Search results for: state machine

Authors: Jipeng Yan, Xingchen Yang, Xiaowei Zhou, Mengxing Tang, Honghai Liu

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Shear elastic modulus of skeletal muscles can be obtained by shear wave elastography (SWE) and has been linearly related to muscle force. However, SWE is currently implemented using array probes. Price and volumes of these probes and their driving equipment prevent SWE from being used in wearable human-machine interfaces (HMI). Moreover, beamforming processing for array probes reduces the real-time performance. To achieve SWE by wearable HMIs, a customized three-element probe is adopted in this work, with one element for acoustic radiation force generation and the others for shear wave tracking. In-phase quadrature demodulation and 2D autocorrelation are adopted to estimate velocities of tissues on the sound beams of the latter two elements. Shear wave speeds are calculated by phase shift between the tissue velocities. Three agar phantoms with different elasticities were made by changing the weights of agar. Values of the shear elastic modulus of the phantoms were measured as 8.98, 23.06 and 36.74 kPa at a depth of 7.5 mm respectively. This work verifies the feasibility of measuring shear elastic modulus by wearable devices.

Keywords: Shear elastic modulus, skeletal muscle, ultrasound, wearable human-machine interface.

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Authors: Maitham Al-Safwani

Abstract:

Motor vibration issues were analyzed and correlated to poor equipment installation. We had a water injection pump tested in the factory and exceeded the pump vibration limit. Once the pump was brought to the site, its half-size shim plates were replaced with full-size shims plate that drastically reduced the vibration. In this study, vibration data were recorded for several and similar motors run at the same and different speeds. The vibration values were recorded — for two and a half hours — and the vibration readings analyzed to determine when the readings become consistent. This was as well supported by recording the audio noises produced by some machines seeking a relationship between changes in machine noises and machine abnormalities, such as vibration.

Keywords: Vibration, noise, shaft unbalance, shaft misalignment.

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Authors: Renju Gangadharan, G. N. Pillai, Indra Gupta

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In this paper a novel method for finding the fault zone on a Thyristor Controlled Series Capacitor (TCSC) incorporated transmission line is presented. The method makes use of the Support Vector Machine (SVM), used in the classification mode to distinguish between the zones, before or after the TCSC. The use of Discrete Wavelet Transform is made to prepare the features which would be given as the input to the SVM. This method was tested on a 400 kV, 50 Hz, 300 Km transmission line and the results were highly accurate.

Keywords: Flexible ac transmission system (FACTS), thyristorcontrolled series-capacitor (TCSC), discrete wavelet transforms(DWT), support vector machine (SVM).

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: Visual search, deep learning, convolutional neural network, machine learning.

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

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The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic Language acquisition and learning, natural language processing, morphological analyzer, part-of-speech.

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Authors: I. Otete, A. I. Ejere, I. S. Okunzuwa

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In this work, we used the Hulthen-Yukawa potential to obtain the bound state energy eigenvalues of the Schrödinger equation in D-dimensions within the frame work of the Nikiforov-Uvarov (NU) method. We demonstrated the graphical behaviour of the Hulthen and the Yukawa potential and investigated how the screening parameter and the potential depth affected the structure and the nature of the bound state eigenvalues. The results we obtained showed that increasing the screening parameter lowers the energy eigenvalues. Also, the eigenvalues acted as an inverse function of the potential depth. That is, increasing the potential depth reduces the energy eigenvalues.

Keywords: Schrödinger's equation, bound state, Hulthen-Yukawa potential, Nikiforov-Uvarov, D-dimensions

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Authors: P. Ramana , K. Alice Mary, M. Surya Kalavathi, M. Phani Kumar

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This paper discusses two observers, which are used for the estimation of parameters of PMSM. Former one, reduced order observer, which is used to estimate the inaccessible parameters of PMSM. Later one, full order observer, which is used to estimate all the parameters of PMSM even though some of the parameters are directly available for measurement, so as to meet with the insensitivity to the parameter variation. However, the state space model contains some nonlinear terms i.e. the product of different state variables. The asymptotic state observer, which approximately reconstructs the state vector for linear systems without uncertainties, was presented by Luenberger. In this work, a modified form of such an observer is used by including a non-linear term involving the speed. So, both the observers are designed in the framework of nonlinear control; their stability and rate of convergence is discussed.

Keywords: Permanent magnet synchronous motor, Mathematicalmodelling, Rotor reference frame, parameter estimation, Luenbergerobserver, reduced order observer, full order observer

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

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Authors: A. Bahar Ganiyeva, M. Sabuhi Tanriverdiyev

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The article analyzes state support and policy mechanisms aimed at driving tourism as one of the vibrant and rapidly developing economies. State programs and long-range strategic roadmaps and previous programs execution, results and their impact on the particular countries economy have been raised during the research. This theme provides a useful framework for discussions with a wider range of stakeholders as the implications arising are of importance both for academics and practitioners engaged in hospitality and tourism development and research. The impact that tourism has on sustainable regional development in emerging markets is highly substantial. For Azerbaijan, Turkey, Georgia, and Russia, with their rich natural resources and cultural heritage, tourism can be an important basis for economic expansion, and a way to form an acceptable image of the countries as safe, open, hospitable, and complex.

Keywords: Sustainable tourism, hospitality, destination, strategic roadmap, tourism, economy, growth, state support, mechanism, policy formation, state program.

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Authors: N. Kaewpraek, W. Assawinchaichote

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This paper considers an H_∞ TS fuzzy state-derivative feedback controller for a class of nonlinear dynamical systems. A Takagi-Sugeno (TS) fuzzy model is used to approximate a class of nonlinear dynamical systems. Then, based on a linear matrix inequality (LMI) approach, we design an H_∞ TS fuzzy state-derivative feedback control law which guarantees L₂-gain of the mapping from the exogenous input noise to the regulated output to be less or equal to a prescribed value. We derive a sufficient condition such that the system with the fuzzy controller is asymptotically stable and H_∞ performance is satisfied. Finally, we provide and simulate a numerical example is provided to illustrate the stability and the effectiveness of the proposed controller.

Keywords: H∞ fuzzy control, LMI, Takagi-Sugano (TS) fuzzy model, nonlinear dynamic systems, state-derivative feedback.

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Authors: J. Kaabi, Y. Harrath

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This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm. 

Keywords: Flow shop scheduling, maintenance, genetic algorithm, priority rules.

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: Akram Hosseinian, Hourieh Rezaei, Ali Reza Mahjoub

Abstract:

Nanostructured Iron Oxide with different morphologies of rod-like and granular have been suc-cessfully prepared via a solid-state reaction in the presence of NaCl, NaBr, NaI and NaN3, respectively. The added salts not only prevent a drastic increase in the size of the products but also provide suitable conditions for the oriented growth of primary nanoparticles. The formation mechanisms of these materials by solid-state reaction at ambient temperature are proposed. The photocatalytic experiments for congo red (CR) have demonstrated that the mixture of α-Fe2O3 and Fe3O4 nanostructures were more efficient than α-Fe2O3 nanostructures.

Keywords: Nano, Iron Oxide, Solid-State, Halide salts, Congored

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Authors: Sarah Beaver, Renee Bryce

Abstract:

Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Anant Oonsivilai, Kenedy A. Greyson

Abstract:

The growth and interconnection of power networks in many regions has invited complicated techniques for energy management services (EMS). State estimation techniques become a powerful tool in power system control centers, and that more information is required to achieve the objective of EMS. For the online state estimator, assuming the continuous time is equidistantly sampled with period Δt, processing events must be finished within this period. Advantage of Kalman Filtering (KF) algorithm in using system information to improve the estimation precision is utilized. Computational power is a major issue responsible for the achievement of the objective, i.e. estimators- solution at a small sampled period. This paper presents the optimum utilization of processors in a state estimator based on KF. The model used is presented using Petri net (PN) theory.

Keywords: Kalman filters, model, Petri Net, power system, sequential State estimator.

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Authors: Z. Shaaban

Abstract:

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: C. Bay, A. Mahr, H. Groneberg, F. Döpper

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Additive Manufacturing processes are becoming increasingly established in the industry for the economic production of complex prototypes and functional components. Laser beam melting (LBM), the most frequently used Additive Manufacturing technology for metal parts, has been gaining in industrial importance for several years. The LBM process chain – from material storage to machine set-up and component post-processing – requires many manual operations. These steps often depend on the manufactured component and are therefore not standardized. These operations are often not performed in a standardized manner, but depend on the experience of the machine operator, e.g., levelling of the build plate and adjusting the first powder layer in the LBM machine. This lack of standardization limits the reproducibility of the component quality. When processing metal powders with inhalable and alveolar particle fractions, the machine operator is at high risk due to the high reactivity and the toxic (e.g., carcinogenic) effect of the various metal powders. Faulty execution of the operation or unintentional omission of safety-relevant steps can impair the health of the machine operator. In this paper, all the steps of the LBM process chain are first analysed in terms of their influence on the two aforementioned challenges: reproducibility and work safety. Standardization to avoid errors increases the reproducibility of component quality as well as the adherence to and correct execution of safety-relevant operations. The corresponding lean method 5S will therefore be applied, in order to develop approaches in the form of recommended actions that standardize the work processes. These approaches will then be evaluated in terms of ease of implementation and their potential for improving reproducibility and work safety. The analysis and evaluation showed that sorting tools and spare parts as well as standardizing the workflow are likely to increase reproducibility. Organizing the operational steps and production environment decreases the hazards of material handling and consequently improves work safety.

Keywords: Additive manufacturing, lean production, reproducibility, work safety.

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Authors: Kianoosh Samimi, Siham Kamali-Bernard, Ali Akbar Maghsoudi

Abstract:

This paper aims to contribute to the characterization and the understanding of fresh state, compressive strength and chloride penetration tendency of high strength self-compacting concretes (HSSCCs) where Portland cement type II is partially substituted by 10% and 15% of natural pumice and zeolite. First, five concrete mixtures with a control mixture without any pozzolan are prepared and tested in both fresh and hardened states. Then, resistance to chloride penetration for all formulation is investigated in non-steady state and steady state by measurement of chloride penetration and diffusion coefficient. In non-steady state, the correlation between initial current and chloride penetration with diffusion coefficient is studied. Moreover, the relationship between diffusion coefficient in non-steady state and electrical resistivity is determined. The concentration of free chloride ions is also measured in steady state. Finally, chloride penetration for all formulation is studied in immersion and tidal condition. The result shows that, the resistance to chloride penetration for HSSCC in immersion and tidal condition increases by incorporating pumice and zeolite. However, concrete with zeolite displays a better resistance. This paper shows that the HSSCC with 15% pumice and 10% zeolite is suitable in fresh, hardened, and durability characteristics.

Keywords: Chloride penetration, immersion, pumice, HSSCC, tidal, zeolite.

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Authors: Varun Gupta, Ankit Pandey

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Crude oil market is an immensely complex and dynamic environment and thus the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the core in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. In this paper, we have tried to predict crude oil prices using Long Short-Term Memory (LSTM) based recurrent neural networks. We have tried to experiment with different types of models using different epochs, lookbacks and other tuning methods. The results obtained are promising and presented a reasonably accurate prediction for the price of crude oil in near future.

Keywords: Crude oil price prediction, deep learning, LSTM, recurrent neural networks.

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Authors: M. Shakir Saat, M. Noh Ahmad, Dr, Amat Amir

Abstract:

A cart-ball system is a challenging system from the control engineering point of view. This is due to the nonlinearities, multivariable, and non-minimum phase behavior present in this system. This paper is concerned with the problem of modeling and control of such system. The objective of control strategy is to place the cart at a desired position while balancing the ball on the top of the arc-shaped track fixed on the cart. A State-Feedback Controller (SFC) with a pole-placement method will be designed in order to control the system. At first, the mathematical model of a cart-ball system in the state-space form is developed. Then, the linearization of a model will be established in order to design a SFC. The integral control strategy will be performed as to control the cart position of a system. Simulation work is then performed using MATLAB/SIMULINK software in order to study the performance of SFC when applied to the system.

Keywords: Cart-Ball System, Integral Control, Pole-PlacementMethod, State-Feedback Controller.

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

Abstract:

Planning the order picking lists for warehouses to achieve some operational performances is a significant challenge when the costs associated with logistics are relatively high, and it is especially important in e-commerce era. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, to define features for supervised machine learning algorithms is not a simple task. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A double zone picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Nooshin Salari, Viliam Makis

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In this paper, we propose a condition-based maintenance policy for multi-unit systems considering the existence of economic dependency among units. We consider a system composed of N identical units, where each unit deteriorates independently. Deterioration process of each unit is modeled as a three-state continuous time homogeneous Markov chain with two working states and a failure state. The average production rate of units varies in different working states and demand rate of the system is constant. Units are inspected at equidistant time epochs, and decision regarding performing maintenance is determined by the number of units in the failure state. If the total number of units in the failure state exceeds a critical level, maintenance is initiated, where units in failed state are replaced correctively and deteriorated state units are maintained preventively. Our objective is to determine the optimal number of failed units to initiate maintenance minimizing the long run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A numerical example is developed to demonstrate the proposed policy and the comparison with the corrective maintenance policy is presented.

Keywords: Reliability, production, maintenance optimization, Semi-Markov Decision Process.

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Authors: D. Almazo, M. Toledo, C. Rodríguez

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This work presents a methodology for the design and manufacture of propellers oriented to the experimental verification of theoretical results based on the combined model. The design process begins by using algorithms in Matlab which output data contain the coordinates of the points that define the blade airfoils, in this case the NACA 6512 airfoil was used. The modeling for the propeller blade was made in NX7, through the imported files in Matlab and with the help of surfaces. Later, the hub and the clamps were also modeled. Finally, NX 7 also made possible to create post-processed files to the required machine. It is possible to find the block of numbers with G & M codes about the type of driver on the machine. The file extension is .ptp. These files made possible to manufacture the blade, and the hub of the propeller.

Keywords: Airfoil, CAM, manufacturing, mathematical algorithm, numeric control, propeller design, simulation.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

Abstract:

Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.

Keywords: Bioinformatics, Protein-protein interactions, Bayesian Kernel, Support Vector Machines.

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Authors: Rong Zhou, Shun’ich Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu

Abstract:

In this paper, we propose a robust disease detection method, called adaptive orientation code matching (Adaptive OCM), which is developed from a robust image registration algorithm: orientation code matching (OCM), to achieve continuous and site-specific detection of changes in plant disease. We use two-stage framework for realizing our research purpose; in the first stage, adaptive OCM was employed which could not only realize the continuous and site-specific observation of disease development, but also shows its excellent robustness for non-rigid plant object searching in scene illumination, translation, small rotation and occlusion changes and then in the second stage, a machine learning method of support vector machine (SVM) based on a feature of two dimensional (2D) xy-color histogram is further utilized for pixel-wise disease classification and quantification. The indoor experiment results demonstrate the feasibility and potential of our proposed algorithm, which could be implemented in real field situation for better observation of plant disease development.

Keywords: Cercospora Leaf Spot (CLS), Disease detection, Image processing, Orientation Code Matching (OCM), Support Vector Machine (SVM).

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Authors: S. Ge, T. Wu, HY. Tang, X. Xiao, K. Iramina, W. Wu

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It is crucial to quantitatively evaluate the treatment of epilepsy patients. This study was undertaken to test the hypothesis that compared to the healthy control subjects, the epilepsy patients have abnormal resting-state connectivity. In this study, we used the imaginary part of coherency to measure the resting-state connectivity. The analysis results shown that compared to the healthy control subjects, epilepsy patients tend to have abnormal rhythm brain connectivity over their epileptic focus.

Keywords: Coherence, connectivity, resting-state, epilepsy

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Authors: A. El Shahat, A. Keyhani, H. El Shewy

Abstract:

High Speed PM Generators driven by micro-turbines are widely used in Smart Grid System. So, this paper proposes comparative study among six classical, optimized and genetic analytical design cases for 400 kW output power at tip speed 200 m/s. These six design trials of High Speed Permanent Magnet Synchronous Generators (HSPMSGs) are: Classical Sizing; Unconstrained optimization for total losses and its minimization; Constrained optimized total mass with bounded constraints are introduced in the problem formulation. Then a genetic algorithm is formulated for obtaining maximum efficiency and minimizing machine size. In the second genetic problem formulation, we attempt to obtain minimum mass, the machine sizing that is constrained by the non-linear constraint function of machine losses. Finally, an optimum torque per ampere genetic sizing is predicted. All results are simulated with MATLAB, Optimization Toolbox and its Genetic Algorithm. Finally, six analytical design examples comparisons are introduced with study of machines waveforms, THD and rotor losses.

Keywords: High Speed, Micro - Turbines, Optimization, PM Generators, Smart Grid, MATLAB.

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Authors: Kairat Zatov, Tursun Gabitov, Maral Botaeva, Moldagaliyev Bauyrzhan, Saira Shamahay

Abstract:

Unlike Christianity and Buddhism, Islam, being one of the three universal world religions, actively penetrates into people-s everyday life. The main reason for this is that in Islam the religion and ideology, philosophy, religious organizations and state bodies are closely interrelated. In order to analyze the state of being of interrelations of religion and civil society in Kazakhstan, it is necessary to study Islam and its relations with spiritual culture of the society. According to the Constitution of the Republic of Kazakhstan the religion is separated from the state, i.e. each performs its own function without interfering into each other-s affairs. The right of the citizens of our republic to freedom of thinking and faith is based on the Constitution of the RK, Civil Code, Law “On freedom of faith and religious unions in the Republic of Kazakhstan". Legislatively secured separation of the mosque and church from the state does not mean that religion has no influence on the latter. The state, consisting of citizens with their own beliefs, including religious ones, cannot be isolated from the influence of religion. Nowadays it is commonly accepted that it is not possible to understand and forecast key social processes without taking into account the religious factor.

Keywords: Kazakhstan, Islam, Shamanism, tradition and innovation, fundamentalism, religious culture, spirit worship, tolerance, sectarianism, extremism and civilization.

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