Search results for: forecasting models

Authors: Päivi J. Tossavainen , Leena Alakoski, Katri Ojasalo

Abstract:

Building a service-centric business model requires new knowledge and capabilities in companies. This paper enlightens the challenges small and medium sized firms (SMEs) face when developing their service-centric business models. This paper examines the premise for knowledge transfer and capability development required. The objective of this paper is to increase knowledge about SME-s transformation to service-centric business models.This paper reports an action research based case study. The paper provides empirical evidence from three case companies. The empirical data was collected through multiple methods. The findings of the paper are: First, the developed model to analyze the current state in companies. Second, the process of building the service – centric business models. Third, the selection of suitable service development methods. The lack of a holistic understanding on service logic suggests that SMEs need practical and easy to use methods to improve their business

Keywords: service-centric business model, service development, action research, case study

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Authors: Alireza Azimi Sani

Abstract:

This paper discusses the design characteristics management accounting systems should have to be useful for strategic planning and control and provides brief introductions to strategic variance analysis, profit-linked performance measurement models and balanced scorecard. It shows two multi-period, multiproduct models are specified, can be related to Porter's strategy framework and cost and revenue drivers, and can be used to support strategic planning, control and cost management.

Keywords: Accounting, balanced scorecard, profit-linked, strategic management, variance analysis.

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: Actual cost and duration, attribute selection, bridge projects, neural networks, predicting models, FANN TOOL, WEKA.

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Authors: Saima Ali, Pathamanathan Rajeev, Imteaz A. Monzur

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In this paper, a brief review of the corrosion mechanism in buried pipe and modes of failure is provided together with the available corrosion models. Moreover, the sensitivity analysis is performed to understand the influence of corrosion model parameters on the remaining life estimation. Further, the probabilistic analysis is performed to propagate the uncertainty in the corrosion model on the estimation of the renaming life of the pipe. Finally, the comparison among the corrosion models on the basis of the remaining life estimation will be provided to improve the renewal plan.

Keywords: Corrosion, pit depth, sensitivity analysis, exposure period.

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Authors: Yasaman Tohidi, Rouzbeh Riazi, Shidvash Vakilipour, Masoud Mohammadi

Abstract:

The objective of the present study is to numerically investigate the effect of CO₂ replacement of N₂ in air stream on the flame characteristics of the CH₄ turbulent diffusion flame. The Open source Field Operation and Manipulation (OpenFOAM) has been used as the computational tool. In this regard, laminar flamelet and modified k-ε models have been utilized as combustion and turbulence models, respectively. Results reveal that the presence of CO₂ in air stream changes the flame shape and maximum flame temperature. Also, CO₂ dilution causes an increment in CO mass fraction.

Keywords: CH4 diffusion flame, CO2 dilution, OpenFOAM, turbulent flame.

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Authors: Lakshmi Keshav, V. G. Srisanthi

Abstract:

In this experimental investigation shake table tests were conducted on two reduced models that represent normal single room building constructed by Compressed Stabilized Earth Block (CSEB) from locally available soil. One model was constructed with earthquake resisting features (EQRF) having sill band, lintel band and vertical bands to control the building vibration and another one was without Earthquake Resisting Features. To examine the seismic capacity of the models particularly when it is subjected to long-period ground motion by large amplitude by many cycles of repeated loading, the test specimen was shaken repeatedly until the failure. The test results from Hi-end Data Acquisition system show that model with EQRF behave better than without EQRF. This modified masonry model with new material combined with new bands is used to improve the behavior of masonry building.

Keywords: Earth Quake Resisting Features, Compressed Stabilized Earth Blocks, Masonry structures, Shake table testing, Horizontal and vertical bands.

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Authors: Hy Dang, Bo Mei

Abstract:

The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

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Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the creditscoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: Credit-scoring Models, Multidimensional Subordinated Lévy Model, Probability of Default.

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Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: Data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks, WSN.

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Authors: Chih-Wei Lin, Duen-Ren Liu, Hui-Fang Chen

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Effective knowledge support relies on providing operation-relevant knowledge to workers promptly and accurately. A knowledge flow represents an individual-s or a group-s knowledge-needs and referencing behavior of codified knowledge during operation performance. The flow has been utilized to facilitate organizational knowledge support by illustrating workers- knowledge-needs systematically and precisely. However, conventional knowledge-flow models cannot work well in cooperative teams, which team members usually have diverse knowledge-needs in terms of roles. The reason is that those models only provide one single view to all participants and do not reflect individual knowledge-needs in flows. Hence, we propose a role-based knowledge-flow view model in this work. The model builds knowledge-flow views (or virtual knowledge flows) by creating appropriate virtual knowledge nodes and generalizing knowledge concepts to required concept levels. The customized views could represent individual role-s knowledge-needs in teamwork context. The novel model indicates knowledge-needs in condensed representation from a roles perspective and enhances the efficiency of cooperative knowledge support in organizations.

Keywords: cooperative knowledge support, knowledge flow, knowledge-flow view, role-based models

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Authors: Maumita Bhattacharya

Abstract:

Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build meta models or use an approximation of the actual fitness functions to be evaluated. These meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present two evolutionary algorithm frameworks which involve use of meta models for fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The second model, DAFHEA-II, an enhanced version of the original DAFHEA framework, incorporates a multiple-model based learning approach for the support vector machine approximator to handle noisy functions [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions demonstrate their efficiency

Keywords: Meta model, Evolutionary algorithm, Stochastictechnique, Fitness function, Optimization, Support vector machine.

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Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

Abstract:

In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: GPS based household surveys, transportation infrastructure, origin-destination trip matrices, traffic forecasts, transportation demand modeling, travel behavior patterns.

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Authors: R. Senthilkumar

Abstract:

Most simple nonlinear thresholding rules for wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper compares different Shrinkage functions used for image-denoising. The second part of the paper compares different bivariate models and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill, Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values are calculated for each noise level.

Keywords: BiShrink, Image-Denoising, PSNR, Shrinkage function

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Authors: G. C. Mahata, A. Goswami

Abstract:

This paper deals with infinite time horizon fuzzy Economic Order Quantity (EOQ) models for deteriorating items with  stock dependent demand rate and nonlinear holding costs by taking deterioration rate θ0 as a triangular fuzzy number  (θ0 −δ 1, θ0, θ0 +δ 2), where 1 2 0 0 <δ ,δ <θ are fixed real numbers. The traditional parameters such as unit cost and ordering  cost have been kept constant but holding cost is considered to vary. Two possibilities of variations in the holding cost function namely, a non-linear function of the length of time for which the item is held in stock and a non-linear function of the amount of on-hand inventory have been used in the models. The approximate optimal solution for the fuzzy cost functions in both these cases have been obtained and the effect of non-linearity in holding costs is studied with the help of a numerical example.

Keywords: Inventory Model, Deterioration, Holding Cost, Fuzzy Total Cost, Extension Principle.

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Authors: Irwin Ma, Tony Wong, Thiagas Sankar

Abstract:

By systematically applying different engineering methods, difficult financial problems become approachable. Using a combination of theory and techniques such as wavelet transform, time series data mining, Markov chain based discrete stochastic optimization, and evolutionary algorithms, this work formulated a strategy to characterize and forecast non-linear time series. It attempted to extract typical features from the volatility data sets of S&P100 and S&P500 indices that include abrupt drops, jumps and other non-linearity. As a result, accuracy of forecasting has reached an average of over 75% surpassing any other publicly available results on the forecast of any financial index.

Keywords: Discrete stochastic optimization, genetic algorithms, genetic programming, volatility forecast

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Authors: S. K. Mohan, T. Viruthagiri, C. Arunkumar

Abstract:

Tannase (tannin acyl hydrolase, E.C.3.1.1.20) is an important hydrolysable enzyme with innumerable applications and industrial potential. In the present study, a kinetic model has been developed for the batch fermentation used for the production of tannase by A.flavus MTCC 3783. Maximum tannase activity of 143.30 U/ml was obtained at 96 hours under optimum operating conditions at 35oC, an initial pH of 5.5 and with an inducer tannic acid concentration of 3% (w/v) for a fermentation period of 120 hours. The biomass concentration reaches a maximum of 6.62 g/l at 96 hours and further there was no increase in biomass concentration till the end of the fermentation. Various unstructured kinetic models were analyzed to simulate the experimental values of microbial growth, tannase activity and substrate concentration. The Logistic model for microbial growth , Luedeking - Piret model for production of tannase and Substrate utilization kinetic model for utilization of substrate were capable of predicting the fermentation profile with high coefficient of determination (R2) values of 0.980, 0.942 and 0.983 respectively. The results indicated that the unstructured models were able to describe the fermentation kinetics more effectively.

Keywords: Aspergillus flavus, Batch fermentation, Kinetic model, Tannase, Unstructured models.

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Authors: M. H. Pol, A. Bidi, A. V. Hoseini

Abstract:

Estimation of natural frequency of structures is very important and isn-t usually calculated simply and sometimes complicated. Lack of knowledge about that caused hard damage and hazardous effects. In this paper, with using from two different models in FEM method and based on hydrodynamic mass of fluids, natural frequency of an especial bearing (Fig. 1) in an electric field (or, a periodic force) is calculated in different stiffness and different geometric. In final, the results of two models and analytical solution are compared.

Keywords: Natural frequency of the bearing, Hydrodynamic mass of fluid method.

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Authors: M. A. Omoloye, M. I. Yusuff, O. K. S. Emiola

Abstract:

The use of mathematical models for solving biological problems varies from simple to complex analyses, depending on the nature of the research problems and applicability of the models. The method is more common nowadays. Many complex models become impractical when transmitted analytically. However, alternative approach such as numerical method can be employed. It appropriateness in solving linear and non-linear model equation in Differential Transformation Method (DTM) which depends on Taylor series make it applicable. Hence this study investigates the application of DTM to solve dynamic transmission of Lassa fever model in a population. The mathematical model was formulated using first order differential equation. Firstly, existence and uniqueness of the solution was determined to establish that the model is mathematically well posed for the application of DTM. Numerically, simulations were conducted to compare the results obtained by DTM and that of fourth-order Runge-Kutta method. As shown, DTM is very effective in predicting the solution of epidemics of Lassa fever model.

Keywords: Differential Transform Method, Existence and uniqueness, Lassa fever, Runge-Kutta Method.

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Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias

Abstract:

Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.

Keywords: regression algorithms, supervised machine learning.

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Authors: Fulya Aydın, Ayşe Kuleyin

Abstract:

The purpose of this study is to investigate the capacity of natural Turkish zeolite for NH4-N removal from landfill leachate. The effects of modification and initial concentration on the removal of NH4-N from leachate were also investigated. The kinetics of adsorption of NH4-N has been discussed using three kinetic models, i.e., the pseudo-second order model, the Elovich equation, the intraparticle diffuion model. Kinetic parameters and correlation coefficients were determined. Equilibrium isotherms for the adsorption of NH4-N were analyzed by Langmuir, Freundlich and Tempkin isotherm models. Langmuir isotherm model was found to best represent the data for NH4-N.

Keywords: Leachate, Ammonium, zeolite

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Authors: Yiannis G. Smirlis

Abstract:

The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

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Authors: Shiann-Shiun Jeng, Chen-Wan Tsung, Hong-You Liou, Chun-Chieh Chang, Jia-Ming Chen

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The coverage probability and range of IEEE 802.16 systems depend on different wireless scenarios. Evaluating the performance of IEEE 802.16 systems over Stanford University Interim (SUI) channels is suggested by IEEE 802.16 specifications. In order to derive an effective method for forecasting the coverage probability and range, this study uses the SUI channel model to analyze the coverage probability with Rayleigh fading for an IEEE 802.16 system. The BER of the IEEE 802.16 system is shown in the simulation results. Then, the maximum allowed path loss can be calculated and substituted into the coverage analysis. Therefore, simulation results show the coverage range with and without Rayleigh fading.

Keywords: OFDM, coverage, SUI channel, IEEE 802.16

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Authors: Teh Raihana Nazirah Roslan, Siti Zulaiha Ibrahim, Sharmila Karim

Abstract:

A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study.

Keywords: Cox-Ingersoll-Ross model, equity warrants, Heston model, hybrid models, stochastic.

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Authors: S. Cagin, X. Fischer, E. Delacourt, N. Bourabaa, C. Morin, D. Coutellier, B. Carré, S. Loumé

Abstract:

The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).

Keywords: Diesel engine, Design optimization, Model reduction, Neural network, NTF algorithm, Scavenging.

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Authors: Jaime R. S. Fonseca

Abstract:

Nevertheless the widespread application of finite mixture models in segmentation, finite mixture model selection is still an important issue. In fact, the selection of an adequate number of segments is a key issue in deriving latent segments structures and it is desirable that the selection criteria used for this end are effective. In order to select among several information criteria, which may support the selection of the correct number of segments we conduct a simulation study. In particular, this study is intended to determine which information criteria are more appropriate for mixture model selection when considering data sets with only categorical segmentation base variables. The generation of mixtures of multinomial data supports the proposed analysis. As a result, we establish a relationship between the level of measurement of segmentation variables and some (eleven) information criteria-s performance. The criterion AIC3 shows better performance (it indicates the correct number of the simulated segments- structure more often) when referring to mixtures of multinomial segmentation base variables.

Keywords: Quantitative Methods, Multivariate Data Analysis, Clustering, Finite Mixture Models, Information Theoretical Criteria, Simulation experiments.

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Authors: B.A.Gobin, R.K.Subramanian

Abstract:

This paper gives an overview of the mapping mechanism of SEAM-a methodology for the automatic generation of knowledge models and its mapping onto Java codes. It discusses the rules that will be used to map the different components in the knowledge model automatically onto Java classes, properties and methods. The aim of developing this mechanism is to help in the creation of a prototype which will be used to validate the knowledge model which has been generated automatically. It will also help to link the modeling phase with the implementation phase as existing knowledge engineering methodologies do not provide for proper guidelines for the transition from the knowledge modeling phase to development phase. This will decrease the development overheads associated to the development of Knowledge Based Systems.

Keywords: KBS, OWL, ontology, knowledge models

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: Masoud Sadeghian, Alireza Fatehi

Abstract:

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.

Keywords: Cement Rotary Kiln, Fault Detection, Delay Estimation Method, Locally Linear Neuro Fuzzy Model, LOLIMOT.

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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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Authors: Mamoun Sqali, Mohamed Wassim Trojet

Abstract:

The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.

Keywords: Scenarios, DEVS, synthesis, validation and verification, simulation, formal verification, z notation.

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