• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.847-857
• /
• 2007

This paper provides a statistical algorithm which detects COD (chemical oxygen demand) measurement machine error on real-time. For this we propose to use regression model fitting and check its validity against the current observations. The main idea is that the normal regression relation between COD measurement and other parameters inside the machine will be violated when the machine is out of order.

• Journal of Advanced Marine Engineering and Technology
• /
• v.37 no.7
• /
• pp.760-770
• /
• 2013

Detecting grammatical errors from a text is a long-history application. In this paper, we compare the performance of two grammatical error detection techniques, which are implemented as a sub-module of an automated English scoring system. One is to use a full syntactic parser, which has not only grammatical rules but also extra-grammatical rules in order to detect syntactic errors while paring. The other one is to use a finite state machine which can identify an error covering a small range of an input. In order to compare the two approaches, grammatical errors are divided into three parts; the first one is grammatical error that can be handled by both approaches, and the second one is errors that can be handled by only a full parser, and the last one is errors that can be done only in a finite state machine. By doing this, we can figure out the strength and the weakness of each approach. The evaluation results show that a full parsing approach can detect more errors than a finite state machine can, while the accuracy of the former is lower than that of the latter. We can conclude that a full parser is suitable for detecting grammatical errors with a long distance dependency, whereas a finite state machine works well on sentences with multiple grammatical errors.

• Journal of Korean Society for Quality Management
• /
• v.47 no.4
• /
• pp.783-793
• /
• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.375-378
• /
• 1995

Although laser interferometer measurement system has the advantage of range and accuracy, the traditional error measurement methods for geometric errors(two straightness and three angular errors) of a machine tool measures error components one at a time. It may also create an optical path difference and affect the measurement accuracy. In order to identify and compensate for geometric error of a moving body, an on-line measurement system for simultaneous detection of the five error components of a moving axis is required. An on-line measurement system with 5 degrees of freedom was developed for geometric error detection. Performance verification of the system was performed on an error generating mechanism. Experimental results show the feasibility of this system for identifying geometric errors of a side of machine tool.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.603-622
• /
• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.12
• /
• pp.38-44
• /
• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.16 no.1 s.106
• /
• pp.27-33
• /
• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.5
• /
• pp.93-99
• /
• 1998

Although laser interferometer measurement system has advantages of measurement range and accuracy, it has some disadvantages when measurement of multi degrees of freedom of motion are required. Because the traditional error measurement methods for geometric errors (two straightness and three angular errors) of a slide of machine tools measures error components one at a time. It may also create an optical path difference and affect the measurement accuracy. In order to identify and compensate for geometric errors of a moving rigid body in real time processes, an on-line error measurement system for simultaneous detection of the five error components of a moving object is required. Using laser alignment technique and some optoelectronic components, an on-line measurement system with 5 degrees of freedom was developed for the geometric error detection in this study Performance verification of the system has been performed on an error generating mechanism. Experimental results show the feasibility of this system for identifying geometric errors of a slide of machine tools.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.3
• /
• pp.1-10
• /
• 1995

In this paper, a fault-tolerant Mini-MAP system with high reliability is proposed. For fault-tolerance, the LLC sublayer, MAC sublayer, and physical layer of the Mini-MAP system are dualized. The detection of faults, the replacement of the failed network, and the management of the network are three major functions required for the dualization, and they are performed by ESM(Error Supervisory Machine), EMM(Error Management Machine), and NMM(Network Management Machine) of the proposed fault-tolerant Mini-MAP system, respectively. The ring maintenance function of the MAC sublayer is used for the detection of the faults. In the proposed fault-tolerant Mini-MAP system, the data are received from both of the dualized networks and transmitted to the selected one of the two. We analyze the reliability and the MTTF(Mean Time To Failure) of the proposed fault-tolerant Mini-MAP system and show that it has better performance compared to a general Mini-MAP system.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.4
• /
• pp.135-141
• /
• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.