• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.79-88
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• 2017

One of the important applications of activity sensing in the home is energy monitoring. Many previous methodologies for detecting and recognizing household appliances have been proposed. This paper presents an approach that uses an energy metering circuit (EMC) to classify and identify the various electrical devices in home based on root-mean-square (RMS) consumed current value. EMC gathers the RMS current values created by appliance state transition (e.g., on to off) and apparatus operating process. In this paper, an identification algorithm is proposed to detect a change in current levels using the standard deviation of current signals and their average values. In addition, characteristic of the appliance is extracted concerning four feature parameters concerning the number of current levels, the minimum level, the maximum level, and signal-to-noise ratio (SNR) of them. Experiment results validate the reliable performance of the proposed identification method for 11 representative appliances.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.59-67
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• 2020

This study aims to develop the fuzzy risk assessment model of the debris flow to verify the accuracy of risk assessment in order to help related organizations reduce losses caused by landslides. In this study, actual cases of landslides that occurred are utilized as the database. The established models help us assess the occurrence of debris flows using computed indicators, and to verify the model errors. In addition, comparisons are made between the models to determine the best one to use in practical applications. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.243-243
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• 2000

This paper introduces a visual inspecting and monitoring system based on an image processing technique. We propose an image processing method for analyzing landslide movement in real time. The method adopts Laplacian of Gaussian operator to extract linear features for the captured images and uses a linear matching algorithm to distinguish the matching error for those features. When the algorithm is processed, motion parameters such as displacement area and its direction are computed. Once movement is recognized, displacements are estimated graphically with statistical amount in the image plane. The simulation results are shown us to verify the effectiveness of the developed method.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.168-176
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• 1996

A diagnosis system which can monitor tool breakage and chipping in real time was developed using a DSP(Digital Signal Processor) board in face milling operation. AR modelling and band energy method were used to extract the feature of tool states from cutting force signals. Artificial neural network embedded on DSP board discriminates different patterns from features got after signal processing. The features extracted from AR modelling are more accurate for the malfunction of a process than those from band energy method, even though the computing speed of the former is slow. From the processed features, we can construct the real time diagnosis system which monitors malfunction by using a DSP board having a parallel processing capability.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.793-794
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• 2003

Forest fire monitoring prototype system was developed by National Development Agency of Japan (NASDA) and the Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF) to verify the usefulness of interoperabile system to study new services of Earth observation satellite data distribution for a practical application. In this system, a standard interface of Web based GIS technology, OpenGIS Consortium (OGC) technology, was adopted. This system is also expected to encourage data sharing activities in Digital Asia Network (DAN) as a demonstration system.

• Smart Structures and Systems
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• v.29 no.6
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.23-34
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• 1995

This paper is concerned with effective and reliable tool breakage detection method using pattern characteristics of feed motor current in milling operations. Correlation coefficient is derived from the feature vector of signal for two consecutive which are extracted feed motor current over three spindle revolutions. The changing pattern of correlation coefficient is continuously compared to detect tool breakage and monitor cutting conditions. This proposed monitoring scheme is not affected by different tools, friction of motion, and varying cutting conditions and material shapes. Experimental results are presented to support the proposed monitoring scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.176-180
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• 2004

The ST-segment that the beginning part of T wave is the important diagnostic parameter to finding myocardial ischemia. Abnormal ST appears in two types. One is the level change, and the other is the pattern change. In this paper, we describe the monitoring of abnormal ST using PC based system. Hardware of this system consists of transmitter, receiver and PC. The function of transmitter is measuring ECG in three channels which are selected manually and transmitting the data to receiver by digital radio way. Connection with receiver and PC is by RS232C, and the data received on the PC is analyzed automatically by ECG analysis algorithm and saved to file. In the algorithm part for detecting abnormal ST, ST-segments are approximated by a polynomial. This method can detect all of the deviation and pattern change of ST-segment regardless the change in the heart rate or sampling rate. To gain algorithm reliability, the method rejects distorted polynomial approximation by calculation the difference between the approximated ST-segment and original ST-segment. In pre-signal processing, the wavelet transformation separates high frequency bands including QRS complex from the original ECG. Consequently, the process improves the performance of detecting each feature points.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.64-71
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• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1229-1248
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• 2016

With the feature of convenience and low cost, remote healthcare monitoring (RHM) has been extensively used in modern disease management to improve the quality of life. Due to the privacy of health data, it is of great importance to implement RHM based on a secure and dependable network. However, the network connectivity of existing RHM systems is unreliable in disaster area because of the unforeseeable damage to the communication infrastructure. To design a secure RHM system in disaster area, this paper presents a Secure VANET-Assisted Remote Healthcare Monitoring System (SVC) by utilizing the unique "store-carry-forward" transmission mode of vehicular ad hoc network (VANET). To improve the network performance, the VANET in SVC is designed to be a two-level network consisting of two kinds of vehicles. Specially, an innovative two-level key management model by mixing certificate-based cryptography and ID-based cryptography is customized to manage the trust of vehicles. In addition, the strong privacy of the health information including context privacy is taken into account in our scheme by combining searchable public-key encryption and broadcast techniques. Finally, comprehensive security and performance analysis demonstrate the scheme is secure and efficient.