• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.174-175
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• 2008

본 논문에서는 반도체 패키징 기술의 일종인 TCP, COF의 제품 결함을 영상 처리를 이용하여 검출하는 알고리즘을 제시하고, 신뢰성을 확보 후 실제 검사 공정에 적용하는 방법론을 제시한다. 제안된 방법으로는 TCP, COF의 양품 패턴을 기준 영상으로 취득하고, 제품의 생산 과정에서 라인 스캔 카메라를 이용한 실시간 제품 영상을 취득한 후, 그레이 레벨 영상으로 변환하고, 노이즈를 제거하기 위한 다양한 필터를 적용한다. 그리고 기준 영상과 비교하기 위한 이진화와 라벨링을 통해 제품의 불량을 검출하여, 사용자에게 시각적으로 표현해 주게 된다. 마지막으로 TCP, COF의 다양한 불량 항목 중에서 10여 가지의 불량패턴을 대상으로 제안된 방법의 타당성을 검증하였다.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.416-417
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• 2018

Brushless DC(BLDC) 모터는 브러시가 없어 고속 회전에 유리하기 때문에 핸드피스 모터에 많이 적용하는 추세이다. 핸드피스 모터는 부피가 작을수록 유리하여 홀 센서를 장착하지 않은 경우가 많기 때문에 센서리스 제어를 하여야 한다. BLDC 모터의 상 역 기전력 zero crossing 감지 방법을 이용한 센서리스 제어를 할 시 40,000rpm에 달하는 고속 회전에서 상 전압 floating 구간이 $250{\mu}s$밖에 안되기 때문에 샘플링이 부족하고 스위칭 노이즈도 강해 zero crossing에 대한 감지가 어려울 수 있다. 이 논문에서는 역 기전력에 대한 비선형 필터인 majority function을 응용함으로써 모터 상 역기전력의 zero crossing을 감지해내고 고속 BLDC 모터 센서리스 제어를 하는 방법을 소개한다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.21-27
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• 2018

Wearing degree and uneven wearing detection algorithm using horizontal edge information is proposed in this paper. The noise in the input image is removed by bilateral filter, and then edges are extracted from the filtered image by using the proposed mask. As the tire is worn, grooves of tire shoulder or sipes are changed more than the vertical grooves. Therefore the edges from grooves of tire shoulder or sipes have more information about the tire wearing than the edges from vertical grooves. Proposed mask that is reflected this feature is used to extract the horizontal edges. After edge extraction, the edge image is represented in two-level system. The edge pixels of the binarization image are used to decide the wearing degree and uneven wearing. This proposed method can be used easily without any other equipments. The proposed method is conducted with a real vehicle, and the experimental results show the good performance of the proposed method in detecting wearing degree and uneven wearing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.299-306
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• 2016

In this paper, a method of shape prediction of an endoscope handling robot that can imitate a surgeon's behavior using a sensor network is suggested. Unit sensors, which are composed of a 3-axis magnetometer and 3-axis accelerometer pair comprise the network through CAN bus communication. Each unit of the sensor is used to detect the angle of the points in the longitudinal direction of the robot, which is made from a flexible tube. The signals received from the sensor network were filtered using a low pass Butterworth filter. Here, a Butterworth filter was designed for noise removal. Finally, the Euler angles were extracted from the signals, in which the noise was filtered by the low path Butterworth filter. Using this Euler angle, the position of each sensor on the sensor network is estimated. The robot body was assumed to consist of links and joints. The position of each sensor can be assumed to be attached to the center of each link. The position of each link was determined using the Euler angle and kinematics equation. The interpolation was carried out between the positions of the sensors to be able to connect each point smoothly and obtain the final posture of the endoscope in operation. The experimental results showed that the shape of the colonoscope can be visualized using the Euler angles evaluated from the sensor network suggested and the shape of serial link estimated from the kinematics chain model.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.257-264
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• 2013

In this study, we have designed the variable amplitude threshold algorithm that can enhance the accuracy of step count using variable amplitude. This algorithm converts the x, y, z sensor values into a single energy value($E_t$) by using SVM(Signal Vector Magnitude) algorithm and can pick step count out over 99% of accuracy through the peak data detection algorithm and fixed peak threshold. To prove the results, We made the noise filtering with the fixed amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it's the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the variable amplitude threshold algorithm can improve the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.121-126
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• 2017

Recently, a lot of people use touch-pen devices such as smart phones and tab computers. To generate the picture and text, a user can give input or control the touch-pen device through simple or multi-touch gestures by touching the screen with a special stylus pen and/or one or more fingers. The accuracy and response time from the moment of contact with the touch board is very important to the touch device. Therefore, research is needed to find a way of removing the noise included in the touch signal quickly and efficiently. In this paper, we propose a method for removing a noise mixed in with a touch point coordinate which has been caused by a input pen on the touch screen. For effective filtering, the fast sampling of the coordinate corresponding to the noise from the input signal is required primarily. Secondly the total compensation of the touch coordinates using the characteristics of the B-Spline curve is applied to correct coordinates of the points. This method can ensure a real-time response than other algorithms. The applied performance evaluation method is comparing error pixels with evaluation values by dividing 10 intervals on the touch pad diagonally. Usually the average error is 7.1 pixels but our proposed method shows an average 4.1 errors. Therefore, our proposed touch pen method can express the input signal on the coordinates more correctly.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1603-1615
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• 2018

In order to achieve the low complexity and area, power in the design of Optical Image Stabilization (OIS) suitable for the smart phone, this paper presents the following design explorations, such as; optimization of gyroscope sampling rate, simple and accurate gyroscope filters, and reduced operating frequency of motion compensation, optimized bit width in ADC and DAC, evaluation of noise effects due to PWM driving. In experiments of gyroscope sampling frequencies, it is found that error values are unvaried in the frequency above 5KHz. The gyroscope filter is efficiently designed by combining the Fuzzy algorithm, to illustrate the reasonable compensation for the angle and phase errors. Further, in the PWM design, the power consumption of 2MHz driving is shown to decrease up to 50% with respect to the linear driving, and the imaging noises are reduced in the driving frequency above 2MHz driving frequency. The operating frequency could be reduced to 5KHz in controller and 10KHz in driver, respectively, in the motion compensation. For ADC and DAC, the optimized exploration experiments verify the minimum bit width of 11bits in ADC as well as 10bits in DAC without the performance degradation.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.157-164
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• 2017

Mobile ECG signal measurement is a technique to measure small signals of several mV, and many studies have been conducted to remove noise including wandering scheme. Removal of the equipotential line noise caused by shaking or movement of the electrode cable is one of the core research contents for the electrocardiogram measurement. In this study, we proposed a modified step-size of combined NLMS(normalized least squares) and DLMS(delayed least squares) adaptive filter to eliminate baseline noise from ECG signals. The proposed method mainly adjusts initial filter step-size to reduce distortion of original ECG signals characteristic after eliminating baseline noise. The modified filter step-size is scaled by filter order size and distortion minimization factor. This method is suitable for portable ECG device with a small processor and less power consumption. This technique also decreases computation time which is essential for real-time filtering. The proposed filter also increase the signal to noise ratio (SNR) compared to conventional NLMS filter.

• Journal of KIISE
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• v.43 no.12
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• pp.1351-1355
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• 2016

The number of CCTV units is rapidly increasing annually, and the demand for intelligent video-analytics system is also increasing continuously for the effective monitoring of them. The existing analytics engines, however, require considerable computing resources and cannot provide a sufficient detection accuracy. For this paper, a light analytics engine was employed to analyze video and we collected metadata, such as an object's location and size, and the dwell time from the engine. A further data analysis was then performed to filter out the target of interest; as a result, it was possible to verify that a light engine and the heavy data analytics of the metadata from that engine can reject an enormous amount of environmental noise to extract the target of interest effectively. The result of this research is expected to contribute to the development of active intelligent-monitoring systems for the future.