Search results for: small series assembly.
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2533

Search results for: small series assembly.

2443 Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning

Authors: Tahseen Ahmed Jilani, Syed Muhammad Aqil Burney, C. Ardil

Abstract:

In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on frequency density based partitioning of the historical enrollment data. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting enrollments than the existing methods.

Keywords: Fuzzy logical groups, fuzzified enrollments, fuzzysets, fuzzy time series.

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2442 Forecasting Enrollment Model Based on First-Order Fuzzy Time Series

Authors: Melike Şah, Konstantin Y.Degtiarev

Abstract:

This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.

Keywords: Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model.

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2441 Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents

Authors: Tahseen A. Jilani, S. M. Aqil Burney, C. Ardil

Abstract:

In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.

Keywords: Average forecasting error rate (AFER), Fuzziness offuzzy sets Fuzzy, If-Then rules, Multivariate fuzzy time series.

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2440 Determination of the Pullout/Holding Strength at the Taper-Trunnion Junction of Hip Implants

Authors: Obinna K. Ihesiulor, Krishna Shankar, Paul Smith, Alan Fien

Abstract:

Excessive fretting wear at the taper-trunnion junction (trunnionosis) apparently contributes to the high failure rates of hip implants. Implant wear and corrosion lead to the release of metal particulate debris and subsequent release of metal ions at the tapertrunnion surface. This results in a type of metal poisoning referred to as metallosis. The consequences of metal poisoning include; osteolysis (bone loss), osteoarthritis (pain), aseptic loosening of the prosthesis and revision surgery. Follow up after revision surgery, metal debris particles are commonly found in numerous locations. Background: A stable connection between the femoral ball head (taper) and stem (trunnion) is necessary to prevent relative motions and corrosion at the taper junction. Hence, the importance of component assembly cannot be over-emphasized. Therefore, the aim of this study is to determine the influence of head-stem junction assembly by press fitting and the subsequent disengagement/disassembly on the connection strength between the taper ball head and stem. Methods: CoCr femoral heads were assembled with High stainless hydrogen steel stem (trunnion) by Push-in i.e. press fit; and disengaged by pull-out test. The strength and stability of the two connections were evaluated by measuring the head pull-out forces according to ISO 7206-10 standards. Findings: The head-stem junction strength linearly increases with assembly forces.

Keywords: Wear, modular hip prosthesis, taper head-stem, force assembly, force disassembly.

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2439 Comparison of Artificial Neural Network Architectures in the Task of Tourism Time Series Forecast

Authors: João Paulo Teixeira, Paula Odete Fernandes

Abstract:

The authors have been developing several models based on artificial neural networks, linear regression models, Box- Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels" of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.

Keywords: Artificial Neural Network Architectures, time series forecast, tourism.

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2438 A 4-Element Corporate Series Feed Millimeter-Wave Microstrip Antenna Array for 5G Applications

Authors: G. Viswanadh Raviteja

Abstract:

In this paper, a microstrip antenna array is designed for 5G applications. A corporate series feed is considered to operate with a center frequency between 27 to 28 GHz to be able to cover the 5G frequency bands 24.25-27.5 GHz, 26.5-29.5 GHz and 27.5-28.35 GHz. The substrate is taken to be Rogers RT/Duroid 6002. The corporate series 5G antenna array is designed stage by stage by taking into consideration a conventional antenna designed at 28 GHz, thereby constructing the 2X1 antenna array before arriving at the final design structure of 4-element corporate series feed antenna array. The discussions concerning S11 parameter, gain and voltage standing wave ratio (VSWR) for the design structures are considered and all the important findings are tabulated. The proposed antenna array’s S11 parameter was found to be -29.00 dB at a frequency of 27.39 GHz with a good directional gain of 12.12 dB.

Keywords: Corporate series feed, millimeter wave antenna array, 5G applications, millimeter-wave (mm-wave) applications

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2437 Multi-Context Recurrent Neural Network for Time Series Applications

Authors: B. Q. Huang, Tarik Rashid, M-T. Kechadi

Abstract:

this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.

Keywords: Gradient descent method, recurrent neural network, learning algorithms, time series, BP

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2436 Application of Robotics to Assemble a Used Fuel Container in the Canadian Used Fuel Packing Plant

Authors: Dimitrie Marinceu

Abstract:

The newest Canadian Used Fuel Container (UFC)- (called also “Mark II”) modifies the design approach for its Assembly Robotic Cell (ARC) in the Canadian Used (Nuclear) Fuel Packing Plant (UFPP). Some of the robotic design solutions are presented in this paper. The design indicates that robots and manipulators are expected to be used in the Canadian UFPP. As normally, the UFPP design will incorporate redundancy of all equipment to allow expedient recovery from any postulated upset conditions. Overall, this paper suggests that robot usage will have a significant positive impact on nuclear safety, quality, productivity, and reliability.

Keywords: Used fuel packing plant, robotic assembly cell, used fuel container, deep geological repository.

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2435 Effects of Variations in Generator Inputs for Small Signal Stability Studies of a Three Machine Nine Bus Network

Authors: Hemalan Nambier a/l Vijiyan, Agileswari K. Ramasamy, Au Mau Teng, Syed Khaleel Ahmed

Abstract:

Small signal stability causes small perturbations in the generator that can cause instability in the power network. It is generally known that small signal stability are directly related to the generator and load properties. This paper examines the effects of generator input variations on power system oscillations for a small signal stability study. Eigenvaules and eigenvectors are used to examine the stability of the power system. The dynamic power system's mathematical model is constructed and thus calculated using load flow and small signal stability toolbox on MATLAB. The power system model is based on a 3-machine 9-bus system that was modified to suit this study. In this paper, Participation Factors are a means to gauge the effects of variation in generation with other parameters on the network are also incorporated.

Keywords: Eigen-analysis, generation modeling, participationfactor, small signal stability.

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2434 Influence of Technology Parameters on Properties of AA6061/SiC Composites Produced By Kobo Method

Authors: J. Wozniak, M. Kostecki, K. Broniszewski, W. Bochniak, A. Olszyna

Abstract:

The influence of extrusion parameters on surface quality and properties of AA6061+x% vol. SiC (x = 0; 2,5; 5; 7,5;10) composites was discussed in this paper. The averages size of AA6061 and SiC particles were 10.6 μm and 0.42 μm, respectively. Two series of composites (I - compacts were preheated at extrusion temperature through 0.5 h and cooled by water directly after process; II - compacts were preheated through 3 hours and were not cooled) were consolidated via powder metallurgy processing and extruded by KoBo method. High values of density for both series of composites were achieved. Better surface quality was observed for II series of composites. Moreover, for these composites lower (compared to I series) but more uniform strength properties over the cross-section of the bar were noticed. Microstructure and Young-s modulus investigations were made.

Keywords: aluminum alloy, extrusion, metal matrix composites, microstructure

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2433 Managing of Work Risk in Small and Medium-Size Companies

Authors: Janusz K. Grabara, Bartłomiej Okwiet, Sebastian Kot

Abstract:

The purpose of the article is presentation and analysis of the aspect of job security in small and medium-size enterprises in Poland with reference to other EU countries. We show the theoretical aspects of the risk with reference to managing small and medium enterprises, next risk management in small and medium enterprises in Poland, which were subjected to a detailed analysis. We show in detail the risk associated with the operation of the mentioned above companies, as well as analyses its levels on various stages and for different kinds of conducted activity.

Keywords: Job safety, small and medium-size companies, SME, work risk, risk management.

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2432 The Impact of Bank Consolidation on Lending to SMES in Nigeria

Authors: Chimaobi Valentine Okolo

Abstract:

This paper seeks to assess the implications of bank consolidation on lending, which largely determine the survival and performance of small and medium scale enterprises and in turn the development of the Nigerian economy. Ordinary least square technique, correlation matrix test and Granger –causality test were employed to measure the extent to which lending to small and medium scale enterprises were influenced. The result showed that bank deposit (BD) impacted on lending to small and medium scale enterprises. Commercial and merchant bank lending rate had statistically insignificant effect on the dependent variable. There is a shift of focus by commercial banks from small and medium scale enterprises (small customers) to major investors (big customers). While micro finance banks work hard at providing funds to small and medium scale entrepreneurs, their capacity to meet the needs of these entrepreneurs is constrained. The capital and deposits of micro finance bank should be boosted in order to effectively support small and medium scale enterprises through loans.

Keywords: Asset size, bank consolidation, lending, small and medium enterprises.

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2431 Application of De-Laval Nozzle Transonic Flow Field Computation Approaches

Authors: A. Haddad, H. Kbab

Abstract:

A supersonic expansion cannot be achieved within a convergent-divergent nozzle if the flow velocity does not reach that of the sound at the throat. The computation of the flow field characteristics at the throat is thus essential to the nozzle developed thrust value and therefore to the aircraft or rocket it propels. Several approaches were developed in order to describe the transonic expansion, which takes place through the throat of a De-Laval convergent-divergent nozzle. They all allow reaching good results but showing a major shortcoming represented by their inability to describe the transonic flow field for nozzles having a small throat radius. The approach initially developed by Kliegel & Levine uses the velocity series development in terms of the normalized throat radius added to unity instead of solely the normalized throat radius or the traditional small disturbances theory approach. The present investigation carries out the application of these three approaches for different throat radiuses of curvature. The method using the normalized throat radius added to unity shows better results when applied to geometries integrating small throat radiuses.

Keywords: De-Laval nozzles, transonic calculations, transonic flow, supersonic nozzle.

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2430 A Fuzzy Time Series Forecasting Model for Multi-Variate Forecasting Analysis with Fuzzy C-Means Clustering

Authors: Emrah Bulut, Okan Duru, Shigeru Yoshida

Abstract:

In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.

Keywords: C-means clustering, Fuzzy time series, Multi-variate design

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2429 The Impact of Bank Consolidation on the Performance of SMES in Nigeria

Authors: Okolo Chimaobi Valentine

Abstract:

This paper seeks to assess the implications of bank consolidation on the performance of small and medium scale enterprises in the Nigerian economy. Multiple linear regression technique and correlation matrix test were employed to measure the extent to which small and medium scale enterprises asset size, survival and access to credit were influenced. The result showed that bank deposit (BD) and bank credit (L or BC) impacted on asset size and survival of small and medium scale enterprises. None of the variables had significant impact on SMEs access to credit. There is a shift of focus by commercial banks away from small and medium scale enterprises (small customers), which is evidenced by the significant negative influence of bank credit to both the survival and asset size of small and medium enterprises. While micro finance banks work hard at providing funds to small and medium scale entrepreneurs, their capacity to meet the needs of these entrepreneurs is constrained. CBN should make policies that will boost micro finance bank’s capital and also monitor closely the management of the banks to ensure prudent financing of small and medium scale investments.

Keywords: Bank consolidation, small and medium enterprises.

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2428 The Marketing Mix in Small Sized Hotels: A Case of Pattaya, Thailand

Authors: Anyapak Prapannetivuth

Abstract:

The purpose of this research is to investigate the marketing mix that is perceived to be important for the small sized hotels in Pattaya. This research provides insights through a review of the marketing activities performed by the small sized hotels. Nine owners & marketing manager of small sized hotels and resorts, all local Chonburi people, were selected for an in-depth interview. The research suggests that seven marketing mixes (e.g. Product, Price, Place, Promotion, People, Physical Evidence and Process) were commonly used by these hotels, however, three types – People, Price and Physical Evidence were considered most important by the owners.

Keywords: Marketing Mix, Marketing Tools, and Small Sized Hotels.

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2427 Implementation of Neural Network Based Electricity Load Forecasting

Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw

Abstract:

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.

Keywords: Neural network, Load forecast, Time series, wavelettransform.

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2426 Detecting the Nonlinearity in Time Series from Continuous Dynamic Systems Based on Delay Vector Variance Method

Authors: Shumin Hou, Yourong Li, Sanxing Zhao

Abstract:

Much time series data is generally from continuous dynamic system. Firstly, this paper studies the detection of the nonlinearity of time series from continuous dynamics systems by applying the Phase-randomized surrogate algorithm. Then, the Delay Vector Variance (DVV) method is introduced into nonlinearity test. The results show that under the different sampling conditions, the opposite detection of nonlinearity is obtained via using traditional test statistics methods, which include the third-order autocovariance and the asymmetry due to time reversal. Whereas the DVV method can perform well on determining nonlinear of Lorenz signal. It indicates that the proposed method can describe the continuous dynamics signal effectively.

Keywords: Nonlinearity, Time series, continuous dynamics system, DVV method

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2425 Study about the Development of Small Towns in the Metropolitan Fringe in Developed Area of China–A Case Study of Sandun Town in Hangzhou

Authors: Haifeng Jiao, Chuankun Rao

Abstract:

Due to the Rapid Urbanization in China, the influence of metropolises to surrounding areas grows by a tremendous speed in developed region. One of the most obvious influences is the expansion of the urban built-up areas which covers the land belongs to small towns. Around metropolitan fringe, the boundary between city and village becomes more and more obscure. So being the most sensitive area, the small towns on the fringe of metropolises have the special meaning on the research into the small towns- development. This paper chooses Sandun Town in Hangzhou of Zhejiang Province as an example, emphatically focus on aspects such as the central area proliferation, the industrial shift, the position effect, the subway effect and the commercial development, reviews a few problems of small towns in the future and the important problems in their planning by the analysis of the characteristics of the present conditions and the developing motive mechanism, so that guides small towns to develop properly by liking with these small towns and center metropolises.

Keywords: Metropolitan fringe, Small towns, Sandun Town, Motive mechanism.

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2424 Retail Strategy to Reduce Waste Keeping High Profit Utilizing Taylor's Law in Point-of-Sales Data

Authors: Gen Sakoda, Hideki Takayasu, Misako Takayasu

Abstract:

Waste reduction is a fundamental problem for sustainability. Methods for waste reduction with point-of-sales (POS) data are proposed, utilizing the knowledge of a recent econophysics study on a statistical property of POS data. Concretely, the non-stationary time series analysis method based on the Particle Filter is developed, which considers abnormal fluctuation scaling known as Taylor's law. This method is extended for handling incomplete sales data because of stock-outs by introducing maximum likelihood estimation for censored data. The way for optimal stock determination with pricing the cost of waste reduction is also proposed. This study focuses on the examination of the methods for large sales numbers where Taylor's law is obvious. Numerical analysis using aggregated POS data shows the effectiveness of the methods to reduce food waste maintaining a high profit for large sales numbers. Moreover, the way of pricing the cost of waste reduction reveals that a small profit loss realizes substantial waste reduction, especially in the case that the proportionality constant  of Taylor’s law is small. Specifically, around 1% profit loss realizes half disposal at =0.12, which is the actual  value of processed food items used in this research. The methods provide practical and effective solutions for waste reduction keeping a high profit, especially with large sales numbers.

Keywords: Food waste reduction, particle filter, point of sales, sustainable development goals, Taylor's Law, time series analysis.

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2423 The Small Strain Effects to the Shear Strength and Maximum Stiffness of Post-Cyclic Degradation of Hemic Peat Soil

Authors: Z. Adnan, M. M. Habib

Abstract:

The laboratory tests for measuring the effects of small strain to the shear strength and maximum stiffness development of post-cyclic degradation of hemic peat are reviewed in this paper. A series of laboratory testing has been conducted to fulfil the objective of this research to study the post-cyclic behaviour of peat soil and focuses on the small strain characteristics. For this purpose, a number of strain-controlled static, cyclic and post-cyclic triaxial tests were carried out in undrained condition on hemic peat soil. The shear strength and maximum stiffness of hemic peat are evaluated immediately after post-cyclic monotonic testing. There are two soil samples taken from West Johor and East Malaysia peat soil. Based on these laboratories and field testing data, it was found that the shear strength and maximum stiffness of peat soil decreased in post-cyclic monotonic loading than its initial shear strength and stiffness. In particular, degradation in shear strength and stiffness is more sensitive for peat soil due to fragile and uniform fibre structures. Shear strength of peat soil, τmax = 12.53 kPa (Beaufort peat, BFpt) and 36.61 kPa (Parit Nipah peat, PNpt) decreased than its initial 58.46 kPa and 91.67 kPa. The maximum stiffness, Gmax = 0.23 and 0.25 decreased markedly with post-cyclic, Gmax = 0.04 and 0.09. Simple correlations between the Gmax and the τmax effects due to small strain, ε = 0.1, the Gmax values for post-cyclic are relatively low compared to its initial Gmax. As a consequence, the reported values and patterns of both the West Johor and East Malaysia peat soil are generally the same.

Keywords: Post-cyclic, strain, shear strength, maximum stiffness.

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2422 Signal Processing Approach to Study Multifractality and Singularity of Solar Wind Speed Time Series

Authors: Tushnik Sarkar, Mofazzal H. Khondekar, Subrata Banerjee

Abstract:

This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1st January, 1997 to 28th October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal.

Keywords: Detrended fluctuation analysis, generalized Hurst exponent, holder exponents, multifractal exponent, multifractal spectrum, singularity spectrum, time series analysis.

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2421 Decision Location and Resource Requirement for Relief Goods Assembly

Authors: Glenda Minguito, Jenith Banluta

Abstract:

One of the critical aspects of humanitarian operations is the distribution of relief goods to an affected community. The common assumption is that relief goods are prepositioned during disasters which are not applicable in developing countries like the Philippines. During disasters, the on-the-ground government agencies and responders have to procure, sort, weigh and pack the relief goods. There is a need to review the relief goods preparation as it seriously affects the delivery of necessary aid for human survival. This study also identifies the ideal location of the assembly hub to minimize the distance to the affected community. This paper reveals that location and resources are dependent on the type of disasters encountered at the local level. The Center-of-Gravity method and Multiple Activity Chart were applied in the analysis.

Keywords: Humanitarian supply chain, location decision, resource allocation, local level.

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2420 Dynamic Stability Assessment of Different Wheel Sized Bicycles Based on Current Frame Design Practice with ISO Requirement for Bicycle Safety

Authors: Milan Paudel, Fook Fah Yap, Anil K. Bastola

Abstract:

The difficulties in riding small wheel bicycles and their lesser stability have been perceived for a long time. Although small wheel bicycles are designed using the similar approach and guidelines that have worked well for big wheel bicycles, the performance of the big wheelers and the smaller wheelers are markedly different. Since both the big wheelers and small wheelers have same fundamental geometry, most blame the small wheel for this discrepancy in the performance. This paper reviews existing guidelines for bicycle design, especially the front steering geometry for the bicycle, and provides a systematic and quantitative analysis of different wheel sized bicycles. A validated mathematical model has been used as a tool to assess the dynamic performance of the bicycles in term of their self-stability. The results obtained were found to corroborate the subjective perception of cyclists for small wheel bicycles. The current approach for small wheel bicycle design requires higher speed to be self-stable. However, it was found that increasing the headtube angle and selecting a proper trail could improve the dynamic performance of small wheel bicycles. A range of parameters for front steering geometry has been identified for small wheel bicycles that have comparable stability as big wheel bicycles. Interestingly, most of the identified geometries are found to be beyond the ISO recommended range and seem to counter the current approach of small wheel bicycle design. Therefore, it was successfully shown that the guidelines for big wheelers do not translate directly to small wheelers, but careful selection of the front geometry could make small wheel bicycles as stable as big wheel bicycles.

Keywords: Big wheel bicycle, design approach, ISO requirements, small wheel bicycle, stability and performance.

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2419 Application of Extreme Learning Machine Method for Time Series Analysis

Authors: Rampal Singh, S. Balasundaram

Abstract:

In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

Keywords: Chaotic time series, Extreme learning machine, Generalization performance.

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2418 Power Series Solution to Sliding Velocity in Three-Dimensional Multibody Systems with Impact and Friction

Authors: Hesham A. Elkaranshawy, Amr M. Abdelrazek, Hosam M. Ezzat

Abstract:

The system of ordinary nonlinear differential equations describing sliding velocity during impact with friction for a three-dimensional rigid-multibody system is developed. No analytical solutions have been obtained before for this highly nonlinear system. Hence, a power series solution is proposed. Since the validity of this solution is limited to its convergence zone, a suitable time step is chosen and at the end of it a new series solution is constructed. For a case study, the trajectory of the sliding velocity using the proposed method is built using 6 time steps, which coincides with a Runge- Kutta solution using 38 time steps.

Keywords: Impact with friction, nonlinear ordinary differential equations, power series solutions, rough collision.

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2417 The Operating Behaviour of Unbalanced Unpaced Merging Assembly Lines

Authors: S. Shaaban, T. McNamara, S. Hudson

Abstract:

This paper reports on the performance of deliberately unbalanced, reliable, non-automated and assembly lines that merge, whose workstations differ in terms of their mean operation times. Simulations are carried out on 5- and 8-station lines with 1, 2 and 4 buffer capacity units, % degrees of line imbalance of 2, 5 and 12, and 24 different patterns of means imbalance. Data on two performance measures, namely throughput and average buffer level were gathered, statistically analysed and compared to a merging balanced line counterpart. It was found that the best configurations are a balanced line arrangement and a monotone decreasing order for each of the parallel merging lines, with the first generally resulting in a lower throughput and the second leading to a lower average buffer level than those of a balanced line.

Keywords: Average buffer level, merging lines, simulation, throughput, unbalanced.

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2416 Experimental Study of Open Water Non-Series Marine Propeller Performance

Authors: M. A. Elghorab, A. Abou El-Azm Aly, A. S. Elwetedy, M. A. Kotb

Abstract:

Later marine propeller is the main component of ship propulsion system. For a non-series propeller, it is difficult to indicate the open water marine propeller performance without an experimental study to measure the marine propeller parameters. In the present study, the open water performance of a non-series marine propeller has been carried out experimentally. The geometrical aspects of a commercial non-series marine propeller have been measured for a propeller blade area ratio of 0.3985. The measured propeller performance parameters were the thrust and torque coefficients for different propeller rotational speed and different water channel flow velocity, then the open water performance for the propeller has been plotted. In addition, a direct comparison between the obtained experimental results and a theoretical study of a B-series marine propeller of the same blade area ratio has been carried out. A correction factor has been introduced to apply the operating conditions of the experimental results to that of the theoretical study for the studied marine propeller.

Keywords: Advance speed, marine propeller, open water performance, thrust coefficient, torque coefficient.

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2415 An Improved Prediction Model of Ozone Concentration Time Series Based On Chaotic Approach

Authors: N. Z. A. Hamid, M. S. M. Noorani

Abstract:

This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly Ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: Chaotic approach, phase space, Cao method, local linear approximation method.

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2414 The Application of an Ensemble of Boosted Elman Networks to Time Series Prediction: A Benchmark Study

Authors: Chee Peng Lim, Wei Yee Goh

Abstract:

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Keywords: AdaBoost, Elman network, neural network ensemble, time series regression.

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