• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.44-54
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• 2012

In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.

• Journal of KIISE:Information Networking
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• v.36 no.4
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• pp.339-350
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• 2009

In wireless sensor networks, the limited battery resources of sensor nodes have a direct impact on network lifetime. To reduce unnecessary power consumption, it is often the case that only a minimum number of sensor nodes operate in active mode while the others are kept in sleep mode. In such a case, however, the network service can be easily unreliable if any active node is unable to perform its sensing or communication function because of an unexpected failure. Thus, for achieving reliable sensing, it is important to maintain the sensing level even when some sensor nodes fail. In this paper, we propose a new fault-tolerance scheme, called FCP(Fault-tolerant Coverage Preserving), that gives an efficient way to handle the degradation of the sensing level caused by sensor node failures. In the proposed FCP scheme, a set of backup nodes are pre-designated for each active node to be used to replace the active node in case of its failure. Experimental results show that the FCP scheme provides enhanced performance with reduced overhead in terms of sensing coverage preserving, the number of backup nodes and the amount of control messages. On the average, the percentage of coverage preserving is improved by 87.2% while the additional number of backup nodes and the additional amount of control messages are reduced by 57.6% and 99.5%, respectively, compared with previous fault-tolerance schemes.

• Journal of the Korean Society of Radiology
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• v.8 no.7
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• pp.371-376
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• 2014

The process of flip-chip bump bonding, Au wire bonding and encapsulation were sucessfully developed and modularized. The CdTe sensor and ROIC were optimally jointed together at $150^{\circ}C$ and $270^{\circ}C$ respectively under24.5 N for 30s. To make SnAg bump on ROIC easy to be bonded, the higher bonding temperature was established than CdTe sensor's. In addition, the bonding pressure was lowered minimally because CdTe Sensor is easier to break than Si Sensor. CdTe multi-energy sensor module observed were no electrical failures in the joints using developed flip chip bump bonding and Au wire bonding process. As a result of measurement, shearing force was $2.45kgf/mm^2$ and, it is enough bonding force against threshold force, $2kgf/mm^2s$.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.114-123
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• 2002

Called VISTA (Variable-stability In-flight Simulator Test Aircraft), the one-of-a-kind NF-l6D has a simulation system that can mimic several aircraft. Though housed in an F-l6 Fighting Falcon airframe, VISTA can also act like the F-15 Eagle or the Navy's F-14 Tomcat. More importantly, such flexibility allows for improved training and consolidation of some sorties. Consequently USAF Test Pilot School students will have an opportunity to learn how to test future integrated cockpits. In this paper we will use the multiple model adaptive estimation (MMAE) and the multiple model adaptive controller (MMAC) techniques to model the aircraft's flight control system containing the longitudinal and lateral-directional axes. Single and dual actuator and sensor failures will also be included in the simulation. White Gaussian noise will be included to simulate the effects of atmospheric disturbances.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1414-1419
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• 2011

In this paper we study the modeling to quantitatively assess the failure rate of USN system designed for fault-tolerant architecture, aiming at applying the world's best domestic USN technology to safety-critical railways. In order to apply the USN system to the safety-critical field like a train control sector that the failures of controllers may cause severe railway accidents such as train collision and derailment, the quantitative reliability and safety evaluation recommended in IEC 62278 must be preceded. We also develop the evaluation model for overall system failure rate for the distributed network structure, which is the characteristics of USN system. Especially, we allocate reliability targets to component units, and present an availability evaluation plan through the plan on the quantitative achievement of failure rate for sensor nodes, gateways, radio-communication network and servers, along with the failure rate model of the overall system considering network operational features.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.300-304
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• 1993

Chip formation control is an important problem in the automation of manufacturing process, since the continuous chip can cause catastrophic failures of the tooling and entangle the workpiece causing damage. However, it is impossible to predict chip form correctly due to the complex nature of cutting process. In order to detect the chip form for unmanned manufacturing, a new identification method is proposed. The feasibility of using acoustic emission signals from the sensing plate for identification of chip form is investigated. Experiments were conducted under the various cutting conditions. When the acoustic emission sensor is attached to the sensing plate, it turns out that the moving averaged AE signals correlated well with the collision of segmented chips with the plate. The sensitivity of moving averaged AE signals to chip congestions due to continuous chip formation is illustrated as well.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.8
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• pp.361-367
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• 2002

Many failures in high voltage equipment are preceded by partial discharge activity. In this paper deals with the application of the high frequency partial discharge measurement technique in motorette. HFPD measurement is very effective method to detect the PD occurred in motorette which is the called name of test specimen for accelerating test of stator winding[1] In this study, CT type HFPD sensor is used to detect the partial discharges and a measured HFPD pattern is analyzed by fractal mathematics. The neural network algorithm is used to pattern recognition and ageing diagnosis. As a result of this study, the fractal dimensions are increased along to applied voltage and HFPD pattern recognition using neural network shown excellent recognition rate. Also, the ageing diagnosis of motorette has been Possible.

• Proceedings of the KIEE Conference
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• 2000.11b
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• pp.280-283
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• 2000

In this paper, a fault tolerant control problem is considered for a class of nonlinear system formulated in a gain scheduling form with LMI-based H-inf control technique Key benefits of this proposed scheme are demonstrated in the simulation of an electromagnetic suspension system with actuator and/or sensor failures, and the method is compared with the convensional state-feedback and output-feedback controller. It is clearly observed that the proposed control scheme shows an improved output performance in comparision with convensional methods.

• Journal of Navigation and Port Research
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• v.32 no.2
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• pp.133-140
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• 2008

In this paper, a fault detection method for operator failures using the observation technique is proposed. The suggested algorithm is extended using the conventional sensor/actuator fault detection method. First, it is assumed that operator failure affects human work operations, as it is an external input signal. With this assumption, a human work model with operator failure is suggested. Second, an unknown input observer with proportional and integral gains is introduced. The characteristic of this observer of estimating an external signal without an exact input is shown, and the conditions for the detection of an operator failure are proposed. Finally, by simulating the container crane operations, it is verified that the observer can accurately detect an operator failure and estimate its magnitude from the given internal signal.