Search results for: robust estimation

Authors: Mahdi Fakoor, Seyed Mohammad Navid Ghoreishi

Abstract:

Investigation of fracture of wood components can prevent from catastrophic failures. Created fracture process zone (FPZ) in crack tip vicinity has important effect on failure of cracked composite materials. In this paper, a failure criterion for fracture investigation of cracked wood specimens under mixed mode I/II loading is presented. This criterion is based on maximum strain energy release rate and material nonlinearity in the vicinity of crack tip due to presence of microcracks. Verification of results with available experimental data proves the coincidence of the proposed criterion with the nature of fracture of wood. To simplify the estimation of nonlinear properties of FPZ, a damage factor is also introduced for engineering and application purposes.

Keywords: Fracture criterion, mixed mode loading, damage zone, microcracks.

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Authors: Safaa R. Amer

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Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design

Keywords: Complex survey, estimate, imputation, neural networks, variance.

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Authors: Hatem. Elmeddeb, Noureddine, Hamdi, Ammar. Bouallègue

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In this paper, a joint source-channel coding (JSCC) scheme for time-varying channels is presented. The proposed scheme uses hierarchical framework for both source encoder and transmission via QAM modulation. Hierarchical joint source channel codes with hierarchical QAM constellations are designed to track the channel variations which yields to a higher throughput by adapting certain parameters of the receiver to the channel variation. We consider the problem of still image transmission over time-varying channels with channel state information (CSI) available at 1) receiver only and 2) both transmitter and receiver being informed about the state of the channel. We describe an algorithm that optimizes hierarchical source codebooks by minimizing the distortion due to source quantizer and channel impairments. Simulation results, based on image representation, show that, the proposed hierarchical system outperforms the conventional schemes based on a single-modulator and channel optimized source coding.

Keywords: Channel-optimized VQ (COVQ), joint optimization, QAM, hierarchical systems.

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Authors: John Florescu

Abstract:

A challenged control problem is when the performance is pushed to the limit. The state-derivative feedback control strategy directly uses acceleration information for feedback and state estimation. The derivative part is concerned with the rateof- change of the error with time. If the measured variable approaches the set point rapidly, then the actuator is backed off early to allow it to coast to the required level. Derivative action makes a control system behave much more intelligently. A sensor measures the variable to be controlled and the measured in formation is fed back to the controller to influence the controlled variable. A high gain problem can be also formulated for proportional plus derivative feedback transformation. Using MATLAB Simulink dynamic simulation tool this paper examines a system with a proportional plus derivative feedback and presents an automatic implementation of finding an acceptable controlled system. Using feedback transformations the system is transformed into another system.

Keywords: Feedback, PD, state-space, derivative.

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Authors: D.M. Weeraddana, K.S. Walgama, E.C. Kulasekere

Abstract:

A framework to estimate the state of dynamically varying environment where data are generated from heterogeneous sources possessing partial knowledge about the environment is presented. This is entirely derived within Dempster-Shafer and Evidence Filtering frameworks. The belief about the current state is expressed as belief and plausibility functions. An addition to Single Input Single Output Evidence Filter, Multiple Input Single Output Evidence Filtering approach is introduced. Variety of applications such as situational estimation of an emergency environment can be developed within the framework successfully. Fire propagation scenario is used to justify the proposed framework, simulation results are presented.

Keywords: Dempster-Shafer Belief theory, Evidence Filtering, Evidence Fusion, Sensor Modalities, Wireless Sensor Networks

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

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Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: Ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model.

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Authors: G. Rezai, P.K.Teng, Z. Mohamed, M.N Shamsudin

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Changing in consumers lifestyles and food consumption patterns provide a great opportunity in developing the functional food sector in Malaysia. There is only a little knowledge about whether Malaysian consumers are aware of functional food and if so what image consumers have of this product. The objective of this research is to determine the extent to which selected socioeconomic characteristics and attitudes influence consumers- awareness of functional food. A survey was conducted in the Klang Valley, Malaysia where 439 respondents were interviewed using a structured questionnaire. The result shows that most respondents have a positive attitude towards functional food. For the binary logistic estimation, the results indicate that age, income and other factors such as concern about food safety, subscribing to cooking or health magazines, being a vegetarian and consumers who have been involved in a food production company significantly influence Malaysian consumers- awareness towards functional food.

Keywords: Binary logistic model, functional foods, knowledge and awareness, perception

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Authors: R. Khaniki, M.B. Menhaj, H. Eliasi

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This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Keywords: Generalized Predictive Control (GPC), TemperatureControl, Global Linearizing Control (GLC), Batch Reactor.

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Authors: R. Khaniki, M.B. Menhaj, H. Eliasi

Abstract:

This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Keywords: Generalized Predictive Control (GPC), TemperatureControl, Global Linearizing Control (GLC), Batch Reactor.

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Authors: Sae-Rom Pak, Seung Hwan Park, Jeong Ho Cho, Daewoong An, Cheong-Sool Park, Jun Seok Kim, Jun-Geol Baek

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It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.

Keywords: Yield Prediction, Semiconductor Test Process, Support Vector Machine, Under Sampling

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Authors: P. Nagendra, S. Halder nee Dey, S. Paul, T. Datta

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This paper presents a methodology to assess the voltage stability status combined with optimal power flow technique using an instantaneous two-bus equivalent model of power system incorporating static var compensator (SVC) and thyristor controlled series compensator (TCSC) controllers. There by, a generalized global voltage stability indicator being developed has been applied to a robust practical Indian Eastern Grid 203-bus system. Simulation results have proved that the proposed methodology is promising to assess voltage stability of any power system at any operating point in global scenario. Voltage stability augmentation with the application of SVC at the weakest bus and TCSC at critical line connected to the weakest bus is compared with the system having no compensation. In the proposed network equivalent model the generators have been modeled more accurately considering economic criteria.

Keywords: Equivalent two-bus model, global voltage security indicator, optimal power flow, SVC, TCSC.

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Authors: Zefry Darmawan, Shigeyuki Haruyama, Ken Kaminishi

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Product development is a big issue in the industrial competition and takes a serious part in development of technology. Product development process could adapt high changes of market needs and transform into engineering concept in order to produce high-quality product. One of the latest methods in product development is Analysis-Led-Design (ALD). It utilizes digital engineering design tools with finite analysis to perform product robust analysis and valuable for product reliability assurance. Heavy machinery which operates under severe condition should maintain safety to the customer when faced with potential hazard. Cab frame should able to absorb the energy while collision. Through ALD, a series of improvement of cab frame to increase energy absorption was made and analyzed. Improvement was made by modifying shapes of frame and-or install absorption device in certain areas. Simulation result showed that install absorption device could increase absorption energy than modifying shape.

Keywords: ALD, ROPS, energy absorption, cab frame.

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Authors: Shaher Bano, Samia Fida, Asif Israr

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The synergistic effect of properties of metals and fibers reinforced laminates has diverted attention of the world towards use of robust composite materials known as fiber-metal laminates in many high performance applications. In this study, modification of an adhesively bonded joint as a single lap joint of carbon fibers based CARALL FML has done to increase interlaminar shear strength of the joint. The effect of different configurations of joint designs such as spews, stepped and modification in adhesive by addition of nano-fillers was studied. Both experimental and simulation results showed that modified joint design have superior properties as maximum force experienced stepped joint was 1.5 times more than the simple lap joint. Addition of carbon nano-tubes as nano-fillers in the adhesive joint increased the maximum force due to crack deflection mechanism.

Keywords: Adhesive joint, carbon reinforced aluminium laminate, CARALL, fiber metal laminates, spews.

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Authors: C. Ardil

Abstract:

Fuzzy set and intuitionistic fuzzy set are dealing with the imprecision and uncertainty inherent in a complex decision problem. However, sometimes these theories are not sufficient to model indeterminate and inconsistent information encountered in real-life problems. To overcome this insufficiency, the neutrosophic set, which is useful in practical applications, is proposed, triangular neutrosophic numbers and trapezoidal neutrosophic numbers are examined, their definitions and applications are discussed. In this study, a decision making algorithm is developed using neutrosophic set processes and an application is given in fighter aircraft selection as an example of a decision making problem. The estimation of the fighter aircraft selection with the neutrosophic multiple criteria decision analysis method is examined.  

Keywords: neutrosophic set, multiple criteria decision making analysis, fighter aircraft selection, MCDMA, neutrosophic numbers

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Alexandros Leontitsis, Archana P. Sangole

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This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Jyh-Da Wei, Hsin-Chen Tsai

Abstract:

This work presents a novel means of extracting fixedlength parameters from voice signals, such that words can be recognized in linear time. The power and the zero crossing rate are first calculated segment by segment from a voice signal; by doing so, two feature sequences are generated. We then construct an FIR system across these two sequences. The parameters of this FIR system, used as the input of a multilayer proceptron recognizer, can be derived by recursive LSE (least-square estimation), implying that the complexity of overall process is linear to the signal size. In the second part of this work, we introduce a weighting factor λ to emphasize recent input; therefore, we can further recognize continuous speech signals. Experiments employ the voice signals of numbers, from zero to nine, spoken in Mandarin Chinese. The proposed method is verified to recognize voice signals efficiently and accurately.

Keywords: Speech Recognition, FIR system, Recursive LSE, Multilayer Perceptron

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Authors: Pranav Ravikumar, Arunabha Bera, Vijay K. Mehra, Anand Kumar

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This paper solves the Non Linear Schrodinger Equation using the Split Step Fourier method for modeling an optical fiber. The model generates a complex wave of optical pulses and using the results obtained two graphs namely Loss versus Wavelength and Dispersion versus Wavelength are generated. Taking Chromatic Dispersion and Polarization Mode Dispersion losses into account, the graphs generated are compared with the graphs formulated by JDS Uniphase Corporation which uses standard values of dispersion for optical fibers. The graphs generated when compared with the JDS Uniphase Corporation plots were found to be more or less similar thus verifying that the model proposed is right. MATLAB software was used for doing the modeling.

Keywords: Modulation, Non Linear Schrodinger Equation, Optical fiber, Split Step Fourier Method.

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Authors: C. Ardil

Abstract:

In this paper, a new MCDMA approach, the permanent analytics process is proposed to assess the immovable valuation criteria and their significance in the placement of the healthcare facility. Five decision factors are considered for the value and selection of immovables. In the multiple factor selection problems, the priority vector of the criteria used to compare several immovables is first determined using the permanent analytics method, a mathematical model for the multiple criteria decisionmaking process. Then, to demonstrate the viability and efficacy of the suggested approach, twenty potential candidate locations were evaluated using the hospital site selection problem's decision criteria. The ranking accuracy of estimation was evaluated using composite programming, which took into account both the permanent analytics process and the weighted multiplicative model. 

Keywords: Hospital Facility Location Selection, Permanent Analytics Process, Multiple Criteria Decision Making (MCDM)

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Authors: Amit Gil, Helman Stern, Yael Edan

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A cognitive collaborative reinforcement learning algorithm (CCRL) that incorporates an advisor into the learning process is developed to improve supervised learning. An autonomous learner is enabled with a self awareness cognitive skill to decide when to solicit instructions from the advisor. The learner can also assess the value of advice, and accept or reject it. The method is evaluated for robotic motion planning using simulation. Tests are conducted for advisors with skill levels from expert to novice. The CCRL algorithm and a combined method integrating its logic with Clouse-s Introspection Approach, outperformed a base-line fully autonomous learner, and demonstrated robust performance when dealing with various advisor skill levels, learning to accept advice received from an expert, while rejecting that of less skilled collaborators. Although the CCRL algorithm is based on RL, it fits other machine learning methods, since advisor-s actions are only added to the outer layer.

Keywords: Robot learning, human-robot collaboration, motion planning, reinforcement learning.

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Authors: Mehdi Hosseinzadeh, Parisa Khoshvaght

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In some applications, such as image recognition or compression, segmentation refers to the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. Image segmentation is to classify or cluster an image into several parts (regions) according to the feature of image, for example, the pixel value or the frequency response. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Several image segmentation algorithms were proposed to segment an image before recognition or compression. Up to now, many image segmentation algorithms exist and be extensively applied in science and daily life. According to their segmentation method, we can approximately categorize them into region-based segmentation, data clustering, and edge-base segmentation. In this paper, we give a study of several popular image segmentation algorithms that are available.

Keywords: Image Segmentation, hierarchical segmentation, partitional segmentation, density estimation.

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Authors: Ze-Jun Hu, Ting-Zhu Huang, Ning-Bo Tan

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To solve saddle point systems efficiently, several preconditioners have been published. There are many methods for constructing preconditioners for linear systems from saddle point problems, for instance, the relaxed dimensional factorization (RDF) preconditioner and the augmented Lagrangian (AL) preconditioner are used for both steady and unsteady Navier-Stokes equations. In this paper we compare the RDF preconditioner with the modified AL (MAL) preconditioner to show which is more effective to solve Navier-Stokes equations. Numerical experiments indicate that the MAL preconditioner is more efficient and robust, especially, for moderate viscosities and stretched grids in steady problems. For unsteady cases, the convergence rate of the RDF preconditioner is slightly faster than the MAL perconditioner in some circumstances, but the parameter of the RDF preconditioner is more sensitive than the MAL preconditioner. Moreover the convergence rate of the MAL preconditioner is still quite acceptable. Therefore we conclude that the MAL preconditioner is more competitive than the RDF preconditioner. These experiments are implemented with IFISS package. 

Keywords: Navier-Stokes equations, Krylov subspace method, preconditioner, dimensional splitting, augmented Lagrangian preconditioner.

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Authors: Refaat M Mohamed, Ayman El-Baz, Aly A. Farag

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This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Keywords: Image Modeling, MRF, Parameters Estimation, SVM Learning.

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Authors: Y. Santosa, D. A. Rahman, C. Wulan, A. H. Mustari

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The global demand for long-tailed macaques for medical experimentation has continued to increase. Fulfillment of Indonesian export demands has been mostly from natural habitats, based on a harvesting quota. This quota has been determined according to the total catch for a given year, and not based on consideration of any demographic parameters or physical environmental factors with regard to the animal; hence threatening the sustainability of the various populations. It is therefore necessary to formulate a method for calculating a sustainable harvesting quota, based on population parameters in natural habitats. Considering the possibility of variations in habitat characteristics and population parameters, a time series observation of demographic and physical/biotic parameters, in various habitats, was performed on 13 groups of long-tailed macaques, distributed throughout the West Java, Lampung and Yogyakarta areas of Indonesia. These provinces were selected for comparison of the influence of human/tourism activities. Data on population parameters that was collected included data on life expectancy according to age class, numbers of individuals by sex and age class, and ‘ratio of infants to reproductive females’. The estimation of population growth was based on a population dynamic growth model: the Leslie matrix. The harvesting quota was calculated as being the difference between the actual population size and the MVP (minimum viable population) for each sex and age class. Observation indicated that there were variations within group size (24–106 individuals), gender (sex) ratio (1:1 to 1:1.3), life expectancy value (0.30 to 0.93), and ‘ratio of infants to reproductive females’ (0.23 to 1.56). Results of subsequent calculations showed that sustainable harvesting quotas for each studied group of long-tailed macaques, ranged from 29 to 110 individuals. An estimation model of the MVP for each age class was formulated as Log Y = 0.315 + 0.884 Log Ni (number of individual on ith age class). This study also found that life expectancy for the juvenile age class was affected by the humidity under tree stands, and dietary plants’ density at sapling, pole and tree stages (equation: Y=2.296 – 1.535 RH + 0.002 Kpcg – 0.002 Ktg – 0.001 Kphn, R2 = 89.6% with a significance value of 0.001). By contrast, for the sub-adult-adult age class, life expectancy was significantly affected by slope (equation: Y=0.377 = 0.012 Kml, R2 = 50.4%, with significance level of 0.007). The infant-toreproductive- female ratio was affected by humidity under tree stands, and dietary plant density at sapling and pole stages (equation: Y = - 1.432 + 2.172 RH – 0.004 Kpcg + 0.003 Ktg, R2 = 82.0% with significance level of 0.001). This research confirmed the importance of population parameters in determining the minimum viable population, and that MVP varied according to habitat characteristics (especially food availability). It would be difficult therefore, to formulate a general mathematical equation model for determining a harvesting quota for the species as a whole.

Keywords: Harvesting, long-tailed macaque, population, quota.

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Authors: Afzel Noore

Abstract:

A new digital watermarking technique for images that are sensitive to blocking artifacts is presented. Experimental results show that the proposed MDCT based approach produces highly imperceptible watermarked images and is robust to attacks such as compression, noise, filtering and geometric transformations. The proposed MDCT watermarking technique is applied to fingerprints for ensuring security. The face image and demographic text data of an individual are used as multiple watermarks. An AFIS system was used to quantitatively evaluate the matching performance of the MDCT-based watermarked fingerprint. The high fingerprint matching scores show that the MDCT approach is resilient to blocking artifacts. The quality of the extracted face and extracted text images was computed using two human visual system metrics and the results show that the image quality was high.

Keywords: Digital watermarking, data hiding, modified discretecosine transformation (MDCT).

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Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat

Abstract:

This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system.

Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.

Keywords: ANFIS, Fault location, Underground Cable, Wavelet Transform.

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Authors: Murugan Marimuthu, Jeyabharath Rajaih

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This paper describes a simple way to control the speed of PMBLDC motor using Fuzzy logic control method. In the conventional PI controller the performance of the motor system is simulated and the speed is regulated by using PI controller. These methods used to improve the performance of PMSM drives, but in some cases at different operating conditions when the dynamics of the system also vary over time and it can change the reference speed, parameter variations and the load disturbance. The simulation is powered with the MATLAB program to get a reliable and flexible simulation. In order to highlight the effectiveness of the speed control method the FLC method is used. The proposed method targeted in achieving the improved dynamic performance and avoids the variations of the motor drive. This drive has high accuracy, robust operation from near zero to high speed. The effectiveness and flexibility of the individual techniques of the speed control method will be thoroughly discussed for merits and demerits and finally verified through simulation and experimental results for comparative analysis.

Keywords: Hall position sensors, permanent magnet brushless DC motor, PI controller, Fuzzy Controller.

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Authors: Yeon-Jea Cho, Sang-Uk Park, Won-Chul Choi, Dong-Jo Park

Abstract:

This paper proposes a smart design strategy for a sequential detector to reliably detect the primary user-s signal, especially in fast fading environments. We study the computation of the log-likelihood ratio for coping with a fast changing received signal and noise sample variances, which are considered random variables. First, we analyze the detectability of the conventional generalized log-likelihood ratio (GLLR) scheme when considering fast changing statistics of unknown parameters caused by fast fading effects. Secondly, we propose an efficient sensing algorithm for performing the sequential probability ratio test in a robust and efficient manner when the channel statistics are unknown. Finally, the proposed scheme is compared to the conventional method with simulation results with respect to the average number of samples required to reach a detection decision.

Keywords: Cognitive radio, fast fading, sequential detection, spectrum sensing.

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Authors: Anant Oonsivilai, Sanom Saichoomdee

Abstract:

To determine the presence and location of faults in a transmission by the adaptation of protective distance relay based on the measurement of fixed settings as line impedance is achieved by several different techniques. Moreover, a fast, accurate and robust technique for real-time purposes is required for the modern power systems. The appliance of radial basis function neural network in transmission line protection is demonstrated in this paper. The method applies the power system via voltage and current signals to learn the hidden relationship presented in the input patterns. It is experiential that the proposed technique is competent to identify the particular fault direction more speedily. System simulations studied show that the proposed approach is able to distinguish the direction of a fault on a transmission line swiftly and correctly, therefore suitable for the real-time purposes.

Keywords: radial basis function neural network, transmission lines protection, relaying, power system.

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Authors: A. Mostafa

Abstract:

This paper presents a numerical study on determination of ballistic limit velocity (V₅₀) of stainless steel 304 (SS 304) used in manufacturing security screens. The simulated ballistic impact tests were conducted on clamped sheets with different thicknesses using ABAQUS/Explicit nonlinear finite element (FE) package. The ballistic limit velocity was determined using three approaches, namely: numerical tests based on material properties, FE calculated residual velocities and FE calculated residual energies. Johnson-Cook plasticity and failure criterion were utilized to simulate the dynamic behaviour of the SS 304 under various strain rates, while the well-known Lambert-Jonas equation was used for the data regression for the residual velocity and energy model. Good agreement between the investigated numerical methods was achieved. Additionally, the dependence of the ballistic limit velocity on the sheet thickness was observed. The proposed approaches present viable and cost-effective assessment methods of the ballistic performance of SS 304, which will support the development of robust security screen systems.

Keywords: Ballistic velocity, stainless steel, numerical approaches, security screen.

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