• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.531-538
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• 2005

This paper proposes an expert system which is able to enhance the accuracy and productivity by determining the test strategy based on heuristic rules for test of the hybrid electronic circuit board producted massively in production line. The test heuristic rules are obtained from test system designer, test experts and experimental results. The guarding method separating the tested device with circumference circuit of the device is adopted to enhance the accuracy of measurements in the test of analog devices. This guarding method can reduce the error occurring due to the voltage drop in both the signal input line and the measuring line by utilizing heuristic rules considering the device impedance and the parallel impedance. Also, PSA(Parallel Signature Analysis) technique Is applied for test of the digital devices and circuits. In the PSA technique, the real-time test of the high integrated device is possible by minimizing the test time forcing n bit output stream from the tested device to LFSR continuously. It is implemented in Visual C++ computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. Finally, the effectiveness of the builded expert system is proved by simulating the several faults occurring in the mounting process the electronic devices to the surface of PCB for a typical hybrid electronic board and by identifying the results.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.464-466
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• 2001

The advantage of the squirrel cage induction motor is the brushless rotor. This advantage for operation and maintenance turns out to be a disadvantage for the detection of the cage rotor bar and endring defects, which means that the detection of cage faults is due to the measurement and analysis of only the stator input signals. The monitoring task in an inverter drive is complicated mainly because the voltage and current waveforms are nonsinusoidal and the high dv/dt values from fast switching inverterd distort the measurements. in this paper, we are going to discuss the detection method of broken rotor bar of squirrel cage induction motor by the motor current signal analysis(MCSA).

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1612-1614
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• 2003

80pF capacitive couplers were connected to six 6.6kV motor model coil terminals. The voltage applied to the coils were 3.81kv, 4.76 kV and 6.6kV, respectively. These stator coils have various types of artificial insulation defects such as large voids, semi-conductive coating damage and strand insulation fault. Digital PD detector(PDD) and turbine generator analyzer(TGA) were used to measure PD activity. TGA summarizes each plot with two quantities such as the normalized quantity number(NQN) and the peak PD magnitude(Qm). The PD levels in PD were measured with a conventional digital PD detector. Most of the defect mechanism of large motor stator winding can be associated with PD patterns such as internal and slot discharges. PD patterns coincide with PDD and TGA. These instruments have an input bandwidth of 40-400kHz and 0.1-350MHz. Surge testing detects faults in inter-turn winding of high voltage motor model coils.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.151-160
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• 2013

iOS applications refer to the apps operating on iOS, a mobile OS developed by Apple. As iOS provides graphic user interfaces based on touch screens, most of iOS apps support GUIs. GUIs become increasingly important for iOS apps. So are GUI tests. As GUI functions are performed by event handlers, faulty event handlers could cause defects in GUIs. Hence, this study detects faults in event handlers as a way to test GUIs for iOS apps, and suggests how to generate test cases by re-defining input domains of event handlers.

• 한국지구물리탐사학회:학술대회논문집
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• 2010.10a
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• pp.27-27
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• 2010

There have been several studies about empirical relation between seismic source parameters(e.g., focal mechanisms, depths, magnitudes, etc.) and T-wave observation. In order to delineate the relation, numerical and theoretical approaches to figure out T-wave excitation mechanism are required. In an attempt to investigate source radiation and wave scattering effects in the oceanic crust on T-wave envelopes, we perform three-dimensional numerical modeling to synthesize T-wave envelopes. We first calculate seismic P- and SV-wave energy on the seafloor using the Direct Simulation Monte Carlo based on the Radiative Transfer Theory, which enables us to take into account both realistic seismic source parameters and wave scattering in heterogeneous media, and then estimate excited T-wave energy by normal mode computation. The numerical simulation has been carried out considering the following different conditions: source types (strike and normal faults), source depths (shallow and deep), and wave propagation through homogeneous and heterogeneous Earth media. From the results of numerical modeling, we confirmed that T-wave envelopes vary according to spatial seismic energy distributions on the seafloor for the various input parameters. Furthermore, the synthesized T-wave envelopes show directional patterns due to anisotropic source radiation, and the slope change of T-wave envelopes caused by focal depth. Seismic wave scattering in the oceanic crust is likely to control the shape of envelopes.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2153-2158
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• 2008

XML is one of standard web languages. XML has a syntax architecture consisted of tags, which are used to descript contents and structures of a XML document. In XML documents, missing of markup tag is one of common factors generating incomplete inputs. Usually, editors will recognize incomplete inputs as syntax errors. And so, when editors find them, they will highlight lines in which syntax errors happened, and execute appropriate error handling routines. But, there are no more parsing actions. In this paper, we propose a method to recognize incomplete input strings and keep parsing phases going. To recognize pars missed grammatically in incomplete inputs and create them newly, we use an expanding parsing table. It includes additional parsing actions for newly generated input symbols. Through the information, incomplete inputs will be completed and parsing steps will be finished successively. Therefore, users can be assured that they make always correct XML documents, even if inputs are incomplete, and can not be nervous about input faults.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.10
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• pp.45-51
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• 2003

In this paper, a scan-based low power BIST (Built-In Self-Test) architecture is proposed. The proposed architecture is based on STUMPS, which uses a LFSR (Linear Feedback Shift Register) as the test generator, a MISR(Multiple Input Shift Register) as the reponse compactor, and SRL(Shift Register Latch) channels as multiple scan paths. In the proposed BIST a degenerate MISR structure is used for every SRL channel; this offers reduced area overheads and has less impact on performance than the STUMPS techniques. The proposed BIST is designed to support both test-per-clock and test-per-scan techniques, and in test-per-scan the total power consumption of the circuit can be reduced dramatically by suppressing the effects of scan data on the circuits. Results of the experiments on ISCAS 89 benchmark circuits show that this architecture is also suitable for detecting path delay faults, when the hamming distance of the data in the SRL channel is considered.