposed procedure was applied to develop a modular curriculum (a cluster of several related courses) covering various subjects in relation to innovative product design and development. The procedure seems quite effective and useful for developing a curriculum that is strategically well differentiated based on the unique characteristics of a particular educational institute and its applicability seems not limited to a specific domain.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.77-95
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• 2023

The regional infrastructure industries of Gwangyang City, the subject of this study, are Gwangyang Port and Gwangyang Steel Mill. Therefore, it is necessary to analyze the regional economic ripple effects of the port logistics industry in Gwangyang City. In this study, a multi-stage approach using the RW and the LQ methodology using the national input-output tables in 2015 and 2019 is used to prepare the regional interindustry analysis chart in Gwangyang City, and an exogenous demand induction model that reclassified the port logistics industry was applied. Through this, the purpose of this study was to provide policy implications by figuring out the regional economic ripple effects of the port logistics industry quantitatively in Gwangyang City. As a result of the analysis, the industries with high production inducement effect and forward/backward linkage effect of the port logistics industry in Gwangyang City were analyzed as manufacturing, transportation, land and air logistics sectors. And the industries in which the added value inducement effect and the employment inducement effect were analyzed as an industry related to the service industry. Therefore, it is necessary to prepare support measures to foster the port logistics industry as a way to promote these industries and revitalize the local economy of Gwangyang City. To this end, it is desirable to improve policies and systems for the vitalization of the Gwangyang port maritime cluster and provide various policy support for the port logistics industry in Gwangyang City. This study is meaningful in suggesting policy implications for the regional economy of Gwangyang City based on the results of exogenous analysis of the port logistics industry in small and medium-sized cities. However, It seems that further studies related to this will be needed in the future.

• Journal of Food Hygiene and Safety
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• v.35 no.5
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• pp.430-437
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• 2020

This is an approach study on the sensory properties (taste and odor) of 15 types of Korean conventional soups and stews using electronic nose and tongue. The relative sensor intensity for the taste components of the samples using electronic tongue was demonstrated. By SRS (sourness) sensor, sogogi-baechuguk (beef and cabbage soup) had the highest rate of 9.0. The STS (saltiness) sensor showed the highest score of 8.2 for ojingeoguk (squid soup). For the UMS (umami) sensor, which identifies savoriness, the sogogi-baechuguk was the highest at 10.1. The SWS (sweetness) sensors showed relatively little difference, with sigeumchi-doenjangguk (spinach and bean paste soup) at the highest of 7.3. According to the BRS sensor, which tests for bitterness, the siraegi-doenjangguk (dried radish green and bean paste soup) was the highest at 7.8. By principal component analysis (PCA), we observed variances of 56.21% in principal component 1 (PC1) and 25.23% in PC2. For each flavor component, we observed -0.95 and -0.20 for factor loading of PC1 and PC2 for SRS sensors, 0.96 and 0.14 for STS sensors, and -0.94 and 0.22 for PC1 and PC2 for UMS sensors, and PC1 and 0.22 for PC1 and PC2 loading for SWS sensors. The similarity between the samples identified by clustering analysis was largely identified by 4 clusters. A total of 25 kinds of volatile compounds in 15 samples were identified, and the ones showing the highest relative content in all samples were identified as ethanol and 2-methylthiophhene. The main ingredient analysis confirmed variances of 28.54% in PC1 and 20.80% in PC2 as a result of the pattern for volatile compounds in 15 samples. In the cluster analysis, it was found to be largely classified into 3 clusters. The data in this study can be used for a sensory property database of conventional Korean soups and stews using electronic sensors.