• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.679-685
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• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• Journal of Ocean Engineering and Technology
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• v.24 no.3
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• pp.1-10
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• 2010

In this study, hybrid smart sensor nodes were developed for the autonomous structural health monitoring of prestressed concrete (PSC) girders. In order to achieve the objective, the following approaches were implemented. First, we show how two types of smart sensor nodes for the hybrid health monitoring were developed. One was an acceleration-based smart sensor node using an MEMS accelerometer to monitor the overall damage in concrete girders. The other was an impedance-based smart sensor node for monitoring the local damage in prestressing tendons. Second, a hybrid monitoring algorithm using these smart sensor nodes is proposed for the autonomous structural health monitoring of PSC girders. Finally, we show how the performance of the developed system was evaluated using a lab-scaled PSC girder model for which dynamic tests were performed on a series of prestress-loss cases and girder damage cases.

• Annual Conference of KIPS
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• 2016.04a
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• pp.129-132
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• 2016

The importance of the infusion pump monitoring system has become amplified in accordance with the growth of IoT(Internet of Things) technology and medical devices. A monitoring system can be described as an essential part of infusion pump system because the patient must be observed all the time. Infusion pump monitoring system is significant to have better safety and efficiency. In this paper, we propose an efficient algorithm and scheme in the infusion monitoring system. In particular, the proposed algorithm based on the time of the database interlocking part was found to have a 30% higher efficiency than the conventional method.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.593-601
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• 2012

This paper develops and analyzes a new multiple-hypothesis Receiver Autonomous Integrity Monitoring (RAIM) algorithm as a candidate for future standard architecture. The proposed algorithm can handle simultaneous multiple failures as well as a single failure. It uses measurement residuals and satellite observation matrices of several consecutive epochs for Failure Detection and Exclusion (FDE). The proposed algorithm redueces the Minimum Detectable Bias (MDB) via the Relative RAIM (RRAIM) scheme. Simulation results show that the proposed algorithm can detect and filter out multiple failures in tens of meters.

• Smart Structures and Systems
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• v.15 no.1
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• pp.191-207
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• 2015

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.207-208
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• 2008

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is improved to about 10%, compared with well-known methods.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.3
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• pp.131-137
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• 2005

This paper describes a new approach to the algorithm and fundamental characteristics of the device for monitoring the leakage currents flowing through zinc oxide (ZnO) surge arresters. In order to obtain a technique for a new on-line monitoring device that can be used in the deterioration diagnosis of ZnO surge arresters, the new algorithm and on-line leakage current detection device for extracting the resistive and capacitive currents using the phase shift addition method were proposed. The computer-based on-line monitoring device can sense accurately the power frequency leakage currents flowing through ZnO surge arresters. The on-line leakage current monitoring device of ZnO surge arresters proposed in this work has the high sensitivity compared to the third harmonic leakage current detection devices. As a consequence, it was found that the proposed leakage current monitoring device would be useful for forecasting the defects and degradation of ZnO surge arresters.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.32-38
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• 2020

The deployment of geographically distributed wireless sensors has greatly elevated the capability of monitoring structural health in social-overhead capital (SOC) public infrastructures. This paper deals with the utilization of a distributed mobility management (DMM) approach for the deployment of wireless sensing devices in a structural health monitoring system (SHM). Then, a wireless sensing mechanism utilizing low-energy adaptive clustering hierarchy (LEACH)-based clustering algorithm for smart sensors has been analyzed to support the seamless data transmission of structural health information which is essentially important to guarantee public safety. The clustering of smart sensors will be able to provide real-time monitoring of structural health and a filtering algorithm to boost the transmission of critical information over heterogeneous wireless and mobile networks.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1382-1385
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• 2007

In this paper, we study about the measuring algorithm that can implement Structural Health Monitoring (SHM) more efficiently by two measurement methods using smart sensor. Through the impedance measurement method, the damage condition of structures on wide area is monitored first, and then it changes the mode to guided wave measurement mode by mode switching algorithm when impedance measurement mode detects abnormal signals. Efficient handling of the real-time data would be available by analyzing location and shape of damage through guided wave measurement.