• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.385-390
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• 2021

The gantry is a device that moves fine chips from the feeder to the PCB. While the gantry is moving the part, the camera checks the condition of the part. The purpose of this paper is to find the path with the shortest travel time of the gantry and calculate the travel time according to the path. stop_motion is a way to check the status of the parts currently in use. This paper presents the moving_motion method and the fly_motion method with maximum speed in front of the camera. In addition the signature method was used to inspect the condition of the parts. When comparing the moving time of the three types of gantry, the moving_motion method improved by 9.42% and the fly_motion method by 17.73% compared to stop_motion. When the fly_motion method proposed in this paper is used for the gantry movement path, it is expected that productivity will be improved.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.167-174
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• 2021

Recently, deep learning technology is widely used in various computer vision applications, such as object recognition, classification, and image generation. In particular, the deep learning-based super-resolution has been gaining significant performance improvement. Fast super-resolution convolutional neural network (FSRCNN) is a well-known model as a deep learning-based super-resolution algorithm that output image is generated by a deconvolutional layer. In this paper, we propose an FPGA-based convolutional neural networks accelerator that considers parallel computing efficiency. In addition, the proposed method proposes Optimal-FSRCNN, which is modified the structure of FSRCNN. The number of multipliers is compressed by 3.47 times compared to FSRCNN. Moreover, PSNR has similar performance to FSRCNN. We developed a real-time image processing technology that implements on FPGA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.43-49
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• 2019

The wired and wireless Internet is a useful window to easily acquire various types of media data. On the other hand, the public can easily get the media data including the object to which the personal information is exposed, which is a social problem. In this paper, we propose a method to robustly detect a target object that has exposed personal information using a learning algorithm and effectively block the detected target object area. In the proposed method, only the target object containing the personal information is detected using a neural network-based learning algorithm. Then, a grid-like mosaic is created and overlapped on the target object area detected in the previous step, thereby effectively blocking the object area containing the personal information. Experimental results show that the proposed algorithm robustly detects the object area in which personal information is exposed and effectively blocks the detected area through mosaic processing. The object blocking method presented in this paper is expected to be useful in many applications related to computer vision.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.323-330
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• 2019

In order to improve the level of the IT organization, CMMi played a lot of roles in improving the IT efficiency and quality step by step. In a university organization if you use a customized IT governance framework for the concept of CMMi, it will be a university IT governance to realize the vision of the university. In this paper, we propose Staged Framework for the university developed for applying to university with reference to this staged model of CMMi. The university has applied its own process step by step and it can be uses in the university field. So it can be applied to university IT plan and budget in compliance with not only the person in charge of IT service center but the person in charge of university headquarters. The staged framework is classified into the maturity and process of university IT project and suggests ways to apply it to the improvement of university IT system levels.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.525-531
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• 2019

Efforts to utilize human gestures to effectively implement a more natural and interactive interface between humans and computers have been ongoing in recent years. In this paper, we propose a new algorithm that accepts consecutive three-dimensional (3D) depth images, defines a hand model, and robustly extracts the human hand region based on six palm joints and 15 finger joints. Then, the 3D depth images are adaptively binarized to exclude non-interest areas, such as the background, and accurately extracts only the hand of the person, which is the area of interest. Experimental results show that the presented algorithm detects only the human hand region 2.4% more accurately than the existing method. The hand region extraction algorithm proposed in this paper is expected to be useful in various practical applications related to computer vision and image processing, such as gesture recognition, virtual reality implementation, 3D motion games, and sign recognition.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.11
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• pp.911-920
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• 2018

In this study, we performed a study to track the angle between the guided weapon and the target by using the strap-down image seeker, and constructed a test bed that can simulate it visually. This paper describes a method to maintain high-performance feature distribution in the implementation of sparse feature tracking algorithm such as Lucas Kanade's optical flow algorithm for target tracking using image information. We have extended the feature tracking problem to the concept of feature management. To realize this, we constructed visual environment using Unity3D engine and developed image processing simulation using OpenCV. For the co-simulation, dynamic system modeling was performed with Matlab Simulink, the visual environment using Unity3D was constructed, and computer vision work using OpenCV was performed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.355-361
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• 2020

Purpose: In this study, we added two kinds of photoinitiators (CQ and TPO) to prepare two kinds of denture base resins (Bis-GMA series and UDMA series) for three-dimensional (3D) printing to compare and analyze their mechanical and biological properties and to find the optimal composition. Methods: Control specimens were made using the mold made of polyvinyl siloxane of the same size. Light curing was performed twice for 20 seconds on both the upper and lower surfaces with LED (light emitting diode) light-curing unit (n=10). Experimental 3D printing dental resins were prepared, to which two photoinitiators were added. Digital light processing type 3D printer (EMBER, Autodesk, CA, USA) was used for 3D printing. The specimen size was 64 mm×10 mm×3.3 mm according to ISO 20795-1. The final specimens were tested for flexural strength and flexural modulus, and MTT test was performed. Furthermore, one-way analysis of variance was performed, and the post-test was analyzed by Duncan's test at α=0.05. Results: The flexural strength of both Bis-GMA+CQ (97.12±6.47 MPa) and UDMA+TPO (97.40±3.75 MPa) was significantly higher (p<0.05) in the experimental group. The flexural modulus in the experimental group of UDMA+TPO (2.56±0.06 GPa) was the highest (p<0.05). MTT test revealed that all the experimental groups showed more than 70% cell activity. Conclusion: The composition of UDMA+TPO showed excellent results in flexural strength, flexural modulus, and biocompatibility.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.637-644
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• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.