Abstract: In this paper, Speed Sensorless Indirect Field Oriented Control (IFOC) of a Permanent Magnet Synchronous machine (PMSM) is studied. The closed loop scheme of the drive system utilizes fuzzy speed and current controllers. Due to the well known drawbacks of the speed sensor, an algorithm is proposed in this paper to eliminate it. In fact, based on the model of the PMSM, the stator currents and rotor speed are estimated simultaneously using adaptive Luenberger observer for currents and MRAS (Model Reference Adaptive System) observer for rotor speed. To overcome the sensivity of this algorithm against parameter variation, adaptive for on line stator resistance tuning is proposed. The validity of the proposed method is verified by an extensive simulation work.
Abstract: This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.
Abstract: Abrasive waterjet is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. The pairwise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.
Abstract: The applications on numbers are across-the-board that there is much scope for study. The chic of writing numbers is diverse and comes in a variety of form, size and fonts. Identification of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], machine printed or handwritten characters/numerals are recognized. There are plentiful approaches that deal with problem of detection of numerals/character depending on the sort of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent our work focused on a technique in feature extraction i.e. Local-based approach, a method using 16-segment display concept, which is extracted from halftoned images & Binary images of isolated numerals. These feature vectors are fed to neural classifier model that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. Experimentation result shows that recognition rate of halftoned images is 98 % compared to binary images (95%).
Abstract: The purpose of study was to design and construction
the semi-automatic sliced ginger machine for reduce production times
in sheet and slice ginger procedure furthermore, reduced amount of
labor of slides and cutting method. Take consider into clean and safety of workers and consumers. The principle of machines, used 1
horsepower motor, rotation speed of sliced blade 967 rpm, the diameter of sliced dish 310 mm, consists of 2 blades for sheet cutting
ginger and the power from motor which transfer to rotate the sliced blade roller, rotation speed 440 rpm. The slice cutter roller was sliced
ginger from sheet ginger to line ginger. The conveyer could
adjustment level of motors, used to the beginning area that sheet
ginger was transference to the roller for sheet and sliced cutting in next process. The cover of sliced cutting had channel for 1 tuber of
ginger. The semi-automatic sliced ginger machine could produced sheet ginger 81.8 kg/h (6.2 times of labor) and line ginger 17.9 kg/h
(2.5 times of labor) compare with, labor work could produced sheet
ginger 13.2 kg/h and line ginger 7.1 kg/h, and when timekeeper, the
total times of semi auto machine 30.86 kg/h and labor 4.6 kg/h, there
for the semi auto machine was 6.7 times of labor. The semiautomatic
sliced ginger machine convenient, easy for use and
maintain, in addition to reduce fatigue of body and seriousness from
works; must be used high skill, and protection accident in slicing
procedure. Beside, machine could used with other vegetables for
example potato, carrot .etc
Abstract: In this paper, we present an innovative scheme of
blindly extracting message bits from an image distorted by an attack.
Support Vector Machine (SVM) is used to nonlinearly classify the
bits of the embedded message. Traditionally, a hard decoder is used
with the assumption that the underlying modeling of the Discrete
Cosine Transform (DCT) coefficients does not appreciably change.
In case of an attack, the distribution of the image coefficients is
heavily altered. The distribution of the sufficient statistics at the
receiving end corresponding to the antipodal signals overlap and a
simple hard decoder fails to classify them properly. We are
considering message retrieval of antipodal signal as a binary
classification problem. Machine learning techniques like SVM is
used to retrieve the message, when certain specific class of attacks is
most probable. In order to validate SVM based decoding scheme, we
have taken Gaussian noise as a test case. We generate a data set using
125 images and 25 different keys. Polynomial kernel of SVM has
achieved 100 percent accuracy on test data.
Abstract: Implementation of response surface methodology (RSM) was employed to study the effects of two factor (rubber clearance and round per minute) in brown rice peeling machine of The optimal BROKENS yield (19.02, average of three repeats),.The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α = 0.05, the values of Regression coefficient, R 2 (adj)were 97.35 % and standard deviation were 1.09513. The independent variables are initial rubber clearance, and round per minute parameters namely. The investigating responses are final rubber clearance, and round per minute (RPM). The restriction of the optimization is the designated.
Abstract: Intrusion detection is a mechanism used to protect a
system and analyse and predict the behaviours of system users. An
ideal intrusion detection system is hard to achieve due to
nonlinearity, and irrelevant or redundant features. This study
introduces a new anomaly-based intrusion detection model. The
suggested model is based on particle swarm optimisation and
nonlinear, multi-class and multi-kernel support vector machines.
Particle swarm optimisation is used for feature selection by applying
a new formula to update the position and the velocity of a particle;
the support vector machine is used as a classifier. The proposed
model is tested and compared with the other methods using the KDD
CUP 1999 dataset. The results indicate that this new method achieves
better accuracy rates than previous methods.
Abstract: Calcium [Ca2+] is an important second messenger
which plays an important role in signal transduction. There are
several parameters that affect its concentration profile like buffer
source etc. The effect of stationary immobile buffer on Ca2+
concentration has been incorporated which is a very important
parameter needed to be taken into account in order to make the
model more realistic. Interdependence of all the important parameters
like diffusion coefficient and influx over [Ca2+] profile has been
studied. Model is developed in the form of advection diffusion
equation together with buffer concentration. A program has been
developed using finite volume method for the entire problem and
simulated on an AMD-Turion 32-bit machine to compute the
numerical results.
Abstract: Electro Discharge Sawing is a hybrid process
combining the features of SEDM and ECM. Its major characteristic is
extremely fast erosion rate compare to either of the above processes.
This paper brings out its relative feature of SEDM and EDS about
their erosion rates, surface roughness, and morphology of machined
surfaces.
Abstract: This work presents a methodology for the design and
manufacture of propellers oriented to the experimental verification of
theoretical results based on the combined model. The design process
begins by using algorithms in Matlab which output data contain the
coordinates of the points that define the blade airfoils, in this case the
NACA 6512 airfoil was used. The modeling for the propeller blade
was made in NX7, through the imported files in Matlab and with the
help of surfaces. Later, the hub and the clamps were also modeled.
Finally, NX 7 also made possible to create post-processed files to the
required machine. It is possible to find the block of numbers with G
& M codes about the type of driver on the machine. The file
extension is .ptp. These files made possible to manufacture the blade,
and the hub of the propeller.
Abstract: Medicinal plants are most suitable crops for ecological production systems because of their role in human health and the aim of sustainable agriculture to improve ecosystem efficiency and its products quality. Calculations include energy output (contents of energy in seed) and energy inputs (consumption of fertilizers, pesticides, labor, machines, fuel and electricity). The ratio of output of the production to inputs is called the energy outputs / inputs ratio or energy efficiency. One way to quantify essential parts of agricultural development is the energy flow method. The output / input energy ratio is proposed as the most comprehensive single factor in pursuing the objective of sustainability. Sylibum marianum L. is one of the most important medicinal plants in Iran and has effective role on health of growing population in Iran. The objective of this investigation was to find out energy efficiency in conventional and low input production system of Milk thistle. This investigation was carried out in the spring of 2005 – 2007 in the Research Station of Rangelands in Hamand - Damavand region of IRAN. This experiment was done in split-split plot based on randomized complete block design with 3 replications. Treatments were 2 production systems (Conventional and Low input system) in the main plots, 3 planting time (25 of March, 4 and 14 of April) in the sub plots and 2 seed types (Improved and Native of Khoozestan) in the sub-sub plots. Results showed that in conventional production system energy efficiency, because of higher inputs and less seed yield, was less than low input production system. Seed yield was 1199.5 and 1888 kg/ha in conventional and low input systems, respectively. Total energy inputs and out puts for conventional system was 10068544.5 and 7060515.9 kcal. These amounts for low input system were 9533885.6 and 11113191.8 kcal. Results showed that energy efficiency for seed production in conventional and low input system was 0.7 and 1.16, respectively. So, milk thistle seed production in low input system has 39.6 percent higher energy efficiency than conventional production system. Also, higher energy efficiency were found in sooner planting time (25 of March) and native seed of Khoozestan.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
Abstract: Pressure wave velocity in a hydraulic system was
determined using piezo pressure sensors without removing fluid from
the system. The measurements were carried out in a low pressure
range (0.2 – 6 bar) and the results were compared with the results of
other studies. This method is not as accurate as measurement with
separate measurement equipment, but the fluid is in the actual
machine the whole time and the effect of air is taken into
consideration if air is present in the system. The amount of air is
estimated by calculations and comparisons between other studies.
This measurement equipment can also be installed in an existing
machine and it can be programmed so that it measures in real time.
Thus, it could be used e.g. to control dampers.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Abstract: This paper presents a model for an unreliable
production line, which is operated according to demand with constant
work-in-process (CONWIP). A simulation model is developed based
on the discrete model and several case problems are analyzed using
the model. The model is utilized to optimize storage space capacities
at intermediate stages and the number of kanbans at the last stage,
which is used to trigger the production at the first stage. Furthermore,
effects of several line parameters on production rate are analyzed
using design of experiments.
Abstract: In this paper we present our results on the performance analysis of a multi-product manufacturing line. We study the influence of external perturbations, intermediate buffer content and the number of manufacturing stages on the production tracking error of each machine in the multi-product line operated under a surplusbased production control policy. Starting by the analysis of a single machine with multiple production stages (one for each product type), we provide bounds on the production error of each stage. Then, we extend our analysis to a line of multi-stage machines, where similarly, bounds on each production tracking error for each product type, as well as buffer content are obtained. Details on performance of the closed-loop flow line model are illustrated in numerical simulations.
Abstract: Doubly fed induction machines DFIM are used
mainly for wind energy conversion in MW power plants. This paper
presents a new strategy of field oriented control ,it is based on the
principle of a double flux orientation of stator and rotor at the same
time. Therefore, the orthogonality created between the two oriented
fluxes, which must be strictly observed, leads to generate a linear and
decoupled control with an optimal torque. The obtained simulation
results show the feasibility and the effectiveness of the suggested
method.
Abstract: The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.
Abstract: Haptics has been used extensively in many applications especially in human machine interaction and virtual reality systems. Haptic technology allows user to perceive virtual reality as in real world. However, commercially available haptic devices are expensive and may not be suitable for educational purpose. This paper describes the design and development of a low cost haptic knob, with only one degree of freedom, for use in rehabilitation or training hand pronation and supination. End-effectors can be changed to suit different applications or variation in hand sizes and hand orientation.