• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.694-699
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• 2009

Combined cycle plants which feature distinct advantages for power generation such as fast response, high efficiency, environmental friendliness, fuel flexiblity represent the majority of new generating plant installations across the globe. Combined cycle plants have different operating modes where the operating parameters can differ greatly depending which mode is operating at the time. This paper addresses the bidding strategy model of combined cycle plants in a competitive electricity market by using a characteristic of multiple operating modes of combined cycle plants. Simulation results of case studies show that an operating mode among multiple ones is selected strategically in generation bidding for more profit of generation company.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.103-105
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• 1992

The new approach using fuzzy linear programming with fuzzy multi-criterion is proposed for the best generation mix of a power system. A chracteristic feature of the presented approach is that not only cost but also reliability for goal function can be taken into account by using fuzzy multi-criterion and so more realistic solution can be obtained. The effectiveness of the proposed approach is demonstrated by the best generation mix problem of KEPCO-system size model which contains nuclear, coal, LNG, oil and pump-generator hydro plant in multi-years.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.2
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• pp.202-207
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• 2015

A CMOS relaxation oscillator, with high robustness over process, voltage and temperature (PVT) variations, is designed in $0.18{\mu}m$ CMOS. The proposed oscillator, consisting of full-differential charge-discharge timing circuit and switched-capacitor based voltage-to-current conversion, could be expanded to a simple open-loop frequency synthesizer (FS) with output frequency digitally tuned. Experimental results show that the proposed oscillator conducts subcarrier generation for frequency-modulated ultra-wideband (FM-UWB) transmitters with triangular amplitude distortion less than 1%, and achieves frequency deviation less than 8% under PVT and phase noise of -112 dBc/Hz at 1 MHz offset frequency. Under oscillation frequency of 10.5 MHz, the presented design has the relative FS error less than 2% for subcarrier generation and the power dissipation of 0.6 mW from a 1.8 V supply.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.51-56
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• 2010

As the price of image acquisition component becomes low enough, the compact and easily accessible handheld devices are generally equipped with image acquisition functionality. This trend speeds up various applications in diverse areas such as image related services and software. Therefore users strongly need to identify their images effectively and efficiently so that the duplicated images are perceived as one physical entity. In order to handle this environment, we propose a number of methods that generate image identifiers utilizing fundamental image features. In this paper, we analyze the identifier generation methods in terms of various size patterns, especially for tiny size cases, since the small images does not contain abundant pixels for feature extraction. In this paper, experimental evaluation over identifier generation methods' behavior according to different sizes is demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1464-1485
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• 2021

Emotional style of multimedia art works are abstract content information. This study aims to explore emotional style transfer method and find the possible way of matching music with appropriate images in respect to emotional style. DCNNs (Deep Convolutional Neural Networks) can capture style and provide emotional style transfer iterative solution for affective image generation. Here, we learn the image emotion features via DCNNs and map the affective style on the other images. We set image emotion feature as the style target in this style transfer problem, and held experiments to handle affective image generation of eight emotion categories, including dignified, dreaming, sad, vigorous, soothing, exciting, joyous, and graceful. A user study was conducted to test the synesthesia emotional image style transfer result with ground truth user perception triggered by the music-image pairs' stimuli. The transferred affective image result for music-image emotional synesthesia perception was proved effective according to user study result.

• International Commerce and Information Review
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• v.12 no.3
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• pp.55-78
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• 2010

This study analyses recent development of digital contents industry. The purpose of this study is to show how the convergence phenomenon is occurring in the digital contents industry. Furthermore, this study examines the influence of digital convergence on the digital contents industry. The characteristics of the digital contents industry falls roughly into three features. To begin with, technical aspect of the industrial feature is that digitalized contents can be used in various digital devices, namely OSMU(One Source Multi Use). The second feature is related to protection of copyright against illegal file sharing and downloading. One final point is that platform for distribution channels has been universal by digital convergence. To sum up, the notable feature of digital contents industry is high value-added. Also, digital contents industry is composed of users, digital device, network, and universal contents. Users are the key component of digital contents industry, who is distinguished from consumers. Digital devices such as mobile phone, PDA can play all kinds of digital contents and make users communicate in two-ways. Portable devices also allow the users to consume digital contents at any place. Digital contents can be distributed by both wire and wireless networks. And most of transactions can be made through networks. There are three key issues about digital convergence. Entry barriers for market become lowered; the age of contents users is changed from old generation to young generation. And the form of contents devices is changing rapidly. Traditional contents field such as movie, music, broadcasting, publishing, animations are combined into one digital contents territory. As a result, this paper suggests that digital convergence phenomenon will be accelerating for the future. According to the result of this study, the advent of digital convergence and e-Commerce will have significant influence on trade of digital contents.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.316-328
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• 2014

In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and its application to fast panoramic image generation on the latest embedded GPU. Parallelized algorithms are implemented using recently developed OpenCL as the embedded GPGPU software platform. We compare the implementation efficiency and speed performance of conventional OpenGL Shading Language and OpenCL. Experimental result shows that implementation on OpenCL has comparable performance with GLSL. Compared with the performance on the embedded CPU in the same application processor, the embedded GPU runs 3~4 times faster. As an example of using feature extraction, panorama image synthesis is performed on embedded GPU by applying image matching using detected features.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.267-274
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• 2020

Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.789-799
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• 2008

For the communication between human and computer in an interactive computing environment, the gesture recognition has been studied vigorously. The algorithms which use the 2D features for the feature extraction and the feature comparison are faster, but there are some environmental limitations for the accurate recognition. The algorithms which use the 2.5D features provide higher accuracy than 2D features, but these are influenced by rotation of objects. And the algorithms which use the 3D features are slow for the recognition, because these algorithms need the 3d object reconstruction as the preprocessing for the feature extraction. In this paper, we propose a method to extract the 3D features combined with the 3D object reconstruction in real-time. This method generates three kinds of 3D projection maps using the modified GPU-based visual hull generation algorithm. This process only executes data generation parts only for the gesture recognition and calculates the Hu-moment which is corresponding to each projection map. In the section of experimental results, we compare the computational time of the proposed method with the previous methods. And the result shows that the proposed method can apply to real time gesture recognition environment.