Search results for: time – varying feed back

Authors: Chen-Yu Wang, Tsung-Chien Chen, Ming-Hui Lee, Jen-Feng Huang

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In this paper, the innovative intelligent fuzzy weighted input estimation method (FWIEM) can be applied to the inverse heat transfer conduction problem (IHCP) to estimate the unknown time-varying heat flux efficiently as presented. The feasibility of this method can be verified by adopting the temperature measurement experiment. We would like to focus attention on the heat flux estimation to three kinds of samples (Copper, Iron and Steel/AISI 304) with the same 3mm thickness. The temperature measurements are then regarded as the inputs into the FWIEM to estimate the heat flux. The experiment results show that the proposed algorithm can estimate the unknown time-varying heat flux on-line.

Keywords: Fuzzy Weighted Input Estimation Method, IHCP andHeat Flux.

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Authors: Andrew Hwang

Abstract:

The average American worker sits for thirteen hours a day, often with poor posture and infrequent breaks, which can lead to health issues and back problems. The Smart Cushion was created to alert individuals of their poor postures, and may potentially alleviate back problems and correct poor posture. The Smart Cushion is a portable, rectangular, foam cushion, with five strategically placed pressure sensors, that utilizes an Arduino Uno circuit board and specifically designed software, allowing it to collect data from the five pressure sensors and store the data on an SD card. The data is then compiled into graphs and compared to controlled postures. Before volunteers sat on the cushion, their levels of back pain were recorded on a scale from 1-10. Data was recorded for an hour during sitting, and then a new, corrected posture was suggested. After using the suggested posture for an hour, the volunteers described their level of discomfort on a scale from 1-10. Different patterns of sitting postures were generated that were able to serve as early warnings of potential back problems. By using the Smart Cushion, the areas where different volunteers were applying the most pressure while sitting could be identified, and the sitting postures could be corrected. Further studies regarding the relationships between posture and specific regions of the body are necessary to better understand the origins of back pain; however, the Smart Cushion is sufficient for correcting sitting posture and preventing the development of additional back pain.

Keywords: Arduino Sketch Algorithm, biomedical technology, pressure sensors, Smart Cushion.

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Authors: Manop Vorasri, Komson Jirapattarasilp, Sittichai Kaewkuekool

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The objective of this research was to study factors, which were affected on surface roughness in high speed milling of hardened tool steel. Material used in the experiment was tool steel JIS SKD 61 that hardened on 60 ±2 HRC. Full factorial experimental design was conducted on 3 factors and 3 levels (3 3 designs) with 2 replications. Factors were consisted of cutting speed, feed rate, and depth of cut. The results showed that influenced factor affected to surface roughness was cutting speed, feed rate and depth of cut which showed statistical significant. Higher cutting speed would cause on better surface quality. On the other hand, higher feed rate would cause on poorer surface quality. Interaction of factor was found that cutting speed and depth of cut were significantly to surface quality. The interaction of high cutting speed associated with low depth of cut affected to better surface quality than low cutting speed and high depth of cut.

Keywords: High-speed milling, Tool steel, SKD 61 Steel, Surface roughness, Cutting speed, Feed rate, Depth of cut

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Authors: Liming Zhang

Abstract:

In this paper, a new adaptive Fourier decomposition (AFD) based time-frequency speech analysis approach is proposed. Given the fact that the fundamental frequency of speech signals often undergo fluctuation, the classical short-time Fourier transform (STFT) based spectrogram analysis suffers from the difficulty of window size selection. AFD is a newly developed signal decomposition theory. It is designed to deal with time-varying non-stationary signals. Its outstanding characteristic is to provide instantaneous frequency for each decomposed component, so the time-frequency analysis becomes easier. Experiments are conducted based on the sample sentence in TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results show that the AFD based time-frequency distribution outperforms the STFT based one.

Keywords: Adaptive fourier decomposition, instantaneous frequency, speech analysis, time-frequency distribution.

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Authors: Mahsa Ja’fari, Mohammad R. Khosravi-Nikou, Mohsen Motavassel

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A comprehensive development towards the production of ultra-clean fuels as a feed stoke is getting to raise due to the increasing use of diesel fuels and global air pollution. Production of environmental-friendly fuels can be achievable by some limited single methods and most integrated ones. Oxidative desulfurization (ODS) presents vast ranges of technologies possessing suitable characteristics with regard to the Fenton process. Using toluene as a model fuel feed with dibenzothiophene (DBT) as a sulfur compound under various operating conditions is the attempt of this study. The results showed that this oxidative process followed a pseudo-first order kinetics. Removal efficiency of 77.43% is attained under reaction time of 40 minutes with (Fe⁺²/H₂O₂) molar ratio of 0.05 in acidic pH environment. In this research, temperature of 50 °C represented the most influential role in proceeding the reaction.

Keywords: Design of experiment, dibenzothiophene, optimization, oxidative desulfurization.

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Authors: Theodor D. Popescu

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The paper presents a method for multivariate time series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. The forecasting can be done separately and with a different method for each component, depending on its time structure. The paper gives also a review of the main algorithms for independent component analysis in the case of instantaneous mixture models, using second and high-order statistics. The method has been applied in simulation to an artificial multivariate time series with five components, generated from three sources and a mixing matrix, randomly generated.

Keywords: Independent Component Analysis, second order statistics, simulation, time series forecasting

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Authors: K. Kusakana

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A hybrid energy system is a combination of renewable energy sources with back up, as well as a storage system used to respond to given load energy requirements. Given that the electrical output of each renewable source is fluctuating with changes in weather conditions, and since the load demand also varies with time; one of the main attributes of hybrid systems is to be able to respond to the load demand at any time by optimally controlling each energy source, storage and back-up system. The induced optimization problem is to compute the optimal operation control of the system with the aim of minimizing operation costs while efficiently and reliably responding to the load energy requirement. Current optimization research and development on hybrid systems are mainly focusing on the sizing aspect. Thus, the aim of this paper is to report on the state-of-the-art of optimal operation control of hybrid renewable energy systems. This paper also discusses different challenges encountered, as well as future developments that can help in improving the optimal operation control of hybrid renewable energy systems.

Keywords: Renewable energies, hybrid systems, optimization, operation control.

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Authors: Dharmendra Chourishi “Maitraya”, Sridevi Seshadri

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Bluetooth is a personal wireless communication technology and is being applied in many scenarios. It is an emerging standard for short range, low cost, low power wireless access technology. Current existing MAC (Medium Access Control) scheduling schemes only provide best-effort service for all masterslave connections. It is very challenging to provide QoS (Quality of Service) support for different connections due to the feature of Master Driven TDD (Time Division Duplex). However, there is no solution available to support both delay and bandwidth guarantees required by real time applications. This paper addresses the issue of how to enhance QoS support in a Bluetooth piconet. The Bluetooth specification proposes a Round Robin scheduler as possible solution for scheduling the transmissions in a Bluetooth Piconet. We propose an algorithm which will reduce the bandwidth waste and enhance the efficiency of network. We define token counters to estimate traffic of real-time slaves. To increase bandwidth utilization, a back-off mechanism is then presented for best-effort slaves to decrease the frequency of polling idle slaves. Simulation results demonstrate that our scheme achieves better performance over the Round Robin scheduling.

Keywords: Piconet, Medium Access Control, Polling algorithm, Scheduling, QoS, Time Division Duplex (TDD).

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Authors: Sukrit Shankar, Chetana Shanta Patsa, Jaydev Sharma

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Fractional Fourier Transform is a generalization of the classical Fourier Transform. The Fractional Fourier span in general depends on the amplitude and phase functions of the signal and varies with the transform order. However, with the development of the Fractional Fourier filter banks, it is advantageous in some cases to have different transform orders for different filter banks to achieve better decorrelation of the windowed and overlapped time signal. We present an expression that is useful for finding the perturbation in the Fractional Fourier span due to the erroneous transform order and the possible variation in the window shape and length. The expression is based on the dependency of the time-Fractional Fourier span Uncertainty on the amplitude and phase function of the signal. We also show with the help of the developed expression that the perturbation of span has a varying degree of sensitivity for varying degree of transform order and the window coefficients.

Keywords: Fractional Fourier Transform, Perturbation, Fractional Fourier span, amplitude, phase, transform order, filterbanks.

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Authors: Zixin Liu, Shu Lü, Shouming Zhong, Mao Ye

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In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

Keywords: Robust exponential stability, delay-dependent stability, discrete-time neutral networks, time-varying delays.

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Authors: N. M. Nawi, M. R. Ransing, R. S. Ransing

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The conjugate gradient optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (CGFR/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back propagation algorithm by introducing gain variation term of the activation function, (2) Calculating the gradient descent on error with respect to the weights and gains values and (3) the determination of the new search direction by exploiting the information calculated by gradient descent in step (2) as well as the previous search direction. The proposed method improved the training efficiency of back propagation algorithm by adaptively modifying the initial search direction. Performance of the proposed method is demonstrated by comparing to the conjugate gradient algorithm from neural network toolbox for the chosen benchmark. The results show that the number of iterations required by the proposed method to converge is less than 20% of what is required by the standard conjugate gradient and neural network toolbox algorithm.

Keywords: Back-propagation, activation function, conjugategradient, search direction, gain variation.

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Authors: Sitalakshmi Venkataraman

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Recent scientific investigations indicate that multimodal biometrics overcome the technical limitations of unimodal biometrics, making them ideally suited for everyday life applications that require a reliable authentication system. However, for a successful adoption of multimodal biometrics, such systems would require large heterogeneous datasets with complex multimodal fusion and privacy schemes spanning various distributed environments. From experimental investigations of current multimodal systems, this paper reports the various issues related to speed, error-recovery and privacy that impede the diffusion of such systems in real-life. This calls for a robust mechanism that caters to the desired real-time performance, robust fusion schemes, interoperability and adaptable privacy policies. The main objective of this paper is to present a framework that addresses the abovementioned issues by leveraging on the heterogeneous resource sharing capacities of Grid services and the efficient machine learning capabilities of artificial neural networks (ANN). Hence, this paper proposes a Grid-based neural network framework for adopting multimodal biometrics with the view of overcoming the barriers of performance, privacy and risk issues that are associated with shared heterogeneous multimodal data centres. The framework combines the concept of Grid services for reliable brokering and privacy policy management of shared biometric resources along with a momentum back propagation ANN (MBPANN) model of machine learning for efficient multimodal fusion and authentication schemes. Real-life applications would be able to adopt the proposed framework to cater to the varying business requirements and user privacies for a successful diffusion of multimodal biometrics in various day-to-day transactions.

Keywords: Back Propagation, Grid Services, MultimodalBiometrics, Neural Networks.

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: Model predictive control, sampled-data control, linear parameter varying systems, LPV.

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Authors: N. Tadrisi Parsa, A. R. Vali, R. Ghasemi

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Diabetes is a growing health problem in worldwide. Especially, the patients with Type 1 diabetes need strict glycemic control because they have deficiency of insulin production. This paper attempts to control blood glucose based on body mathematical body model. The Bergman minimal mathematical model is used to develop the nonlinear controller. A novel back-stepping based sliding mode control (B-SMC) strategy is proposed as a solution that guarantees practical tracking of a desired glucose concentration. In order to show the performance of the proposed design, it is compared with conventional linear and fuzzy controllers which have been done in previous researches. The numerical simulation result shows the advantages of sliding mode back stepping controller design to linear and fuzzy controllers.

Keywords: Back stepping, Bergman Model, Nonlinear control, Sliding mode control.

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Authors: Jin Choi, Zahra Aminikhoei, Yi-Oh Kim, Sang-Min Lee

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A feeding experiment was conducted to determine the optimum dietary protein and lipid levels for juvenile fancy carp. Eight experimental diets were formulated to contain four protein levels (200, 300, 400 and 500 g kg-1) with two lipid levels (70 and 140 g kg-1). Triplicate groups of fish (initial weight, 12.1±0.2 g fish-1) were hand-fed the diets to apparent satiation for 8 weeks. Fish growth performance, feed utilization and feed intake were significantly (P<0.0001) affected by dietary protein level, but not by dietary lipid level (P>0.05). Weight gain and feed efficiency ratio tended to increase as dietary protein level increased up to 400 and 500 g kg-1, respectively. Daily feed intake of fish decreased with increasing dietary protein level and that of fish fed diet contained 500 g kg-1 protein was significantly lower than other fish groups. The protein efficiency ratio of fish fed 400 and 500 g kg-1 protein was lower than that of fish fed 200 and 300 g kg-1 protein. Moisture, crude protein and crude lipid contents of muscle and liver were significantly affected by dietary protein, but not by dietary lipid level (P>0.05). The increase in dietary lipid level resulted in an increase in linoleic acid in liver and muscle paralleled with a decrease in n-3 highly unsaturated fatty acids content in muscle of fish. In considering these results, it was concluded that the diet containing 400 g kg-1 protein with 70 g kg-1 lipid level is optimal for growth and efficient feed utilization of juvenile fancy carp.

Keywords: Fancy carp, Dietary protein, Dietary lipid, Fatty acid.

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Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo

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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

Keywords: Neural networks, groundwater depth, forecast.

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Authors: Saurav Bhattacharjee, Syam Nair

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Lime stabilization is a sustainable and economically viable option to address strength deficiencies of subgrade soils. However, exposure of stabilized layers to environmental elements can lead to a reduction in post-stabilization strength gain expected in these layers. The current study investigates the impact of carbonation on the strength properties of lime-treated soils. Manufactured soils prepared using varying proportions of bentonite silica mixtures were used in the study. Lime-treated mixtures were exposed to different atmospheric conditions created by varying the concentrations of CO₂ in the testing chamber. The impact of CO₂ diffusion was identified based on changes in carbonate content and unconfined compressive strength (UCS) properties. Changes in soil morphology were also investigated as part of the study. The rate of carbonation was observed to vary polynomially (2nd order) with exposure time. The strength properties of the mixes were observed to decrease with exposure time.

Keywords: Manufactured soil, carbonation, morphology, unconfined compressive strength.

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Authors: S. Hu, J. Mortensen, Bernard F. Buxton

Abstract:

A real-time tracking system was built to track performers on an interactive stage. Using an ordinary, up to date, desktop workstation, the performers- silhouette was segmented from the background and parameterized by calculating the normalized central image moments. In the stage system, the silhouette moments were then sent to a parallel workstation, which used them to generate corresponding 3D virtual geometry and projected the generated graphic back onto the stage.

Keywords: Image moment, interactive stage, real-time, silhouette.

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Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan

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This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.

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Authors: Zoheir Tir, Rachid Abdessemed

Abstract:

This paper presents a new control scheme to control a brushless doubly fed induction generator (BDFIG) using back-to-back PWM converters for wind power generation. The proposed control scheme is a New Self-Tuning Fuzzy Proportional-Derivative Controller (NSTFPDC). The goal of BDFIG control is to achieve a similar dynamic performance to the doubly fed induction generator (DFIG), exploiting the well-known induction machine vector control philosophy. The performance of NSTFPDC controller has been investigated and compared with the two controllers, called Proportional–Integral (PI) and PD-like Fuzzy Logic controller (PD-like FLC) based BDFIG. The simulation results demonstrate the effectiveness and the robustness of the NSTFPDC controller.

Keywords: Brushless Doubly Fed Induction Generator (BDFIG), PI controller, PD-like Fuzzy Logic controller, New Self-Tuning Fuzzy Proportional-Derivative Controller (NSTFPDC), Scaling factor, back-to-back PWM converters, wind energy system.

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Authors: O. A. Anjola, M. A. Adejobi, A. Ogunbameru, F. P. Agbaye, R. O. Odunukan

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This experiment was conducted for 8 weeks to evaluate the growth performance and economics of pullets fed on different dietary energy sources. A total of 300 Harco black was used for this experiment. The birds were completely randomized and divided into four diet treatment groups. Each treatment group had three replicates of twenty-five birds per replicate. Four diets containing maize, spaghetti, noodles, and biscuit was formulated to represent diet 1, 2, 3 and 4 respectively. Diet 1 containing maize is the control, while diet 2, 3, and 4 contains spaghetti, noodles, and biscuit waste meal at 100% replacement for maize on weight for weight basis. Performance indices on Feed intake, body weight, weight gain, feed conversion ratio (FCR) and economy of production were measured. Blood samples were also collected for heamatology and serum biochemistry assessment. The result of the experiment indicated that different dietary energy source fed to birds significantly (P < 0.05) affect feed intake, body weight, weight gain, and feed conversion ratio (FCR). The best cost of feed per kilogram of body weight gain was obtained in Spaghetti based diet (₦559.30). However, the best performance were obtained from diet 1(maize), it can be concluded that spaghetti as a replacement for maize in diet of pullet is most economical and profitable for production without any deleterious effects attached. Blood parameters of birds were not significantly (p > 0.05) influenced by the use of the dietary energy sources used in this experiment.

Keywords: Growth performance, spaghetti, noodles, biscuit, profit, hematology and serum biochemistry.

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Authors: M. Govindarajan, R. M. Chandrasekaran

Abstract:

Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.

Keywords: Back propagation, classification accuracy, textmining, time complexity.

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Authors: Birendra Kumar Bohara

Abstract:

The hillside building shows different behavior as a flat ground building in lateral loading. Especially the step back building in the sloping ground has different seismic behavior. The hillside building 3D model having different types of structural elements is introduced and analyzed with a seismic effect. The structural elements such as the shear wall, steel, and concrete bracing are used to resist the earthquake load and compared with without using any shear wall and bracing system. The X, inverted V, and diagonal bracing are used. The total nine models are prepared in ETABs finite element coding software. The linear dynamic analysis is the response spectrum analysis (RSA) carried out to study dynamic behaviors in means of top story displacement, story drift, fundamental time period, story stiffness, and story shear. The results are analyzed and made some decisions based on seismic performance. It is also observed that it is better to use the X bracing system for lateral load resisting elements.

Keywords: Step-back buildings, bracing system, hill side buildings, response spectrum method.

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Authors: Fadi Eleiwi, Taous Meriem Laleg-Kirati

Abstract:

This paper presents a complete dynamic modeling of a membrane distillation process. The model contains two consistent dynamic models. A 2D advection-diffusion equation for modeling the whole process and a modified heat equation for modeling the membrane itself. The complete model describes the temperature diffusion phenomenon across the feed, membrane, permeate containers and boundary layers of the membrane. It gives an online and complete temperature profile for each point in the domain. It explains heat conduction and convection mechanisms that take place inside the process in terms of mathematical parameters, and justify process behavior during transient and steady state phases. The process is monitored for any sudden change in the performance at any instance of time. In addition, it assists maintaining production rates as desired, and gives recommendations during membrane fabrication stages. System performance and parameters can be optimized and controlled using this complete dynamic model. Evolution of membrane boundary temperature with time, vapor mass transfer along the process, and temperature difference between membrane boundary layers are depicted and included. Simulations were performed over the complete model with real membrane specifications. The plots show consistency between 2D advection-diffusion model and the expected behavior of the systems as well as literature. Evolution of heat inside the membrane starting from transient response till reaching steady state response for fixed and varying times is illustrated.

Keywords: Membrane distillation, Dynamical modeling, Advection-diffusion equation, Thermal equilibrium, Heat equation.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: S. A. Razavi Alavi, M. Torabi Angaji, Z. Gholami

Abstract:

In the process of polyethylene extrusion polymer material similar to powder or granule is under compression, melting and transmission operation and on base of special form, extrudate has been produced. Twin-screw extruders are applicable in industries because of their high capacity. The powder mixing with chemical additives and melting with thermal and mechanical energy in three zones (feed, compression and metering zone) and because of gear pump and screw's pressure, converting to final product in latest plate. Extruders with twin-screw and short distance between screws are better than other types because of their high capacity and good thermal and mechanical stress. In this paper, process of polyethylene extrusion and various tapes of extruders are studied. It is necessary to have an exact control on process to producing high quality products with safe operation and optimum energy consumption. The granule size is depending on granulator motor speed. Results show at constant feed rate a decrease in granule size was found whit Increase in motor speed. Relationships between HDPE feed rate and speed of granulator motor, main motor and gear pump are calculated following as: x = HDPE feed flow rate, yM = Main motor speed yM = (-3.6076e-3) x^4+ (0.24597) x^3+ (-5.49003) x^2+ (64.22092) x+61.66786 (1) x = HDPE feed flow rate, yG = Gear pump speed yG = (-2.4996e-3) x^4+ (0.18018) x^3+ (-4.22794) x^2+ (48.45536) x+18.78880 (2) x = HDPE feed flow rate, y = Granulator motor speed 10th Degree Polynomial Fit: y = a+bx+cx^2+dx^3... (3) a = 1.2751, b = 282.4655, c = -165.2098, d = 48.3106, e = -8.18715, f = 0.84997 g = -0.056094, h = 0.002358, i = -6.11816e-5 j = 8.919726e-7, k = -5.59050e-9

Keywords: Extrusion, Extruder, Granule, HDPE, Polymer, Twin-Screw extruder.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

Abstract:

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.

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Authors: Imen Sfaihi, Noureddine Hamdi

Abstract:

In this paper, we investigate the study of techniques for scheduling users for resource allocation in the case of multiple input and multiple output (MIMO) packet transmission systems. In these systems, transmit antennas are assigned to one user or dynamically to different users using spatial multiplexing. The allocation of all transmit antennas to one user cannot take full advantages of multi-user diversity. Therefore, we developed the case when resources are allocated dynamically. At each time slot users have to feed back their channel information on an uplink feedback channel. Channel information considered available in the schedulers is the zero forcing (ZF) post detection signal to interference plus noise ratio. Our analysis study concerns the round robin and the opportunistic schemes. In this paper, we present an overview and a complete capacity analysis of these schemes. The main results in our study are to give an analytical form of system capacity using the ZF receiver at the user terminal. Simulations have been carried out to validate all proposed analytical solutions and to compare the performance of these schemes.

Keywords: MIMO, scheduling, ZF receiver, spatial multiplexing, round robin scheduling, opportunistic.

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Authors: V Krishnaveni, S Jayaraman, A Gunasekaran, K Ramadoss

Abstract:

The ElectroEncephaloGram (EEG) is useful for clinical diagnosis and biomedical research. EEG signals often contain strong ElectroOculoGram (EOG) artifacts produced by eye movements and eye blinks especially in EEG recorded from frontal channels. These artifacts obscure the underlying brain activity, making its visual or automated inspection difficult. The goal of ocular artifact removal is to remove ocular artifacts from the recorded EEG, leaving the underlying background signals due to brain activity. In recent times, Independent Component Analysis (ICA) algorithms have demonstrated superior potential in obtaining the least dependent source components. In this paper, the independent components are obtained by using the JADE algorithm (best separating algorithm) and are classified into either artifact component or neural component. Neural Network is used for the classification of the obtained independent components. Neural Network requires input features that exactly represent the true character of the input signals so that the neural network could classify the signals based on those key characters that differentiate between various signals. In this work, Auto Regressive (AR) coefficients are used as the input features for classification. Two neural network approaches are used to learn classification rules from EEG data. First, a Polynomial Neural Network (PNN) trained by GMDH (Group Method of Data Handling) algorithm is used and secondly, feed-forward neural network classifier trained by a standard back-propagation algorithm is used for classification and the results show that JADE-FNN performs better than JADEPNN.

Keywords: Auto Regressive (AR) Coefficients, Feed Forward Neural Network (FNN), Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm, Polynomial Neural Network (PNN).

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Authors: Amar Benakmoum, Rosa Larid, Sofiane Zidani

Abstract:

Dried tomato peel (DTP) was tested in vivo (n=10) in 42 week-old laying hens at rates of 0, 40, 70, 100 and 130g/kg DM feed. Laying hens were fed in group 120 g DM/day/animal for 26 days. After 21 days, feed intake was not affected after DTP incorporation (97% of the offered feed in the five groups). Laying rate was not significantly different after DTP incorporation at 4 and 10% from the control group. Egg yolk resulting from DTP-enriched diets, contained lower amounts of cholesterol (14 to 17mg/g) and triglyceride (188mg/g) compared to the control group (22 and 241 mg/g, respectively) (P<0.0001). After DTP-enriched diets, content in total phenol was 2.0 to 3.6-fold higher, β-carotene 1.7 to 2.7-fold higher, and lycopene increased between 26.5 and 42.8μg/g compared to the control (P<0.0001). The optimal incorporation rate was 7% DTP.

Keywords: Carotenoid, dried tomato peel, lycopene, laying hens, phenols.

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