Search results for: Tool Configuration Vector

Authors: Syed Munvar Ali, V. Vijaya Kumar Reddy, M. Surya Kalavathi

Abstract:

In this paper, a dual inverter configuration has been implemented for induction motor drive. This isolated dual inverter is capable to produce high quality of output voltage and minimize common mode voltage (CMV). To this isolated dual inverter a decoupled space vector based pulse width modulation (PWM) technique is proposed. Conventional space vector based PWM (SVPWM) techniques require reference voltage vector calculation and sector identification. The proposed decoupled SVPWM technique generates gating pulses from instantaneous phase voltages and gives a CMV of ±v_dc/6. To evaluate proposed algorithm MATLAB based simulation studies are carried on indirect vector controlled open end winding induction motor drive.

Keywords: Inverter configuration, decoupled SVPWM, common mode voltage, vector control.

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

Abstract:

Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization, This is a hybrid of two product code vector quantization techniques namely the Multi stage vector quantization technique, and Switched split vector quantization technique,. Multi Switched Split Vector Quantization technique quantizes the linear predictive coefficients in terms of line spectral frequencies. From results it is proved that Multi Switched Split Vector Quantization provides better trade off between bitrate and spectral distortion performance, computational complexity and memory requirements when compared to Switched Split Vector Quantization, Multi stage vector quantization, and Split Vector Quantization techniques. By employing the switching technique at each stage of the vector quantizer the spectral distortion, computational complexity and memory requirements were greatly reduced. Spectral distortion was measured in dB, Computational complexity was measured in floating point operations (flops), and memory requirements was measured in (floats).

Keywords: Unconstrained vector quantization, Linear predictiveCoding, Split vector quantization, Multi stage vector quantization, Switched Split vector quantization, Line Spectral Frequencies.

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Authors: T. C. Manjunath, C. Ardil

Abstract:

This paper presents an efficient method of obtaining a straight-line motion in the tool configuration space using an articulated robot between two specified points. The simulation results & the implementation results show the effectiveness of the method.

Keywords: Bounded deviation algorithm, Straight line motion, Tool configuration space, Joint space, TCV.

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Authors: T.C.Manjunath, C. Ardil

Abstract:

This paper deals with the development of a Jacobean model for a 4-axes indigenously developed scara robot arm in the laboratory. This model is used to study the relation between the velocities and the forces in the robot while it is doing the pick and place operation.

Keywords: SCARA, Jacobean, Tool Configuration Vector, Computer Control , Visual Basic , Interfacing , Drivers,

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

Abstract:

Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to split vector quantization (SVQ), multi stage vector quantization(MSVQ) and switched split vector quantization (SSVQ) techniques. It has been proved from results that MSSVQ has better spectral distortion performance, lower computational complexity and lower memory requirements when compared to all the above mentioned product code vector quantization techniques. Computational complexity is measured in floating point operations (flops), and memory requirements is measured in (floats).

Keywords: Linear predictive Coding, Multi stage vectorquantization, Switched Split vector quantization, Split vectorquantization, Line Spectral Frequencies (LSF).

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Authors: Inderpreet Kaur, Neena Gupta

Abstract:

In this paper, the gain spectrum of EDFA has been broadened by implementing HTE configuration for S and C band. On using this configuration an amplification bandwidth of 76nm ranging from 1479nm to 1555nm with a peak gain of 26dB has been obtained.

Keywords: C band, DWDM system, EDFA, Gain, HTE, Hybrid Fiber Amplifier, S band.

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Authors: Erna T. Herdiani, Maman A. Djauhari

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In recent years, the use of vector variance as a measure of multivariate variability has received much attention in wide range of statistics. This paper deals with a more economic measure of multivariate variability, defined as vector variance minus all duplication elements. For high dimensional data, this will increase the computational efficiency almost 50 % compared to the original vector variance. Its sampling distribution will be investigated to make its applications possible.

Keywords: Asymptotic distribution, covariance matrix, likelihood ratio test, vector variance.

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Authors: Bhupendra C. S. Chauhan, P. S. Bisht, O. P. S. Negi

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According to celebrated Hurwitz theorem, there exists four division algebras consisting of R (real numbers), C (complex numbers), H (quaternions) and O (octonions). Keeping in view the utility of octonion variable we have tried to extend the three dimensional vector analysis to seven dimensional one. Starting with the scalar and vector product in seven dimensions, we have redefined the gradient, divergence and curl in seven dimension. It is shown that the identity n(n - 1)(n - 3)(n - 7) = 0 is satisfied only for 0, 1, 3 and 7 dimensional vectors. We have tried to write all the vector inequalities and formulas in terms of seven dimensions and it is shown that same formulas loose their meaning in seven dimensions due to non-associativity of octonions. The vector formulas are retained only if we put certain restrictions on octonions and split octonions.

Keywords: Octonions, Vector Space and seven dimensions

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Authors: Süha Yılmaz, Emin Özyılmaz, Melih Turgut, Şuur Nizamoğlu

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In this paper, position vector of a partially null unit speed curve with respect to standard frame of Minkowski space-time is studied. First, it is proven that position vector of every partially null unit speed curve satisfies a vector differential equation of fourth order. In terms of solution of the differential equation, position vector of a partially null unit speed curve is expressed.

Keywords: Frenet Equations, Partially Null Curves, Minkowski Space-time, Vector Differential Equation.

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Authors: Rui. Zhang, Yiming. Pi

Abstract:

In this paper we present a novel approach for human Body configuration based on the Silhouette. We propose to address this problem under the Bayesian framework. We use an effective Model based MCMC (Markov Chain Monte Carlo) method to solve the configuration problem, in which the best configuration could be defined as MAP (maximize a posteriori probability) in Bayesian model. This model based MCMC utilizes the human body model to drive the MCMC sampling from the solution space. It converses the original high dimension space into a restricted sub-space constructed by the human model and uses a hybrid sampling algorithm. We choose an explicit human model and carefully select the likelihood functions to represent the best configuration solution. The experiments show that this method could get an accurate configuration and timesaving for different human from multi-views.

Keywords: Bayesian framework, MCMC, model based, human body configuration.

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Authors: A. Greco, N. Mammone, F.C. Morabito, M.Versaci

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This paper is mainly concerned with the application of a novel technique of data interpretation for classifying measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artificial Neural Networks and Multi-Class Support Vector Machines have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compared with earlier methods.

Keywords: Tokamak, Classification, Artificial Neural Network, Support Vector Machines.

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studies with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Marc Diesse, Hochschule Heilbronn

Abstract:

We address the question of identifying the configuration space singularities of linkages, i.e., points where the configuration space is not locally a submanifold of Euclidean space. Because the configuration space cannot be smoothly parameterized at such points, these singularity types have a significantly negative impact on the kinematics of the linkage. It is known that Jacobian methods do not provide sufficient conditions for the existence of CS-singularities. Herein, we present several additional algebraic criteria that provide the sufficient conditions. Further, we use those criteria to analyze certain classes of planar linkages. These examples will also show how the presented criteria can be checked using algorithmic methods.

Keywords: Linkages, configuration space singularities, real algebraic geometry, analytic geometry, computer algebra.

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Authors: Prof. Chokri SLIM

Abstract:

A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.

Keywords: Neural network, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression.

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Authors: Ali Sarosh, Dong Yun-Feng, Muhammad Umer

Abstract:

Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.

Keywords: TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, aerothermodynamics, mass-modeling, TSTO vehicle.

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Authors: Mei Li, Fahui Zhai

Abstract:

In this paper, the definitions of the quaternion measure and the quaternion vector measure are introduced. The relation between the quaternion measure and the complex vector measure as well as the relation between the quaternion linear functional and the complex linear functional are discussed respectively. By using these relations, the necessary and sufficient condition to determine the quaternion vector measure is given.

Keywords: Quaternion, Quaternion measure, Quaternion vector measure, Quaternion Banach space, Quaternion linear functional.

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Authors: Emin Özyılmaz

Abstract:

In this work, position vector of a time-like dual curve according to standard frame of D31 is investigated. First, it is proven that position vector of a time-like dual curve satisfies a dual vector differential equation of fourth order. The general solution of this dual vector differential equation has not yet been found. Due to this, in terms of special solutions, position vectors of some special time-like dual curves with respect to standard frame of D31 are presented.

Keywords: Classical Differential Geometry, Dual Numbers, DualFrenet Equations, Time-like Dual Curve, Position Vector, DualLorentzian Space.

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Authors: Ju-Hong Lee, Ching-Wei Liao, Kun-Che Lee

Abstract:

This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

Keywords: Adaptive beamforming, antenna array, linearly constrained minimum variance, robustness, steering vector.

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Authors: A. Habiboghli, R. Mostafaei, M. R.Meybodi

Abstract:

Optical network uses a tool for routing which is called Latin router. These routers use particular algorithms for routing. In this paper, we present algorithm for configuration of optical network that is optimized regarding previous algorithm. We show that by decreasing the number of hops for source-destination in lightpath number of satisfied request is less. Also we had shown that more than single-hop lightpath relating single-hop lightpath is better.

Keywords: Latin Router, Constraint Satisfied, Wavelength, Optical Network

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Authors: S.Esakkirajan, T. Veerakumar, V. Senthil Murugan, P.Navaneethan

Abstract:

In this paper, image compression using hybrid vector quantization scheme such as Multistage Vector Quantization (MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A combined MSVQ and PVQ are utilized to take advantages provided by both of them. In the wavelet decomposition of the image, most of the information often resides in the lowest frequency subband. MSVQ is applied to significant low frequency coefficients. PVQ is utilized to quantize the coefficients of other high frequency subbands. The wavelet coefficients are derived using lifting scheme. The main aim of the proposed scheme is to achieve high compression ratio without much compromise in the image quality. The results are compared with the existing image compression scheme using MSVQ.

Keywords: Lifting Scheme, Multistage Vector Quantization and Pyramid Vector Quantization.

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Authors: Nagi Mekhiel

Abstract:

Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory organization, parallel processors, serial code, vector processing.

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Authors: Tomoya Okubo, Kuninori Nakamura, Shin-ichi Mayekawa

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In this research, a latent class vector model for pairwise data is formulated. As compared to the basic vector model, this model yields consistent estimates of the parameters since the number of parameters to be estimated does not increase with the number of subjects. The result of the analysis reveals that the model was stable and could classify each subject to the latent classes representing the typical scales used by these subjects.

Keywords: finite mixture models, latent class analysis, Thrustone's paired comparison method, vector model

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Authors: Ami Kim, Oubong Gwun, Juwhan Song

Abstract:

In this paper, we proposed the distribution of mesh normal vector direction as a feature descriptor of a 3D model. A normal vector shows the entire shape of a model well. The distribution of normal vectors was sampled in proportion to each polygon's area so that the information on the surface with less surface area may be less reflected on composing a feature descriptor in order to enhance retrieval performance. At the analysis result of ANMRR, the enhancement of approx. 12.4%~34.7% compared to the existing method has also been indicated.

Keywords: Interpolated Normal Vector, Feature Descriptor, 3DModel Retrieval.

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Authors: Makoto Nagatomo, Yasuo Uchida, Makoto Sakamoto, Tuo Zhang, Tatsuma Kurogi, Takao Ito, Tsunehiro Yoshinaga, Satoshi Ikeda, Masahiro Yokomichi, Hiroshi Furutani

Abstract:

This paper presents a four-dimensional computational model, k-neighborhood template A-type three-dimensional bounded cellular acceptor (abbreviated as A-3BCA(k)), and discusses the hierarchical properties. An A-3BCA(k) is a four-dimensional automaton which consists of a pair of a converter and a configuration-reader. The former converts the given four-dimensional tape to the three- and two- dimensional configuration and the latter determines the acceptance or nonacceptance of given four-dimensional tape whether or not the derived two-dimensional configuration is accepted. We mainly investigate the difference of the accepting power based on the difference of the configuration-reader. It is shown that the difference of the accepting power of the configuration-reader tends to affect directly that of the A-3BCA(k) for the case when the converter is deterministic. On the other hand, results are not analogous for the nondeterministic case.

Keywords: Cellular acceptor, configuration-reader, converter, finite automaton, four-dimension, on-line tessellation acceptor, parallel/sequential array acceptor, turing machine.

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Authors: Vinod Kumar Khera, R. K. Sharma, A. K. Gupta

Abstract:

Power dissipation increases exponentially during test mode as compared to normal operation of the circuit. In extreme cases, test power is more than twice the power consumed during normal operation mode. Test vector generation scheme is key component in deciding the power hungriness of a circuit during testing. Test vector count and consequent leakage current are functions of test vector generation scheme. Fault based test vector count optimization has been presented in this work. It helps in reducing test vector count and the leakage current. In the presented scheme, test vectors have been reduced by extracting essential child vectors. The scheme has been tested experimentally using stuck at fault models and results ensure the reduction in test vector count.

Keywords: Low power VLSI testing, independent fault, essential faults, test vector reduction.

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Authors: Shams Ul Islam, Chao Ying Zhou, Farooq Ahmad

Abstract:

A two-dimensional numerical simulation of crossflow around four cylinders in an in-line rectangular configuration is studied by using the lattice Boltzmann method (LBM). Special attention is paid to the effect of the spacing between the cylinders. The Reynolds number ( Re ) is chosen to be e 100 R = and the spacing ratio L / D is set at 0.5, 1.5, 2.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 and 10.0. Results show that, as in the case of four cylinders in an inline rectangular configuration , flow fields show four different features depending on the spacing (single square cylinder, stable shielding flow, wiggling shielding flow and a vortex shedding flow) are observed in this study. The effects of spacing ratio on physical quantities such as mean drag coefficient, Strouhal number and rootmean- square value of the drag and lift coefficients are also presented. There is more than one shedding frequency at small spacing ratios. The mean drag coefficients for downstream cylinders are less than that of the single cylinder for all spacing ratios. The present results using the LBM are compared with some existing experimental data and numerical studies. The comparison shows that the LBM can capture the characteristics of the bluff body flow reasonably well and is a good tool for bluff body flow studies.

Keywords: Four square cylinders, Lattice Boltzmann method, rectangular configuration, spacing ratios, vortex shedding.

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Authors: J. S. Kim

Abstract:

This paper presents a 0.13-µm Complementary Metal-Oxide Semiconductor (CMOS) vector modulator for beamforming system. The vector modulator features a 360° phase and gain range of -10 dB to 10 dB with a root mean square phase and amplitude error of only 2.2° and 0.45 dB, respectively. These features make it a suitable for wireless backhaul system in the 5 GHz industrial, scientific, and medical (ISM) bands. It draws a current of 20.4 mA from a 1.2 V supply. The total chip size is 1.87x1.34 mm².

Keywords: CMOS, vector modulator, beamforming, wireless backhaul, ISM.

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Authors: Yuan L. Lai, Yong R. Chen, Jui P. Hung, Tzuo L. Luo, Hsi H. Hsiao

Abstract:

Chatter vibration has been a troublesome problem for a machine tool toward the high precision and high speed machining. Essentially, the machining performance is determined by the dynamic characteristics of the machine tool structure and dynamics of cutting process. Therefore the dynamic vibration behavior of spindle tool system greatly determines the performance of machine tool. The purpose of this study is to investigate the influences of the machine frame structure on the dynamic frequency of spindle tool unit through finite element modeling approach. To this end, a realistic finite element model of the vertical milling system was created by incorporated the spindle-bearing model into the spindle head stock of the machine frame. Using this model, the dynamic characteristics of the milling machines with different structural designs of spindle head stock and identical spindle tool unit were demonstrated. The results of the finite element modeling reveal that the spindle tool unit behaves more compliant when the excited frequency approaches the natural mode of the spindle tool; while the spindle tool show a higher dynamic stiffness at lower frequency that may be initiated by the structural mode of milling head. Under this condition, it is concluded that the structural configuration of spindle head stock associated with the vertical column of milling machine plays an important role in determining the machining dynamics of the spindle unit.

Keywords: Machine tools, Compliance, Frequency response function, Machine frame structure, Spindle unit

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Authors: Kheldoun Aissa, Khodja Djalal Eddine

Abstract:

The iron loss is a source of detuning in vector controlled induction motor drives if the classical rotor vector controller is used for decoupling. In fact, the field orientation will not be satisfied and the output torque will not truck the reference torque mostly used by Loss Model Controllers (LMCs). In addition, this component of loss, among others, may be excessive if the vector controlled induction motor is driving light loads. In this paper, the series iron loss model is used to develop a vector controller immune to iron loss effect and then an LMC to minimize the total power loss using the torque generated by the speed controller.

Keywords: Field Oriented Controller, Induction Motor, Loss ModelController, Series Iron Loss.

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