• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.117-125
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• 2015

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.51-58
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• 2018

Recently, various satellite sensors have been developed and it is becoming more convenient to acquire multitemporal satellite images. Therefore, various researches are being actively carried out in the field of utilizing change detection techniques such as disaster and land monitoring using multitemporal satellite images. In particular, researches related to the development of unsupervised change detection techniques capable of extracting rapidly change regions have been conducted. However, there is a disadvantage that false detection occurs due to a spectral difference such as a seasonal change. In order to overcome the disadvantages, this study aimed to reduce the false alarm detection due to seasonal effects using the direction vector generated by applying the $S^2CVA$ (Sequential Spectral Change Vector Analysis) technique, which is one of the unsupervised change detection methods. $S^2CVA$ technique was applied to RapidEye images of the same and different seasons. We analyzed whether the change direction vector of $S^2CVA$ can remove false positives due to seasonal effects. For the quantitative evaluation, the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) value were calculated for the change detection results and it was confirmed that the change detection performance was improved compared with the change detection method using only the change magnitude vector.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.61-69
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• 2012

Smart Grid is the next-generation intelligent power grid that maximizes energy efficiency with the convergence of IT technologies and the existing power grid. Smart Grid is created solution for standardization and interoperability. Smart Grid industry enables consumers to check power rates in real time for active power consumption. It also enables suppliers to measure their expected power generation load, which stabilizes the operation of the power system. Smart industy was ecolved actively cause Wireless communication is being considered for AMI system and wireless communication using ZigBee sensor has been applied in various industly. In this paper, we proposed efficient addressing scheme for improving the performance of the routing algorithm using ZigBee in Smart Grid environment. A distributed address allocation scheme used an existing algorithm has wasted address space. Therefore proposing x, y coordinate axes from divide address space of 16 bit to solve this problem. Each node was reduced not only bitwise but also multi hop using the coordinate axes while routing than Cskip algorithm. I compared the performance between the standard and the proposed mechanism through the numerical analysis. Simulation verify performance about decrease averaging multi hop count that compare proposing algorithm and another. The numerical analysis results show that proposed algorithm reduce multi hop than ZigBee distributed address assignment and another.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.4
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• pp.42-50
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• 2008

The real-time bio-signal monitoring system based on the ZigBee and SIP/RTP has proposed and implemented for telemedicine but that has some problems at the stabilities to transmit bio-signal from the sensors to the other sides. In this paper, we designed and implemented a real-time bio-signal monitoring system that is focused on the reliability and efficiency for transmitting bio-signal at real-time. We designed the system to have enhanced architecture and performance in the ubiquitous sensor network, SIP/RTP real-time transmission and management of the database. The Bluetooth network is combined with ZigBee network to distribute traffic of the ECG and the other bio-signal. The modified and multiplied RTP session is used to ensure real-time transmission of ECG, other bio-signals and speech information on the internet. The modified ECG compression method based on DWLT and MSVQ is used to reduce data rate for storing ECG to the database. Finally we implemented a system that has improved performance for transmitting bio-signal from the sensors to the monitoring console and database. This implemented system makes possible to make various applications to serve U-health care services.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.945-953
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• 2018

In this paper, we use 3-D LIDAR for obstacle detection and avoidance maneuver for autonomous unmanned operation. It is aimed to avoid obstacle avoidance in unmanned water under marine condition using only single sensor. 3D lidar uses Quanergy's M8 sensor to collect surrounding obstacle data and includes layer information and intensity information in obstacle information. The collected data is converted into a three-dimensional Cartesian coordinate system, which is then mapped to a two-dimensional coordinate system. The data including the obstacle information converted into the two-dimensional coordinate system includes noise data on the water surface. So, basically, the noise data generated regularly is defined by defining a hypothetical region of interest based on the assumption of unmanned water. The noise data generated thereafter are set to a threshold value in the histogram data calculated by the Vector Field Histogram, And the noise data is removed in proportion to the amount of noise. Using the removed data, the relative object was searched according to the unmanned averaging motion, and the density map of the data was made while keeping one cell on the virtual grid map. A polar histogram was generated for the generated obstacle map, and the avoidance direction was selected using the boundary value.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.185.1-185.1
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• 2010

원격탐사(remote sensing)란 관측 대상과의 접촉 없이 멀리서 정보를 얻어내는 기술을 말한다. 기상관측분야에는 이미 소다(SODAR) 장비가 폭넓게 사용되거 왔으나 최근 풍력자원평가(wind resource assessment)를 위한 풍황측정에 SODAR와 더불어 라이다(LIDAR)가 적극적으로 활용되기 시작하고 있다. 참고로 SODAR(SOnic Detection And Ranging)는 수직 및 동서 남북 방향으로 음파를 발생시키고 대기유동에 의해 산란 반사된 에코를 수신하여 진동수 변화와 반사에코 강도를 측정하여 각 방향의 에코자료를 벡터 합성함으로써 풍향 및 풍속을 산출하는 원리이다. 반면 LIDAR(Light Detection And Ranging)는 비교적 최근에 풍황측정 용도로 개발된 레이저 탐지에 바탕을 둔 원거리 센서로, 공기입자(먼지, 수증기, 구름, 안개, 오염물질 등)에 의해 산란된 레이저 발산의 도플러 쉬프트(Doppler shift)를 이용하여 풍향 및 풍속을 측정하는 원격탐사 장비이다. 풍력자원평가 측면에서 라이다는 그 정확도가 IEC61400-12에 의거한 풍황탑(met-mast) 측정자료 다수와의 비교검증 실측평가(Albers et al., 2009)를 통하여 입증된 바 있다. 한편 한국에너지기술연구원에서 운용 중인 라이다 시스템은 그림 1의 우측 그림과 같이 1초에 $360^{\circ}$를 스캔하여 50지점에서 반사되는 레이저를 스펙트럼으로 측정하되 설정된 관측높이에서 풍속은 샘플링 부피(sampling volume)의 평균값으로 정의된다. 그런데 샘플링 부피는 설정된 관측높이로부터 상하 12.5m, 총 25m의 높이구간에서 관측한 스펙트럼의 평균값을 그 중앙지점에서의 풍속으로 환산하는 알고리듬(algorithm)을 채택하고 있다. 따라서 비선형적으로 변화하는 풍속연직분포 관측 시 풍속환산 알고리듬에 의한 측정오차가 개입될 가능성이 존재하는 것이다. 이에 본 연구에서는 라이다에 의한 풍속연직분포 측정 시 샘플링 부피의 구간 평균화 과정에서 발생하는 불확도(uncertainty)를 정량적으로 분석함으로써 라이다에 의한 풍속연직분포 관측의 불확도를 정량평가하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.