• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.9
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• pp.71-79
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• 1993

This paper proposes a new test pattern generation methodology for combinational logic circuits using neural networks based on a modular structure. The CUT (Circuit Under Test) is described in our gate level hardware description language. By conferring neural database, the CUT is compiled to an ATPG (Automatic Test Pattern Generation) neural network. Each logic gate in CUT is represented as a discrete Hopfield network. Such a neual network is called a gate module in this paper. All the gate modules for a CUT form an ATPG neural network by connecting each module through message passing paths by which the states of modules are transferred to their adjacent modules. A fault is injected by setting the activation values of some neurons at given values and by invalidating connections between some gate modules. A test pattern for an injected fault is obtained when all gate modules in the ATPG neural network are stabilized through evolution and mutual interactions. The proposed methodology is efficient for test generation, known to be NP-complete, through its massive paralelism. Some results on combinational logic circuits confirm the feasibility of the proposed methodology.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.400-425
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• 2018

This study presents the research results and current status of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to simulate the coupled hydro-mechanical behavior of fault, including slip or reactivation, induced by water injection. The first research step of Task B is a benchmark simulation which is designed for the modelling teams to familiarize themselves with the problem and to set up their own codes to reproduce the hydro-mechanical coupling between the fault hydraulic transmissivity and the mechanically-induced displacement. We reproduced the coupled hydro-mechanical process of fault slip using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. A methodology to formulate the hydro-mechanical coupling relations of two different hydraulic aperture models and link the solid element of TOUGH2 and the interface element of FLAC3D was suggested. In addition, we developed a coupling module to update the changes in geometric features (mesh) and hydrological properties of fault caused by water injection at every calculation step for TOUGH-FLAC simulator. Then, the transient responses of the fault, including elastic deformation, reactivation, progressive evolutions of pathway, pressure distribution and water injection rate, to stepwise pressurization were examined during the simulations. The results of the simulations suggest that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOLVAEX-2019 Task B and validated using the field data from fault activation experiments in a further study.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.1
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• pp.73-81
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• 2020

For the fault tree analysis (FTA) analysis of the packaged hydrogen filling station, the composition of the charging station was analyzed and the fault tree (FT) diagram was prepared. FT diagrams were created by dividing the causes of events into external factors and internal factors with the hydrogen event as the top event. The external factors include the effects of major disasters caused by natural disasters and external factors as OR gates. Internal factors are divided into tube tailer, compressor & storage tank, and dispenser, which are composed of mistakes in operation process and causes of accidents caused by parts leakage. In this study, the purpose was to improve the hydrogen station. The subjects of this study were domestic packaged hydrogen stations and FTA study was conducted based on the previous studies, failure mode & effect analysis (FMEA) and hazard & operability study (HAZOP). Top event as a hydrogen leaking event and constructed the flow of events based on the previous study. Refer to "Off shore and onshore reliability data 6th edition", "European Industry Reliability Data Bank", technique for human error rate prediction (THERP) for reliability data. We hope that this study will help to improve the safety and activation of the hydrogen station.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.120-124
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• 2002

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency (rf) impedance match data. Using a match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with variations in process factors, which include rf source power, pressure, Ar, and $O_$2 flow rates. As an input to neural networks, two means and standard deviations of positions were used as well as a reflected power. Diagnostic accuracy was measured as a function of training factors, which include the number of hidden neurons, the magnitude of initial weights, and two gradients of neuron activation functions. The accuracy was the most sensitive to the number of hidden neurons. Interaction effects on the accuracy were also examined by performing a 2$^$4 full factorial experiment. The experiments were performed on multipole inductively coupled plasma equipment.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.606-609
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• 1995

This paper presents discriminate between magnetizing inrush and internal faults of power transformer by artificial neural networks trained with preprocessing of fault discriminant. The proposed neural networks contain multi-layer perceptron using back-propagation learning algorithm with logistic sigmoid activation function. For this training and test, we used the relaying signals obtained from the EMTP simulation of model power system. It is shown that the proposed transformer protection system by neural networks never misoperated.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Korean Journal of Materials Research
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• v.11 no.4
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• pp.278-282
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• 2001

The recombination motion of Partial dislocations on basal slip (0001) 1/3<1120> in sapphire ($\alpha$-Al$_2$$O_3$) single crystals was investigated using the four-point bending test with the prism plane (1120) samples. These bending experiments were carried but in the temperature range from $1200^{\circ}C$ to $1400^{\circ}C$ at various engineering stresses 90MPa, 120MPa, and 150MPa. During these tests it was shown that an incubation time was needed for basal slip to be activated. The activation energy for the incubation time was 5.6-6.0eV in the temperature range from $1200^{\circ}C$ to $1400^{\circ}C$. The incubation time is believed to be related to recombination of climb dissociated partial dislocations via self-climb. In addition, these activation energies are nearly same as those for oxygen self-diffusion in $Al_2$$O_3$ (approximately 6.3 eV). Thus, the recombination of the two partial dislocations would be possibly controlled by oxygen diffusion on the stacking fault between the partials.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.1-7
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• 2019

Due to the highly concentrated power plants integrated through the limited transmission lines in Korea, a Special Protection System(SPS) has been applied to stabilize the power systems by instantly tripping the pre-determined generators in a large-scaled power plant when a fault occurs on the drawing transmission lines. Moreover, power outputs of those generators are constrained to avoid any activation of Under Frequency Load Shedding(UFLS) even after those generators are tripped by SPS action. For this, this paper proposes a method for calculating the required capacity of Energy Storage System(ESS) expected to relieve the operating constraints to generators using its fast response for controlling power system frequency. The proposed method uses the generator acceleration energy to derive the stable condition during the SPS action. In addition, its effectiveness is verified by the case studies adopting actual SPS operations in Korean power systems.

• Korean Journal of Materials Research
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• v.3 no.3
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• pp.230-236
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• 1993

We have the homoepitaxiallayers on the surfaces of Si(111) with and without the adsorbed surfactants, for example, Ag or Sn. In this paper, We have studied the difference of growth for these two cases by the observation of intensity oscillations of RHEED specular spots during the growing processes. In the case of growth without the adsorbed surfactants, the Si atoms fill first the stacking fault layer of Si(111) 7 ${\times}$7 structure. Therefore, the irregular oscillations are observed in the early stage of growing process. However, in the case of growth with the adsorbed surfactants, the surfactants already have the ${\sqrt}{3}$ ${\times}$ ${\sqrt}{3}$ structures on the surfaces of Si(111) at the adjucate temperatures of 300`$600^{\circ}C$ and 190~$860^{\circ}C$ for the surfactants of Ag and Sn, respectively. We also find that the number of oscillations is a little larger for the case of growth with the adsorbed surfactants. The reason for this is that for the case of growth with the adsorbed surfactants, the activation energies of Si atoms decrease due to the segregation of surfactants toward the growing surfaces.