• Analytical Science and Technology
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• v.10 no.1
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• pp.1-8
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• 1997

We identified nicotine, cotinine and toluene in high school volunteer's urine by using GC/NPD, GC/FID and GC/MS. To analyze of nicotine and cotinine, urine samples were extracted with diethylether and centrifuged on a benchtop centrifuge for 5 min. The upper organic layer was injected into a GC. The distributions of nicotine and cotinine were $4{\sim}630{\mu}g/L$ and $63{\sim}1,602{\mu}g/L$ in smoking-group, respectively. To analyze of toluene, head space vial was filled with 2mL sodium citrate solution and 1mL of urine. The vial was warmed in a water bath at $55^{\circ}C$ for 20min, and then $250{\mu}L$ of head space air was injected into a GC. The result show that toluene was not detected in all of the volunteers' samples. However, the range of toluene was 0.1~28.0mg/L in glue sniffer's urine samples(NISI data).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.12
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• pp.1040-1047
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• 2012

This paper presents a fault diagnosis algorithm of control surfaces of small fixed-wing aircraft to reduce maintenance cost or to improve repair efficiency by estimation of fault occurrence or part replacement periods. The proposed fault diagnosis algorithm consists of ANPSD (Averaged Normalized Power Spectral Density), PCA (Principle Component Analysis), and GC (Geometric Classifier). ANPSD is used for frequency-domain vibration testing. PCA has advantage to extract compressed information from ANPSD. GC has good properties to minimize errors of the fault detection and isolation. The algorithm was verified by the accelerometer measurements of the scaled normal and faulty ailerons and the test results show that the algorithm is suitable for the detection and isolation of the control surface faults. This paper also proposes solutions for some kind of implementation problems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.486-492
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• 2015

Radiation source imaging system is essential for protecting of radiation leakage accidents and minimizing damages from the radioactive materials, and is expected to play an important role in the nuclear plant decommissioning area. In this study, the stereoscopic camera principle was applied to develop a new radiation imaging device technology that can extract the radiation three-dimensional position information. This radiation three-dimensional imaging device (K3-RIS) was designed as a compact structure consisting of a radiation sensor, a CCD camera, and a pan-tilt only. It features the acquisition of stereoscopic radiation images by position change control, high-resolution detection by continuous scan mode control, and stereoscopic image signal processing. The performance analysis test of K3-RIS was conducted for a gamma-ray source(Cs-137) in radiation calibration facility. The test result showed that a performance error with less than 3% regardless of distances of the objects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2586-2592
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• 2014

Lost fishing gears become a major cause of marine pollution, and many policy and technical efforts have been conducted for that. For efficient retrieving lost fishing gears in underwater, It is important to know the current position. Using GPS in the sub-sea environment is impossible and localization requires the use of special systems, and mobility due to water currents for underwater localization also has to be considered. In this paper, described with respect to the system for a self-generated location informations without using an external signal, such as a GPS and Sonar and storing them. Using the characteristics of the geomagnetic and INS principle, proposed informations and a way for estimating self position during movement. Embedded based system suggested and implemented in this study is tested for validating it's functionality.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4455-4461
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• 2013

This paper investigates the diagnosis performance of Artificial Neural Network (ANN) depending on the structure and the input vector type of ANN, which has been used to detect the partial discharge to lead to the electric machinery deterioration. The diagnosis performance of one hidden layer and two hidden layer in ANN are compared. The performance using the 2048 time-series data and the performance using the feature input vector are compared. For measuring the partial discharge signal, the tip-to-plate, the sphere-to-sphere, the tip-to-tip, the tip-to-sphere and the sphere-to-plate electrodes are used respectively. For ANN's learning, Matlab and C-code program are used. For evaluating the diagnosis performance of ANNs, the simulation studies are performed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• Land and Housing Review
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• v.2 no.4
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• pp.407-413
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• 2011

This paper reports a novel structural health monitoring (SHM) technique for detecting de-bonding between a concrete beam and CFRP (Carbon Fiber Reinforced Polymer) sheet that is attached to the concrete surface. To achieve this, a multi-scale actuated sensing system with a self-sensing circuit using piezoelectric active sensors is applied to the CFRP laminated concrete beam structure. In this self-sensing based multi-scale actuated sensing, one scale provides a wide frequency-band structural response from the self-sensed impedance measurements and the other scale provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. To quantify the de-bonding levels, the supervised learning-based statistical pattern recognition was implemented by composing a two-dimensional (2D) plane using the damage indices extracted from the impedance and guided wave features.

• Journal of Korean Society of Transportation
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• v.23 no.1
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• pp.83-92
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• 2005

This study if the first trial toward realizing a new methodology for vehicle re-identification based on heterogeneous sensor systems. A major interest of the author is how to effectively utilize information obtained from different sensors to derive accurate and reliable section travel times. The 'blade' sensor that is a newly developed sensor for capturing vehicle wheel information and the existing square loop sensor are employed to extract the inputs of the proposed vehicle re-identification algorithm. The fundamental idea of the algorithm developed in this study, which is so called 'anonumous vehicle re-identification,' it to match vehicle features obtained from both sensors. The results of the algorithm evaluation reveal that the proposed methodology could be successfully implemented in the field. The proposed methodology would be an invaluable tool for operating agencies in support of traffic monitoring systems and traveler information systems.