Search results for: Artificial roughness

Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

Abstract:

There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 418

Authors: M. Ahmadi Marvast, M. Sohrabi, H. Ganji

Abstract:

The rate of production of main products of the Fischer-Tropsch reactions over Fe/HZSM5 bifunctional catalyst in a fixed bed reactor is investigated at a broad range of temperature, pressure, space velocity, H2/CO feed molar ratio and CO2, CH4 and water flow rates. Model discrimination and parameter estimation were performed according to the integral method of kinetic analysis. Due to lack of mechanism development for Fisher – Tropsch Synthesis on bifunctional catalysts, 26 different models were tested and the best model is selected. Comprehensive one and two dimensional heterogeneous reactor models are developed to simulate the performance of fixed-bed Fischer – Tropsch reactors. To reduce computational time for optimization purposes, an Artificial Feed Forward Neural Network (AFFNN) has been used to describe intra particle mass and heat transfer diffusion in the catalyst pellet. It is seen that products' reaction rates have direct relation with H2 partial pressure and reverse relation with CO partial pressure. The results show that the hybrid model has good agreement with rigorous mechanistic model, favoring that the hybrid model is about 25-30 times faster.

Keywords: Fischer-Tropsch, heterogeneous modeling, kinetic study.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2780

Authors: Christian Endisch, Christoph Hackl, Dierk Schröder

Abstract:

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.

Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1892

Authors: A. F. Ali, D. M. Shawky

Abstract:

Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.

Keywords: Bioinformatics, Artificial Neural Networks, Protein Sequence Analysis, Feature Extraction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2303

Authors: Khalid Ahmed Elrabie Abdelrasoul

Abstract:

The present study was carried out on Sahiwal cattle bulls maintained at the Artificial Breeding Complex, NDRI, Karnal, Hayana, India, to assess the effect of cooling using mist cooling and fanning on Sahiwal bulls in the dry hot summer season. Fourteen Sahiwal bulls were divided into two groups of seven each. Sexual behavior and semen quality traits considered were: Reaction time (RT), Dismounting time (DMT), Total time taken in mounts (TTTM), Flehmen response (FR), Erection Score (ES), Protrusion Score (PS), Intensity of thrust (ITS), Temperament Score (TS), Libido Score (LS), Semen volume, Physical appearance, Mass activity, Initial progressive motility, Non-eosinophilic spermatozoa count (NESC) and post thaw motility percent. Data were analyzed by least squares technique. Group-1 was the control, whereas group-2 (treatment group) bulls were exposed to mist cooling and fanning (thrice a day 15 min each) in the dry hot summer season. Group-2 showed significantly (p < 0.01) higher value in DMT (sec), ES, PS, ITS, LS, semen volume (ml), semen color density, mass activity, initial motility, progressive motility and live sperm.

Keywords: Mist cooling, Sahiwal bulls, semen quality, sexual behavior.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1012

Authors: Saad M. Al-Garni, Adel A. Abdennour

Abstract:

Freeways are originally designed to provide high mobility to road users. However, the increase in population and vehicle numbers has led to increasing congestions around the world. Daily recurrent congestion substantially reduces the freeway capacity when it is most needed. Building new highways and expanding the existing ones is an expensive solution and impractical in many situations. Intelligent and vision-based techniques can, however, be efficient tools in monitoring highways and increasing the capacity of the existing infrastructures. The crucial step for highway monitoring is vehicle detection. In this paper, we propose one of such techniques. The approach is based on artificial neural networks (ANN) for vehicles detection and counting. The detection process uses the freeway video images and starts by automatically extracting the image background from the successive video frames. Once the background is identified, subsequent frames are used to detect moving objects through image subtraction. The result is segmented using Sobel operator for edge detection. The ANN is, then, used in the detection and counting phase. Applying this technique to the busiest freeway in Riyadh (King Fahd Road) achieved higher than 98% detection accuracy despite the light intensity changes, the occlusion situations, and shadows.

Keywords: Background Extraction, Neural Networks, VehicleDetection, Freeway Traffic.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1863

Authors: Mohamed A. Mahfouz, M. A. Ismail

Abstract:

Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1914

Authors: Guojian Cheng, Tianshi Liu, Jiaxin Han, Zheng Wang

Abstract:

The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen-s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are topology preserving, feature mappings and probability distribution approximation of input patterns. To overcome some limitations of SOFM, e.g., a fixed number of neural units and a topology of fixed dimensionality, Growing Self-Organizing Neural Network (GSONN) can be used. GSONN can change its topological structure during learning. It grows by learning and shrinks by forgetting. To speed up the training and convergence, a new variant of GSONN, twin growing cell structures (TGCS) is presented here. This paper first gives an introduction to competitive learning, SOFM and its variants. Then, we discuss some GSONN with fixed dimensionality, which include growing cell structures, its variants and the author-s model: TGCS. It is ended with some testing results comparison and conclusions.

Keywords: Artificial neural networks, Competitive learning, Growing cell structures, Self-organizing feature maps.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1505

Authors: Gaoyong Luo

Abstract:

The approach based on the wavelet transform has been widely used for image denoising due to its multi-resolution nature, its ability to produce high levels of noise reduction and the low level of distortion introduced. However, by removing noise, high frequency components belonging to edges are also removed, which leads to blurring the signal features. This paper proposes a new method of image noise reduction based on local variance and edge analysis. The analysis is performed by dividing an image into 32 x 32 pixel blocks, and transforming the data into wavelet domain. Fast lifting wavelet spatial-frequency decomposition and reconstruction is developed with the advantages of being computationally efficient and boundary effects minimized. The adaptive thresholding by local variance estimation and edge strength measurement can effectively reduce image noise while preserve the features of the original image corresponding to the boundaries of the objects. Experimental results demonstrate that the method performs well for images contaminated by natural and artificial noise, and is suitable to be adapted for different class of images and type of noises. The proposed algorithm provides a potential solution with parallel computation for real time or embedded system application.

Keywords: Edge strength, Fast lifting wavelet, Image denoising, Local variance.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1977

Authors: S. Anna Durai, E. Anna Saro

Abstract:

Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2511

Authors: Kevin Wongleedee

Abstract:

Culinary culture differences can cause health problems for international tourists in Thailand. This paper drew upon data collected from an international tourist survey conducted in Bangkok, Thailand during summer of 2012. Summer is the period that a variety food safety issues and incidents are often publicized in Thailand. The survey targeted European Union tourists- concerns toward a variety of food safety issues that they encountered during their trip in Thailand. A total of 400 respondents were elicited as data input for t-test, and one way ANOVA test. The findings revealed an astonishing result that up to 46.5 percent of respondents were sick at least one time or more in Thailand. However, the majority of respondents trusted that the Thai hotel and Thai restaurants would ensure food safety, but they did not trust street vendors to ensure food safety. The level of food safety concern can be ranked from most concern to least concern by using the value of mean scores as follows: 1) artificial coloring, 2) use of preservatives, 3) antibiotics, 4) growth hormones, 5) chemical residues, and 6) bacterial contamination. The overall mean score for level of concerns was 3.493 with standard deviation of 1.677 which did not indicate a very high level of concern. In addition, the result for t-test and one way ANOVA test revealed that there was not much effect from the demographic differences to level of food safety concerns.

Keywords: Concerns, European Union Tourists, Food Safety Management.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2830

Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

Abstract:

In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network.

The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters.

Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output.

This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc.

From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: Project profitability, multi-objective optimization, genetic algorithm, Pareto set, Neural Networks.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2020

Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: Building energy prediction, data mining, demand response, electricity market.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2160

Authors: Mandlenkosi George Robert Mahlobo, Peter Apata Olubambi, Philippe Refait

Abstract:

The aim of this study was to use voltammetry as a method to understand the behavior of steel in unsaturated soil in the presence of cathodic protection (CP). Three carbon steel coupons were buried in artificial soil wetted at 65-70% of saturation for 37 days. All three coupons were left at open circuit potential (OCP) for the first seven days in the unsaturated soil before CP which was only applied on two of the three coupons at the protection potential -0.8 V vs. Cu/CuSO4 for the remaining 30 days of the experiment. Voltammetry was performed weekly on the coupon without CP while electrochemical impedance spectroscopy (EIS) was performed daily to monitor and correct the applied CP potential from ohmic drop. Voltammetry was finally performed the last day on the coupons under CP. All the voltammograms were modeled with mathematical equations in order to compute the electrochemical parameters and subsequently deduce the corrosion rate of the steel coupons. For the coupon without CP, the corrosion rate was determined at 300 µm/y. For the coupons under CP, the residual corrosion rate under CP was estimated at 12 µm/y while the corrosion rate of the coupons, after interruption of CP, was estimated at 25 µm/y. This showed that CP was efficient due to two effects: a direct effect, from the decreased potential, and an induced effect, associated with the increased interfacial pH that promoted the formation of a protective layer on the steel surface.

Keywords: Carbon steel, cathodic protection, voltammetry, unsaturated soil, Raman spectroscopy.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 61

Authors: P. D. Pastuszak

Abstract:

The present work is devoted to thermographic studies of curved composite panels (unidirectional GFRP) with subsurface defects. Various artificial defects, created by inserting PTFE stripe between individual layers of a laminate during manufacturing stage are studied. The analysis is conducted both with the use finite element method and experiments. To simulate transient heat transfer in 3D model with embedded various defect sizes, the ANSYS package is used. Pulsed Thermography combined with optical excitation source provides good results for flat surfaces. Composite structures are mostly used in complex components, e.g., pipes, corners and stiffeners. Local decrease of mechanical properties in these regions can have significant influence on strength decrease of the entire structure. Application of active procedures of thermography to defect detection and evaluation in this type of elements seems to be more appropriate that other NDT techniques. Nevertheless, there are various uncertainties connected with correct interpretation of acquired data. In this paper, important factors concerning Infrared Thermography measurements of curved surfaces in the form of cylindrical panels are considered. In addition, temperature effects on the surface resulting from complex geometry and embedded and real defect are also presented.

Keywords: Active thermography, finite element analysis, composite, curved structures, defects.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1665

Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

Abstract:

Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1567

Authors: Sheena Christabel Pravin, M. Palanivelan

Abstract:

Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: Bilingual, children who stutter, children with language impairment, Hidden Markov Models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 979

Authors: Endri Rama, Genci Capi, Shigenori Kawahara

Abstract:

The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: Brain machine interface, decision-making, local field potentials, mobile robot, navigation, neural network, rat, signal processing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1423

Authors: Gang Li, Ghulam Mubashar Hassan, Arcady Dyskin, Cara MacNish

Abstract:

Digital image correlation (DIC) is a contactless fullfield displacement and strain reconstruction technique commonly used in the field of experimental mechanics. Comparing with physical measuring devices, such as strain gauges, which only provide very restricted coverage and are expensive to deploy widely, the DIC technique provides the result with full-field coverage and relative high accuracy using an inexpensive and simple experimental setup. It is very important to study the natural patterns effect on the DIC technique because the preparation of the artificial patterns is time consuming and hectic process. The objective of this research is to study the effect of using images having natural pattern on the performance of DIC. A systematical simulation method is used to build simulated deformed images used in DIC. A parameter (subset size) used in DIC can have an effect on the processing and accuracy of DIC and even cause DIC to failure. Regarding to the picture parameters (correlation coefficient), the higher similarity of two subset can lead the DIC process to fail and make the result more inaccurate. The pictures with good and bad quality for DIC methods have been presented and more importantly, it is a systematic way to evaluate the quality of the picture with natural patterns before they install the measurement devices.

Keywords: Digital image correlation (DIC), Deformation simulation, Natural pattern, Subset size.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2749

Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

Abstract:

Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2784

Authors: A. Bagheri Garmarudi, M. Khanmohammadi, N. Khoddami, K. Shabani

Abstract:

Determination of nano particle size is substantial since the nano particle size exerts a significant effect on various properties of nano materials. Accordingly, proposing non-destructive, accurate and rapid techniques for this aim is of high interest. There are some conventional techniques to investigate the morphology and grain size of nano particles such as scanning electron microscopy (SEM), atomic force microscopy (AFM) and X-ray diffractometry (XRD). Vibrational spectroscopy is utilized to characterize different compounds and applied for evaluation of the average particle size based on relationship between particle size and near infrared spectra [1,4] , but it has never been applied in quantitative morphological analysis of nano materials. So far, the potential application of nearinfrared (NIR) spectroscopy with its ability in rapid analysis of powdered materials with minimal sample preparation, has been suggested for particle size determination of powdered pharmaceuticals. The relationship between particle size and diffuse reflectance (DR) spectra in near infrared region has been applied to introduce a method for estimation of particle size. Back propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near infrared diffuse reflectance spectra. Thirty five different nano TiO2 samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP- ANN.

Keywords: near infrared, particle size, chemometrics, neuralnetwork, nano structure.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1798

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

Abstract:

Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2512

Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

Abstract:

Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1639

Authors: Farhad Asadi, Hossein Sadati

Abstract:

In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 502

Authors: Dimitrios E. Kontaxis, George Litainas, Dimitrios P. Ptochos, Vaggelis P. Ptochos, Sotirios P. Ptochos, Dimitrios Beletsis, Konstantinos Kritikakis, Milan Sunaric

Abstract:

Real-time monitoring of the supply chain conditions and procedures is a critical element for the optimal coordination and safety of the deliveries, as well as for the minimization of the delivery time and cost. Real time monitoring requires IoT data streams, which are related to the conditions of the products and the means of transport (e.g., location, temperature/humidity conditions, kinematic state, ambient light conditions, etc.). These streams are generated by battery-based IoT tracking devices, equipped with appropriate sensors, and are transmitted to a cloud-based back-end system. Proper handling and processing of the IoT data streams, using predictive and artificial intelligence algorithms, can provide significant and useful results, which can be exploited by the supply chain stakeholders in order to enhance their financial benefits, as well as the efficiency, security, transparency, coordination and sustainability of the supply chain procedures. The technology, the features and the characteristics of a complete, proprietary system, including hardware, firmware and software tools - developed in the context of a co-funded R&D program - are addressed and presented in this paper. 

Keywords: IoT embedded electronics, real-time monitoring, tracking device, sensor platform

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 552

Authors: Hikmat A. M. Abd-El-Jaber, Tengku M. T. Sembok

Abstract:

The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are not really satisfied with their search results. Using ontologies for semantic-based searching is likely the key solution. Adopting profiling approach and using ontology base characteristics, this work proposes a strategy for finding the exact meaning of the query terms in order to retrieve relevant information according to user needs. The evaluation of conducted experiments has shown the effectiveness of the suggested methodology and conclusion is presented.

Keywords: information retrieval, user profiles, semantic Web, ontology, search engine.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3168

Authors: Adnan Muharemovic, Hidajet Salkic, Mario Klaric, Irfan Turkovic, Aida Muharemovic

Abstract:

In nature, electromagnetic fields always appear like atmosphere static electric field, the earth's static magnetic field and the wide-rang frequency electromagnetic field caused by lightening. However, besides natural electromagnetic fields (EMF), today human beings are mostly exposed to artificial electromagnetic fields due to technology progress and outspread use of electrical devices. To evaluate nuisance of EMF, it is necessary to know field intensity for every frequency which appears and compare it with allowed values. Low frequency EMF-s around transmission and distribution lines are time-varying quasi-static electromagnetic fields which have conservative component of low frequency electrical field caused by charges and eddy component of low frequency magnetic field caused by currents. Displacement current or field delay are negligible, so energy flow in quasi-static EMF involves diffusion, analog like heat transfer. Electrical and magnetic field can be analyzed separately. This paper analysis the numerical calculations in ELF-400 software of EMF in distribution substation in shopping center. Analyzing the results it is possible to specify locations exposed to the fields and give useful suggestion to eliminate electromagnetic effect or reduce it on acceptable level within the non-ionizing radiation norms and norms of protection from EMF.

Keywords: Electromagnetic Field, Density of Electromagnetic Flow, Place of Proffesional Exposure, Place of Increased Sensitivity

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3813

Authors: Taposh Roy, Dipankar Choudhury, Belinda Pingguan-Murphy

Abstract:

The tribological outcomes of micro dent are found to be outstanding in many engineering and natural surfaces. Ceramic (Al₂O₃) is considered one of the most potential material to bearing surfaces particularly, artificial hip or knee implant. A well-defined micro dent on alumina oxide interface could further decrease friction and wear rate, thus increase their stability and durability. In this study we fabricated circular micro dent surface profiles (Dia: 400µm, Depth 20µm, P: 1.5mm; Dia: 400µm, Depth 20µm, P: 2mm) on pure Al₂O₃ (99.6%) substrate by using a micro tool machines. A preliminary tribological experiment was carried out to compare friction coefficient of these fabricated dent surfaces with that of non-textured surfaces. The experiment was carried on well know pin-on-disk specimens while other experimental parameters such as hertz pressure, speed, lubrication, and temperature were maintained to standard of simulated hip joints condition. The experiment results revealed that micro dent surface texture reduced 15%, 8% and 4% friction coefficient under 0.132,0.162, 0.187 GPa contact pressure respectively. Since this is a preliminary tribological study, we will pursue further experiments considering higher ranges of dent profiles and longer run experiments. However, the preliminary results confirmed the suitability of fabricating dent profile to ceramic surfaces by using micro tooling, and also their improved tribological performance in simulated hip joints.

Keywords: Micro dent, tribology, ceramic on ceramic hipjoints.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2290

Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

Abstract:

The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: Optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1384

Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

Abstract:

Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: Artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2090