Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.
As one of alternative sources of renewable energy, wind energy has an excellence prospect in Indonesia, particularly in coastal and hilly areas which have potential wind to generate electricity for residential uses. There is urgent need to locally develop low cost inverter of wind generator system for residential use. Recent developments in power electronic converters and embedded computing allow improvement of power electronic converter devices that enable integration of microcontrollers in its design. In this project, an inverter circuit with suitable control scheme design was developed. The circuit was to be used with a selected topology of Wind Energy Conversion System (WECS) to convert electricity generated by a 500W direct-drive permanent magnet type wind generator which is typical for residential use. From single phase AC output of the generator, a rectifier circuit is designed to convert AC to DC voltage. Then a DC-DC boost converter is used to step up the voltage to a nominal DC voltage suitable for domestic use. The proposed inverter then will convert the DC voltage to sinusoidal AC. The duty cycle of sinusoidal Pulse-Width Modulated (SPWM) signal controlling switches in the inverter was generated by a microcontroller. The lab-scale experimental rig involves simulation of wind generator by running a geared DC motor coupled with 500W wind generator where the prototype circuit was connected at the generator output. The experimental circuit produced single phase 240V sinusoidal AC voltage with frequency of 50Hz. Measured total harmonics distortion (THD) of the voltage across load was 4.0% which is within the limit of 5% as recommended by IEEE Standard 519-1992. Muhida et al. / Mechatronics, Electrical Power, and Vehicular Technology 03 (2012)
This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.
Ground Penetrating Radar (GPR) is very useful for underground object detection as its signal able to penetrate surfaces in obtaining the underneath information. However, its radargram output in hyperbolic signal are very challenging to be analyzed. This work investigates the suitability of selected data processing methods in extracting important features of the signal in order to understand and reconstruct it to make it more beneficial. Results show that with suitable combination of data processing, it able to extract the peak of the hyperbolic signal accordingly and further reconstruction can be made.
Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator’s knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD & DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
Ground penetrating radar is one of the promising non-destructive investigation for shallow subsurface exploration in locating buried utilities. However, interpreting hyperbolic signature of buried objects in GPR images remains a challenging task since the GPR signals are easily corrupted by environmental noise and cause misinterpretation of the size and geometry of subsurface object from the GPR raw profile. Therefore, this paper proposes Discrete Wavelet Transform (DWT) and principal component analysis (PCA) to classify geometry of buried object using k-nearest neighbour. (k-NN). The GPR images firstly being pre-processed. Then, the GPR images are decomposed using DWT into four sub-bands which are LL (Low-Low), LH (Low-High), HL (High-Low) and HH (High-High). The sub-bands LL or a coarse approximation coefficients was extracted as DWT features in order to classify the shape of buried objects. Since DWT features do contain high dimensional data, thus PCA is used to reduce the dimensional features from higher to lower space by linear transformation. The new projected features were then classify using k-NN classifier into four shapes which is cubic, cylinder, disc and sphere. A series of experiments have been conducted on extracted DWT and PCA features from hyperbolic signature of buried objects having different shapes. Based on the results, the proposed method had achieved the average recognition rate of 99.41%.
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