Search results for: Fuzzy Weighted Input Estimation Method

Authors: Devdutt, M. L. Aggarwal

Abstract:

The development of vehicles having best ride comfort and safety of travelling passengers is of great interest for automotive manufacturers. The effect of transmitted vibrations from car body to passenger seat is required to be controlled for achieving the same. The application of magneto-rheological (MR) shock absorber in suspension system has been considered to achieve significant benefits in this regard. This paper introduces a secondary suspension controlled semi-active quarter car system using MR shock absorber for effective vibration control. Fuzzy logic control system is used for design of controller for actual damping force generation by MR shock absorber. Performance evaluations are done related to passenger seat acceleration and displacement in time and frequency domains, in order to see the effectiveness of the proposed semi-active suspension system. Simulation results show that the semi-active suspension system provides better results compared to passive suspension system in terms of passenger ride comfort improvement.

Keywords: Fuzzy logic control, MR shock absorber, Quarter car model, Semi-active suspension system.

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Authors: Orhan Feyzioğlu, Gülçin Büyüközkan

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As a vital activity for companies, new product development (NPD) is also a very risky process due to the high uncertainty degree encountered at every development stage and the inevitable dependence on how previous steps are successfully accomplished. Hence, there is an apparent need to evaluate new product initiatives systematically and make accurate decisions under uncertainty. Another major concern is the time pressure to launch a significant number of new products to preserve and increase the competitive power of the company. In this work, we propose an integrated decision-making framework based on neural networks and fuzzy logic to make appropriate decisions and accelerate the evaluation process. We are especially interested in the two initial stages where new product ideas are selected (go/no go decision) and the implementation order of the corresponding projects are determined. We show that this two-staged intelligent approach allows practitioners to roughly and quickly separate good and bad product ideas by making use of previous experiences, and then, analyze a more shortened list rigorously.

Keywords: Decision Making, Neural Networks, Fuzzy Theory and Systems, Choquet Integral, New Product Development.

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: Classifier ensemble, breast cancer survivability, data mining, SEER.

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Authors: Danijela Tuljak-Suban, Valter Suban

Abstract:

Food as perishable goods represents a specific and sensitive part in the supply chain theory, since changing physical or chemical characteristics considerably influence the approach to stock management. The most delicate phase of this process is transportation, where it becomes difficult to ensure the stable conditions which limit deterioration, since the value of the deterioration rate could be easily influenced by the mode of transportation. The fuzzy definition of variables allows one to take these variations into account. Furthermore, an appropriate choice of the defuzzification method permits one to adapt results to real conditions as far as possible. In this article those methods which take into account the relationship between the deterioration rate of perishable goods and transportation by ship will be applied with the aim of (a) minimizing the total cost function, defined as the sum of the ordering cost, holding cost, disposing cost and transportation costs, and (b) improving the supply chain sustainability by reducing environmental impact and waste disposal costs.

Keywords: Perishable goods, fuzzy reasoning, transport by ship, supply chain sustainability.

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Authors: Thomas O. Olwal, Michael A. van Wyk, Barend J. van Wyk

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In this paper we present a soft timing phase estimation (STPE) method for wireless mobile receivers operating in low signal to noise ratios (SNRs). Discrete Polyphase Matched (DPM) filters, a Log-maximum a posterior probability (MAP) and/or a Soft-output Viterbi algorithm (SOVA) are combined to derive a new timing recovery (TR) scheme. We apply this scheme to wireless cellular communication system model that comprises of a raised cosine filter (RCF), a bit-interleaved turbo-coded multi-level modulation (BITMM) scheme and the channel is assumed to be memory-less. Furthermore, no clock signals are transmitted to the receiver contrary to the classical data aided (DA) models. This new model ensures that both the bandwidth and power of the communication system is conserved. However, the computational complexity of ideal turbo synchronization is increased by 50%. Several simulation tests on bit error rate (BER) and block error rate (BLER) versus low SNR reveal that the proposed iterative soft timing recovery (ISTR) scheme outperforms the conventional schemes.

Keywords: discrete polyphase matched filters, maximum likelihood estimators, soft timing phase estimation, wireless mobile systems.

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Authors: Gaurav Kumar, Dharmbir Prasad, Rudra Pratap Singh

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In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.

Keywords: Adaptive, AEKF, ballistic missile, EKF, re-entry phase, target tracking.

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Authors: Ashish Saini, A.K. Saxena

Abstract:

The genetic algorithm (GA) based solution techniques are found suitable for optimization because of their ability of simultaneous multidimensional search. Many GA-variants have been tried in the past to solve optimal power flow (OPF), one of the nonlinear problems of electric power system. The issues like convergence speed and accuracy of the optimal solution obtained after number of generations using GA techniques and handling system constraints in OPF are subjects of discussion. The results obtained for GA-Fuzzy OPF on various power systems have shown faster convergence and lesser generation costs as compared to other approaches. This paper presents an enhanced GA-Fuzzy OPF (EGAOPF) using penalty factors to handle line flow constraints and load bus voltage limits for both normal network and contingency case with congestion. In addition to crossover and mutation rate adaptation scheme that adapts crossover and mutation probabilities for each generation based on fitness values of previous generations, a block swap operator is also incorporated in proposed EGA-OPF. The line flow limits and load bus voltage magnitude limits are handled by incorporating line overflow and load voltage penalty factors respectively in each chromosome fitness function. The effects of different penalty factors settings are also analyzed under contingent state.

Keywords: Contingent operation state, Fuzzy rule base, Genetic Algorithms, Optimal Power Flow.

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Authors: Abu Bakar Sambah, Fusanori Miura

Abstract:

Remote sensing image processing, spatial data analysis through GIS approach, and analytical hierarchy process were introduced in this study for assessing the vulnerability area and inundation area due to tsunami hazard in the area of Rikuzentakata, Iwate Prefecture, Japan. Appropriate input parameters were derived from GSI DEM data, ALOS AVNIR-2, and field data. We used the parameters of elevation, slope, shoreline distance, and vegetation density. Five classes of vulnerability were defined and weighted via pairwise comparison matrix. The assessment results described that 14.35km² of the study area was under tsunami vulnerability zone. Inundation areas are those of high and slightly high vulnerability. The farthest area reached by a tsunami was about 7.50km from the shoreline and shows that rivers act as flooding strips that transport tsunami waves into the hinterland. This study can be used for determining a priority for land-use planning in the scope of tsunami hazard risk management.

Keywords: AHP, GIS, remote sensing, tsunami vulnerability.

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Authors: E. Mike Dison, T. Pathinathan

Abstract:

Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.

Keywords: Appositive, computing with words, PRUF, semantic sentiment analysis, set theoretic interpretations.

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Authors: Evangelos G. Karvelas, Christos Liosis, Andreas Theodorakakos, Theodoros E. Karakasidis

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In the present work, a numerical method for the estimation of the appropriate gradient magnetic fields for optimum driving of the particles into the desired area inside the human body is presented. The proposed method combines Computational Fluid Dynamics (CFD), Discrete Element Method (DEM) and Covariance Matrix Adaptation (CMA) evolution strategy for the magnetic navigation of nanoparticles. It is based on an iteration procedure that intents to eliminate the deviation of the nanoparticles from a desired path. Hence, the gradient magnetic field is constantly adjusted in a suitable way so that the particles’ follow as close as possible to a desired trajectory. Using the proposed method, it is obvious that the diameter of particles is crucial parameter for an efficient navigation. In addition, increase of particles' diameter decreases their deviation from the desired path. Moreover, the navigation method can navigate nanoparticles into the desired areas with efficiency approximately 99%.

Keywords: CFD, CMA evolution strategy, DEM, magnetic navigation, spherical particles.

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Authors: M. Reza Bagerzadeh Karimi, M. Mahdi Bagerzadeh Karimi

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In the present paper, active control system is used in different heights of the building and the most effective part was studied where the active control system is applied. The mathematical model of the building is established in MATLAB and in order to active control the system FLC method was used. Three different locations of the building are chosen to apply active control system, namely at the lowest story, the middle height of the building, and at the highest point of the building with TMD system. The equation of motion was written for high rise building and it was solved by statespace method. Also passive control was used with Tuned Mass Damper (TMD) at the top floor of the building to show the robustness of FLC method when compared with passive control system.

Keywords: Fuzzy Logic Controller (FLC), Tuned Mass Damper(TMD), Active control, passive control

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Authors: José M. Merigó, Montserrat Casanovas

Abstract:

We study the problem of decision making with Dempster-Shafer belief structure. We analyze the previous work developed by Yager about using the ordered weighted averaging (OWA) operator in the aggregation of the Dempster-Shafer decision process. We discuss the possibility of aggregating with an ascending order in the OWA operator for the cases where the smallest value is the best result. We suggest the introduction of the ordered weighted geometric (OWG) operator in the Dempster-Shafer framework. In this case, we also discuss the possibility of aggregating with an ascending order and we find that it is completely necessary as the OWG operator cannot aggregate negative numbers. Finally, we give an illustrative example where we can see the different results obtained by using the OWA, the Ascending OWA (AOWA), the OWG and the Ascending OWG (AOWG) operator.

Keywords: Decision making, aggregation operators, Dempster- Shafer theory of evidence, Uncertainty, OWA operator, OWG operator.

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Authors: Young-Seok Choi

Abstract:

The Adaptive Line Enhancer (ALE) is widely used for enhancing narrowband signals corrupted by broadband noise. In this paper, we propose novel ALE methods to improve the enhancing capability. The proposed methods are motivated by the fact that the output of the ALE is a fine estimate of the desired narrowband signal with the broadband noise component suppressed. The proposed methods preprocess the input signal using ALE filter to regenerate a finer input signal. Thus the proposed ALE is driven by the input signal with higher signal-to-noise ratio (SNR). The analysis and simulation results are presented to demonstrate that the proposed ALE has better performance than conventional ALE’s.

Keywords: Adaptive filter, adaptive line enhancer, noise, feedback.

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Authors: A. Jabbari, M. Shakeri, A. Nabavi

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In this paper, a tooth shape optimization method for cogging torque reduction in Permanent Magnet (PM) motors is developed by using the Reduced Basis Technique (RBT) coupled by Finite Element Analysis (FEA) and Design of Experiments (DOE) methods. The primary objective of the method is to reduce the enormous number of design variables required to define the tooth shape. RBT is a weighted combination of several basis shapes. The aim of the method is to find the best combination using the weights for each tooth shape as the design variables. A multi-level design process is developed to find suitable basis shapes or trial shapes at each level that can be used in the reduced basis technique. Each level is treated as a separated optimization problem until the required objective – minimum cogging torque – is achieved. The process is started with geometrically simple basis shapes that are defined by their shape co-ordinates. The experimental design of Taguchi method is used to build the approximation model and to perform optimization. This method is demonstrated on the tooth shape optimization of a 8-poles/12-slots PM motor.

Keywords: PM motor, cogging torque, tooth shape optimization, RBT, FEA, DOE.

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Authors: Tariq Ali Gujar, Anita Hökelmann

Abstract:

Postural balance plays essential role throughout life in daily activities. Somatosensory, visual and vestibular inputs play the fundamental role in maintaining body equilibrium to balance the posture. The aim of this study was to find out electroencephalography (EEG) responses during balance activity of young people during Sensory Organization Balance Test. The outcome of this study will help to create the fitness and neurorehabilitation plan. 25 young people (25 ± 3.1 years) have been analyzed on Balance Master NeuroCom® with the coupling of Brain Vision 32 electrode wireless EEG system during the Sensory Organization Test. From the results it has been found that the balance score of samples is significantly higher under the influence of somatosensory input as compared to visual and vestibular input (p < 0.05). The EEG between somatosensory and visual input to balance the posture showed significantly higher (p < 0.05) alpha and beta activities during somatosensory input in somatosensory, attention and visual functions of the cortex whereas executive and motor functions of the cerebral cortex showed significantly higher (p < 0.05) alpha EEG activity during the visual input. The results suggest that somatosensory and attention function of the cerebral cortex has alpha and beta activity, respectively high during somatosensory and vestibular input in maintaining balance. In patients with balance impairments both physical and cognitive training, including neurofeedback will be helpful to improve balance abilities.

Keywords: Balance, electroencephalography activity, somatosensory, visual, vestibular.

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Authors: Nur Syibrah Muhamad Naim, Mohd Lazim Abdullah, Che Mohd Imran Che Taib, Abu OsmanMd. Tap

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Decision making preferences to certain criteria usually focus on positive degrees without considering the negative degrees. However, in real life situation, evaluation becomes more comprehensive if negative degrees are considered concurrently. Preference is expected to be more effective when considering both positive and negative degrees of preference to evaluate the best selection. Therefore, the aim of this paper is to propose the conflicting bifuzzy preference relations in group decision making by utilization of a novel score function. The conflicting bifuzzy preference relation is obtained by introducing some modifications on intuitionistic fuzzy preference relations. Releasing the intuitionistic condition by taking into account positive and negative degrees simultaneously and utilizing the novel score function are the main modifications to establish the proposed preference model. The proposed model is tested with a numerical example and proved to be simple and practical. The four-step decision model shows the efficiency of obtaining preference in group decision making.

Keywords: Fuzzy preference relations, score function, conflicting bifuzzy, decision making.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

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Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: Petr Morávek, Miloš Šeda

Abstract:

In practice, we often come across situations where it is necessary to make decisions based on incomplete or uncertain data. In control systems it may be due to the unknown exact mathematical model, or its excessive complexity (e.g. nonlinearity) when it is necessary to simplify it, respectively, to solve it using a rule base. In the case of databases, searching data we compare a similarity measure with of the requirements of the selection with stored data, where both the select query and the data itself may contain vague terms, for example in the form of linguistic qualifiers. In this paper, we focus on the processing of uncertain data in databases and demonstrate it on the example multi-criteria decision making in the selection of variants, specified by higher number of technical parameters.

Keywords: fuzzy logic, linguistic variable, multicriteria decision

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Authors: Takashi Sakai, Shinsaku Kikuta, Jun-ichi Koyama

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Using a texture database, a statistical estimation of spring-back was conducted in this study on the basis of statistical analysis. Both spring-back in bending deformation and experimental data related to the crystal orientation show significant dispersion. Therefore, a probabilistic statistical approach was established for the proper quantification of these values. Correlation was examined among the parameters F(x) of spring-back, F(x) of the buildup fraction to three orientations after 92° bending, and F(x) at an as-received part on the basis of the three-parameter Weibull distribution. Consequent spring-back estimation using a texture database yielded excellent estimates compared with experimental values.

Keywords: Bending, Spring-back, Database, Crystallographic Orientation, Texture, SEM-EBSD, Weibull distribution, Statistical analysis.

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Authors: Maja Sarevska, Abdel-Badeeh M. Salem

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This paper considers the problem of Null-Steering beamforming using Neural Network (NN) approach for antenna array system. Two cases are presented. First, unlike the other authors, the estimated Direction Of Arrivals (DOAs) are used for antenna array weights NN-based determination and the imprecise DOAs estimations are taken into account. Second, the blind null-steering beamforming is presented. In this case the antenna array outputs are presented at the input of the NN without DOAs estimation. The results of computer simulations will show much better relative mean error performances of the first NN approach compared to the NNbased blind beamforming.

Keywords: Beamforming, DOAs, neural network.

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Authors: Ali Asadian, Behzad Moshiri, Ali Khaki Sedigh, Caro Lucas

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Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites- outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

Keywords: Adaptive Network based Fuzzy Inference System (ANFIS), Genetic optimization, Global Positioning System (GPS), Inertial Navigation System (INS).

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Authors: Jonqlan Lin, C. Y. Tasi, K. H. Lin

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This paper addresses the issue of the autonomous mobile robot (AMR) navigation task based on the hybrid control modes. The novel hybrid control mode, based on multi-sensors information by using the fuzzy approach, has been presented in this research. The system operates in real time, is robust, enables the robot to operate with imprecise knowledge, and takes into account the physical limitations of the environment in which the robot moves, obtaining satisfactory responses for a large number of different situations. An experiment is simulated and carried out with a pioneer mobile robot. From the experimental results, the effectiveness and usefulness of the proposed AMR obstacle avoidance and navigation scheme are confirmed. The experimental results show the feasibility, and the control system has improved the navigation accuracy. The implementation of the controller is robust, has a low execution time, and allows an easy design and tuning of the fuzzy knowledge base.

Keywords: Autonomous mobile robot, obstacle avoidance, MEMS, hybrid control mode, navigation control.

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Authors: Firas M. Tuaimah, Huda M. Abdul Abbas

Abstract:

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.

Keywords: Short term load forecasting, prediction interval, type 2 fuzzy logic systems.

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Authors: Li Xu, Shouming Zhong

Abstract:

The reachable set bounding estimation for distributed delay systems with disturbances is a new problem. In this paper,we consider this problem subject to not only time varying delay and polytopic uncertainties but also distributed delay systems which is not studied fully untill now. we can obtain improved non-ellipsoidal reachable set estimation for neural networks with time-varying delay by the maximal Lyapunov-Krasovskii fuctional which is constructed as the pointwise maximum of a family of Lyapunov-Krasovskii fuctionals corresponds to vertexes of uncertain polytope.On the other hand,matrix inequalities containing only one scalar and Matlabs LMI Toolbox is utilized to give a non-ellipsoidal description of the reachable set.finally,numerical examples are given to illustrate the existing results.

Keywords: Reachable set, Distributed delay, Lyapunov-Krasovskii function, Polytopic uncertainties.

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Authors: Yin Yin Phyo, Tun Lin Naing

Abstract:

This paper investigated the simulation of two-phase interleaved boost converter (IBC) with free and open-source software Scilab/Xcos. By using interleaved method, it can reduce current stress on components, components size, input current ripple and output voltage ripple. The required mathematical model is obtained from the equivalent circuit of its different four modes of operation for simulation. The equivalent circuits are considered in continuous conduction mode (CCM). The average values of the system variables are derived from the state-space equation to find the equilibrium point. Scilab is now becoming more and more popular among students, engineers and scientists because it is open-source software and free of charge. It gives a great convenience because it has powerful computation and simulation function. The waveforms of output voltage, input current and inductors current are obtained by using Scilab/Xcos.

Keywords: Two-phase boost converter, continuous conduction mode, free and open-source, interleaved method, dynamic simulation.

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Authors: N. Ouldcherchali, M. S. Boucherit, L. Barazane, A. Morsli

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In this study, we proposed two techniques to track the maximum power point (MPPT) of a photovoltaic system. The first is an intelligent control technique, and the second is robust used for variable structure system. In fact the characteristics I-V and P–V of the photovoltaic generator depends on the solar irradiance and temperature. These climate changes cause the fluctuation of maximum power point; a maximum power point tracking technique (MPPT) is required to maximize the output power. For this we have adopted a control by fuzzy logic (FLC) famous for its stability and robustness. And a Siding Mode Control (SMC) widely used for variable structure system. The system comprises a photovoltaic panel (PV), a DC-DC converter, which is considered as an adaptation stage between the PV and the load. The modelling and simulation of the system is developed using MATLAB/Simulink. SMC technique provides a good tracking speed in fast changing irradiation and when the irradiation changes slowly or it is constant the panel power of FLC technique presents a much smoother signal with less fluctuations.

Keywords: Fuzzy logic controller, maximum power point, photovoltaic system, tracker, sliding mode controller.

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Authors: Dipen A. Mistry, Bhupelly Dheeraj, Ravit Gautam, Manmohan Singh Meena, Suresh Mikkili

Abstract:

In recent years the large scale use of the power electronic equipment has led to an increase of harmonics in the power system. The harmonics results into a poor power quality and have great adverse economical impact on the utilities and customers. Current harmonics are one of the most common power quality problems and are usually resolved by using shunt active filter (SHAF). The main objective of this work is to develop PI and Fuzzy logic controllers (FLC) to analyze the performance of Shunt Active Filter for mitigating current harmonics under balanced and unbalanced sinusoidal source voltage conditions for normal load and increased load. When the supply voltages are ideal (balanced), both PI and FLC are converging to the same compensation characteristics. However, the supply voltages are non-ideal (unbalanced), FLC offers outstanding results. Simulation results validate the superiority of FLC with triangular membership function over the PI controller.

Keywords: DC link voltage, Fuzzy logic controller, Harmonics, PI controller, Shunt Active Filter.

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Authors: M. Fakharian, M. I. Khodakarami

Abstract:

In this paper, a new trend for improvement in semianalytical method based on scale boundaries in order to solve the 2D elastodynamic problems is provided. In this regard, only the boundaries of the problem domain discretization are by specific subparametric elements. Mapping functions are uses as a class of higherorder Lagrange polynomials, special shape functions, Gauss-Lobatto- Legendre numerical integration, and the integral form of the weighted residual method, the matrix is diagonal coefficients in the equations of elastodynamic issues. Differences between study conducted and prior research in this paper is in geometry production procedure of the interpolation function and integration of the different is selected. Validity and accuracy of the present method are fully demonstrated through two benchmark problems which are successfully modeled using a few numbers of DOFs. The numerical results agree very well with the analytical solutions and the results from other numerical methods.

Keywords: 2D Elastodynamic Problems, Lagrange Polynomials, G-L-Lquadrature, Decoupled SBFEM.

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Authors: Khalil Monfaredi, Hassan Faraji Baghtash, Majid Abbasi

Abstract:

In this paper a novel high output impedance, low input impedance, wide bandwidth, very simple current mirror with input and output voltage requirements less than that of a simple current mirror is presented. These features are achieved with very simple structure avoiding extra large node impedances to ensure high bandwidth operation. The circuit's principle of operation is discussed and compared to simple and low voltage cascode (LVC) current mirrors. Such outstanding features of this current mirror as high output impedance ~384K, low input impedance~6.4, wide bandwidth~178MHz, low input voltage ~ 362mV, low output voltage ~ 38mV and low current transfer error ~4% (all at 50μA) makes it an outstanding choice for high performance applications. Simulation results in BSIM 0.35μm CMOS technology with HSPICE are given in comparison with simple, and LVC current mirrors to verify and validate the performance of the proposed current mirror.

Keywords: Analog circuits, Current mirror, high frequency, Low power, Low voltage.

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Authors: Fatemeh Amiri, Caro Lucas, Nasser Yazdani

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As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure - linear correlation coefficient and mutual information- is investigated respectively

Keywords: anomaly Detection, feature selection, Locally Linear Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation coefficient.

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