• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1257-1268
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• 1993

본 논문에서는 퍼지보상(compensative) 연산자를 이용하는 정보융합(information fusion) 방법을 제안하였다. 제안된 정보융합 방법에서는 보상적인 성질을 갖는 퍼지 총체화(aggregation) 연산자를 역오류전파(back-propagation)신경회로망의 활성화함수(activation function)로 간주하고, 이들 연산자에 수반된 파라메터들을 학습에 의해 결정한다. 결정된 연산자의 파라메터들은 학습자료에 나타난 의사 결정에 수반된 보상도를 표현할 수 있으며, 평가에 불필요한 정보원을 제거하는 성질도 가지고 있다. 제안된 정보융합 구조는 평가지수(sub-criterion)들의 만족도를 입력으로 학습에 의해 결정된 보상연산자에 의해 총체화된 만족도를 제공한다. 제안된 방법은 패턴 인식 문제와 칼라영상의 분할과 인식등 컴퓨터비죤 문제에 적용하여 그 정당성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.255-258
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• 2012

모바일 로봇 센서가 개입하여 네트워크의 단절을 억제하여 모바일 로봇 센서를 통해 센서 네트워크의 생존시간을 향상시키는 것을 목표로 한다. 센서노드가 비교적 적은 희소한 환경에서는 베터리 소모 등의 다양한 원인으로 인하여 매개체 역할을 수행하는 브릿지 노드가 제거 될 경우 해당 브릿지 하부의 네트워크는 단절되어 더 이상 쓸모없게 되는 심각한 상황을 초래하기 쉽다. 본 논문에서는, 모바일 로봇 센서가 개입하여 단절된 브릿지를 효율적으로 찾아가 그 역할을 대행함으로써 네트워크의 생존시간을 연장 시키는 것을 목적으로 하는 센서 네트워크를 설계하고 구현했다.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.189-196
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• 2010

In this paper, we proposed the adaptive local sigma filter reducing noises generated by an image sensor. The small noises generated by the image sensor are amplified by increased an analog gain and an exposure time of the image sensor together with information. And the goal of this work was the system design that is reduce the these amplified noises. Edge data are extracted by Flatness Index Map algorithm. We made the threshold adaptively changeable by the luminance average in this algorithm that extracts the edge data not in high luminance, but just low luminance. The Local Sigma Filter performed only about the edge pixel that were extracted by Flatness Index Map algorithm. To verify the performance of the designed filter, we made the Window test program. The hardware was designed with HDL language. We verified the hardware performance of Local Sigma Filter system using FPGA Demonstration board and HD image sensor, $1280{\times}720$ image size and 30 frames per second.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.1-9
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• 2008

One of the problems for implementing the spot detection phase in the 2-DE gel image analysis program is the eliminating noises in the image. Remained noises after the preprocessing phase cause the over-segmented regions by the segmentation phase. To identify and exclude the over-segmented background regions, if we use the fixed thresholding method that is choosing an intensity value for the threshold, the spots that is invisible by the eyes but mean a very small amount proteins which have important role in the biological samples could be eliminated. This paper propose an adaptive thresholding method that come from an idea that is got on statistical analysing for the prominences of the peaks. The adaptive thresholding method works as following. Firstly we calculate an average prominence value curve and fit it to exponential function curve, as a result we get parameters for the exponential function. And then we calculate a threshold value by using the parameters and probability distribution of errors. Lastly we apply the threshold value to the region for determining the region is a noise or not. According to the probability distribution of errors, the reliability is 99.85% and we show the correctness of the proposed method by representing experiment results.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.125-131
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• 2019

Through the 4th Industrial Revolution, various digital equipments are being distributed, and accordingly, the importance of data processing is increasing. As data processing has a great effect on the reliability of equipment, its importance is increasing, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN in consideration of the noise in the image. The proposed algorithm is used in the filtering process by inferring the standard deviation of the image noise. The noise is removed by dividing the filter for the high frequency component and the filter for the low frequency component compared with the standard deviation of the filtering mask. The proposed algorithm is simulated with the existing methods for evaluation and compared and analyzed by difference image, PSNR and profile. The proposed algorithm minimizes the effect of noise and preserves the important characteristics of the image and shows the performance of efficient noise removal.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.44-49
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• 2022

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• Journal of the Korea Convergence Society
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• v.7 no.3
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• pp.77-85
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• 2016

We propose a digital signal processing technic that can compensate the non-linearity inherent in audio amplifiers, and present the result of the simulation. The inherent non-linearity of the audio power amplifier arising from analog devices is compensated via a digital signal processing technic consisting of indirect learning architecture and an adaptive filter. The simulation results show that the compensator can be realized using a third-order polynomial and compensates odd-order non-linearity efficiently. The even-oder non-linearity is mainly due to the dc offset at the output, which is difficult to eliminate with the proposed method. Care must be taken in designing the bias circuit to avoid the DC offset at the output. The proposed technic has significance in that digital signal processing technic can compensate for the impairment that is an inherent characteristic of an analog system.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.209-214
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• 2017

The purpose of this study was to investigate the effect of oral health status on daily life of elderly women. The subjects of this study were 142 elderly women aged 60 years or older who were surveyed through questionnaires, interviews, counseling, and oral examinations. The SPSS program was used for frequency analysis, descriptive statistics and regression analysis. The results of the study showed that the number affected on daily life were 1.4 in average and the oral health condition was the highest at scaling (59.4%). When the oral status was unsatisfactory, it was found to be uncomfortable in daily life. In case of oral pain, oral status and poor removal of calculus, the number and frequency in daily life were increased. In conclusion, the oral health of elderly women can improve their daily life ability and affect the quality of life. Therefore, a systematic oral care plan is needed through the expansion of oral health promotion education for elderly women and the development of continuous preventive programs.