• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.107-116
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• 2015

In compliance with digital device growth, the proliferation of high-tech computers, the availability of high quality and inexpensive video cameras, the demands for automated video analysis is increasing, especially in field of intelligent monitor system, video compression and robot vision. That is why object tracking of computer vision comes into the spotlight. Tracking is the process of locating a moving object over time using a camera. The consideration of object's scale, rotation and shape deformation is the most important thing in robust object tracking. In this paper, we propose a robust object tracking scheme using Random Forest. Specifically, an object detection scheme based on region covariance and ZNCC(zeros mean normalized cross correlation) is adopted for estimating accurate object location. Next, the detected region will be divided into five regions for random forest-based learning. The five regions are verified by random forest. The verified regions are put into the model pool. Finally, the input model is updated for the object location correction when the region does not contain the object. The experiments shows that the proposed method produces better accurate performance with respect to object location than the existing methods.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.411-418
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• 1992

The purpose of this study is to develop dynamic-stochastic models that can forecast the inflow into reservoir during low/drought periods and flood periods. For the formulation of the models, the discrete transfer function is utilized to construct the deterministic characteristics, and the ARIMA model is utilized to construct the stochastic characteristics of residuals. The stochastic variations and structures of time series on hydrological data are examined by employing the auto/cross covariance function and auto/cross correlation function. Also, general modeling processes and forecasting method are used the model building methods of Box and Jenkins. For the verifications and applications of the developed models, the Chungju multi-purpose reservoir which is located in the South Han river systems is selected. Input data required are the current and past reservoir inflow and Yungchun water levels. In order to transform the water level at Yungchon into streamflows, the water level-streamflows rating curves at low/drought periods and flood periods are estimated. The models are calibrated with the flood periods of 1988 and 1989 and hourly data for 1990 flood are analyzed. Also, for the low/drought periods, daily data of 1988 and 1989 are calibrated, and daily data for 1989 are analyzed.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.606-608
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• 1998

Digital watermarking is the technique which embeds the invisible signal into multimedia data such as audio, video, images, for copyright protection, including owner identification and copy control information. In this paper, a new watermark detection algorithm by local masking cross covariance between watermarked signal and pseudo noise signal is proposed. The proposed algorithm enhances the detection probability for embedding information. Since reducing detection errors for the weak embedding signals, the algorithm improves the image quality and robusts against illegal attack to delete the embedding information and data compression applications such as JPEG and MPEGs.

• Wind and Structures
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• v.30 no.6
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• pp.633-648
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• 2020

The objective of this work was to investigate the interference effects of two-parallel bridge decks on aerodynamic coefficients, vortex-induced vibration, flutter instability and flutter derivatives. The two bridges have significant difference in cross-sections, dynamic properties, and flutter speeds of each isolate bridge. The aerodynamic static tests and aeroelastic tests were performed in TU-AIT boundary layer wind tunnel in Thammasat University (Thailand) with sectional models in a 1:90 scale. Three configuration cases, including the new bridge stand-alone (case 1), the upstream new bridge and downstream existing bridge (case 2), and the downstream new bridge and the upstream existing bridge (case 3), were selected in this study. The covariance-driven stochastic subspace identification technique (SSI-COV) was applied to identify aerodynamic parameters (i.e., natural frequency, structural damping and state space matrix) of the decks. The results showed that, interference effects of two bridges decks on aerodynamic coefficients result in the slightly reduction of the drag coefficient of case 2 and 3 when compared with case 1. The two parallel configurations of the bridge result in vortex-induced vibrations (VIV) and significantly lower the flutter speed compared with the new bridge alone. The huge torsional motion from upstream new bridge (case 2) generated turbulent wakes flow and resulted in vertical aerodynamic damping H1^* of existing bridge becomes zero at wind speed of 72.01 m/s. In this case, the downstream existing bridge was subjected to galloping oscillation induced by the turbulent wake of upstream new bridge. The new bridge also results in significant reduction of the flutter speed of existing bridge from the 128.29 m/s flutter speed of the isolated existing bridge to the 75.35 m/s flutter speed of downstream existing bridge.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.833-838
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• 2012

TDOA which is one of the position estimation methods is used on indoor positioning, jammer localization, rescue of life, etc. due to high accuracy and simple structure. This paper proposes the super-resolution TDOA estimator using MPM(Matrix Pencil Method). The proposed estimator has more accuracy and is applicable to narrowband signal compared with the conventional cross-correlation. Furthermore, its complexity is low because obtained data directly is used for construction of matrix unlike the MUSIC(Multiple Signal Classification) which is one of the well-known super-resolution estimator using covariance matrix. To validate the performance of proposed estimator, errors of estimation and computational burden is compared to MUSIC through software simulation.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.313-342
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• 2014

A Nuclear Energy Agency (NEA), Organization for Economic Co-operation and Development (OECD) benchmark for Uncertainty Analysis in Modeling (UAM) is defined in order to facilitate the development and validation of available uncertainty analysis and sensitivity analysis methods for best-estimate Light water Reactor (LWR) design and safety calculations. The benchmark has been named the OECD/NEA UAM-LWR benchmark, and has been divided into three phases each of which focuses on a different portion of the uncertainty propagation in LWR multi-physics and multi-scale analysis. Several different reactor cases are modeled at various phases of a reactor calculation. This paper discusses Phase I, known as the "Neutronics Phase", which is devoted mostly to the propagation of nuclear data (cross-section) uncertainty throughout steady-state stand-alone neutronics core calculations. Three reactor systems (for which design, operation and measured data are available) are rigorously studied in this benchmark: Peach Bottom Unit 2 BWR, Three Mile Island Unit 1 PWR, and VVER-1000 Kozloduy-6/Kalinin-3. Additional measured data is analyzed such as the KRITZ LEU criticality experiments and the SNEAK-7A and 7B experiments of the Karlsruhe Fast Critical Facility. Analyzed results include the top five neutron-nuclide reactions, which contribute the most to the prediction uncertainty in keff, as well as the uncertainty in key parameters of neutronics analysis such as microscopic and macroscopic cross-sections, six-group decay constants, assembly discontinuity factors, and axial and radial core power distributions. Conclusions are drawn regarding where further studies should be done to reduce uncertainties in key nuclide reaction uncertainties (i.e.: $^{238}U$ radiative capture and inelastic scattering (n, n') as well as the average number of neutrons released per fission event of $^{239}Pu$).

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1239-1246
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• 2003

Registration is the process aligning the range data sets from different views in a common coordinate system. In order to achieve a complete 3D model, we need to refine the data sets after coarse registration. One of the most popular refinery techniques is the iterative closest point (ICP) algorithm, which starts with pre-estimated overlapping regions. This paper presents an improved ICP algorithm that can automatically register multiple 3D data sets from unknown viewpoints. The sensor projection that represents the mapping of the 3D data into its associated range image is used to determine the overlapping region of two range data sets. By combining ICP algorithm with the sensor projection constraint, we can make an automatic registration of multiple 3D sets without pre-procedures that are prone to errors and any mechanical positioning device or manual assistance. The experimental results showed better performance of the proposed method on a couple of 3D data sets than previous methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2763-2782
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• 2014

In this paper, a generalized likelihood ratio test (GLRT) is proposed for cyclostationary multi-antenna spectrum sensing in cognitive radio systems, which takes into account the cyclic autocorrelations obtained from all the receiver antennas and the cyclic cross-correlations obtained from all pairs of receiver antennas. The proposed GLRT employs a different hypotheses problem formulation and a different asymptotic covariance estimation method, which are proved to be more suitable for multi-antenna systems than those employed by the $Dandawat{\acute{e}}$-Giannakis algorithm. Moreover, we derive the asymptotic distributions of the proposed test statistics, and prove the constant false alarm rate property of the proposed algorithm. Extensive simulations are conducted, showing that the proposed GLRT can achieve better detection performance than the $Dandawat{\acute{e}}$-Giannakis algorithm and its extension for multi-antenna cases.

• Journal of Preventive Medicine and Public Health
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• v.47 no.1
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• pp.64-71
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• 2014

Objectives: Physical activity behavior begins to decline during adolescence and continues to decrease throughout young adulthood. This study aims to explain factors that influence physical activity behavior in a sample of female adolescents using a health promotion model framework. Methods: This cross-sectional survey was used to explore physical activity behavior among a sample of female adolescents. Participants completed measures of physical activity, perceived self-efficacy, self-esteem, social support, perceived barriers, and perceived affect. Interactions among the variables were examined using path analysis within a covariance modeling framework. Results: The final model accounted for an $R^2$ value of 0.52 for physical activity and offered a good model-data fit. The results indicated that physical activity was predicted by self-esteem (${\beta}$=0.46, p<0.001), perceived self-efficacy (${\beta}$=0.40, p<0.001), social support (${\beta}$=0.24, p<0.001), perceived barriers (${\beta}$=-0.19, p<0.001), and perceived affect (${\beta}$=0.17, p<0.001). Conclusions: The findings of this study showed that the health promotion model was useful to predict physical activity behavior among the Iranian female adolescents. Information related to the predictors of physical activity behavior will help researchers plan more tailored culturally relevant health promotion interventions for this population.