• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.279-285
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• 2020

The study on the temporal and spatial monitoring of passing vessels is important in terms of protection and management the marine ecosystem in the coastal area. In this paper, we propose the automatic detection technique of passing vessel by utilizing an artificial intelligence technology and broadband striation patterns which are characteristic of broadband noise radiated by passing vessel. Acoustic measurements to collect underwater noise spectrum images and ship navigation information were conducted in the southern region of Jeju Island in South Korea for 12 days (2016.07.15-07.26). And the convolution neural network model is optimized through learning and validation processes based on the collected images. The automatic detection performance of passing vessel is evaluated by precision (0.936), recall (0.830), average precision (0.824), and accuracy (0.949). In conclusion, the possibility of the automatic detection technique of passing vessel is confirmed by using an artificial intelligence technology, and a future study is proposed from the results of this study.

• Communications of Mathematical Education
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• v.24 no.3
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• pp.503-523
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• 2010

The current mathematics curriculum are consist of the following domains: 'Characteristics', 'Objectives', 'Contents', 'Teaching and learning method', and 'Assessment'. The mathematics standards which students have to learn in the school are presented in the domain of 'Contents'. 'Contents' are consist of the following sub-domains: 'Number and Operation', 'Geometric Figures', 'Measures', 'Probability and Statistics', and 'Pattern and Problem-Solving' (Elementary School); 'Number and Operation', 'Geometry', 'Letter and Formula', 'Function', and 'Probability and Statistics' (Junior and Senior High School). These sub-domains of 'Contents' are dealing with mathematical subjects, except 'Problem-Solving' at the elementary school level. In this study, the sub-domain of 'mathematical process' was suggested in an equal position to the typical sub-domains of 'Contents'.

• Journal of the Korean housing association
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• v.23 no.4
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• pp.59-67
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• 2012

Developing the modern design of Hanok and providing support for the commercialization model development in recent years propelled by the New Hanok Support Strategies of the central government in conjunction with the New Hanok revitalization related projects reflecting local goverments. New Hanok revitalization, the rekindling and revaluing of human behaviors and interests in local goverments following the social and cultural changes of the past decades, has emeraged as an increasingly traditional area of concerning in New Hanok planning. In this paper we attempt to this discussion by describing recent projects in New Hanok revitalization in Jeollanam-do Province. Therefore, this study aims to examine the classification of compound knowledges based multidimensional relationship by using Self-Organizing Maps (SOM). SOM is an unsupervised learning neural network model for the analysis of high-dimensional input data. By using SOM, we were able to create a cluster map reflecting the characteristics of the New Hanok. In this case the pattern of the preference data was easily understood by visual analysis. Liking for compound knowledge deduced from this data was classified into 8 categories according to the compound knowledge properties of New Hanok. As a result, a systematic approach for analysis the characteristics of individual family and living environment of New Hanoks and 10 space usage patterns the changes in some aspects of New Hanok.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5B
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• pp.542-551
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• 2009

We proposed the context based user profile which is aware of its user's situation and based on user's situation it recommends personalized services. The user profile which consists of (context, service) pair can be acquired by the context and the service usage of a user; it then can be used to recommend personalized services for the user. In this paper, we show how they can be evolved without previously known user information so that not to violate privacy during the learning phase; in the result our user profile can be applied to any new environment without any modification to model only except context profiles. Using context-awareness based user profile, the service usage pattern of a user can be learned by the union of contexts and the preferred services can be recommended by the current environments. Finally, we evaluate the precision of proposed approach using simulation with data sets of UCI depository and Weka tool-kit.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.85-90
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• 2014

In this paper, we propose a virtual violin implementation using the detection of bowing and finger dropping position from the estimated finger tip and finger board information with the 3D image data from a Kinect. Violin finger board pattern and depth information are extracted from the color image and depth image to detect the touch event on the violin finger board and to identify the touched position. Final decision of activated musical alphabet is carried out with the finger drop position and bowing information. Our virtual violin uses PC MIDI to output synthesized violin sound. The experimental results showed that the proposed method can detect finger drop position and bowing detection with high accuracy. Virtual violin can be utilized for the easy and convenient interface for a beginner to learn playing violin with the PC-based learning software.

• MALSORI
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• no.4
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• pp.20-30
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• 1982

The aim of this paper is to analyse difficulties of the pronunciation in swedish vowels encountered by Koreans learners and to seek solutions in order to correct the possible errors. In the course of the analysis the swedish and Korean vowels in question are compared with the purpose of describing differences aha similarities between these two systems. This contrastive description is largely based on the students' articulatory speech level ana the writer's auditory , judgement . The following points are discussed : 1 ) Vowel length as a distinctive feature in Swedish compared with that of Korean. 2) A special attention is paid on the Swedish vowel [w:] that is characterized by its peculiar type of lip rounding. 3) The six pairs of Swedish vowels that are phonologically contrastive but difficult for Koreans to distinguish one from the other: [y:] ~ [w:], [i:] ~ [y:], [e:] ~ [${\phi}$:], [w;] ~ [u:] [w:] ~ [$\theta$], [$\theta$] ~ [u] 4) The r-colored vowel in the case of the postvocalic /r/ that is very common in American English is not allowed in English sound sequences. The r-colored vowel in the American English pattern has to be broken up and replaced hi-segmental vowel-consonant sequences . Korean accustomed to the American pronunciation are warned in this respect. For a more distinct articulation of the postvocalic /r/ trill [r] is preferred to fricative [z]. 5) The front vowels [e, $\varepsilon, {\;}{\phi}$) become opener variants (${\ae}, {\;}:{\ae}$] before / r / or supradentals. The results of the analysis show that difficulties of the pronunciation of the target language (Swedish) are mostly due to the interference from the Learner's source language (Korean). However, the Learner sometimes tends to get interference also from the other foreign language with which he or she is already familiar when he or she finds in that language more similarity to the target language than in his or her own mother tongue. Hence this foreign language (American English) in this case functions as a second language for Koreans in Learning Swedish.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.