• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.375-378
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• 2017

Shipping and logistics safety, security system is strengthening worldwide, the development of shipping and logistics safety security core technology for national security logistics system construction has been carried out. In addition, it is necessary to localize the Array Detection System, which is a core component of the container search machine, to cope with the 100% pre-inspection of the container scheduled for 2018 in the United States. In this paper, we propose a study on a self-developed Digital Signal Processing Board among the array detection systems that replace foreign products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.379-382
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• 2017

Shipping and logistics safety, security system is strengthening worldwide, the development of shipping and logistics safety security core technology for national security logistics system construction has been carried out. In addition, it is necessary to localize the Array Detection System, which is a core component of the container search machine, to cope with the 100% pre-inspection of the container scheduled for 2018 in the United States. In this paper, we propose a test software program developed by using TI-RTOS (Texas Instruments - Real Time Operating System) with a test digital signal processing board which is developed self development.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.16-24
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• 2011

In this paper, we developed a auto sorting system for T type welding nut using a vision inspector. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. As a result we performed numeric inspection of 0.1mm accuracy. This is impossible in old sorting system and inspector with naked eye. Also, we reduced the manufacturing unit cost to 25% and improved a production efficiency to 330%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.141-146
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• 1998

Recently, the computer vision such as part measurement, and product inspection is very popular to achieve the factory automation since the labor cost is dramatically increasing. In this paper, the diameter and the length of rope are measured by CCD camera which is orthogonally mounted on the ceiling. Two parameters which are the diameter and the length of rope are used to measure the weight of rope. If the weight of rope is reached to predetermined weight, the information is transmitted to PLC(programmable logic control) to cut the rope on the wheel. The cutting machine cuts the rope according to the information obtained from the CCD camera. To measure the diameter and length of rope on real time, the searching space for image segmentation is restricted the predetermined area according to the camera calibration position. Finally, to estimate the weight of rope, the knowledge base system which depends on the diameter, the length of rope, and weight relation between these information are constructed according to diameters of rope. This method contributes to achieve the factory automation, and reduce the production cost since the operators are unnecessary to measure the weight of rope by try-and-error method.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1626-1628
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• 2003

The lightning arrester is a very important overvoltage protection device in the electric power system. Therefore, the inspection of lightning arrester whether it keeps its performance or not properly has close related to verifying the safety confidence of the electric power system. But the development of the deterioration measuring method and on-line detecting system, is necessary to monitor the deterioration of the lightening arrestor. In this paper, we developed the lightning arrester degradation monitoring system. This system detected leakage current of lightning arrester by using the ZCT, and analyze the third harmonics ingredient of leakage current using DFT method in the Data Acquisition Unit(DAU). The analyzed current signal is transmit to the Human-Machine Interface(HMI), and HMI alarmed when accident are occurred and informed with the amplitude of leakage current to the operator.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.1-8
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• 2015

Security has become an issue due to the rapid increases in internet traffic data network. Especially P2P traffic data poses a great challenge to network systems administrators. Preemptive measures are necessary for network quality of service(QoS) and efficient resource management like blocking suspicious traffic data. Deep packet inspection(DPI) is the most exact way to detect an intrusion but it may pose a private security problem that requires time. We used several machine learning methods to compare the performance in classifying network traffic data accurately over time. The Random Forest method shows an excellent performance in both accuracy and time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.6
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• pp.918-924
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• 1997

A method for measuring the accuracy of rotating objects was studied. Rotating axis errors are significant; such as the spindle error of a manufacturing machine which results in the surface roughness of machined work pieces. Three capacitance type displacement sensors were used to measure the rotating master ball position. The sensors were mounted to the three orthogonal points on the spindle axis. The measurement data were analyzed and shown for rotating spindle accuracy, not only for average roundness error but also for spindle volumetric positional error during the revolutions. This method is simple and economical for industrial field use with regular inspection of rotating machines using portable equipment. Measuring and analyzing time using this method takes only a couple of hours. This method can also measure microscopic amplitude and 3-dimensional direction of vibrating objects.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.369-372
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• 2010

평판 디스플레이(flat panel display)의 크기가 커짐에 따라 다양한 기판을 이용한 제조 방법이 개발되고 있다. 디스플레이 제조 공정 중 기판의 결함을 찾아서 분류하는 검사 시스템은 최종 제품의 품질을 결정하는 매우 중요한 부분이다. 본 연구는 머신비전 기술을 이용하여 불투명하고 반사율이 높은 기판 표면의 결함을 찾아내고, 이 결함을 스크래치(scratch), 흑결함(dark defect), 백결함(white defect)으로 분류하는 장치를 구현하는데 목적이 있다. 이를 구현하기 위해 본 논문에서는 정밀 스테이지(stage)와 라인 카메라(line CCD camera)을 이용한 광학계를 활용하여 검사 시스템을 구현하였다. 구축된 시스템을 이용하여 취득한 이미지를 12 개의 영역으로 등분하여 각각의 국부 영역에 대한 문턱값 연산(thresholding)을 적용함으로써 조명의 불균일을 의한 검출 에러율을 획기적으로 낮추었다. 간단한 컴퓨터비전 알고리듬의 채용으로도 검사 시스템의 구현이 가능함을 보였다.