• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.149-156
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• 2017

Various kinds of accessories(e.g., clock, radio, automatic door locks, alarm devices, etc.) or unit components (e.g., black box, navigation system, alarm, private audio, etc.) require dark current even when the vehicle power is turned off. However, accessories or unit components can be the causes of excessive dark current generation. It results in battery discharge and the vehicle's failure to start. Therefore, immediate detection of abnormal dark current and response are very important for a successful repair job. In this paper, we can increase the maintenance efficiency by presenting a standardized diagnostic process for the measurement of the dark current and the existing problem. As a result of the absence of a system to block the dark current in a vehicle, diagnosis and repair were performed immediately by using a standardized dark current diagnostic process.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.101-108
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• 1999

In this paper, a real time diagnostic algorithm for estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). First, ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. Second, IEM by use of the arrival time method estimates the impact location of loose parts. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.775-778
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• 2017

This research describes system implementation for automatic inspection on wind pressure sensor production based on reliability improvement. The reliability is minimized by the automatic inspection system on the wind pressure sensor against the manual system. The system consists of the control system and monitoring system to be scanning the inspection processing. The inspection system for reliability is evaluated in Gage Repeatability and Reproducibility. The experimental results are improved about 2 times speed, measured error ${\pm}0.02V$, effectiveness 15%, missing probability 17% and false alarm 12% respectively. The system will be also improved by database and the product barcodes for the total quality control system based on the effective reliability in the future.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.47-54
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• 1999

Tool wear is one of major causes occurring defectives in NC machining. In this paper we developed an automatic tool compensation system for the NC lathe machining. The system compensates machining error without any help of operators whenever the specification of a part is out of a tolerance. The configuration of the automatic compensation system consists of a NC lathe, an autoloader, a sensor, and a PLC. The system is operated as follows. A workpiece loaded by the autoloader is machining on the NC lathe. Once the workpiece is machined to be turned to a part, it is moved onto the sensor to be measured. If the sensor detects a part out of tolerance, a tool compensation is made in the NC controller. The system gives a help in increasing the productivity by reducing occurrence of defective parts as well as by eliminating time for the tool compensation. Besides the productivity increase, the system calculates cumulative usage time of the tool and notices the tool replace time to a worker by an alarm signal. A case is introduced to show that the system can be applied effectively in a shop.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.47-52
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• 2011

Constant false alarm rate (CFAR) is an automatic detection algorithm for active sonar system. Among several CFAR algorithms, ordered statistics (OS) CFAR has the best performance over cell averaging (CA), smallest of (SO), greatest of (GO) algorithms at non-homogeneous environments. However, OS CFAR has the disadvantage of bad detection performance in multiple target conditions. We suggest an ordered statistics ratio (OSR) CFAR algorithm that is robust to multiple target environments. The proposed and conventional schemes are compared with computer simulations.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.296-298
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• 2008

Currently SCADA System faces various fault situation. Operator must recognize all fault state and management plans. But it is not easy to recognize all category and acquired error data. So it is needed that automatic fault estimation. Automatic fault estimation is possible to data flow control. Data flow control method is two type. One is alarm processing and the other one is topology processing. This paper provide two type processing method in SCADA data flow control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7B
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• pp.1351-1360
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• 1999

In frequency selective channels, conventional PN code acquisition schemes are not ideal candidates. This is because they are primarily designed for the AWGN channel. In this paper, a direct-sequence spread-spectrum(DSSS) PN code acquisition system based on digital matched filtering (DMF) with automatic threshold control(ATC) algorithm is presented and analyzed with regards to probability of detection and probability of false alarm. These two important probabilities, the probability of detection ($P_D$) and the probability of false alarm ($P_{FA}$) are derived and analyzed in considering Doppler shift, sampling rate, mean acquisition time, and PN chip rate in frequency selective Rayleigh fading channel when using serial-search method as detection technique. From computer simulation results of a DMF acquisition system model, it is shown that the performance of the acquisition system using ATC algorithm is better than that of constant threshold system.

• Reproductive and Developmental Biology
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• v.33 no.3
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• pp.139-146
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• 2009

This study tried to develop the system (device) that automatically notify a manager of condition just before and after farrowing to extend ubiquitous-based technology and to increase efficiency of delivery care and productivity by reducing human labor and time on standby when farrowing management is done in the difficult and hard working environment of farrowing such as night or holidays in field sand especially in pig industry. In this test, selected 10 gilts were executed timed artificial insemination and were set up each temperature sensor and load sensor to them 3 days before the estimated farrowing day and were observed the farrowing situation. This study was embodied the NESPOT-based (KT Corporation) monitoring system, the system to transmit data in real time by utilization of wireless LAN and the sensor module to apply the ubiquitous environment to them. And this study was observed the situation to automatically notify situations of 10 gilts that first bore just before and after farrowing. The result obtained the farrowing situations of them in real time by setup of the NESPOT-based monitoring system to check farrowing situation directly is as follow. The average time of the automatic notice about situation just before farrowing by the temperature sensor was 27.5 minutes before the beginning of farrowing (the expulsion time of a piglet). 6 of 8 pregnant gilts that first bore automatically were notified situations just before farrowing and the temperature sensors inserted into 2 ones before farrowing were omitted. (The automatic notice rate 75%) The average time of the automatic notice of situation just after farrowing by the load sensor was taken 46.5 minutes after the beginning of farrowing (the expulsion time of a first piglet). The average gestation period of 8 ones that first bore and were tested by the automatic notice of farrowing situation was 115.6 days. This result found that the automatic farrowing notice system by the temperature sensor is more efficient than the load sensor as the automatic farrowing alarm device and sanitary treatment and improvement of the omission rate were required.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.189-196
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• 2024

Frequent false positives alarm from the Intelligent Selective Control System have raised significant concerns. These persistent issues have led to declines in operational efficiency and market credibility among agents. Developing a new model or replacing the existing one to mitigate false positives alarm entails substantial opportunity costs; hence, improving the quality of the training dataset is pragmatic. However, smaller organizations face challenges with inadequate capabilities in dataset collection and refinement. This paper proposes an automatic human pose data collection system centered around a human pose estimation model, utilizing camera-based sensor fusion techniques and edge devices. The system facilitates the direct collection and real-time processing of field data at the network periphery, distributing the computational load that typically centralizes. Additionally, by directly labeling field data, it aids in constructing new training datasets.

• ETRI Journal
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• v.37 no.3
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.