• Journal of Digital Contents Society
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• v.19 no.4
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• pp.821-827
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• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.57-67
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• 1998

In order to extract the contour of interesting object in the image, Kass suggested the Active Contour Model called "Snakes" The speed of this model is slow and this model is sensitive of initialization. In order to improve these problems, Gunn extracted the accurate contour by using two initializations, and operated to less sensitive of initialization. This method could extract more accurate contour than the existing method, but it had no effect in the speed and it was sensitive of noise. This paper applied to the Energy Minimization Algorithm about only the pixel within the window applying the window of $8{\times}8$ size at each contour point consisting Snakes in order to solve these problems. In order to less sensitive of noise which exists within image, it suggests a method that moves the window to vertical direction for the gradient of each contour point.our point.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.962-968
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• 2016

The large scaled lead-acid battery is widely used for efficient operation of the photovoltaic system in many islands. However, lithium-ion battery is now being introduced to mitigate the fluctuation of wind power and to replace lead-acid battery. Therefore, hybrid ESS(Energy Storage system) that combines lithium-ion battery with lead-acid battery is being required because lithium-ion battery is costly in present stage. Under this circumstance, this paper presents the optimal algorithm to create composition rate of hybrid ESS by considering fixed and variable costs in order to maximize advantage of each battery. With minimization of total cost including fixed and variable costs, the optimal composition rate can be calculated based on the various scenarios such as load variation, life cycle and cost trend. From simulation results, it is confirmed that the proposed algorithms are an effective tool to produce a optimal composition rate.

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

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.12
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• pp.1537-1542
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• 2011

This paper presents the power management strategies for a compound hybrid excavator. The compound hybrid excavator has been replaced the hydraulic swing motor to the electric swing motor. This excavator requires a proper control algorithm to regulate the energy flow between the mechanical coupling and the electric devices. The controller should improve fuel economy and maintain the super capacitor voltage within a proper range. A thermostat controller and ECMS controller are designed such that these objectives can be achieved. The thermostat controller regulates the power of the engine-assist motor on the basis of the super capacitor voltage, and the ECMS controller determines it using the real-time fuel minimization strategy based on the concept of equivalent fuel. Simulation results showed that by using the hybrid excavator, the fuel economy becomes about 20% higher than that obtained using the conventional excavator and that the ECMS controller outperforms the thermostat controller.