Search results for: Multiple regression modeling

Authors: Radek Němec

Abstract:

Today's business environment requires that companies have access to highly relevant information in a matter of seconds. Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by star schemas. Dimensional modeling is already recognized as a leading industry standard in the field of data warehousing although several drawbacks and pitfalls were reported. This paper focuses on the analysis of another data warehouse modeling technique - the anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show information about performance of queries executed on database schemas structured according to principles of each database modeling technique.

Keywords: Data warehousing, anchor modeling, star schema, anchor schema, query performance.

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Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: Bayesian, Forecast, Stock, BART.

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool.

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Authors: Farhad Kolahan, A. Hamid Khajavi

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In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.

Keywords: AWJ machining, Mathematical modeling, Simulated Annealing, Optimization

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Authors: O. Poleshchuk, E.Komarov

Abstract:

This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

Keywords: Interval type-2 fuzzy sets, fuzzy regression, weighted interval.

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Authors: Raja Das, M. K. Pradhan

Abstract:

This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Kwa-Sur Tam

Abstract:

This paper reviews various approaches that have been used for the modeling and simulation of large-scale engineering systems and determines their appropriateness in the development of a RICS modeling and simulation tool. Bond graphs, linear graphs, block diagrams, differential and difference equations, modeling languages, cellular automata and agents are reviewed. This tool should be based on linear graph representation and supports symbolic programming, functional programming, the development of noncausal models and the incorporation of decentralized approaches.

Keywords: Interdisciplinary, dynamic, functional programming, object-oriented.

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Authors: Raman Paranjape

Abstract:

We present a new approach to evaluation of Cyber Security in Power Systems using the method of modeling the power systems Infrastructure using software agents. Interfaces between module and the home smart meter are recognized as the primary points of intrusion.

Keywords: Power Systems, Modeling and Simulation, Agent systems.

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Authors: C. Ardil

Abstract:

In this paper, the comparison of composite programming and compromise programming for the aircraft selection problem is discussed using the multiple criteria decision analysis method. The decision making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as aircraft selection problems. The proposed technique gives more efficient results by extending the composite programming and compromise programming, which are widely used in modeling multiple criteria decisions. The proposed model is applied to a practical decision problem for evaluating and selecting aircraft problems.A selection of aircraft was made based on the proposed approach developed in the field of multiple criteria decision making. The model presented is solved by using the following methods: composite programming, and compromise programming. The importance values of the weight coefficients of the criteria are calculated using the mean weight method. The evaluation and ranking of aircraft are carried out using the composite programming and compromise programming methods. In order to determine the stability of the model and the ability to apply the developed composite programming and compromise programming approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ and q in the first part. The second part of the sensitivity analysis relates to the application of different multiple criteria decision making methods, composite programming and compromise programming. In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient of the ranks obtained was calculated which confirms the applicability of all the proposed approaches.

Keywords: composite programming, compromise programming, additive weighted model, multiplicative weighted model, multiple criteria decision making analysis, MCDMA, aircraft selection

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Authors: Cheima Ben Soltane, Ittansa Yonas Kelbesa

Abstract:

Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: Feature Extraction, Speaker Modeling, Feature Matching, Mel Frequency Cepstrum Coefficient (MFCC), Gaussian mixture model (GMM), Vector Quantization (VQ), Linde-Buzo-Gray (LBG), Expectation Maximization (EM), pre-processing, Voice Activity Detection (VAD), Short Time Energy (STE), Background Noise Statistical Modeling, Closed-Set Tex-Independent Speaker Identification System (CISI).

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Authors: Mohammad Najafi, Ali Jamshidi

Abstract:

We employ the idea of Hirota-s bilinear method, to obtain some new exact soliton solutions for high nonlinear form of (2+1)-dimensional potential Kadomtsev-Petviashvili equation. Multiple singular soliton solutions were obtained by this method. Moreover, multiple singular soliton solutions were also derived.

Keywords: Hirota bilinear method, potential Kadomtsev-Petviashvili equation, multiple soliton solutions, multiple singular soliton solutions.

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Authors: C. Ardil

Abstract:

This paper proposes entropic programming for the freighter aircraft selection problem using the multiple criteria decision analysis method. The study aims to propose a systematic and comprehensive framework by focusing on the perspective of freighter aircraft selection. In order to achieve this goal, an integrated entropic programming approach was proposed to evaluate and rank alternatives. The decision criteria and aircraft alternatives were identified from the research data analysis. The objective criteria weights were determined by the mean weight method and the standard deviation method. The proposed entropic programming model was applied to a practical decision problem for evaluating and selecting freighter aircraft. The proposed entropic programming technique gives robust, reliable, and efficient results in modeling decision making analysis problems. As a result of entropic programming analysis, Boeing B747-8F, a freighter aircraft alternative ( a3), was chosen as the most suitable freighter aircraft candidate.   

Keywords: entropic programming, additive weighted model, multiple criteria decision making analysis, MCDMA, TOPSIS, aircraft selection, freighter aircraft, Boeing B747-8F, Boeing B777F, Airbus A350F

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Authors: Adriano Z. Zambom, Preethi Ravikumar

Abstract:

One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: Additive models, local polynomial regression, residuals, mean square error, variable selection.

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Authors: M. Capocelli, A. Caputo, M. De Falco, D. Mazzei, V. Piemonte

Abstract:

This paper presents the experimental results and the related modeling of a thermal energy storage (TES) facility, ideated and realized by ENEA and realizing the thermocline with an innovative geometry. Firstly, the thermal energy exchange model of an equivalent shell & tube heat exchanger is described and tested to reproduce the performance of the spiral exchanger installed in the TES. Through the regression of the experimental data, a first-order thermocline model was also validated to provide an analytical function of the thermocline, useful for the performance evaluation and the comparison with other systems and implementation in simulations of integrated systems (e.g. power plants). The experimental data obtained from the plant start-up and the short-cut modeling of the system can be useful for the process analysis, for the scale-up of the thermal storage system and to investigate the feasibility of its implementation in actual case-studies.

Keywords: Thermocline, modelling, heat exchange, spiral, shell, tube.

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Authors: Stefanie Heinke, Carina Pereira, Jan Spillner, Steffen Leonhardt

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Severe heart failure is a common problem that has a significant effect on health expenditures in industrialized countries; moreover it reduces patient-s quality of life. However, current research usually focuses either on detailed modeling of the heart or on detailed modeling of the cardiovascular system. Thus, this paper aims to present a sophisticated model of the heart enhanced with an extensive model of the cardiovascular system. Special interest is on the pressure and flow values close to the heart since these values are critical to accurately diagnose causes of heart failure. The model is implemented in Dymola an object-oriented, physical modeling language. Results achieved with the novel model show overall feasibility of the approach. Moreover, results are illustrated and compared to other models. The novel model shows significant improvements.

Keywords: Cardiovascular system, heart, modeling.

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Authors: Vipin Saxena, Deepa Raj

Abstract:

Unified Modeling language (UML) is one of the important modeling languages used for the visual representation of the research problem. In the present paper, UML model is designed for the Instruction pipeline which is used for the evaluation of the instructions of software programs. The class and sequence diagrams are designed & performance is evaluated for instructions of a sample program through a case study.

Keywords: UML, Instruction Pipeline, Class Diagram &Sequence Diagram.

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Authors: Michal Cerny

Abstract:

We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.

Keywords: Linear regression, interval-censored data, computational complexity.

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Authors: Shankar Kambhampaty, Venkata Srinivas Modali

Abstract:

When architecting an application, key nonfunctional requirements such as performance, scalability, availability and security, which influence the architecture of the system, are some times not adequately addressed. Performance of the application may not be looked at until there is a concern. There are several problems with this reactive approach. If the system does not meet its performance objectives, the application is unlikely to be accepted by the stakeholders. This paper suggests an approach for performance modeling for web based J2EE and .Net applications to address performance issues early in the development life cycle. It also includes a Performance Modeling Case Study, with Proof-of-Concept (PoC) and implementation details for .NET and J2EE platforms.

Keywords: Performance Measures, Performance Modeling, Performance Testing, Resource Utilization, Response Time, Throughput.

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Authors: Ivana T. Karabegović, Saša S. Stojičević, Dragan T. Veličković, Nada Č. Nikolić, Miodrag L. Lazić

Abstract:

Optimization of a microwave-assisted extraction of cherry laurel (Prunus laurocerasus) fruit using methanol was studied. The influence of process parameters (microwave power, plant material-to-solvent ratio and the extraction time) on the extraction efficiency were optimized by using response surface methodology. The predicted maximum yield of extractive substances (41.85 g/100 g fresh plant material) was obtained at microwave power of 600 W and plant material to solvent ratio of 0.2 g/cm3 after 26 minutes of extraction, while a mean value of 40.80±0.41 g/100 g fresh plant material was obtained from laboratory experiments. This proves applicability of the model in predicting optimal extraction conditions with minimal laborious and time consuming. The results indicated that all process parameters were effective on the extraction efficiency, while the most important factor was extraction time. In order to rationalize production the optimal economical condition which gave a large total extract yield with minimal energy and solvent consumption was found.

Keywords: Cherry laurel, Extraction, Multiple regression modeling, Microwave.

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Authors: Abdullah A. AlShaher

Abstract:

In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: Shape recognition, Arabic handwritten characters, regression curves, expectation maximization algorithm.

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Authors: Vipin Saini, Sunil Kamboj, Suman Bala, A. Pandurangan

Abstract:

The present invention relates to multiple-unit tablet dosage forms, which is composed of several subunits (multiparticulates/pellets). Each small multiparticulate further composed of many layers. Some layer contains drug substance; others are rate controlling polymer. The resulting multiple-unit tablet dosage forms of pantoprazole were satisfactory fabricated. Pelletization technique has some advantages over coated tablet formulation. In coated tablet the coating may be damaged and a pinhole possibly formed that would result in increased release of drug in stomach and may be deactivated in stomach juices. If the coat of some pellets may be damaged that would not affect the release properties of the multiple-unit tablet. Hence they are beneficial in this aspect. The results confirmed the successful preparation of stable and bioequivalent once daily controlled release multiple-unit tablets of pantoprazole.

Keywords: Controlled release, multiple unit tablets, pantoprazole, pelletization.

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Authors: C. Patrascioiu

Abstract:

The paper describes the modeling and simulation of the heat pumps domain processes. The main objective of the study is the use of the heat pump in propene–propane distillation processes. The modeling and simulation instrument is the Unisim^® Design simulator. The paper is structured in three parts: An overview of the compressing gases, the modeling and simulation of the freezing systems, and the modeling and simulation of the heat pumps. For each of these systems, there are presented the Unisim^® Design simulation diagrams, the input–output system structure and the numerical results. Future studies will consider modeling and simulation of the propene–propane distillation process with heat pump.

Keywords: Distillation, heat pump, simulation, Unisim Design.

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Authors: C. Ardil, A. M. Pashaev, R.A. Sadiqov, P. Abdullayev

Abstract:

In this paper, multiple criteria decision making analysis technique, is presented for ranking and selection of a set of determined alternatives - fighter aircraft - which are associated with a set of decision factors. In fighter aircraft design, conflicting decision criteria, disciplines, and technologies are always involved in the design process. Multiple criteria decision making analysis techniques can be helpful to effectively deal with such situations and make wise design decisions. Multiple criteria decision making analysis theory is a systematic mathematical approach for dealing with problems which contain uncertainties in decision making. The feasibility and contributions of applying the multiple criteria decision making analysis technique in fighter aircraft selection analysis is explored. In this study, an integrated framework incorporating multiple criteria decision making analysis technique in fighter aircraft analysis is established using entropy objective weighting method. An improved integrated multiple criteria decision making analysis method is utilized to aggregate the multiple decision criteria into one composite figure of merit, which serves as an objective function in the decision process. Therefore, it is demonstrated that the suitable multiple criteria decision making analysis method with decision solution provides an effective objective function for the decision making analysis. Considering that the inherent uncertainties and the weighting factors have crucial decision impacts on the fighter aircraft evaluation, seven fighter aircraft models for the multiple design criteria in terms of the weighting factors are constructed. The proposed multiple criteria decision making analysis model is based on integrated entropy index procedure, and additive multiple criteria decision making analysis theory. Hence, the applicability of proposed technique for fighter aircraft selection problem is considered. The constructed multiple criteria decision making analysis model can provide efficient decision analysis approach for uncertainty assessment of the decision problem. Consequently, the fighter aircraft alternatives are ranked based their final evaluation scores, and sensitivity analysis is conducted.

Keywords: Fighter Aircraft, Fighter Aircraft Selection, Multiple Criteria Decision Making, Multiple Criteria Decision Making Analysis, MCDMA

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Authors: Willem Waegeman, Luc Boullart

Abstract:

Instead of traditional (nominal) classification we investigate the subject of ordinal classification or ranking. An enhanced method based on an ensemble of Support Vector Machines (SVM-s) is proposed. Each binary classifier is trained with specific weights for each object in the training data set. Experiments on benchmark datasets and synthetic data indicate that the performance of our approach is comparable to state of the art kernel methods for ordinal regression. The ensemble method, which is straightforward to implement, provides a very good sensitivity-specificity trade-off for the highest and lowest rank.

Keywords: Ordinal regression, support vector machines, ensemblelearning.

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Authors: Christina G. Siontorou

Abstract:

This paper presents an integrated knowledge-based approach to multi-scale modeling of aquatic systems, with a view to enhancing predictive power and aiding environmental management and policy-making. The basic phases of this approach have been exemplified in the case of a bay in Saronicos Gulf (Attiki, Greece). The results showed a significant problem with rising phytoplankton blooms linked to excessive microbial growth, arisen mostly due to increased nitrogen inflows; therefore, the nitrification/denitrification processes of the benthic and water column sub-systems have provided the quality variables to be monitored for assessing environmental status. It is thereby demonstrated that the proposed approach facilitates modeling choices and implementation option decisions, while it provides substantial support for knowledge and experience capitalization in long-term water management.

Keywords: Aquatic ecosystem, integrated modeling, multi-scale modeling, ontological platform.

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Authors: A. K. Al-Othman

Abstract:

This study presents a new approach based on Tanaka's fuzzy linear regression (FLP) algorithm to solve well-known power system economic load dispatch problem (ELD). Tanaka's fuzzy linear regression (FLP) formulation will be employed to compute the optimal solution of optimization problem after linearization. The unknowns are expressed as fuzzy numbers with a triangular membership function that has middle and spread value reflected on the unknowns. The proposed fuzzy model is formulated as a linear optimization problem, where the objective is to minimize the sum of the spread of the unknowns, subject to double inequality constraints. Linear programming technique is employed to obtain the middle and the symmetric spread for every unknown (power generation level). Simulation results of the proposed approach will be compared with those reported in literature.

Keywords: Economic Dispatch, Fuzzy Linear Regression (FLP)and Optimization.

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Authors: Elsayeda M. Elgaml, Dina M. Ibrahim, Elsayed A. Sallam

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Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy. 

Keywords: Association Rules, FP-growth, Multiple minimum supports, Weka Tool

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Authors: Aree Thongpukdee, Ekasit Nisayan, Chockpisit Thepsithar

Abstract:

Shoots, with three leaves, of Paphiopedilum 'Delrosi' were used as explants for multiple shoot induction. Modified Hyponex medium was supplemented with thidiazuron (TDZ), N6- benzyladenine (BA) or kinetin (Kn) alone and in combinations with 2,4-dichlorophenoxyacetic acid (2,4-D). All explants were cultured for 15 weeks. It was found that TDZ alone at the concentration of 0.45μM or in combination with 4.52μM 2,4-D and 8.88μM BA in combination with 13.56μM 2,4-D promoted multiple shoots. The highest shoot sprouting efficiencies (80.0, 90.0 and 80.0%) and new shoot numbers (1.5, 1.3 and 1.1) were obtained, respectively. Fresh weight, height, numbers of leaf and root of new shoots and initial explants were discussed.

Keywords: Paphiopedilum, terrestrial orchids, in vitro culture, micropropagation, multiple shoot induction

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Authors: Ibrahim A. Aref, Tarek A. El-Mihoub

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This paper shows how we can integrate communication modeling into the design modeling at early stages of the design flow. We consider effect of incorporating noise such as impulsive noise on system stability. We show that with change of the system model and investigate the system performance under the different communication effects. We modeled a unmanned aerial vehicle (UAV) as a demonstration using SystemC methodology. Moreover the system is modeled by joining the capabilities of UML and SystemC to operate at system level.

Keywords: Modelling, SoC, SystemC, UAV, Simulation, SoC.

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Authors: Sina Babadi, K. M. Ebrahimi

Abstract:

The aim of this paper is to determine the stress levels at the end of a long slender shaft such as a drilling assembly used in the oil or gas industry using a mathematical model in real-time. The torsional deflection experienced by this type of drilling shaft (about 4 KM length and 20 cm diameter hollow shaft with a thickness of 1 cm) can only be determined using a distributed modeling technique. The main objective of this project is to calculate angular velocity and torque at the end of the shaft by TLM method and also analyzing of the behavior of the system by transient response. The obtained result is compared with lumped modeling technique the importance of these results will be evident only after the mentioned comparison. Two systems have different transient responses and in this project because of the length of the shaft transient response is very important.

Keywords: Distributed Lumped modeling, Lumped modeling, Drill string, Angular Velocity, Torque.

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