• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.851-860
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• 2009

Recently, among the computational methods of protein-protein interaction prediction, vast amounts of domain based methods originated from domain-domain relation consideration have been developed. However, it is true that multi domains collaboration is avowedly ignored because of computational complexity. In this paper, we implemented a protein interaction prediction system based the Interaction Significance matrix, which quantified an influence of domain combination pair on a protein interaction. Unlike conventional domain combination methods, IS matrix contains weighted domain combinations and domain combination pair power, which mean possibilities of domain collaboration and being the main body on a protein interaction. About 63% of sensitivity and 94% of specificity were measured when we use interaction data from DIP, IntAct and Pfam-A as a domain database. In addition, prediction accuracy gradually increased by growth of learning set size, The prediction software and learning data are currently available on the web site.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1749-1756
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• 2014

Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.9
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• pp.1281-1286
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• 2010

Power plants have many components and equipment. It is difficult for operators to know the time of failure or the equipment that fails. Plants incur heavy economic losses due to unexpected failure. The equipment in power plants is constantly monitored by various sensors and instruments. However, prevention of failure is very difficult. Therefore, engineers are developing many types of failure-alarm systems that can detect the abnormal functioning of equipment. Such failure-alarm systems inform only about the abnormal functioning of equipment and do not indicate the cause of failure or the parts that have failed. In this study, we have developed a failure-prediction system that can provide details on the cause of trouble and the maintenance method.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.6
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• pp.779-785
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• 2011

Prediction of the transient pressure variations and performance parameters has been carried out for an SI engine using one of commercial software, GT-POWER. Various models were applied for the calculation of properties of the plenum chamber, exhaust manifold and catalytic convertor which are very important components included in the intake and exhaust systems.

• International Journal of Contents
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• v.7 no.1
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• pp.1-7
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• 2011

A new power control algorithm for wireless communication which can be applied to various near distance communications and USN/RFID systems is proposed. This technique has been applied and tested to lithium coin battery operated UHF/microwave transceiver systems to show extremely long communication life time without battery exchange. The power control algorithm is based on the dynamic prediction method of arrival time for incoming packet at the receiver. We obtain 16mA current consumption in the TX module and 20mA current consumption in the RX module. The advantage provided by this method compared to others is that both master transceiver and slave transceiver can be low power consumption system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.393-398
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• 2015

City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.43-51
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• 2008

In this paper, we are implemented low-power structure for Inter prediction, Intra prediction, Deblocking filter, Transform and Quantization blocks in H.264/AVC Encoder. The proposed Inter/Intra prediction blocks are shown 60.2% cell area reduction by adder reduction through Distributed Arithmetic, 44.3% add operation reduction using MUX for hardware share in Deblocking filter block. Furthermore we applied CSD and CSS process to reduce the cell area instead of multipliers that take a lot of area. The FPGA(Field Programmable Gate Array) and ARM Process based H.264/AVC encoder is implemented using proposed low power IPs. The proposed structure Platforms are implemented to interlock with FPGA and ARM processors. H.264/AVC Encoder implementation using Platforms shows that proposed low-power IPs can use H.264/AVC Encoder SoC effectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.723-726
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• 2009

This paper describes a non-causal interpolative prediction method for B-picture encoding. Interpolative prediction uses correlations between neighboring pixels, including non-causal pixels, for high prediction performance, in contrast to the conventional prediction, using only the causal pixels. For the interpolative prediction, the optimal quantizing scheme has been investigated for preventing conding error power from expanding in the decoding process. In this paper, we extend the optimal quantization sceme to inter-frame prediction in video coding. Unlike H.264 scheme, our method uses non-causal frames adjacent to the prediction frame.