• The KIPS Transactions:PartD
• /
• v.15D no.1
• /
• pp.87-98
• /
• 2008

In company with the demand of powerful processing units for embedded systems, the method to develop embedded software is also required to support the demand in new approach. In order to improve the resource utilization and system performance, software modeling techniques have to consider the features of hardware architecture. This paper proposes a partitioning technique of UML-based software models, which focus the generation of the allocatable software components into multiprocessor architecture. Our partitioning technique, at first, transforms UML models to CBCFGs(Constraint-Based Control Flow Graphs), and then slices the CBCFGs with consideration of parallelism and data dependency. We believe that our proposition gives practical applicability in the areas of platform specific modeling and performance estimation in model-driven embedded software development.

• Journal of the Korea Computer Graphics Society
• /
• v.22 no.3
• /
• pp.21-29
• /
• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.6
• /
• pp.473-480
• /
• 2004

In this paper we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the‘data-driven’method to PCMM tot move reliable model combination and introduce a frame-synched version for estimation of environments posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance is examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

• Journal of the Korea Society for Simulation
• /
• v.20 no.3
• /
• pp.59-67
• /
• 2011

This paper proposes a model to analyze the fault factors of air-to-ground weapon delivery utilizing software fault localization methods. In the previous study, to figure out the factors to affect the accuracy of air-to-ground weapon delivery, the FBEL (Factor-based Error Localization) method had been proposed and the fault factors were analyzed based on the method. But in the study, the correlation between weapon delivery accuracy and the fault factors could not be revealed because the firing accuracy among several factors was fixed. In this paper we propose a more precise fault analysis model driven through a study of the correlation among the fault factors of weapon delivery, and a method to estimate the possibility of faults with the limited number of test cases utilizing the model. The effectiveness of proposed method is verified through the simulation utilizing real delivery data. and weapons delivery testing in the evaluation of which element affecting the accuracy of analysis that was available to be used successfully.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.607-617
• /
• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.9 no.6
• /
• pp.51-58
• /
• 2010

This paper suggests a control technique of the two axes precision stage. The stage is supported by four flexible spring hinges and driven by two piezoelectric actuators. The dynamic motion of the stage is analysed by the finite element method and identified by the frequency domain modeling technique based on the experimental data. The sliding mode control with integrator is applied to improve the tracking ability of the stage to the complex reference input signal. Experimental results demonstrate that the proposed modeling schemes and control algorithm can be used effectively for the two axes stage.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.18 no.3
• /
• pp.1-8
• /
• 2010

A design study has been performed to obtain configuration and weight of a compound gyroplane. A study of research trends and characteristics was performed to develop the compound gyroplane sizing program. Based on these results, the sizing program has been developed and its suitability has been validated using existing compound gyroplane data. The subject air vehicles was a Challis Heliplane UAV, Carter Coptet, FB-1 Gyrodyne, and Jet Gyrodyne. As results, the program was suitable to size a compound gyroplane at conceptual design phase, because the greatest error rate was less than 10% and the conceptual design allowance error rate is less than 15%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.27 no.8
• /
• pp.21-29
• /
• 2013

The functional behaviors of human standing postural control were investigated when they were exposed to long-term horizontal vibration in the sagittal plane. For complexity of human postural control, a useful alternative method that has been based on a black-box approach was taken; that is, where the feedback mechanism was lumped into a single element. A motor-driven support platform was designed as a source of vibration. The AC Servo-controlled motors produced continuous anterior/posterior (AP) motion. The data were analyzed both in the time and frequency domain. The cross-correlation and coherency functions were estimated. Subjects behaved as a non-rigid pendulum with a mass and a spring throughout the whole period of the platform motion, as consistent with the plan chosen for this study.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1812-1824
• /
• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.6
• /
• pp.542-553
• /
• 2020

Virtual world and simulation need large scale map rendering. However, rendering too many vertices is a computationally complex and time-consuming process. Some game development companies have developed 3D LOD objects for high-speed rendering based on distance between camera and 3D object. Terrain physics simulation researchers need a way to recognize the original object shape from 3D LOD objects. In this paper, we proposed simply automatic LOD framework using point cloud data (PCD). This PCD was created using a 6-direct orthographic ray. Various experiments are performed to validate the effectiveness of the proposed method. We hope the proposed automatic LOD generation framework can play an important role in game development and terrain physic simulation.