• 정보처리학회논문지D
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• 제10D권3호
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• pp.501-512
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• 2003

본 논문은 다차원 타임 워핑 거리 함수를 이용하여 유사한 이미지 서브시퀀스를 신속하게 검색할 수 있는 색인 방법을 제안한다. 타임 워핑 거리는 시퀀스들의 길이가 다르거나 샘플링 비율이 다른 많은 응용에서 Lp 거리보다 더욱 적합하다. 우리가 제안한 색인 방법은 디스크 기반의 접미어 트리를 색인 구조체로 채택하고, 유사하지 않은 서브시퀀스를 잘못된 누락 없이 잘 여과하기 위해 하한 거리 함수를 사용한다. 이 방법은 특정 차원의 상대적 가중치를 손쉽게 부여하기 위해 정규화를 적용하고 색인 트리를 압축하기 위해 이산화 과정을 수행한다. 메디컬 이미지와 합성 이미지 시퀀스를 대상으로 한 실험은 본 논문에서 제안한 방법이 naive한 방법보다 우수한 성능을 보이고 대용량의 이미지 시퀸스 데이터베이스로의 확장이 용이함을 입증한다.

• 전자공학회논문지
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• 제52권12호
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• pp.67-73
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• 2015

본 논문에서는 근거리 위치 기반 시스템을 위한 3 - 5 GHz IR-UWB(impulse radio-ultra wide band) RFIC를 제안한다. 수신기의 구조는 에너지 검출 방식으로 설계되었고, 고속 sampling을 하기 위해서 4 bit ADC 와 DLL(delay locked loop) 을 이용하여 equivalent-time sampling 기술을 사용하도록 설계되었다. 송신기는 저전력의 디지털 UWB impulse generator 를 설계하였다. 설계된 IR-UWB RFIC 는 CMOS $0.18{\mu}m$ 공정을 이용하여 제작되었다. 측정된 수신기의 감도는 -85.7 dBm 이며, 송신기와 수신기는 1.8 V 전원 전압에서 각각 32 mA 와 25.5 mA 의 전류를 소모한다.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.606-613
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• 2010

This paper proposes a novel current-based maximum power point tracking (CMPPT) method for a single-phase photovoltaic power conditioning system (PV PCS) by using a modified incremental conductance method. The CMPPT method simplifies the entire control structure of the power conditioning system and uses an inherent current source characteristic of solar cell arrays. Therefore, it exhibits robust and fast response under a rapidly changing environmental condition. Digital phase locked loop technique using an all-pass filter is also introduced to detect the phase of grid voltage, as well as the peak voltage. Controllers of dc/dc boost converter, dc-link voltage, and dc/ac inverter are designed for coordinated operation. Furthermore, a current control using a pseudo synchronous d-q transformation is employed for grid current control with unity power factor. A 3 kW prototype PV PCS is built, and its experimental results are given to verify the effectiveness of the proposed control schemes.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.541-545
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• 1997

This paper presents a modified geometric active contour model or edge detection and segmentation of computed tomography(CT) scan images. The method is based on the level setup approach developed by Osher and Sethian and the modeling of propagation fronts with curvature dependent speeds by Malladi. Based on above algorithms, the geometric active contour is obtained through a particular level set of hypersurface lowing along its gradient force and curvature force. This technique retains the attractive feature which is topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. But there are limitations in this algorithm which are being not able to separate the object with weak difference from neighbor object. So we use speed limitation filter to overcome those problems. We apply a 2D model to various synthetic cases and the three cases of real CT scan images in order to segment objects with complicated shapes and topologies. From the results, the presented model confirms that it attracts very naturally and efficiently to the desired feature of CT scan images.

• 대한의용생체공학회:의공학회지
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• 제35권2호
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• pp.14-18
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• 2014

This study is about e-Book program based on human-computer interaction(HCI) system for physically handicapped person. By acquiring background knowledge of HCI, we know that if we use vision-based interface we can replace current computer input devices by extracting any characteristic point and tracing it. We decided betweeneyes as a characteristic point by analyzing facial input image using webcam. But because of three-dimensional structure of glasses, the person who is wearing glasses wasn't suitable for tracing between-eyes. So we changed characteristic point to the bridge of the nose after detecting between-eyes. By using this technique, we could trace rotation of head in real-time regardless of glasses. To test this program's usefulness, we conducted an experiment to analyze the test result on actual application. Consequently, we got 96.5% rate of success for controlling e-Book under proper condition by analyzing the test result of 20 subjects.

• 전력전자학회논문지
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• 제10권6호
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• pp.550-559
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• 2005

매트릭스 컨버터는 VS떼 비하여 장수명, 입력역률 직접제어 및 에너지 회생 등의 장점을 갖고 있으나, 제어의 복잡성, ride-through 대책 및 낮은 전압이용률 등은 상용화를 위해 해결해야 할 난제이다. 본 논문에서는 SVM를 적용한 매트릭스 컨버터의 설계 및 구현방법을 제안한다. 입력 고조파를 저감시키기 위한 입력필터와 입출력의 과전압 방지와 free-wheeling을 위한 클램프 회로의 설계기법을 제시하고, 고속 DSP와 CPLD를 사용하여 공간벡터 제어 및 4 단계 전류(commutation) 제어를 구현하며, 매트릭스 컨버터의 양방향 스위치 구동을 위한 전용의 전원회로를 설계하여, 최적 구조의 전력회로를 제안한다. 그리고 구현된 매트릭스 컨버터를 유도전동기에 적용하여 성공적인 운전 결과를 얻을 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• ETRI Journal
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• 제46권2호
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• pp.165-174
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• 2024

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep-learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three-dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep-learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two-dimensional images and their corresponding light-field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light-field data extracted from holograms of objects with single and multiple depths and mesh-based computer-generated holograms.

• International journal of advanced smart convergence
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• 제4권1호
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.