• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.376-385
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• 2023

Fishing boat capsizing accidents account for more than half of all capsize accidents. These can occur for a variety of reasons, including inexperienced operation, bad weather, and poor maintenance. Due to the size and influence of the industry, technological complexity, and regional diversity, fishing ships are relatively under-researched compared to commercial ships. This study aimed to predict the rolling motion time series of fishing boats using an image-based deep learning model. Image-based deep learning can achieve high performance by learning various patterns in a time series. Three image-based deep learning models were used for this purpose: Xception, ResNet50, and CRNN. Xception and ResNet50 are composed of 177 and 184 layers, respectively, while CRNN is composed of 22 relatively thin layers. The experimental results showed that the Xception deep learning model recorded the lowest Symmetric mean absolute percentage error(sMAPE) of 0.04291 and Root Mean Squared Error(RMSE) of 0.0198. ResNet50 and CRNN recorded an RMSE of 0.0217 and 0.022, respectively. This confirms that the models with relatively deeper layers had higher accuracy.

• Korean Association of Arts Management
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• no.49
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• pp.5-35
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• 2019

The main purpose of this study was to use semantic network analysis to examine the international trend of arts management and other studies pertinent to this field. The subject was based on 357 keywords listed on the abstract of 185 research papers in the International Journal of Arts Management. To examine the most current trends of arts management based studies the time frame was restricted from 2008 to 2017. To briefly summarize the result, first, 'museum' was the most frequently appeared keyword. This was followed by 'performing arts' and 'arts' with more than 20 appearances. 'Motion picture industry' and 'theater' were the next frequently appeared keywords. 'Customer behavior' and 'market strategy', keywords related to management, were also included in the high ranked group along with art related keywords. Second, yearly research trend shows that arts management has been regularly studied for past ten years with average of 19 research papers with about 53 keywords. Keywords such as 'museum' and 'performing arts' has been regularly studied for past ten years. 'Culture', 'theater' and 'motion pictures industry' does not regularly appear in the result of yearly research trend but nevertheless they have sparsely made an appearance along the past decade. 'Art gallery' has not been cited till 2011 but from 2012 it was regularly and continuously made an appearance in the yearly research trend. Overall, the yearly trend result shows that the trend of international arts management studies within IJAM, was at first centered on fine arts but as the time passed there has been diversified keywords related to management. Third, 'performing art' and 'art' has the highest link frequency(34). Fourth, density result was 0.039 which shows that the keyword density is not very high. Fifth, 'art', 'performing art', 'museum', 'theater' and 'brand' were positioned in the middle when looking at the visualized version of centrality result. This means that these five keywords has the highest centrality among other keywords.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.51-59
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• 2012

This paper suggests an efficient method which tracks articulated human body modeled with markov network using disparity map derived from stereo images. The conventional methods which only use color information to calculate likelihood for energy function tend to fail when background has same colors with objects or appearances of object are changed during the movement. In this paper, we present a method evaluating likelihood with both disparity information and color information to find human body parts. Since the human body part are cylinder projected to rectangles in 2D image plane, we use the properties of distribution of disparity of those rectangles that do not have discontinuous distribution. In addition to that we suggest a conditional-messages-update that is able to reduce unnecessary message update of belief propagation. Since the message update has comprised over 80% of the whole computation in belief propagation, the conditional-message-update yields 9~45% of improvements of computational time. Furthermore, we also propose an another speed up method called three dimensional dynamic models assumed the body motion is continuous. The experiment results show that the proposed method reduces the computational time as well as it increases tracking accuracy.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.467-474
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• 2010

Recently, Study of functional clothing for Vital sensing is focused on reducing artifact by human motions, in order to enhance the electrocardiogram(ECG) sensing accuracy. In this study, considering the factors for each element found from the analysis, a 3-lead electrode inside textile embroidered with silver yarn was developed, and draft designs off our types of vital-signal sensing garments, which are 'chest-belt typed' garment, 'cross-typed' garment 'x-typed' garment and 'curved x-typed' garment, were prepared. The draft designs were implemented on a sleeveless male shirt made of an elastic material so that the garment and the electrodes can remain closely attached along the contour of the human body, and the acquired data was sent to the main computer over a wireless network. In order to evaluate the effects caused by body movements and the ECG-sensing capability for each type in static and dynamic states, displacements were measured from one and two dimensional perspectives. ECG measurement evaluation was also performed for Signal-to-noise ratio(SNR) analysis. Applying the experimental results, the draft garment designs were modified and complemented to produce two types of modular approaches 'continuous-attached' and 'insertion-detached' for the ECG-sensing smart clothing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.678-681
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• 2003

In this paper, we aims to develop a microcontroll er-based portable pulse oximeter using Compact Flash Interface. First, portable pulse oxineter system is designed to record 2 channel of biosignals simultaneously, including 1 channel of SpO$_2$ and 1 channel of pulse rate. It is very small and portable. Besides, the system makes it possible to measure a patients condition without an additional medical equipment. We tried to solve the problems generated by a patient's motion. That is, we added an analog circuit to a traditional pulse oximeter in order to eliminate the change of the base line. And we used 2D sector algorithm. As present, SpO$_2$ modules are completed. But there are still many further development needed in order to enhance the function. Especially, compact flash interface remains the most to complete. Second, ECG monitoring system uses almost same as present 3-lead ECG system. But we focus on the analog part, especially in filter. The proposed filter is composed of two parts. One is a filter to remove the power-line interface. The other is a filter to remove the baseline drift. A filter to remove the power-line and the baseline drift is necessarily used in the ECG system. The implemented filter have three features; minimizing the distortion in DC component, removing the harmonic component of power-line frequency. Using compact flash interface, we can easily transfer a patient's personal information and the measured signal data to a network based server environment. That means, it is possible to implement a patient's monitoring system with low cost.

• Earthquakes and Structures
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• v.2 no.3
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• pp.207-232
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• 2011

The development of reliable earthquake mitigation plans and seismic risk management procedures can only be based on the establishment of comprehensive earthquake hazard and loss scenarios. Two cities, Grevena (Greece) and D$\ddot{u}$zce (Turkey), were used as case studies in order to apply a comprehensive methodology for the vulnerability and loss assessment of lifelines. The methodology has the following distinctive phases: detailed inventory, identification of the typology of each component and system, evaluation of the probabilistic seismic hazard, geotechnical zonation, ground response analysis and estimation of the spatial distribution of seismic motion for different seismic scenarios, vulnerability analysis of the exposed elements at risk. Estimating adequate earthquake scenarios for different mean return periods, and selecting appropriate vulnerability functions, expected damages of the water and waste water systems in D$\ddot{u}$zce and of the roadway network and waste water system of Grevena are estimated and discussed; comparisons with observed earthquake damages are also made in the case of D$\ddot{u}$zce, proving the reliability and the efficiency of the proposed methodology. The results of the present study constitute a sound basis for the development of efficient loss scenarios for lifelines and infrastructure facilities in seismic prone areas. The first part of this paper, concerning the estimation of the seismic ground motions, has been utilized in the companion paper by Kappos et al. (2010) in the same journal.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.271-277
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• 2020

There are increasing cases of monitoring the structural response of structures using multiple sensors. However, owing to cost and management problems, limited sensors are installed in the structure. Thus, few structural responses are collected, which hinders analyzing the behavior of the structure. Therefore, a technique to predict responses at a location where sensors are not installed to a reliable level using limited sensors is necessary. In this study, a numerical study is conducted to predict the seismic response of low-rise buildings using limited information. It is assumed that the available response information is only the acceleration responses of the first and top floors. Using both information, the first natural frequency of the structure can be obtained. The acceleration information on the first floor is used as the ground motion information. To minimize the error on the acceleration history response of the top floor and the first natural frequency error of the target structure, the method for predicting the mass and stiffness information of a structure using the genetic algorithm is presented. However, the constraints are not considered. To determine the range of design variables that mean the search space, the parameter prediction method based on artificial neural networks is proposed. To verify the proposed method, a five-story structure is used as an example.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.736-745
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• 2002

In network delivery of compressed video, packets may be lost if the channel is unreliable like Internet. Such losses tend to of cur in burst like continuous bit-stream error. In this paper, we propose an effective error-concealment approach to which an error resilient video encoding approach is applied against burst errors and which reduces a complexity of error concealment at the decoder using data hiding. To improve the performance of error concealment, a temporal and spatial error resilient video encoding approach at encoder is developed to be robust against burst errors. For spatial area of error concealment, block shuffling scheme is introduced to isolate erroneous blocks caused by packet losses. For temporal area of error concealment, we embed parity bits in content data for motion vectors between intra frames or continuous inter frames and recovery loss packet with it at decoder after transmission While error concealment is performed on error blocks of video data at decoder, it is computationally costly to interpolate error video block using neighboring information. So, in this paper, a set of feature are extracted at the encoder and embedded imperceptibly into the original media. If some part of the media data is damaged during transmission, the embedded features can be extracted and used for recovery of lost data with bi-direction interpolation. The use of data hiding leads to reduced complexity at the decoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.79-88
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• 2004

Gaze detection is to locate the position on a monitor screen where a user is looking by computer vision. Gaze detection systems have numerous fields of application. They are applicable to the man-machine interface for helping the handicapped to use computers and the view control in three dimensional simulation programs. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.