• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.159-165
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• 2018

In this study, it is analyzed that keywords of the interest in the 4th industrial revolution for science and Technician K women's university students, areas to prioritize in the strategy of 4th industry revolution, to research compare analyze the recognition of science technology such as the most necessary education, human resource development of universities and companies in Korea and abroad according to the technology trend required in the 4th Industrial revolution era and which area to prepare for the 4th industrial revolution. The survey result shows different thoughts of science and Technician(KOFST) and the women university students. In the 1) 4th industrial revolution, the 96% of former are interested, while 60% of latter are interested. And in the most used keywords, the former group used AI(24%), Fusion new industry(21%) the most, while the latter group used AI(34%), Robot(18%). And, 3) in the strategic priority, the science technology experts are interested in education, R&D system innovation(27%), IoT, Information and Communication(26%) and the university students are interested in IoT, Information and Communication(31%), AI(28%). Finally, 4) the science technology experts thought of Autonomous Vehicle(20%), 3D Printer(7%), AI(16%) important, while the women university students thought of AI(27%), VR/Augmented Reality(17%), and Autonomous Vehicle(16%) the most necessary education. In the 4th industrial revolution, we need people with ability to solve complicated problems with creativity based on understanding and absorbing new knowledge and thinking of converged idea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1310-1316
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• 2021

Recently, with the development of artificial intelligence and IoT technology, the importance of video processing such as object tracking, medical imaging, and object recognition is increasing. In particular, the noise reduction technology used in the preprocessing process demands the ability to effectively remove noise and maintain detailed features as the importance of system images increases. In this paper, we provide a modified weight filter based on pixel matching in an AWGN environment. The proposed algorithm uses a pixel matching method to maintain high-frequency components in which the pixel value of the image changes significantly, detects areas with highly relevant patterns in the peripheral area, and matches pixels required for output calculation. Classify the values. The final output is obtained by calculating the weight according to the similarity and spatial distance between the matching pixels with the center pixel in order to consider the edge component in the filtering process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1625-1631
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• 2020

With the development of IoT technology, various digital equipment is being spread, and accordingly, the importance of data processing is increasing. The importance of data processing is increasing as it greatly affects the reliability of equipment, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN according to the characteristics of the fuzzy membership function. The proposed algorithm calculates the estimated value according to the correlation between the value of the fuzzy membership function between the input image and the pixel value inside the filtering mask, and obtains the final output by adding or subtracting the output of the spatial weight filter. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and analyzed using difference image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves the important characteristics of the image, and shows the performance of efficiently removing noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1263-1270
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• 2021

Recently, in the food culture life, the trend of consumers cooking is changing, and the use rate of induction cookware is increasing. Therefore, in this study, we propose the development of a functional auxiliary device to improve the safety of induction cookware to improve the convenience of cooking according to the increase in the cooking population. The proposed device is linked with IoT through the app. Through the app, the device can control the induction heating power adjustment and time reservation. In addition, an ultrasonic sensor is used to prevent the container from overflowing during cooking, and the user can safely use induction through the fine dust sensor. The implemented device conducts research assuming the actual cooking situation. Finally, it was confirmed that the user's fatigue was reduced during cooking through the device and the user's safety was improved in emergency situations such as overcooking or overflowing of water.

• 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 the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.675-681
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated in various fields, interest in video processing, which is the basis for automation such as object recognition and object classification, is increasing. Various studies have been conducted on noise removal in the video processing process, which has a significant impact on image quality and system accuracy and reliability, but there is a problem that it is difficult to restore images for areas with high impulse noise density. In this paper proposes a filter algorithm based on partial mask and Lagrange interpolation to restore the damaged area of impulse noise in the image. In the proposed algorithm, the filtering process was switched by comparing the filtering mask with the noise estimate and the purge weight was calculated based on the low frequency component and the high frequency component of the image to restore the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.54-61
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• 2023

With the development of IoT technologies and artificial intelligent, diverse digital image equipments are being used in industrial sites. Because image data can be easily damaged by noise while it's obtained with a camera or a sensor and the damaged image has a bad effect on the process of image processing, noise removal is being demanded as preprocessing. In this thesis, for the restoration of image damaged by the noise of random impulse, a switching filter algorithm based on noise estimation was suggested. With the proposed algorithm, noise estimation and error distraction were carried out according to the similarity of the pixel values in the local mask of the image, and a filter was chosen and switched depending on the ratio of noise existing in the local mask. Simulations were conducted to analyze the noise removal performance of the proposed algorithm, and as a result of magnified image and PSNR comparison, it showed superior performance compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.