• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.4
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• pp.25-33
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• 2014

As global warming becoming an environmentally serious issue, more attention is drawn to fuel consumption which is the direct source of green house gas emission. The fuel consumption by aircraft operation is not an exception. Motivated by the societal and environmental context, this paper explains a method for estimation of aircraft fuel consumed during their flights as well as the computational process using real flight track data. Applying so-called 'Total Energy Model' along with aircraft specific parameters provided in EUROCONTROL's Base of Aircraft Data (BADA) to aircraft radar track data, we estimate fuel consumption of individual aircraft flown between Gimpo and Jeju airports. We then assess the estimation accuracy by comparing the estimated fuel consumption with the actual one collected from an airline. The computational results are quite encouraging in that the method is able to estimate the actual fuel consumption within ${\pm}6{\sim}11%$ of error margin. The limitations and possible enhancements of the method are also discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3A
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• pp.163-171
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• 2012

The signal model and weighted-average based estimation techniques are proposed to estimate the angle-of-arrival (AOA) parameters of multiple clusters for a low data rate ultrawide band (LR-UWB) based wireless positioning system. It is observed that the weighted-average based AOA estimation technique gives an optimal AOA estimate under few clusters condition, and the average based AOA estimation technique gives a correct AOA estimate under many clusters condition through computer simulation. Also, we can observe that the variance estimation error decreases as SNR increases, and the proposed techniques are superior to the conventional technique from the viewpoint of performance.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.39-39
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• 2003

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1480-1490
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• 1997

A method by which the system parameter matrices can be estimated from measured time data of excitation force and acceleration has been studied. The acceleration data are integrated numerically to obtain the velocities and displacements, and the systm parameters are estimated from these data by solving equations of motion. The characteristics of the method have been investigated through its application to simulated data of 1 DOF and 2 DOF systems and experimental data measured from a simple structure. It was found that the method is very sensitive to measurement noise and the accuracy of the estimated parameters can be improved by averaging the repeatedly measured data and removing the noise. One of the main advantages of the parameter estimation method is that no a priori information about the system under test is required. The method can be easily extended to non-linear parameter estimation.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.427-435
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• 2023

Cardinality estimation is used in wide range of applications and a fundamental problem processing a large range of data. While the internet moves into the era of big data, the function addressing cardinality estimation use only on-chip cache memory. To use memory efficiently, there have been various methods proposed. However, because of the noises between estimator, which is data structure per flow, loss of accuracy occurs in these algorithms. In this paper, we focus on minimizing noises. We propose multiple data structure that each estimator has the number of estimated value as many as the number of structures and choose the minimum value, which is one with minimum noises, We discover that the proposed algorithm achieves better performance than the best existing work using the same tight memory, such as 1 bit per flow, through experiment.

• Smart Structures and Systems
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• v.32 no.4
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• pp.235-251
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• 2023

A method that can estimate global deformation and internal forces using a limited amount of displacement data and based on the shape superposition technique and a neural network has been recently developed. However, it is difficult to directly measure sufficient displacement data owing to the limitations of conventional displacement meters and the high cost of global navigation satellite systems (GNSS). Therefore, in this study, the previously developed estimation method was extended by combining displacement, slope, and strain to improve the estimation accuracy while reducing the need for high-cost GNSS. To validate the proposed model, the global deformation and internal forces of a cable-stayed bridge were estimated using limited multi-response data. The effect of multi-response data was analyzed, and the estimation performance of the extended method was verified by comparing its results with those of previous methods using a numerical model. The comparison results reveal that the extended method has better performance when estimating global responses than previous methods.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.485-490
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• 1999

Wind vector information over the ocean is currently obtained using multiple beam scatterometer data. The scatterometers on ERS-1/2 generate wind vector information with a spatial resolution of 50km and accuracies of $\pm$2m/s in wind speed and $\pm$20$^{\circ}$ in wind direction. Synthetic aperture radar (SAR) data over the ocean have the potential of providing wind vector information independent of weather conditions with finer resolution. Finer resolution wind vector information can often be useful particularly in coastal regions where the scatterometer wind information is often corrupted because of the lower resolution system characteristics which is often contaminated by the signal returns from the coastal areas or ice in the case of arctic environments. In this paper we tested CMOD_4 and CMOD_IFR2 algorithms for extracting the wind vector from SAR data. These algorithms require precise estimation of normalized radar cross-section and wind direction from the SAR data and the local incidence angle. The CMOD series algorithms were developed for the C-band, VV-Polarized SAR data, typically for the ERS SAR data. Since RADARSAT operates at the same C-band but with HH-Polarization, the CMOD series algorithms should not be used directly. As a preliminary approach of resolving with this problem, we applied the polarization ratio between the HH and VV polarizations in the wind vectors estimation. Two test areas, one in front of Inchon and several sites around Jeju island were selected and investigated for wind vector estimation. The new results were compared with the wind vectors obtained from CMOD algorithms. The wind vector results agree well with the observed wind speed data. However the estimation of wind direction agree with the observed wind direction only when the wind speed is greater than approximately 3.0m/s.