• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1C
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• pp.69-77
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• 2002

Sine digital signal processing for motion pictures requires huge amount of data computation to store, manipulate and transmit, more effective data compression is necessary. Therefore, the ITU-T recommended H.26x as data compression standards for digital motion pictures. The data compression method that eliminates time redundancies by motion estimation using relationship between picture frames has been widely used. Most video conding systems employ block matching algorithm for the motion estimation and compensation, and the algorithm is based on the minimun value of cast functions. Therefore, fast search algorithm rather than full search algorithm is more effective in real time low data rates encodings such as H.26x. In this paper, motion estimation employing the Nearest-Neighbors algorithm is designed to reduce search time using FPGA, coded in VHDL, and simulated and verified using Xilink Foundation.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.835-842
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• 2018

The so-called sliced average variance estimation (SAVE) is a popular methodology in sufficient dimension reduction literature. SAVE is sensitive to the number of slices in practice. To overcome this, a fused SAVE (FSAVE) is recently proposed by combining the kernel matrices obtained from various numbers of slices. In the paper, we consider practical applications of FSAVE to large p-small n data. For this, near-infrared spectroscopy of biscuit dough data is analyzed. In this case study, the usefulness of FSAVE in high-dimensional data analysis is confirmed by showing that the result by FASVE is superior to existing analysis results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.2
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• pp.87-92
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• 2012

Voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in a power system. The PMU technique can not easily get the field data and it is impossible to share information, so that there has been used a FNET(Frequency Monitoring Network) method for the wide-area intelligent protection in USA. It consists of FDR(Fault Disturbance Recorder) and IMS(Information Management System). Therefore, FDR must provide an optimal frequency estimation method that is robust to noise and failure. In this paper, we present comparative studies for the frequency estimation method using IRDWT(Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and DFT(Discrete Fourier Transform). The Republic of Korea345[kV] power system modeling data by EMTP-RV are used to evaluate the performance of the proposed two kinds of RDWT(Recursive Discrete Wavelet Transform) and DFT. The simulation results show that the proposed frequency estimation technique using FRDWT could be the optimal frequency measurement method, and thus be applied to FDR.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.159-165
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• 2008

For deciding the profitability and feasibility of the construction project, the schematic estimation has to not only link the design decision-making but also estimate the cost with reliability. The Object-based schematic estimation system was developed for easily linking with design-making and supports to evaluate the design alternatives in the design development stage but didn't consider the cost estimated by object supplementary and parameter work item. This research presents the Integrated Object-Parameter Schematic Estimation Model in the design development stage that can lead to more accurately estimate the cost through analyzing historical data from the high-storied office buildings. For the development of the proposed model for schematic estimation, after analyzing and classifying the work items from the Bills of Quantities(BOQs) and drawings of historical data, this research proposed the methods of estimating cost in accordance with attributes of each work item using regression analysis. In addition, a case study is performed for the effectiveness as comparing the proposed model with the previous estimating model.

• Journal of Sensor Science and Technology
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• v.26 no.6
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• pp.371-378
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• 2017

An accurate and robust estimation of center of rotation (CoR) using optical marker trajectory is crucial in human biomechanics. In this regard, the performances of the two prevailing least-squares methods, the Gamage and Lasenby (GL) method, and the Chang and Pollard (CP) method, are verified in this paper. While both methods are sphere-fitting approaches in closed form and require no tuning parameters, they have not been thoroughly verified by comparison of their estimation accuracies. Furthermore, while for both methods, results for stationary CoR locations are presented, cases for perturbed CoR locations have not been investigated for any of them. In this paper, the estimation performances of the GL method and CP method are investigated by varying the range of motion (RoM) and noise amount, for both stationary and perturbed CoR locations. The difference in the estimation performance according to the variation in the amount of noise and RoM was clearly shown for both methods. However, the CP method outperformed the GL method, as seen in results from both the simulated and the experimental data. Particularly, when the RoM is small, the GL method failed to estimate the appropriate CoR while the CP method reasonably maintained the accuracy. In addition, the CP method showed a considerably better predictability in CoR estimation for the perturbed CoR location data than the GL method. Accordingly, it may be concluded that the CP method is more suitable than the GL method for CoR estimation when RoM is limited and CoR location is perturbed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.467-474
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• 2011

The performance of pilot-symbol-assisted channel estimation widely used for OFDM systems is degraded due to the small number of pilot symbols used for higher transmission efficiency. In this paper, we propose a pilot symbol based channel estimation using cross-correlation to improve the estimation performance of the OFDM system with small number of pilot symbols. The proposed technique detects a data symbol using the channel estimated by the pilot symbol and estimates the channel using the estimated data symbol and the pilot symbol. It is shown by computer simulations that the proposed technique outperforms the conventional pilot symbol assisted estimation technique.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.183-183
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• 2020

Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1810-1817
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• 2000

The purpose of this research is to develop an estimation model for information systems operating costs. Current cost estimation practices and types of sytem management projects have been reviewed an analyses. Typical operating project types of information systems are determined. They are application system operation, help disk operation, network management and operation, and hardware management. For each type of projects, cost factors ar identified and a structure of cost estimation model is defined. Cost estimation models have been constructed and tested by 24 real operation projects data. Statistical analysis shows derived models are statistically significant. User groups' opinion on these draft cost estimation model has been surveyed and summarized. The results of this research can be used as a cornerstone for future research on operating cost estimation, and for cost estimation guideline of information systems operation projects.