S. M. Elaraby

Laboratory experiments and modeling for industrial radiotracer applications

This paper presents three laboratory experiments, which have been carried out using the Molybdenum-99 (Mo(99)) radiotracer to measure the residence time distribution (RTD), the mixing time and the flow rate in a water flow rig. The results of the RTD measurement experiment are preprocessed using the MATLAB software for background correction, radioactive decay correction, starting point correction, filtering, and data extrapolation. After preprocessing, six mathematical models are investigated on this data using the International Atomic Energy Agency (IAEA) RTD software. The parameters of each model are optimized to calculate the value of the RTD, and to determine the model, which gives the best match with the practical data. The selected model with the best match is used to calculate the RTD in this experiment. The mixing time experiment is carried out for different rotation speeds and repeated three times in each case. The results show that the mixing time is inversely proportional to the rotation speed. The flow rate experiment is carried out to measure the flow rate in the flow rig. The experimental results show a high reliability of the radiotracer used in the RTD, mixing time and flow rate measurements.

Automatic object detection from acoustic to Seismic landmine images

This paper presents two innovative techniques developed and implemented for the automation of the landmine detection in a data scanned by the laser Doppler vibrometer (LDV) based acoustic to seismic (A/S) landmine detection system. These techniques are based on the intensity component of the color landmine images or on grayscale versions of these images. The obtained results are promising in terms of accuracy, consistency, reliability and processing time.

EFFICIENT DETECTION OF LANDMINES FROM ACOUSTIC IMAGES

The Laser Doppler Vibrometer (LDV)-based Acoustic to Seismic (A/S) landmine detection system is one of the reliable and powerful landmine detection systems. The interpretation of the LDVbased A/S data is performed off-line, manually, depending heavily on the skills, experience, alertness and consistency of a trained operator. This takes a long time. The manually obtained results suffer from errors, particularly when dealing with large volumes of data. This paper proposes some techniques for the automatic detection of objects from the acoustic images which are obtained from the LDV-based A/S landmine detection system. These techniques are based on Corresponding author: F. E. Abd El-Samie (fathi sayed@yahoo.com).

Design and Implementation of a Fast General Purpose Fuzzy Processor

In this paper, a Fast Fuzzy processor FP is proposed. This processor, which is implemented using FPGA, has four inputs and one output with 8-bits width for each. The proposed processor is synthesized, functionally verified and implemented using Xilinx Integrated Software Environment ISE and is tested using Xilinx Spartan 3E starter kit. A PC Graphical User Interface GUI is programmed using C# programming language to select and download the parameters of the processor through the serial port communication. The proposed processor is experimentally tested through water sprinkler system example. The experimental results approve the excellent performance of the proposed processor.

C20. Identifying Unique Flatbed Scanner Characteristics for Matching a Scanned Image to its source

Scanner identification is the ability to discern the devices by which an image was scanned. In this paper, a new and robustness individual source scanner identification scheme is proposed. This scheme formulates a unique fingerprint for each scanner using traces of dust, dirt, and scratches over scanner platen on scanned images. A single Support Vector machine (SVM) classifier is implemented and trained using correlation features of scanned images to classify different scanners brands and different models for the same scanner brand, and a 99.68% detection accuracy is obtained. In addition, the robustness of the used individual source scanner identification scheme on resized and different resolutions scanned images is experimentally tested. The experimental results using the proposed classifier for different scanner brands and different models for the same scanner brand approved the validity, efficiency, and robustness of the proposed scheme to match the scanned image to its unique source.

Cepstral detection of buried landmines from acoustic images with a spiral scan

This paper introduces a cepstral approach for the detection of landmines from acoustic images. This approach is based on transforming the 2D landmine images to 1D signals using a spiral scan to make object pixels as close as possible to each other after the scan. The Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1D signals to form a database of features, which can be used to train a neural network. The discrete cosine transform (DCT) and the discrete wavelet transform (DWT) are also investigated in this paper for the possible extraction of features from these transforms of the original images and/or the original images themselves. The detection of landmines can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a landmine exists or not. Experimental results show that, the proposed cepstral approach with features extracted from the 2D DCT are the most robust and reliable features in the detection process because of its strong energy compaction property.

Principle component analysis for radiotracer signal separation.

Radiotracers can be used in several industrial applications by injecting the radiotracer into the industrial system and monitoring the radiation using radiation detectors for obtaining signals. These signals are analyzed to obtain indications about what is happening within the system or to determine the problems that may be present in the system. For multi-phase system analysis, more than one radiotracer is used and the result is a mixture of radiotracers signals. The problem is in such cases is how to separate these signals from each other. The paper presents a proposed method based on Principle Component Analysis (PCA) for separating mixed two radiotracer signals from each other. Two different radiotracers (Technetium-99m (Tc(99m)) and Barium-137m (Ba(137m))) were injected into a physical model for simulation of chemical reactor (PMSCR-MK2) for obtaining the radiotracer signals using radiation detectors and Data Acquisition System (DAS). The radiotracer signals are mixed and signal processing steps are performed include background correction and signal de-noising, then applying the signal separation algorithms. Three separation algorithms have been carried out; time domain based separation algorithm, Independent Component Analysis (ICA) based separation algorithm, and Principal Components Analysis (PCA) based separation algorithm. The results proved the superiority of the PCA based separation algorithm to the other based separation algorithm, and PCA based separation algorithm and the signal processing steps gives a considerable improvement of the separation process.

Distillation Column Malfunctions Identification using Higher Order Statistics

paper presents a proposed approach for distillation column malfunction identification using Higher Order Statistics (HOS). Gamma ray scanning techniques have been used for examining the inner details of a distillation column. In the proposed method, the signals are firstly divided into frames; each frame contains only the signal of one column tray. Secondly, HOS are estimated for these frame signals. Then features are extracted from the HOS estimate. Finally, features are used for training and testing of Artificial Neural Network (ANN) to identify the distillation column malfunctions. The simulation results show that the HOS can be used efficiently for the distillation column malfunction identification especially at high noisy scanning conditions. KeywordsCumulant, moment, and Trispectrum.

RTD signal identification using linear and nonlinear modified periodograms

One of the important applications of radioisotope in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of the industrial system at the design stage and determination of the system malfunctions. The RTD signal may be subject to different sorts of noise; this leads to errors in the RTD calculations and hence leads to wrong analysis in determination of system malfunctions. This paper presents a proposed method for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal and from its linear and nonlinear modified periodograms. The neural networks are used for training and testing the proposed method. The proposed method is tested by RTD signals obtained from measurements carried out using radiotracer technique. The experimental results show that the proposed method with features extracted from the PDS of the RTD signal calculated using nonlinear modified periodogram (multitaper) is the most robust and reliable in RTD signal identification.

Advanced control technique application in U-tube steam generator of nuclear power station

A water level control system for a nuclear steam generator (SG) is proposed. A method to improve the performance of nuclear steam generator in nuclear power station is introduced. Combination of genetic algorithm (GA) technique and fuzzy logic control is carried out. The optimal parameters of fuzzy logic controller are achieved. These parameters include; the membership functions of water level error and changes water level error, the rule base, and the input scaling gains. Steam generator model is implemented using MATLAB/SIMULINK. The optimal controller was applied to control the water level of nuclear SG and its compared with conventional controller. Simulation results indicate that the optimal fuzzy controller greatly improves the performance of nuclear SG. Moreover the proposed controller is robust to any disturbance related to sudden changes in steam flow rate and water level. Moreover the proposed controller is robust to any disturbance related to load variations.