• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.246-253
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• 2003

The initiation and growth of short fatigue cracks in the simulated aircraft structure with a series of rivet holes was detected by acoustic emission (AE). The location and the size of short tracks were determined by AE source location techniques and the measurement with traveling microscope. AE events increased intermittently with the initiation and growth of short cracks to form a stepwise increment curve of cumulative AE events. For the precise determination of AE source locations, a region-of-interest (ROI) was set around the rivet holes based on the plastic zone size in fracture mechanics. Since the signal-to-noise ratio (SNR) was very low at this early stage of fatigue cracks, the accuracy of source location was also enhanced by the wavelet transform do-noising. In practice, the majority of AE signals detected within the ROI appeared to be noise from various origins. The results showed that the effort of structural geometry and SNR should be closely taken into consideration for the accurate evaluation of fatigue damage in the structure.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.49-61
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• 2006

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the UDAR data compression. A newly developed data compression approach to approximate the UDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become an important research topic for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original UDAR data. The results show that this method can be used for significant reduction of data set.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.8-16
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• 2004

An inversion method is presented for the determination of the compressional wave speed, compressional wave attenuation, thickness of the sediment layer and density as a function of depth for a horizontally stratified ocean bottom. An experiment for estimating those properties was conducted in the shallow water of South Sea in Korea. In the experiment, a light bulb implosion and the propagating sound were measured using a VLA (vertical line array). As a method for estimating the geoacoustic properties, a coherent broadband matched field processing combined with Genetic Algorithm was employed. When a time-dependent signal is very short, the Fourier transform results are not accurate, since the frequency components are not locatable in time and the windowed Fourier transform is limited by the length of the window. However, it is possible to do this using the wavelet transform a transform that yields a time-frequency representation of a signal. In this study, this transform is used to identify and extract the acoustic components from multipath time series. The inversion is formulated as an optimization problem which maximizes the cost function defined as a normalized correlation between the measured and modeled signals in the wavelet transform coefficient vector. The experiments and procedures for deploying the light bulbs and the coherent broadband inversion method are described, and the estimated geoacoustic profile in the vicinity of the VLA site is presented.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.97-104
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• 2012

In this paper, the GPS precipitable water vapor was retrieved by estimating of GPS signal delay in the troposphere during the progress of heavy snowfall on the Gangwon Province, 2011. For this period, the time series analysis between GPS precipitable water vapor and fresh snow depth was accomplished. The time series and the comparison with the GPS precipitable water vapor and the fresh snow depth indicates that the temporal change of two variations is closely related to the progress of the heavy snowfall. Also, the periodicity of GPS precipitable water vapor using the wavelet transform method was showed a similar cycle of saturated water vapor pressure as the limitation of this study span. The result shows that the decrement of GPS precipitable water vapor was conflicted with the increment of fresh snow depth at two sites, Gangneung and Uljin. The correlation between the GPS precipitable water vapor and the saturated water vapor pressure for the event was showed a positive correlation, compare with the non-heavy snowfall periods.

• Journal of Korea Water Resources Association
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• v.38 no.2
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• pp.111-119
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• 2005

The climatological characteristics of the area averaged monthly precipitation over the Han- and Nakdong-river basins were investigated. The data used for this study is monthly precipitation data from 51 meteorological stations for the period of 1954 to 2002. The magnitude of area averaged precipitation in the Han-river basin was about 10% larger than that in the Nakdong-river basin. However, the variability of two monthly precipitation time series exhibited similar characteristics: April precipitation tends to decrease and August precipitation increase significantly, while there was no significant trend for the other months. There were some indications of abrupt change around the 1970's in the periodicity of precipitation and relationship with El Nino index. September precipitation showed negative correlation with NINO3 index but November precipitation, positive correlation with NINO3 index, indicating a possible connection with the global-scale phenomena.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.24-33
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• 2017

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.9-20
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• 1999

To investigate the characteristics of internal waves (IWs) and their effects on acoustic wave propagation, a series of sea experiment were performed in the east coast of Donghae city, Korea in 1997 and 1998 where the water depth varies between 130 and 140 m. Thermistor strings were deployed to measure water temperatures simultaneously at 9 depths. CW source signals with the frequencies of 250,670 and 1000 Hz were received by an array of 15 hydrophones. Through the Wavelet transform analysis, the IWs are characterized as having typical periods of 2-17 min and duration of 1-2 hours. The IWs exist in a group of periods rather than in one period. Underwater acoustic signals also show obvious energy peaks in the periods of less than 12 min. Consistency in the periods of the two physical processes implies that acoustic waves react to the IWs through some mechanisms like mode interference and travel time fluctuation. Based on the thermistor string data, mode arriving structures are analyzed. As thermocline depth varies with time, it may cause travel time difference as much as 4-10 ms between mode 1 and 2 over 10 km range. This travel time difference causes interference among modes and thus fluctuation from range-independent stratified ocean structure. In real situations, however, there exist additional spatial variation of IWs. Model simulations with all modes and simple IWs show clear responses of acoustic signals to the IWs, i.e., fluctuations of amplitude and phase.