• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.135-142
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• 2013

Contour detection is important for many computer vision applications, such as shape discrimination and object recognition. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. The novelty of this operator is that dilation is limited to Deluanary triangular. An efficient implementation is presented. The grouping algorithm is then embedded in a multi-threshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contour.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.101-106
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• 2016

This paper proposes the SU-SE-MMA algorithm which applying the concept of selective updaing to the SE-MMA that is possible to reduce the intersymbol interference due to distortion occurred at the channel when transmit the nonconstant modulus 16-QAM signal. The SE-MMA emerged for the simplifying the computational operation from the current MMA adaptation algorithm, then it's has the fast convergence speed and has a problem of increase the residual component in the steady state. The SU-SE-MMA performs the selectively tap updating when the distance of equalizer output and specified transmit signal point is greater than the given threshold value and tap updaing does not occurred in the small distance. By this selective updating process, it is possible to more reduction in the computational operation in the propose algorithm. The improved adaptive equalization performance of SU-SE-MMA like as the equalizer output signal constellation, residual isi, MD, SER were confirmed by computer simulation compared to SE-MMA. As a result of simulation, the AV-SE-MMA has better performance in output signal constellation, residual isi and MD compared to the SE-MMA, but it was confirmed that the AV-SE-MMA has similar in the SER performance that means the robustness to the noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1203-1208
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• 2016

For young children who are not spontaneous or not accurate in verbal communication of their emotions and experiences, drawing is a good means of expressing their status in mind and thus drawing analysis with chromatics is a traditional tool for art therapy. Recently, children enjoy digital drawing via painting tools thus there is a growing needs to develop an automatic digital drawing analysis tool based on chromatics and art therapy theory. In this paper, we propose such an analyzing tool based on dominant color analysis. Technically, we use ART2 clustering and fuzzy logic to understand the fuzziness of subjects' status of mind expressed in their digital drawings. The frequency of color usage is fuzzified with respect to the membership functions. After applying fuzzy logic to this fuzzified central vector, we determine the dominant color and supporting colors from the digital drawings and children's status of mind is then analyzed according to the color-personality relationships based on Alschuler and Hattwick's historical researches.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.591-600
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• 2016

Computing technologies are increasingly applied to most casual human environment networks, as computing technologies are further developed. In addition, the rapidly increasing interest in IoT has led to the wide acceptance of speech recognition as a means of HCI. In this study, we present a novel method for recognizing the Korean vowel 'ㅜ', as a part of a phoneme based Korean speech recognition system. The proposed method involves analyses of bulk indicators calculated in the time domain instead of analysis in the frequency domain, with consequent reduction in the computational cost. Four elementary algorithms for detecting typical waveform patterns of 'ㅜ' using bulk indicators are presented and combined to make final decisions. The experimental results show that the proposed method can achieve 90.1% recognition accuracy, and recognition speed of 0.68 msec per syllable.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.583-590
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• 2016

Recently, the rapidly increasing interest in IoT in almost all areas of casual human life has led to wide acceptance of speech recognition as a means of HCI. Simultaneously, the demand for speech recognition systems for mobile environments is increasing rapidly. The server-based speech recognition systems are typically fast and show high recognition rates; however, an internet connection is necessary, and complicated server computation is required since a voice is recognized by units of words that are stored in server databases. In this paper, we present a novel method for recognizing the Korean vowel 'ㅗ', as a part of a phoneme based Korean speech recognition system. The proposed method involves analyses of waveform patterns in the time domain instead of the frequency domain, with consequent reduction in computational cost. Elementary algorithms for detecting typical waveform patterns of 'ㅗ' are presented and combined to make final decisions. The experimental results show that the proposed method can achieve 89.9% recognition accuracy.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.417-422
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• 2015

A service size of the IoT environment is determined by the number of sensors. The number of sensors increase means increases the amount of data generated by the IoT environment. There are studies to reliably operate a network for research and operational dynamic buffer for data when network congestion control congestion in the network environment. There are also studies of the stream data that has been processed in the connectionless network environment. In this study, we propose a sensor gateway for processing big data of the IoT environment. For this, review the RESTful for designing a sensor middleware, and apply the double-buffer algorithm to process the stream data efficiently. Finally, it generates a big data traffic using the MJpeg stream that is based on the HTTP protocol over TCP to evaluate the proposed system, with open source media player VLC using the image received and compare the throughput performance.

• Journal of Advanced Navigation Technology
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• v.20 no.1
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• pp.45-52
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• 2016

In this paper, the method of interference analysis and its simulation have been suggested for the frequency sharing between aeronautical radio navigation systems and Link-16 platforms. In order to get the criteria for interoperability, the algorithm of interference analysis and protection ratio are derived to assure frequency sharing. Also the receiving power of wireless system has been illustrated with the help of radio propagation model of ITU-R Rec. P.1546 in VHF-UHF band. Finally the required receiving power or separation distance between DME/TACAN beacons and Link-16 ground station terminals has been considered based on system link budget in terms of evaluating interoperability as well as actual applications. As a result, if the suggested interference analysis and test set-up are applied to the field trial, it will lead to easy means to make a decision on interoperability over the existing incumbent systems.