• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.3
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• pp.42-51
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• 1999

An experimental modeling of cutting and structural dynamics and the on-line detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics of cutting process but also for the analytic realization of diagnostic systems. In this regard, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision round shape machining such as turning, drilling and boring in mold and die making. In this study, simulation and experimental work were performed to show the malfunctioned behaviors. For this purpose, two new recursive approach (REIVM, RLSM) were adopted fur the on-line system identification and monitoring of a machining process, we can apply these new algorithm in real process for the detection of abnormal machining behaviors such as chipping, chatter, wear and round shape lobe waviness.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.25-32
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• 2010

The paper proposes a hierarchical fault detection method for the high voltage equipment using a microphone array which detects the location of fault and the thermal imaging and CCD cameras which verifies the fault and stores the image, respectively. There are partial arc discharges on the faulty insulators, which generates a specific pattern of sound. Detecting the signal using the microphone array, the location of the faulty insulator can be estimated. The 6th band-pass filter was applied to remove noise signal from wind or external influence. When the mobile robot carries the thermal and CCD cameras to the possible place of the fault insulator, the fault insulators or power transmission wires can be detected by the thermal images, which are caused by the aging or natural erosion. Finally, the CCD camera captures the image of the fault insulator for the record. The detection scheme of fault location using the microphone array and the thermal images have been proved to be effective through the real experiments. As a result of this research, it becomes possible to use a mobile robot with the integrated sensors to detect the fault insulators instead of a human being.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.107-114
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• 2012

Fire detection system is used for detection and alarm-generation of danger in case of fire. Most fire detection systems being used these days often malfunction from false positive and false negative errors. To improve detection reliability, an integrated fire detection algorithm using multi-senor information of heat, smoke and carbon monoxide detectors is suggested, then built and tested using the LabVIEW environment. Simulated using sensor measurement data offered by National Institute of Standards and Technology (NIST), possibility of reducing false positive and false negative errors is verified.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.1
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• pp.67-74
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• 1998

The primary cause of air pollution by vehicles is imperfect combustion of fuel. One of the most usual causes of this imperfect combustion is the misfire in IC engins. The U.S. EPA(Environment Protection Agency) and the CARB(California air Resources Board) have imposed regulations for the detection of misfiring in automotive engines. The OBD-II regulations require that misfire should be monitored by the engine diagnostic system, and that the goal of OBD-II is to alert the driver to the presence of a malfunction of the emission control system. Several solutions to the misfire detection problem have been proposed for the detection of misfires. However, the performance of these methods in the presence of misfire is not altogether clear. This paper presents a precise method and system for internal combustion engine misfire. Present invention based upon measurements of engine roughness as derived from crankshaft angular velocity measurements with special signal processing method. Crankshaft angular velocity signals are processed by WDPT, so that the more reliable misfire detection than the time domain analysis. Experimental work confirms that it is possible to apply the WDFT for the detection of misfires in no-load idle and road testing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.130-140
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• 2010

SIP (Session Initiation Protocol) is a signaling protocol to provide IP-based VoIP (Voice over IP) service. However, many security vulnerabilities exist as the SIP protocol utilizes the existing IP based network. The SIP Malformed message attacks may cause malfunction on VoIP services by changing the transmitted SIP header information. Additionally, there are several threats such that an attacker can extract personal information on SIP client system by inserting malicious code into SIP header. Therefore, the alternative measures should be required. In this study, we analyzed the existing research on the SIP anomaly message detection mechanism against SIP attack. And then, we proposed a Co-occurrence based SIP packet analysis mechanism, which has been used on language processing techniques. We proposed a association rule generation and an attack detection technique by using the actual SIP session state. Experimental results showed that the average detection rate was 87% on SIP attacks in case of using the proposed technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.584-590
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• 2013

Depending on the operating characteristics, track circuit is installed for the purpose of control directly or indirectly of the signal device, point switch machine and other security device. These are mainly used for train detection, transmission of information, broken train detection and transmission of return current. Especially, the return current is related to signal system, power system and catenary line, and track circuit systems. It is one of the most important component shall be dealt for the safety of track side staff and for the protection of railway-related electrical system according to electrification. Therefore, an accurate analysis of the return current is needed to prevent the return current unbalance and the system induced disorder and failure due to an over current condition. Also, if the malfunction occurred by the return current harmonics, it can cause problems including train operation interruption. In this paper, we presented measurement and analysis method at return current and it's harmonics by train operation. By the test criteria, we evaluated for safety. Hereafter, it is expected to contribute to the field associated with it.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.525-534
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• 2016

The biological wastewater treatment plant, which uses microbial community to remove organic matter and nutrients in wastewater, is known as its nonlinear behavior and uncertainty to operate. Therefore, operation of the biological wastewater treatment process much depends on observation and knowledge of operators. The manual inspection of human operators is essential to manage the process properly, however, it is impossible to detect a fault promptly so that the process can be exposed to improper condition not securing safe effluent quality. Among various process faults, equipment malfunction is critical to maintain normal operational state. To detect equipment faults automatically, the dynamic time warping was tested using on-line oxidation-reduction potential (ORP) and dissolved oxygen (DO) profiles in a sequencing batch reactor (SBR), which is a type of wastewater treatment process. After one cycle profiles of ORP and DO were measured and stored, they were warped to the template profiles which were prepared already and the distance result, accumulated distance (D) values were calculated. If the D values were increased significantly, some kinds of faults could be detected and an alarm could be sent to the operator. By this way, it seems to be possible to make an early detecting of process faults.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.461-471
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• 2004

In recent years, large volumes of data are transferred between a computer system and various subsystems through digital logic circuits and interconnected wires. And there always exist potential errors when data are transferred due to electrical noise, device malfunction, or even timing errors. In general, parity checking circuits are usually employed for detection of single-bit errors. However, it is not sufficient to enhance system reliability and availability for efficient error detection. It is necessary to detect and further correct errors up to a certain level within the affordable cost. In this paper, we report a generation of 3-distance code using the characteristic matrix of a PBCA.