• Advances in Energy Research
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• v.1 no.2
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• pp.97-106
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• 2013

This study designs and tests a photovoltaic system with distributed maximum power point tracking (DMPPT) methodology using a field programmable gate array (FPGA) controller. Each solar panel in the distributed PV system is equipped with a newly designed DC/DC converter and the panel's voltage output is regulated by a FPGA controller using PI control. Power from each solar panel on the system is optimized by another controller where the quadratic maximization MPPT algorithm is used to ensure the panel's output power is always maximized. Experiments are carried out at atmospheric insolation with partial shading conditions using 4 amorphous silicon thin film solar panels of 2 different grades fabricated by Chi-Mei Energy. It is found that distributed MPPT requires only 100ms to find the maximum power point of the system. Compared with the traditional centralized PV (CPV) system, the distributed PV (DPV) system harvests more than 4% of solar energy in atmospheric weather condition, and 22% in average under 19% partial shading of one solar panel in the system. Test results for a 1.84 kW rated system composed by 8 poly-Si PV panels using another DC/DC converter design also confirm that the proposed system can be easily implemented into a larger PV power system. Additionally, the use of NI sbRIO-9642 FPGA-based controller is capable of controlling over 16 sets of PV modules, and a number of controllers can cooperate via the network if needed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.434-445
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• 2004

Motion Estimation is an essential module in video encoders based on international standards such as H.263 and MPEG. Many fast motion estimation algorithms have been proposed in order to reduce the computational complexity of a well-known full search algorithms(FS). However, these fast algorithms can not work efficiently in DSP processors recently developed for video processing. To solve for this. we propose an efficient motion estimation scheme optimized in the DSP processor like Philips TM1300. A motion vector predictor is pre-estimated and a small search range is chosen in the proposed scheme using strong motion vector correlation between a current macro block (MB) and its neighboring MB's to reduce computation time. An MPEG-4 SP@L3(Simple Profile at Level 3) encoding system is implemented in Philips TM1300 to verify the effectiveness of the proposed method. In that processor, we can achieve better performance using our method than other conventional ones while keeping visual quality as good as that of the FS.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.285-291
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• 2010

Flash memory has many good features such as small size, shock-resistance, and low power consumption, but the cost of flash memory is still high to substitute for hard disk entirely. Recently, some mobile devices, such as laptops, attempt to use both flash memory and hard disk together for taking advantages of merits of them. However, existing OSs (Operating Systems) are not optimized to use the heterogeneous storage media. This paper presents a new buffer cache management scheme. First, we allocate buffer cache space according to access patterns of block references and the characteristics of storage media. Second, we prefetch data blocks selectively according to the location of them and access patterns of them. Third, we moves destaged data from buffer cache to hard disk or flash memory considering the access patterns of block references. Trace-driven simulation shows that the proposed schemes enhance the buffer cache hit ratio by up to 29.9% and reduce the total I/O elapsed time by up to 49.5%.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.673-682
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• 2005

Distance browsing queries, namely k-nearest neighbor queries, are the most important queries in spatial database applications, e.g., Geographic Information Systems(GISs). Recently, GIS applications trends to extend themselves toward wide multi-user environments such as the Web. Since many techniques for such queries, where Hjaltason and Samet's algorithm is the most efficient one, were optimized for only one query, we need to complement them suitable for multi-user environments. It can be a good approach that we store many individual query results in a cache, i.e., query result caching and reuse them in evaluating incoming queries, j.e., query result matching. In this paper, we propose a complementary Hjaltason and Samet's algerian capable of reusing previous query results in a cache for answering distance browsing queries in multi-user GIS environments. Our experimental results conform the efficiency of our approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.165-182
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• 2014

An RFID systems employ efficient Anti-Collision Algorithms (ACAs) to enhance the performance in various applications. The EPC-Global G2 RFID system utilizes Frame Slotted Aloha (FSA) as its ACA. One of the common approaches used to maximize the system performance (tag identification efficiency) of FSA-based RFID systems involves finding the optimal value of the frame length relative to the contending population size of the RFID tags. Several analytical models for finding the optimal frame length have been developed; however, they are not perfectly optimized because they lack precise characterization for the timing details of the underlying ACA. In this paper, we investigate this promising direction by precisely characterizing the timing details of the EPC-Global G2 protocol and use it to derive a precise-optimal frame length model. The main objective of the model is to determine the optimal frame length value for the estimated number of tags that maximizes the performance of an RFID system. However, because precise estimation of the contending tags is difficult, we utilize a parametric-heuristic approach to maximize the system performance and propose two simple schemes based on the obtained optimal frame length-namely, Improved Dynamic-Frame Slotted Aloha (ID-FSA) and Exponential Random Partitioning-Frame Slotted Aloha (ERP-FSA). The ID-FSA scheme is based on the tag set estimation and frame size update mechanisms, whereas the ERP-FSA scheme adjusts the contending tag population in such a way that the applied frame size becomes optimal. The results of simulations conducted indicate that the ID-FSA scheme performs better than several well-known schemes in various conditions, while the ERP-FSA scheme performs well when the frame size is small.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.40-48
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• 2015

An optical network-on-chip(ONoC) architecture is emerging as a new paradigm for solving on-chip communication bottleneck. Recent studies on ONoC have been focusing on supporting the parallel transmission and avoiding path collisions using wavelength division multiplexing(WDM). However, since the maximum number of wavelengths, which a single waveguide can accommodate is limited by crosstalk and insertion loss. Therefore previous WDM studies based on incrementing the number of different wavelengths according to the number of nodes would be infeasible due to the implementation complexity. To solve such problems, we combined time division multiplexing(TDM) and wavelength-routed ONoC, along with an optimized channel allocation algorithm, which can minimize the number of extra wavelength channels and latency caused by combining TDM scheme.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.193-203
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• 2009

Snow cover is one of the important parameters since it determines surface energy balance and its variation. To classify snow and cloud from satellite data is very important process when inferring land surface information. Generally, misclassified cloud and snow pixel can lead directly to error factor for retrieval of surface products from satellite data. Therefore, in this study, we perform algorithm for detecting snow cover area with remote sensing data. We just utilize visible reflectance, and infrared channels rather than using NDSI (Normalized Difference Snow Index) which is one of optimized methods to detect snow cover. Because COMS MI (Meteorological Imager) channels doesn't include near infra-red, which is used to produce NDSI. Detecting snow cover with visible channel is well performed over clear sky area, but it is difficult to discriminate snow cover from mixed cloudy pixels. To improve those detecting abilities, brightness temperature difference (BTD) between 11 and 3.7 is used for snow detection. BTD method shows improved results than using only visible channel.

• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.89-98
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• 2011

This study examined the energy performance according to the main design parameters of a solar water heating system for an office building using the life cycle cost (LCC) optimization simulations. The LCC optimization simulations of the system were conducted with TRNSYS and GenOpt employing the Hooke-Jeeves algorithm for cases where water temperature was $60^{\circ}C$ and $50^{\circ}C$. The results showed that for water temperature at $60^{\circ}C$ and $50^{\circ}C$ the global radiation incident on the collector could be decreased by 16.98% and 28.52%, collector useful energy gain could be decreased by 15.04% and 22.59%, energy to load from storage tank could be decreased by 10.86% and 18.06% and AH energy to load could be increased by 16.86% and 38.50% respectively compared to a non-optimized system. The annual average collection efficiency of the collector was increased by 0.88% for $60^{\circ}C$ and 2.78% for $50^{\circ}C$ because of increase of collector slope and decrease of the mass flow rate per collector area. The annual average efficiency of the system was increased by 1.74% and 3.47% compared to the basis system. However, the annual solar fraction of the system was decreased by 6.68% for $60^{\circ}C$ and 11.26% for $50^{\circ}C$ due to decrease of collector area and storage tank volume.