Search results for: approximation algorithms

Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Anastasiia Yu. Timofeeva

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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

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Authors: Boukhatem Mohammed Belkaid, Lahdir Mourad

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In this paper, we propose a new encryption system for security issues medical images. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity, and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every new session of encryption, that will be used to encrypt each frame of the medical image basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, medical images, encryption, decryption, key, correlation

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Authors: Lamia Bourdache, Amar Djema

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The effect of non-Newtonian property (power law index n) on traveling waves of thin layer of power law fluid flowing over an inclined plane is investigated. For this, a simplified second-order two-equation model (SM) is used. The complete model is second-order four-equation (CM). It is derived by combining the weighted residual integral method and the lubrication theory. This is due to the fact that at the beginning of the instability waves, a very small number of waves is observed. Using a suitable set of test functions, second order terms are eliminated from the calculus so that the model is still accurate to the second order approximation. Linear, spatial, and temporal stabilities are studied. For travelling waves, a particular type of wave form that is steady in a moving frame, i.e., that travels at a constant celerity without changing its shape is studied. This type of solutions which are characterized by their celerity exists under suitable conditions, when the widening due to dispersion is balanced exactly by the narrowing effect due to the nonlinearity. Changing the parameter of celerity in some range allows exploring the entire spectrum of asymptotic behavior of these traveling waves. The (SM) model is converted into a three dimensional dynamical system. The result is that the model exhibits bifurcation scenarios such as heteroclinic, homoclinic, Hopf, and period-doubling bifurcations for different values of the power law index n. The influence of the non-Newtonian parameter on the nonlinear development of these travelling waves is discussed. It is found at the end that the qualitative characters of bifurcation scenarios are insensitive to the variation of the power law index.

Keywords: inclined plane, nonlinear stability, non-Newtonian, thin film

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Authors: Dmitry Fedoseyev

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The ultimate success of unmanned aerial vehicles (UAVs) depends largely on the effective control of their flight, especially in variable wind conditions. This paper investigates different approaches to selecting the optimal flight level to maximize the range of UAVs. We propose to consider methods based on mathematical models of atmospheric conditions, as well as the use of sensor data and machine learning algorithms to automatically optimize the flight level in real-time. The proposed approaches promise to improve the efficiency and range of UAVs in various wind conditions, which may have significant implications for the application of these systems in various fields, including geodesy, environmental surveillance, and search and rescue operations.

Keywords: drone, UAV, flight trajectory, wind-searching, efficiency

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Authors: Kais Ben-Ahmed, Mhamed Ali-El-Aroui

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This paper develops a Local Generalized Linear Spatial Model (LGLSM) to describe the spatial variation of Visceral Leishmaniasis (VL) infection risk in northern and central Tunisia. The response from each region is a number of affected children less than five years of age recorded from 1996 through 2006 from Tunisian pediatric departments and treated as a poison county level data. The model includes climatic factors, namely averages of annual rainfall, extreme values of low temperatures in winter and high temperatures in summer to characterize the climate of each region according to each continentality index, the pluviometric quotient of Emberger (Q2) to characterize bioclimatic regions and component for residual extra-poison variation. The statistical results show the progressive increase in the number of affected children in regions with high continentality index and low mean yearly rainfull. On the other hand, an increase in pluviometric quotient of Emberger contributed to a significant increase in VL incidence rate. When compared with the original GLSM, Bayesian locally modeling is improvement and gives a better approximation of the Tunisian VL risk estimation. According to the Bayesian approach inference, we use vague priors for all parameters model and Markov Chain Monte Carlo method.

Keywords: generalized linear spatial model, local model, extra-poisson variation, continentality index, visceral leishmaniasis, Tunisia

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: uncertain transportation problem, efficient solution, ranking function, fuzzy transportation problem

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Authors: Maik Kschischo, Dominik Kahl, Philipp Wendland, Andreas Weber

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Understanding and modelling of real-world complex dynamic systems in biology, engineering and other fields is often made difficult by incomplete knowledge about the interactions between systems states and by unknown disturbances to the system. In fact, most real-world dynamic networks are open systems receiving unknown inputs from their environment. To understand a system and to estimate the state dynamics, these inputs need to be reconstructed from output measurements. Reconstructing the input of a dynamic system from its measured outputs is an ill-posed problem if only a limited number of states is directly measurable. A first requirement for solving this problem is the invertibility of the input-output map. In our work, we exploit the fact that invertibility of a dynamic system is a structural property, which depends only on the network topology. Therefore, it is possible to check for invertibility using a structural invertibility algorithm which counts the number of node disjoint paths linking inputs and outputs. The algorithm is efficient enough, even for large networks up to a million nodes. To understand structural features influencing the invertibility of a complex dynamic network, we analyze synthetic and real networks using the structural invertibility algorithm. We find that invertibility largely depends on the degree distribution and that dense random networks are easier to invert than sparse inhomogeneous networks. We show that real networks are often very difficult to invert unless the sensor nodes are carefully chosen. To overcome this problem, we present a sensor node placement algorithm to achieve invertibility with a minimum set of measured states. This greedy algorithm is very fast and also guaranteed to find an optimal sensor node-set if it exists. Our results provide a practical approach to experimental design for open, dynamic systems. Since invertibility is a necessary condition for unknown input observers and data assimilation filters to work, it can be used as a preprocessing step to check, whether these input reconstruction algorithms can be successful. If not, we can suggest additional measurements providing sufficient information for input reconstruction. Invertibility is also important for systems design and model building. Dynamic models are always incomplete, and synthetic systems act in an environment, where they receive inputs or even attack signals from their exterior. Being able to monitor these inputs is an important design requirement, which can be achieved by our algorithms for invertibility analysis and sensor node placement.

Keywords: data-driven dynamic systems, inversion of dynamic systems, observability, experimental design, sensor node placement

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Authors: Shobha G. Ranjan

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Cloud computing is an emerging, on-demand and internet- based technology. Varieties of services like, software, hardware, data storage and infrastructure can be shared though the cloud computing. This technology is highly reliable, cost effective and scalable in nature. It is a must only the authorized users should access these services. Further the time granted to access these services should be taken into account for proper accounting purpose. Currently many organizations do the security measures in many different ways to provide the best cloud infrastructure to their clients, but that’s not the limitation. This paper presents the multi-level security measure technique which is in accordance with the OSI model. In this paper, details of proposed multilevel security measures technique are presented along with the architecture, activities, algorithms and probability of success in breaking authentication.

Keywords: cloud computing, cloud security, integrity, multi-tenancy, security

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Authors: Masoumeh Mofarrah, Amir NahanMoghadam

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Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise, and improve EOR methods and their application. Recently, Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic, and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization infeasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: Othmane Zarhali, Cécilia Aubrun, Emmanuel Bacry, Jean-Philippe Bouchaud, Jean-François Muzy

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The Nested factor model was introduced by Bouchaud and al., where the asset return fluctuations are explained by common factors representing the market economic sectors and residuals (noises) sharing with the factors a common dominant volatility mode in addition to the idiosyncratic mode proper to each residual. This construction infers that the factors-residuals log volatilities are correlated. Here, we consider the case of a single factor where the only dominant common mode is a S-fbm process (introduced by Peng, Bacry and Muzy) with Hurst exponent H around 0.11 and the residuals having in addition to the previous common mode idiosyncratic components with Hurst exponents H around 0. The reason for considering this configuration is twofold: preserve the Nested factor model’s characteristics introduced by Bouchaud and al. and propose a framework through which the stylized fact reported by Peng and al. is reproduced, where it has been observed that the Hurst exponents of stock indices are large as compared to those of individual stocks. In this work, we show that the Log S-fbm Nested factor model’s construction leads to a Hurst exponent of single stocks being the ones of the idiosyncratic volatility modes and the Hurst exponent of the index being the one of the common volatility modes. Furthermore, we propose a statistical procedure to estimate the Hurst factor exponent from the stock returns dynamics together with theoretical guarantees, with good results in the limit where the number of stocks N goes to infinity. Last but not least, we show that the factor can be seen as an index constructed from the single stocks weighted by specific coefficients.

Keywords: hurst exponent, log S-fbm model, nested factor model, small intermittency approximation

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Z. Ulukan, E. Demircioğlu,

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Facility location is a complex real-world problem which needs a strategic management decision. This paper provides a general review on studies, efforts and developments in Facility Location Problems which are classical optimization problems having a wide-spread applications in various areas such as transportation, distribution, production, supply chain decisions and telecommunication. Our goal is not to review all variants of different studies in FLPs or to describe very detailed computational techniques and solution approaches, but rather to provide a broad overview of major location problems that have been studied, indicating how they are formulated and what are proposed by researchers to tackle the problem. A brief, elucidative table based on a grouping according to “General Problem Type” and “Methods Proposed” used in the studies is also presented at the end of the work.

Keywords: discrete location problems, exact methods, heuristic algorithms, single source capacitated facility location problems

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Authors: Ankit Shai

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CRC or Cyclic Redundancy Check is one of the most common, and one of the most powerful error-detecting codes implemented on modern computers. Most of the modern communication protocols use some error detection algorithms in digital networks and storage devices to detect accidental changes to raw data between transmission and reception. Cyclic Redundancy Check, or CRC, is the most popular one among these error detection codes. CRC properties are defined by the generator polynomial length and coefficients. The aim of this project is to implement an efficient FPGA based CRC-8 that accepts a 32 bit input, taking into consideration optimal chip area and high performance, using VHDL. The proposed architecture is implemented on Xilinx ISE Simulator. It is designed while keeping in mind the hardware design, complexity and cost factor.

Keywords: cyclic redundancy checker, CRC-8, 32-bit input, FPGA, VHDL, ModelSim, Xilinx

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Authors: Hafiz Md. Hasan Babu, Khandaker Mohammad Mohi Uddin, Nitish Biswas, Sarreha Tasmin Rikta, Nuzmul Hossain Nahid

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Nanomedicine and bioengineering use biological systems that can perform computing operations. In a biocomputational circuit, different types of biomolecules and DNA (Deoxyribose Nucleic Acid) are used as active components. DNA computing has the capability of performing parallel processing and a large storage capacity that makes it diverse from other computing systems. In most processors, the multiplier is treated as a core hardware block, and multiplication is one of the time-consuming and lengthy tasks. In this paper, cost-effective DNA multipliers are designed using algorithms of molecular DNA operations with respect to conventional ones. The speed and storage capacity of a DNA multiplier are also much higher than a traditional silicon-based multiplier.

Keywords: biological systems, DNA multiplier, large storage, parallel processing

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Authors: John Joseph S. Martis, Rohanchandra R. Gatty, Aaron Jose Fernandes, Rahul P. Nambiar

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Introduction: Surgery for breast cancer is either mastectomy or breast conservation surgery. The most commonly used incision for modified radical mastectomy is the transverse Stewart incision. But this incision may have the disadvantage of causing disparity between the closure lines of superior and inferior skin flaps in mastectomy and can cause overhanging of soft tissue below and behind the axilla. The curvizigzag incision, on principle, may help in this regard and can prevent scar migration beyond the anterior axillary line. This study aims to compare the two incisions in this regard. Methods: 100 patients with cancer of breast were included in the study after satisfying inclusion and exclusion criteria. They underwent surgery at Father Muller Medical College, Mangalore, India, between November 2019 to September 2021. The patients were divided into two groups. Group A patients were subjected to modified radical mastectomy with curvizigzag incision and group B patients with transverse Stewart incision. Results: Seroma on postoperative day1, day 2 was 0% in both the groups. Seroma on postoperative day 30 was present in 14% of patients in group B. 60% of patients in group B had sag of soft tissue below and behind the axilla, and none of the patients in group A had this problem. In 64% of the patients in group B, the incision crossed the anterior axillary fold, 64% of the patients in group B had tension in the incision site while approximation of the skin flaps. Conclusion: Curvizigzag incision is statistically better with lesser complications when compared to the transverse Stewart incision for modified radical mastectomy for carcinoma breast.

Keywords: breast cancer, curvizigzag incision, transverse Stewart incision, seroma, modified radical mastectomy

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Authors: Y. Xu, L. Xiong, Z. Xu

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In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision.

Keywords: secure image retrieval, secure search, orthogonal decomposition, secure cloud computing

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Authors: Hamsini Pulugurtha, P. V. S. L. Jagadamba

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Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations

Keywords: Siamese neural network, English speech, English text, natural language processing, unsupervised extractive text summarization

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Authors: T. S. Almutairi, Paul May, Neil Allan

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The geometrical, electronic and magnetic properties of the neutral Mg center and MgN₃ cluster in diamond have been studied theoretically in detail by means of an HSE06 Hamiltonian that includes a fraction of the exact exchange term; this is important for a satisfactory picture of the electronic states of open-shell systems. Another batch of the calculations by GGA functionals have also been included for comparison, and these support the results from HSE06. The local perturbations in the lattice by introduced Mg defect are restricted in the first and second shell of atoms before eliminated. The formation energy calculated with HSE06 and GGA of single Mg agrees with the previous result. We found the triplet state with C₃ᵥ is the ground state of Mg center with energy lower than the singlet with C₂ᵥ by ~ 0.1 eV. The recent experimental ZPL (557.4 nm) of Mg center in diamond has been discussed in the view of present work. The analysis of the band-structure of the MgN₃ cluster confirms that the MgN₃ defect introduces a shallow donor level in the gap lying within the conduction band edge. This observation is supported by the EMM that produces n-type levels shallower than the P donor level. The formation energy of MgN₂ calculated from a 2NV defect (~ 3.6 eV) is a promising value from which to engineer MgN₃ defects inside the diamond. Ion-implantation followed by heating to about 1200-1600°C might induce migration of N related defects to the localized Mg center. Temperature control is needed for this process to restore the damage and ensure the mobilities of V and N, which demands a more precise experimental study.

Keywords: empirical marker method, generalised gradient approximation, Heyd–Scuseria–Ernzerhof screened hybrid functional, zero phono line

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Authors: Peter A. L’vov, Roman S. Konovalov, Alexey A. L’vov

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The paper presents the advanced digital modification of the conventional current loop circuit for pressure piezoelectric transducers. The optimal DSP algorithms of current loop responses by the maximum likelihood method are applied for diminishing of measurement errors. The loop circuit has some additional advantages such as the possibility to operate with any type of resistance or reactance sensors, and a considerable increase in accuracy and quality of measurements to be compared with AC bridges. The results obtained are dedicated to replace high-accuracy and expensive measuring bridges with current loop circuits.

Keywords: current loop, maximum likelihood method, optimal digital signal processing, precise pressure measurement

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Authors: Mohammed Hussein, Abualseoud Hanani

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In Low Earth Orbit (LEO) satellite constellation system, routing data from the source all the way to the destination constitutes a daunting challenge because LEO satellite constellation resources are spare and the high speed movement of LEO satellites results in a highly dynamic network topology. This situation limits the applicability of traditional routing approaches that rely on exchanging topology information upon change or setup of a connection. Consequently, in recent years, many routing algorithms and implementation strategies for satellite constellation networks with Inter Satellite Links (ISLs) have been proposed. In this article, we summarize and classify some of the most representative solutions according to their objectives, and discuss their advantages and disadvantages. Finally, with a look into the future, we present some of the new challenges and opportunities for LEO satellite constellations in general and routing protocols in particular.

Keywords: LEO satellite constellations, dynamic topology, IP routing, inter-satellite-links

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Authors: A. Giniatoulline

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A model of the mathematical fluid dynamics which describes the motion of a three-dimensional viscous rotating fluid in a homogeneous gravitational field with the consideration of the salinity and heat transfer is considered in a vertical finite layer. The model is a generalization of the linearized Navier-Stokes system with the addition of the Coriolis parameter and the equations for changeable density, salinity, and heat transfer. An explicit solution is constructed and the proof of the existence and uniqueness theorems is given. The localization and the structure of the spectrum of inner waves is also investigated. The results may be used, in particular, for constructing stable numerical algorithms for solutions of the considered models of fluid dynamics of the Atmosphere and the Ocean.

Keywords: Fourier transform, generalized solutions, Navier-Stokes equations, stratified fluid

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Authors: Giovanni Incerti

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In this paper the dynamic behavior of a synchronous belt drive during start-up is analyzed and discussed. Besides considering the belt elasticity, the mathematical model here proposed also takes into consideration the electrical behaviour of the DC motor. The solution of the motion equations is obtained by means of the modal analysis in state space, which allows to obtain the decoupling of all equations of the mathematical model without introducing the hypothesis of proportional damping. The mathematical model of the transmission and the solution algorithms have been implemented within a computing software that allows the user to simulate the dynamics of the system and to evaluate the effects due to the elasticity of the belt branches and to the electromagnetic behavior of the DC motor. In order to show the details of the calculation procedure, the paper presents a case study developed with the aid of the abovementioned software.

Keywords: belt drive, vibrations, startup, DC motor

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Authors: Md. S. Ansari, S. S. Motsa

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In this paper, an analysis has been made on the flow of non-Newtonian viscoelastic nanofluid over a linearly stretching sheet under the influence of uniform magnetic field. Heat transfer characteristics is analyzed taking into the effect of nonlinear radiation and non-uniform heat source/sink. Transport equations contain the simultaneous effects of Brownian motion and thermophoretic diffusion of nanoparticles. The relevant partial differential equations are non-dimensionalized and transformed into ordinary differential equations by using appropriate similarity transformations. The transformed, highly nonlinear, ordinary differential equations are solved by spectral local linearisation method. The numerical convergence, error and stability analysis of iteration schemes are presented. The effects of different controlling parameters, namely, radiation, space and temperature-dependent heat source/sink, Brownian motion, thermophoresis, viscoelastic, Lewis number and the magnetic force parameter on the flow field, heat transfer characteristics and nanoparticles concentration are examined. The present investigation has many industrial and engineering applications in the fields of coatings and suspensions, cooling of metallic plates, oils and grease, paper production, coal water or coal–oil slurries, heat exchangers’ technology, and materials’ processing and exploiting.

Keywords: magnetic field, nonlinear radiation, non-uniform heat source/sink, similar solution, spectral local linearisation method, Rosseland diffusion approximation

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Stoyan Nedeltchev, Markus Schubert

Abstract:

By means of the ultrafast X-ray tomography facility, data were obtained at different superficial gas velocities U_G in a bubble column (0.1 m in ID) operated with an air-deionized water system at ambient conditions. Raw reconstructed images were treated by both the information entropy (IE) and the reconstruction entropy (RE) algorithms in order to identify the main transition velocities in a bubble column. The IE values exhibited two well-pronounced minima at U_G=0.025 m/s and U_G=0.085 m/s identifying the boundaries of the homogeneous, transition and heterogeneous regimes. The RE extracted from the central region of the column’s cross-section exhibited only one characteristic peak at U_G=0.03 m/s, which was attributed to the transition from the homogeneous to the heterogeneous flow regime. This result implies that the transition regime is non-existent in the core of the column.

Keywords: bubble column, ultrafast X-ray tomography, information entropy, reconstruction entropy

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Authors: Radek Malinský, Ivan Jelínek

Abstract:

The current web has become a modern encyclopedia, where people share their thoughts and ideas on various topics around them. Such kind of encyclopedia is very useful for other people who are looking for answers to their questions. However, with the growing popularity of social networking and blogging and ever expanding network services, there has also been a growing diversity of technologies along with different structure of individual websites. It is, therefore, difficult to directly find a relevant answer for a common Internet user. This paper presents a web application for the real-time end-to-end analysis of selected Internet trends; where the trend can be whatever the people post online. The application integrates fully configurable tools for data collection and analysis using selected webometric algorithms, and for its chronological visualization to user. It can be assumed that the application facilitates the users to evaluate the quality of various products that are mentioned online.

Keywords: Trend, visualizer, web analysis, web 2.0.

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Authors: Masoumeh Mofarrah, Amir NahanMoghadam

Abstract:

Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise and improve EOR methods and their application. Recently Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization in feasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

Procedia PDF Downloads 407