Several incidents that occurred around the world involving power failure
caused by unscheduled line outages were identified as one of the main
contributors to power failure and cascading blackout in electric power
environment. With the advancement of computer technologies, artificial
intelligence (AI) has been widely accepted as one method that can be applied
to predict the occurrence of unscheduled disturbance. This paper presents
the development of automatic contingency analysis and ranking algorithm
for the application in the Artificial Neural Network (ANN). The ANN is
developed in order to predict the post-outage severity index from a set of preoutage
data set. Data were generated using the newly developed automatic
contingency analysis and ranking (ACAR) algorithm. Tests were conducted
on the 24-bus IEEE Reliability Test Systems. Results showed that the developed
technique is feasible to be implemented practically and an agreement was
achieved in the results obtained from the tests. The developed ACAR can be
utilised for further testing and implementation in other IEEE RTS test systems
particularly in the system, which required fast computation time. On the other
hand, the developed ANN can be used for predicting the post-outage severity
index and hence system stability can be evaluated.
We present an algorithm to reduce the number of slices from 2D contour cross sections. The main aim of the algorithm is to filter less significant slices while preserving an acceptable level of output quality and keeping the computational cost to reconstruct surface(s) at a minimal level. This research is motivated mainly by two factors; first 2D cross sections data is often huge in size and high in precisions – the computational cost to reconstruct surface(s) from them is closely related to the size and complexity of this data. Second, we can trades visual fidelity with speed of computations if we can remove visually insignificant data from the original dataset which may contains redundant information. In our algorithm we use the number of contour points on a pair of slices to calculate the distance between them. Selection to retain/reject a slice is based on the value of distance compared against a threshold value. Optimal threshold value is derived to produce set of slices that collectively represent the feature of the dataset. We tested our algorithm over six different set of data, varying in complexities and sizes. The results show slice reduction rate depends on the complexity of the dataset, where highest reduction percentage is achieved for objects with lots of constant local variations. Our derived optimal thresholds seem to be able to produce the right set of slices with the potential of creating surface(s) that traded off the accuracy and speed requirements.
In this paper, a direct integration implicit variable step size method in the form of Adams Moulton Method is developed for solving directly the second order system of ordinary differential equations (ODEs) using variable step size. The existing multistep method involves the computations of the divided differences and integration coefficients in the code when using the variable step size or variable step size and order. The idea of developing this method is to store all the coefficients involved in the code. Thus, this strategy can avoid the lengthy computation of the coefficients during the implementation of the code as well as to improve the execution time. Numerical results are given to compare the efficiency of the developed method with the 1-point method of variable step size and order code (1PDVSO) in Omar (1999).
Coptotermes gestroi, the Asian subterranean termite (AST), is an economically important structural and agricultural pest that has become established in many areas of the world. For the first time, phylogeography was used to illuminate the origins of new found C. gestroi in the US Commonwealth of Puerto Rico; Ohio, USA; Florida, USA; and Brisbane, Australia. Phylogenetic relationships of C. gestroi collected in indigenous locations within Malaysia, Thailand, and Singapore as well as from the four areas of introduction were investigated using three genes (16S rRNA, COII, and ITS) under three optimality criteria encompassing phenetic and cladistic assumptions (maximum parsimony, maximum likelihood, and neighbor-joining). All three genes showed consistent support for a close genetic relationship between C. gestroi samples from Singapore and Ohio, whereas termite samples from Australia, Puerto Rico, and Key West, FL were more closely related to those from Malaysia. Shipping records further substantiated that Singapore and Malaysia were the likely origin of the Ohio and Australia C. gestroi, respectively. These data provide support for using phylogeography to understand the dispersal history of exotic termites. Serendipitously, we also gained insights into concerted evolution in an ITS cluster from rhinotermitid species in two genera.
The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.
For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine the similarity with the DNA sequences available in the databanks. The efficiency of the alignment decides the type of amino acids and its corresponding proteins. In order to evaluate the given DNA sequence for its proteomic identity, a pattern matching approach is proposed in this paper. A block based semi-global alignment scheme is introduced to determine the similarity between the DNA sequences (known and given). The two DNA sequences are divided into blocks of equal length and alignment is performed which minimizes the computational complexity. The efficiency of the alignment scheme is evaluated using the parameter, percentage of similarity (POS). Four essential DNA version of the amino acids that emphasize the importance of proteomic functionalities are chosen as patterns and matching is performed with the known and given DNA sequences to determine the similarity between them. The ratio of amino acid counts between the two sequences is estimated and the results are compared with that of the POS value. It is found from the experimental results that higher the POS value and the pattern matching higher are the similarity between the two DNA sequences. The optimal block is also identified based on the POS value and amino acids count.
This article addresses time-domain ultrawide band (UWB) electromagnetic tomography for reconstructing the unknown spatial characteristic of an object from observations of the arrivals of short electromagnetic (EM) pulses. Here, the determination of the first peak arrival of the EM traces constitutes the forward problem, and the inverse problem aims to reconstruct the EM property distribution of the media. In this article, the finite-difference time-domain method implementing a perfectly matched layer is used to solve the forward problem from which the system sensitivity maps are determined. Image reconstruction is based on the combination of a linearized update and regularized Landweber minimization algorithm. Experimental data from a laboratory UWB system using targets of different contrasts, sizes, and shapes in an aqueous media are presented. The results show that this technique can accurately detect and locate unknown targets in spite of the presence of significant levels of noise in the data.
In this study, a hybridized neuro-genetic optimization methodology realized by embedding finite element analysis (FEA) trained artificial neural networks (ANN) into genetic algorithms (GA), is used to optimize temperature control in a ceramic based continuous flow polymerase chain reaction (CPCR) device. The CPCR device requires three thermally isolated reaction zones of 94 degrees C, 65 degrees C, and 72 degrees C for the denaturing, annealing, and extension processes, respectively, to complete a cycle of polymerase chain reaction. The most important aspect of temperature control in the CPCR is to maintain temperature distribution at each reaction zone with a precision of +/-1 degree C or better, irrespective of changing ambient conditions. Results obtained from the FEA simulation shows good comparison with published experimental work for the temperature control in each reaction zone of the microfluidic channels. The simulation data are then used to train the ANN to predict the temperature distribution of the microfluidic channel for various heater input power and fluid flow rate. Once trained, the ANN analysis is able to predict the temperature distribution in the microchannel in less than 20 min, whereas the FEA simulation takes approximately 7 h to do so. The final optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. Finally, the GA optimized results are used to build a new FEA model for numerical simulation analysis. The simulation results for the neuro-genetic optimized CPCR model and the initial CPCR model are then compared. The neuro-genetic optimized model shows a significant improvement from the initial model, establishing the optimization method's superiority.
Stones in the biliary tract are routinely identified using MRCP (magnetic resonance cholangiopancreatography). The noisy nature of the images, as well as varying intensity, size and location of the stones, defeat most automatic detection algorithms, making computer-aided diagnosis difficult. This paper proposes a multi-stage segment-based scheme for semi-automated detection of choledocholithiasis and cholelithiasis in the MRCP images, producing good performance in tests, differentiating them from "normal" MRCP images. With the high success rate of over 90%, refinement of the scheme could be applicable in the clinical environment as a tool in aiding diagnosis, with possible applications in telemedicine.
This paper discussed security of medical images and reviewed some work done regarding them. A fragile watermarking scheme was then proposed that could detect tamper and subsequently recover the image. Our scheme required a secret key and a public chaotic mixing algorithm to embed and recover a tampered image. The scheme was also resilient to VQ attack. The purposes were to verify the integrity and authenticity of medical images. We used 800 x 600 x 8 bits ultrasound (US) greyscale images in our experiment. We tested our algorithm for up to 50% tampered block and obtained 100% recovery for spread-tampered block.
In fetal heart monitoring using Doppler ultrasound signals the cardiac information is commonly extracted from non-directional signals. As a consequence often some of the cardiac events cannot be observed clearly which may lead to the incorrect detection of the valve and wall motions. Here, directional signals were simulated to investigate their enhancement of cardiac events, and hence provide clearer information regarding the cardiac activities. First, fetal Doppler ultrasound signals were simulated with signals encoding forward and reverse motion then obtained using a pilot frequency. The simulation results demonstrate that the model has the ability to produce realistic Doppler ultrasound signals and a pilot frequency can be used in the mixing process to produce directional signals that allow the simulated cardiac events to be distinguished clearly and correctly.
Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.
Phosphatidylinositol 4-phosphate 5-kinases (PIP5Ks) play diverse roles in the cellular biology of many organisms, including signal transduction, secretion and vesicular trafficking, and regulation of cytoskeleton assembly. Discovery of the PIP5K gene in Eimeria tenella may shed light on its role in the biology of this avian protozoan, and afford further understanding of the cell-host interaction, particularly during the invasion process. In this study, we report the identification of the PIP5K coding region in the genome sequence of Eimeria tenella using in silico gene prediction approaches. Prediction of the PIP5K coding sequence was confirmed by mapping the full-length cDNA sequence, generated via the Rapid Amplification of cDNA Ends (RACE) method, to the genomic sequence. The putative PIP5K gene of Eimeria tenella is located on the complementary strand of the E1080B12.b1 contig, and comprises 12 exons. Further analysis showed that the coding region spans from exon 1 to exon 7, with all exons obeying the adopted 'gt...ag' splicing rule of intronic sequences. Consensus of the hexameric 5' donor-splice site was deduced as GTRDBB... and the consensus for the 3' acceptor-splice sites as ...BHDYAG. The gene encodes a 252-amino acid residue protein. Domain search and protein fold recognition analyses provide compelling evidences that the deduced protein is a PIP5K.
Psoriasis is a skin disorder which is caused by genetic fault. There is no cure for psoriasis, however, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the calculation of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameter, the lesion area. The method isolates healthy (or healed) skin areas from lesion areas by analyzing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. Euclidean distance of all pixels from each centroid is calculated. Each pixel is assigned to the class with minimum Euclidean distance. The study involves patients from three different ethnic origins having different skin tones. Results obtained show that the proposed method is comparable to the dermatologist visual approach.
In this paper, we describe an image processing scheme to analyze and determine areas of skin that have undergone repigmentation in particular, during the treatment of vitiligo. In vitiligo cases, areas of skin become pale or white due to the lack of skin pigment called melanin. Vitiligo treatment causes skin repigmentation resulting in a normal skin color. However, it is difficult to determine and quantify the amount of repigmentation visually during treatment because the repigmentation progress is slow and moreover changes in skin color can only be discerned over a longer time frame typically 6 months. Here, we develop a digital image analysis scheme that can identify and determine vitiligo skin areas and repigmentation progression on a shorter time period. The technique is based on principal component analysis and independent component analysis which converts the RGB skin image into a skin image that represent skin areas due to melanin and haemoglobin only, followed by segmentation process. Vitiligo skin lesions are identified as skin areas that lack melanin (non-melanin areas). In the initial studies of 4 patients, the method has been able to quantify repigmentation in vitiligo lesion. Hence it is now possible to determine repigmentation progression objectively and treatment efficacy on a shorter time cycle.
This paper will study and evaluate watermarking technique by Zain and Fauzi [1]. Recommendations will then be made to enhance the technique especially in the aspect of recovery or reconstruction rate for medical images. A proposal will also be made for a better distribution of watermark to minimize the distortion of the Region of Interest (ROI). The final proposal will enhance AW-TDR in three aspects; firstly the image quality in the ROI will be improved as the maximum change is only 2 bits in every 4 pixels, or embedding rate of 0.5 bits/pixel. Secondly the recovery rate will also be better since the recovery bits are located outside the region of interest. The disadvantage in this is that, only manipulation done in the ROI will be detected. Thirdly the quality of the reconstructed image will be enhanced since the average of 2 x 2 pixels would be used to reconstruct the tampered image.
Effect of amplitude criteria on the operating characteristics of algorithms for detecting OSAH events based on the analysis of oxygen saturation alone is investigated. The objective is to establish that there exists an oxygen desaturation level that leverages these algorithms to be more sensitive or more specific, irrespective of the differences in detection mechanism and database, a first ever attempt. Linear classification of algorithms from previous studies discovered that a drop in oxygen saturation of 3% or less makes the detection algorithms more sensitive while a drop of 4% or more makes it more specific. Results from two algorithms developed here also supported this. This finding explains the contradiction cited in the performance of algorithms from the different authors, which casts doubts on their detection ability. It could lead to the establishment of standard oxygen desaturation levels for screening and diagnosis of moderate/severe OSA, thus providing a more credible comparison basis for automated detection algorithms or even clinical tests.
Cardiovascular disease (CVD) is the leading cause of death worldwide, and due to the lack of early detection techniques, the incidence of CVD is increasing day by day. In order to address this limitation, a knowledge based system with embedded intelligent heart sound analyser (KBHSA) has been developed to diagnose cardiovascular disorders at early stages. The system analyses digitized heart sounds that are recorded from an electronic stethoscope using advanced digital signal processing and artificial intelligence techniques. KBHSA takes into account data including the patient's personal and past medical history, clinical examination, auscultation findings, chest x-ray and echocardiogram, and provides a list of diseases that it has diagnosed. The system can assist the general physician in making more accurate and reliable diagnosis under emergency conditions where expert cardiologists and advanced equipment are not readily available. To test the validity of the system, abnormal heart sound samples and medical data from 40 patients were recorded and analysed. The diagnoses made by the system were counter checked by four senior cardiologists in Malaysia. The results show that the findings of KBHSA coincide with those of cardiologists.
Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes.
An artificial neural network (ANN) was applied for the determination of V(V) based on immobilized fatty hydroxamic acid (FHA) in poly(methyl methacrylate) (PMMA). Spectra obtained from the V(V)-FHA complex at single wavelengths was used as the input data for the ANN. The V(V)-FHA complex shows a limited linear dynamic range of V(V) concentration of 10 - 100 mg/ L. After training with ANN, the linear dynamic range was extended with low calibration error. A three layer feed forward neural network using backpropagation (BP) algorithm was employed in this study. The input layer consisted of single neurons, 30 neurons in hidden a layer and one output neuron was found appropriate for the multivariate calibration used. The network were trained up to 10000 epochs with 0.003 % learning rate. This reagent also provided a good analytical pedormance with reproducibility characters of the method yielding relative standard deviation (RSD) of 9.29% and 7.09% for V(V) at concentrations of 50 mg/ L and 200 mg/ L, respectively. The limit of detection of the method was 8.4 mg/ L.