• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.370-372
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• 2005

Inconveniences which might arise in transmitting measured biological data based on cable protocols generally are recognized critical points in tele-monitoring environment and also restrict the mobility of the user. a. Especially, activity monitoring which is importantly recognized as a core parameter in ubiquitous healthcare arena and weight management, pervasive and wireless measuring technology is most needed. In this paper, we would like to suggest lower power, miniaturized communication system in order to solve the above problems. The suggested system is powered by small coin-size battery. Also, The suggested system is compared with a blue-tooth module which is generally available in the commercial market. Even though, the suggested system didn't have higher transmission rate, its low power consumption make the suggested system would be feasible in ubiquitous monitoring of biological signals in ubiquitous healthcare arena.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.6
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• pp.414-420
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• 2015

Concrete undergoes significant phase changes from liquid to solid states as hydration progresses. These phase changes are known as the setting process. A liquid state concrete is electrically conductive because of the presence of water and ions. However, since the conductive elements in the liquid state of concrete are consumed to produce non-conductive hydration products, the electrical conductivity of hydrating concrete decreases during the setting process. Therefore, the electrical properties of hydrating concrete can be used to monitor the setting process of concrete. In this study, a parameter identification method to estimate electrical parameters such as ohmic resistance of concrete is proposed. The effectiveness of the proposed method for monitoring the setting process of concrete is experimentally validated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.3
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• pp.238-244
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• 2004

To monitor the torque of an induction motor using current, the accurate identification of the motor parameters is very important. In this study, the motor parameters such as rotor resistance, stator and rotor leakage inductance, mutual inductance are estimated for torque monitoring and indirect vector control. Estimated parameters are used to monitor the torque of vector controlled induction motor without any speed measuring sensor. Stator current is measured to estimate the magnetizing current which is used to calculate flux linkage, rotor velocity and motor torque. From the experiments, the proposed method shows a good estimation of the motor parameters and torque under the normal rotational speed.

• Tribology and Lubricants
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• v.18 no.2
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• pp.117-126
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• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.199-206
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• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• IE interfaces
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• v.9 no.1
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• pp.13-24
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• 1996

Quality of injection molded parts is dependent on both mold design and processing conditions. From the mold design point of view, an optimal shrinkage should be used to compensate the shrinkage of molded parts. From the processing point of view, it is important to analyze the priority of processing conditions because a number processing conditions affect the quality of molded parts. Processing analysis employing the design of experiment was performed, and the shrinkage of molded part was considered as a characteristic parameter to improve the quality. As the result of the analysis of variance on SN ratio of a characteristic value, injection speed and bolding pressure were selected as two effective process parameters. Regression analysis on shrinkage and processing conditions was carried out, and an optimal processing condition was obtained by the response surface analysis. Shrinkage at the optimal condition could be used to reduce the number of try-cut at the step of mold making. The ranges of indirect control parameter, such as maximum cavity pressure or weight, measured at the optimal processing condition were used for monitoring the quality of molded parts in process.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.331-350
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• 2017

Based on the alternative stabilization diagram by varying the time lag parameter in the stochastic subspace identification analysis, this study aims to investigate the measurements from several cases of civil structures for extending the applicability of a recently noticed criterion to ensure stable identification results. Such a criterion demands the time lag parameter to be no less than a critical threshold determined by the ratio of the sampling rate to the fundamental system frequency and is firstly validated for its applications with single measurements from stay cables, bridge decks, and buildings. As for multiple measurements, it is found that the predicted threshold works well for the cases of stay cables and buildings, but makes an evident overestimation for the case of bridge decks. This discrepancy is further explained by the fact that the deck vibrations are induced by multiple excitations independently coming from the passing traffic. The cable vibration signals covering the sensor locations close to both the deck and pylon ends of a cable-stayed bridge provide convincing evidences to testify this important discovery.

• Journal of Hydrogen and New Energy
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• v.27 no.1
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• pp.36-41
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• 2016

The development need of new renewable energy is more and more important to resolve exhaustion of chemical fuels and environmental pollution. Polymer electrolyte membrane fuel cell has been widely studied to the extent that it can be used commercially. But there are many problems to be solved. One of them is to enhance the stability of fuel cell stacks. This paper proposes a new fault diagnosis method using Least Square Method (LSM) which is one of parameter estimation methods. The proposed method extracts equivalent circuit parameters from on-line measurements. Parameters of the circuit are estimated according to normal and abnormal states using simulation. The variation of parameters estimated in each states enables the estimation of state in fuel cells. Thus the LSM presented can be a suitable on-line parameter estimation method in PEMFC.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.