• Journal of Korea Foundry Society
• /
• v.42 no.6
• /
• pp.369-376
• /
• 2022

The die-casting process is an important process for various industries, but there are limitations in the profitability and productivity of related companies due to the high defect rate. In order to overcome this, this study has developed die-casting fault detection modules based on industrial AI technologies. The developed module is constructed from three-stage models depending on the characteristics of the dataset. The first-stage model conducts fault detection based on supervised learning from the dataset without labels. The second-stage model realizes one-class classification based on semi-supervised learning, where the dataset only has production success labels. The third-stage model corresponds to fault detection based on supervised learning, where the dataset includes a small amount of production failure cases. The developed fault detection module exhibited outstanding performance with roughly 96% accuracy for actual process data.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.30 no.1
• /
• pp.1-10
• /
• 2002

The purpose of this article is to examine land use change in the fringe area of a metropolitan city through multi-temporal data analysis. Change detection has been regarded as one of the most important applications for utilization of remotely sensed imageries. Conventionally, two images were used for change detection, and Arithmetic calculators were generally used on the process. Meanwhile, multi-temporal change detection for a large number of images has been carried out. In this paper, a digital land-use map and three Landsat TM data were utilized for the multi-temporal change detection Each urban area map was extracted as a base map on the process of multi-temporal change detection. Each urban area map was converted to bit image by using boolean logic. Various urban change types could be obtained by stacking the urban area maps derived from the multi-temporal data using Geographic Information System(GIS). Urban change type map was created by using the process of piling up the bit images. Then the urban change type map was compared with each land cover map for the change detection. Dalseo-gu of Daegu city and Hwawon-eup of Dalsung-gun, the fringe area of Daegu Metropolitan city, were selected for the test area of this multi-temporal change detection method. The districts are adjacent to each other. Dalseo-gu has been developed for 30 yeais and so a large area of paddy land has been changed into a built-up area. Hwawon-eup, near by Dalseo-gu, has been influenced by the urbanization of Dalseo-gu. From 1972 to 1999, 3,507.9ha of agricultural area has been changed into other land uses, while 72.7ha of forest area has been altered. This agricultural area was designated as a 'Semi-agricultural area'by the National landuse Management Law. And it was easy for the preserved area to be changed into a built-up area once it would be included as urban area. Finally, the method of treatment and management of the preserved area needs to be changed to prevent the destruction of paddy land by urban sprawl on the urban fringe.

• Journal of Korean Society for Geospatial Information Science
• /
• v.14 no.4 s.38
• /
• pp.79-84
• /
• 2006

Change detection is a core functions of remote sensing. It can be widely used in public business such as land monitoring, demage assessment from disaster, growth analysis of cities, etc. However, it seems that the change detection using satellite imagery has not been fully used in public business. For the person who are in charge of public business, it would not be easy to implement the change detection because various functions are combined into it. So, to promote the use of the change detection in public business, the standard, the process and the method for the change detection in public business should be established. Also, the software which supports that would be very useful. This study aims to promote the use of satellite imagery in public business by building up the change detection process which are suitable for general public business and developing the change detection software to support the process. The software has been developed using ETRI Components for Satellite Image Processing to support the interoperability.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.431-434
• /
• 2019

In the case of blur processing, it is common to use a tool such as Photoshop to perform processing manually. However, it can be considered very efficient if the blur is processed at one time in the object detection process. Based on this point, we can use the object detection model to blur the objects during the process. The object detection is performed by using the YOLO [3] model. If such blur processing is used, it may be additionally applied to streaming data of video or image.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.3
• /
• pp.135-144
• /
• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.1
• /
• pp.120-127
• /
• 1991

The purpose of this study is to develop a fault detection and diagnosis scheme that can monitor process fault and instrument fault of a steam generator. The suggested scheme consists of a Kalman filter and two bias estimators. Method of detecting process and instrument fault in a steam generator uses the mean test on the residual sequence of Kalman filter, designed for the unfailed system, to make a fault decision. Once a fault is detected, two bias estimators are driven to estimate the fault and to discriminate process fault and instrument fault. In case of process fault, the fault diagnosis of outlet temperature, feed-water heater and main steam control value is considered. In instrument fault, the fault diagnosis of steam genrator's three instruments is considered. Computer simulation tests show that on-line prompt fault detection and diagnosis can be performed very successfully.

• IE interfaces
• /
• v.16 no.2
• /
• pp.248-257
• /
• 2003

In a heated rolling process of a steel mill where steel plates are pressed to a sheet coil by spreading and expanding, an irregularly-shaped head portion as well as a tail portion of the sheet coil need to be cropped. Any crop which is not clearly cut and separated from the sheet coil may cause critical damages to the facilities of the following processes. As the cropping process is performed very fast, human eyes are not proper for continuous monitoring of the cropping process. To solve this problem, we have developed a machine-vision based crop-drop detection system. The system also measures lengths of major and minor axes for the crops and thereby determines the proper crop size to minimize steel sheet losses.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.4
• /
• pp.121-125
• /
• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.3
• /
• pp.202-208
• /
• 2011

Recently the technologies that control the computer system through human computer interaction(HCI) have been widely studied. Their applications usually involve the methods that locate user's positions via face detection and recognize user's gestures, but face detection performance is not good enough. In case that the applications do not locate user's position stably, user interface performance, such as gesture recognition, is significantly decreased. In this paper we propose a new stable face detection algorithm using skin color detection and cumulative distribution of face detection results, whose effectiveness was verified by experiments. The propsed algorithm can be applicable in the area of human tracking that uses correspondence matrix analysis.

• Convergence Security Journal
• /
• v.13 no.6
• /
• pp.11-16
• /
• 2013

Mobile node must move optionally and perform the router and the host functions at the same time. These characteristics of nodes have become a potential threatening element of a variety of attacks. In particular, a black hole which malicious node causes packet loss among them is one of the most important issues. In this paper, we propose distributed detection technique using monitoring tables in all node and cooperative detection technique based cluster for an efficient detection of black hole attack. The proposed technique performs by dividing into local detection and cooperative detection process which is composed of process of step 4 in order to improve the accuracy of the attack detection. Cluster head uses a black hole list to cooperative detection. The performance of the proposed technique was evaluated using ns-2 simulator and its excellent performance could be confirmed in the experiment result.