Search results for: closed-loop model identification

Authors: S. M. Mahbobur Rahman

Abstract:

Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model.

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Authors: S.A. Alqallaf, S.A. Al-Mawsawi, A. Haider

Abstract:

In order to evaluate the performance of a unified power flow controller (UPFC), mathematical models for steady state and dynamic analysis are to be developed. The steady state model is mainly concerned with the incorporation of the UPFC in load flow studies. Several load flow models for UPFC have been introduced in literature, and one of the most reliable models is the decoupled UPFC model. In spite of UPFC decoupled load flow model simplicity, it is more robust compared to other UPFC load flow models and it contains unique capabilities. Some shortcoming such as additional set of nonlinear equations are to be solved separately after the load flow solution is obtained. The aim of this study is to investigate the different control strategies that can be realized in the decoupled load flow model (individual control and combined control), and the impact of the location of the UPFC in the network on its control parameters.

Keywords: UPFC, Decoupled model, Load flow.

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Authors: Reza Davoodi, Jahangir Yadollahi Farsi, Roya Naseri

Abstract:

In the way of growing and developing firms especially high-tech firms, on many occasions manager of firm is mainly involved in solving problems of his business and decision making about executive activities of the firm, while besides executive measures, attention to planning of firm's success and growth way and application of long experience and sagacity in designing business model are vital and necessary success in a business is achieved as a result of different factors, one of the most important of them is designing and performing an optimal business model at the beginning of the firm's work. This model is determining the limit of profitability achieved by innovation and gained value added. Therefore, business model is the process of connecting innovation environment and technology with economic environment and business and is important for succeeding modern businesses considering their traits.

Keywords: Business Model (BM), Nanotechnology, High- TechFirms.

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Authors: Chengjie Wang, Li Yin, Chuanmin Wang

Abstract:

This paper presents a physics-based model for the high-voltage fast recovery diodes. The model provides a good trade-off between reverse recovery time and forward voltage drop realized through a combination of lifetime control and emitter efficiency reduction techniques. The minority carrier lifetime can be extracted from the reverse recovery transient response and forward characteristics. This paper also shows that decreasing the amount of the excess carriers stored in the drift region will result in softer characteristics which can be achieved using a lower doping level. The developed model is verified by experiment and the measurement data agrees well with the model.

Keywords: Emitter efficiency, lifetime control, P-i-N diode, physics-based model

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Authors: A. L. Qureshi, A. A. Mahessar, A. Baloch

Abstract:

Flood wave propagation in river channel flow can be enunciated by nonlinear equations of motion for unsteady flow. It is difficult to find analytical solution of these non-linear equations. Hence, in this paper verification of the finite element model has been carried out against available numerical predictions and field data. The results of the model indicate a good matching with both Preissmann scheme and HEC-RAS model for a river reach of 29km at both sites (15km from upstream and at downstream end) for discharge hydrographs. It also has an agreeable comparison with the Preissemann scheme for the flow depth (stage) hydrographs. The proposed model has also been applying to forecast daily discharges at 400km downstream in the Indus River from Sukkur barrage of Sindh, Pakistan, which demonstrates accurate model predictions with observed the daily discharges. Hence, this model may be utilized for flood warnings in advance.

Keywords: Finite Element Method, Flood Forecasting, HEC-RAS, Indus river.

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Authors: Lei Lei, Hongbo Zhang, Donald J. Bergstrom, Bing Zhang, K. Y. Song, W. J. Zhang

Abstract:

This paper presents a model for a modified T-junction device for microspheres generation. The numerical model is developed using a commercial software package: COMSOL Multiphysics. In order to test the accuracy of the numerical model, multiple variables, such as the flow rate of cross-flow, fluid properties, structure, and geometry of the microdevice are applied. The results from the model are compared with the experimental results in the diameter of the microsphere generated. The comparison shows a good agreement. Therefore the model is useful in further optimization of the device and feedback control of microsphere generation if any.

Keywords: CFD modeling, validation, microsphere generation, modified T-junction.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

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Authors: Junseok Ham, Chansun Jung, Junhyung Park, Jihye Ryeo, Ilju Ko

Abstract:

It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emotions. This paper supposed the Emotion Field for visualizing current emotion with location, and current emotion is expressed by location and color in the Emotion Field. For visualizing changes within current emotion, the artificial emotion model is adjusted to characters in Hamlet.

Keywords: Emotion, Artificial Emotion, Visualizing, EmotionModel.

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Authors: Yanshuang Zhang, Byungho Jeong

Abstract:

In many cases, there are some time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in evaluating the performance of organizations. Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. Multi-periods input(MpI) and Multi-periods output(MpO) models are integrate models to calculate simple efficiency considering time lag effect. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. That is, efficient DMUs can’t be discriminated because their efficiency scores are same. Thus, this paper suggests a super-efficiency model for efficiency evaluation under the consideration of time lag effect based on the MpO model. A case example using a long term research project is given to compare the suggested model with the MpO model.

Keywords: DEA, Super-efficiency, Time Lag.

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Authors: Rachid.Dehini, Abdesselam.Bassou, Brahim.Ferdi

Abstract:

Active Power Filters (APFs) are today the most widely used systems to eliminate harmonics compensate power factor and correct unbalanced problems in industrial power plants. We propose to improve the performances of conventional APFs by using artificial neural networks (ANNs) for harmonics estimation. This new method combines both the strategies for extracting the three-phase reference currents for active power filters and DC link voltage control method. The ANNs learning capabilities to adaptively choose the power system parameters for both to compute the reference currents and to recharge the capacitor value requested by VDC voltage in order to ensure suitable transit of powers to supply the inverter. To investigate the performance of this identification method, the study has been accomplished using simulation with the MATLAB Simulink Power System Toolbox. The simulation study results of the new (SAPF) identification technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion.

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Authors: M. Sadeghzadeh, M. Teshnehlab

Abstract:

Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.

Keywords: Ant colony optimization, Classification, Datamining, Feature selection.

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Authors: A. Badoud, M. Khemliche, S. Latreche

Abstract:

The main objective developed in this paper is to find a graphic technique for modeling, simulation and diagnosis of the industrial systems. This importance is much apparent when it is about a complex system such as the nuclear reactor with pressurized water of several form with various several non-linearity and time scales. In this case the analytical approach is heavy and does not give a fast idea on the evolution of the system. The tool Bond Graph enabled us to transform the analytical model into graphic model and the software of simulation SYMBOLS 2000 specific to the Bond Graphs made it possible to validate and have the results given by the technical specifications. We introduce the analysis of the problem involved in the faults localization and identification in the complex industrial processes. We propose a method of fault detection applied to the diagnosis and to determine the gravity of a detected fault. We show the possibilities of application of the new diagnosis approaches to the complex system control. The industrial systems became increasingly complex with the faults diagnosis procedures in the physical systems prove to become very complex as soon as the systems considered are not elementary any more. Indeed, in front of this complexity, we chose to make recourse to Fault Detection and Isolation method (FDI) by the analysis of the problem of its control and to conceive a reliable system of diagnosis making it possible to apprehend the complex dynamic systems spatially distributed applied to the standard pressurized water nuclear reactor.

Keywords: Bond Graph, Modeling, Simulation, Monitoring, Analytical Redundancy Relations, Pressurized Water Reactor, Directed Graph.

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Authors: Murray L. Ireland, Kevin J. Worrall, Rebecca Mackenzie, Thaleia Flessa, Euan McGookin, Douglas Thomson

Abstract:

Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing input residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances.

Keywords: Fault detection, inverse simulation, rover, ground robot.

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Authors: Huynh Quyet Thang, Le Hai Quan

Abstract:

Interaction Model plays an important role in Modelbased Intelligent Interface Agent Architecture for developing Intelligent User Interface. In this paper we are presenting some improvements in the algorithms for development interaction model of interface agent including: the action segmentation algorithm, the action pair selection algorithm, the final action pair selection algorithm, the interaction graph construction algorithm and the probability calculation algorithm. The analysis of the algorithms also presented. At the end of this paper, we introduce an experimental program called “Personal Transfer System".

Keywords: interface agent, interaction model, user model.

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Authors: Muhibul Haque Bhuyan, Quazi D. M. Khosru

Abstract:

Carrier scatterings in the inversion channel of MOSFET dominates the carrier mobility and hence drain current. This paper presents an analytical model of the subthreshold drain current incorporating the effective electron mobility model of the pocket implanted nano scale n-MOSFET. The model is developed by assuming two linear pocket profiles at the source and drain edges at the surface and by using the conventional drift-diffusion equation. Effective electron mobility model includes three scattering mechanisms, such as, Coulomb, phonon and surface roughness scatterings as well as ballistic phenomena in the pocket implanted n-MOSFET. The model is simulated for various pocket profile and device parameters as well as for various bias conditions. Simulation results show that the subthreshold drain current data matches the experimental data already published in the literature.

Keywords: Linear Pocket Profile, Pocket Implanted n-MOSFET, Subthreshold Drain Current and Effective Mobility Model.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: Ratchada Sopakayang, Gerhard A. Holzapfel

Abstract:

In this study, a new constitutive model is developed to describe the hyperelastic behavior of collagenous tissues with a parallel arrangement of collagen fibers such as ligaments and tendons. The model is formulated using a continuum approach incorporating the structural changes of the main tissue components: collagen fibers, proteoglycan-rich matrix and fiber-matrix interaction. The mechanical contribution of the interaction between the fibers and the matrix is simply expressed by a coupling term. The structural change of the collagen fibers is incorporated in the constitutive model to describe the activation of the fibers under tissue straining. Finally, the constitutive model can easily describe the stress-stretch nonlinearity which occurs when a ligament/tendon is axially stretched. This study shows that the interaction between the fibers and the matrix contributes to the mechanical tissue response. Therefore, the model may lead to a better understanding of the physiological mechanisms of ligaments and tendons under axial loading.

Keywords: Hyperelasticity, constitutive model, fiber-matrix interaction, ligament, tendon.

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Authors: Sherin M. Youssef, Ahmed Abouelfarag, Noha M. Ghatwary

Abstract:

An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of digital images. The model depends on the human visual characteristics of image sub-regions in the frequency multi-resolution wavelet domain. In the proposed model, a multi-variable fuzzy based architecture has been designed to produce a perceptual membership degree for both candidate embedding sub-regions and strength watermark embedding factor. Different sizes of benchmark images with different sizes of watermarks have been applied on the model. Several experimental attacks have been applied such as JPEG compression, noises and rotation, to ensure the robustness of the scheme. In addition, the model has been compared with different watermarking schemes. The proposed model showed its robustness to attacks and at the same time achieved a high level of imperceptibility.

Keywords: Watermarking, The human visual system (HVS), Fuzzy Inference System (FIS), Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT).

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Authors: Katleho Daniel Makatjane, Diteboho Lawrence Xaba, Ntebogang Dinah Moroke

Abstract:

The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.

Keywords: Generalized autoregressive score model, stock returns, time-varying.

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Authors: S. Panda, S. K. Tomar, R. Prasad, C. Ardil

Abstract:

Order reduction of linear-time invariant systems employing two methods; one using the advantages of Routh approximation and other by an evolutionary technique is presented in this paper. In Routh approximation method the denominator of the reduced order model is obtained using Routh approximation while the numerator of the reduced order model is determined using the indirect approach of retaining the time moments and/or Markov parameters of original system. By this method the reduced order model guarantees stability if the original high order model is stable. In the second method Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical examples.

Keywords: Model Order Reduction, Markov Parameters, Routh Approximation, Particle Swarm Optimization, Integral Squared Error, Steady State Stability.

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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: A. Rangan, A. Tansu

Abstract:

This paper generalizes Yeh Lam-s shock model for renewal shock arrivals and random threshold. Several interesting statistical measures are explicitly obtained. A few special cases and an optimal replacement problem are also discussed.

Keywords: shock model, optimal replacement, random threshold, shocks.

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Authors: R. Kongnuy, P. Pongsumpun

Abstract:

We used mathematical model to study the transmission of dengue disease. The model is developed in which the human population is separated into two populations, pregnant and non-pregnant humans. The dynamical analysis method is used for analyzing this modified model. Two equilibrium states are found and the conditions for stability of theses two equilibrium states are established. Numerical results are shown for each equilibrium state. The basic reproduction numbers are found and they are compared by using numerical simulations.

Keywords: Basic reproductive number, dengue disease, equilibrium states, pregnancy.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

Abstract:

This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: Mazhar B. Tayel, Amr H. Yassin

Abstract:

A proposed small-signal model parameters for a pseudomorphic high electron mobility transistor (PHEMT) is presented. Both extrinsic and intrinsic circuit elements of a smallsignal model are determined using genetic algorithm (GA) as a stochastic global search and optimization tool. The parameters extraction of the small-signal model is performed on 200-μm gate width AlGaAs/InGaAs PHEMT. The equivalent circuit elements for a proposed 18 elements model are determined directly from the measured S- parameters. The GA is used to extract the parameters of the proposed small-signal model from 0.5 up to 18 GHz.

Keywords: PHEMT, Genetic Algorithms, small signal modeling, optimization.

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Authors: Neila Mezghani, Philippe Phan, Hubert Labelle, Carl Eric Aubin, Jacques de Guise

Abstract:

The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.

Keywords: Scoliosis, Lenke model, decision-rules, computer aided classifier.

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Authors: Agus Harjoko, Sri Hartati, Henry Dwiyasa

Abstract:

There have been numerous implementations of security system using biometric, especially for identification and verification cases. An example of pattern used in biometric is the iris pattern in human eye. The iris pattern is considered unique for each person. The use of iris pattern poses problems in encoding the human iris. In this research, an efficient iris recognition method is proposed. In the proposed method the iris segmentation is based on the observation that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By detecting the boundary between the pupil and the iris and the boundary between the iris and the sclera, the iris area can be separated from pupil and sclera. A step is taken to reduce the effect of eyelashes and specular reflection of pupil. Then the four levels Coiflet wavelet transform is applied to the extracted iris image. The modified Hamming distance is employed to measure the similarity between two irises. This research yields the identification success rate of 84.25% for the CASIA version 1.0 database. The method gives an accuracy of 77.78% for the left eyes of MMU 1 database and 86.67% for the right eyes. The time required for the encoding process, from the segmentation until the iris code is generated, is 0.7096 seconds. These results show that the accuracy and speed of the method is better than many other methods.

Keywords: Biometric, iris recognition, wavelet transform.

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Authors: Yung C. Ho, Hui C. Fang, Ming J. Hsieh

Abstract:

When consistently innovative business-models can give companies a competitive advantage, longitudinal empirical research, which can reflect dynamic business-model changes, has yet to prove a definitive connection. This study consequently employs a dynamic perspective in conjunction with innovation theory to examine the relationship between the types of business-model innovation and firm value. This study tries to examine various types of business-model innovation in high-end and low-end technology industries such as HTC and the 7-Eleven chain stores with research periods of 14 years and 32 years, respectively. The empirical results suggest that adopting radical business-model innovation in addition to expanding new target markets can successfully lead to a competitive advantage. Sustained advanced technological competences and service/product innovation are the key successful factors in high-end and low-end technology industry business-models respectively. In sum up, the business-model innovation can yield a higher market value and financial value in high-end technology industries than low-end ones.

Keywords: Business-model, Dynamic Perspective, Firm Value, Innovation

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Authors: Karel Frajtak, Miroslav Bures, Ivan Jelinek

Abstract:

Software testing is important stage of software development cycle. Current testing process involves tester and electronic documents with test case scenarios. In this paper we focus on new approach to testing process using automated test case generation and tester guidance through the system based on the model of the system. Test case generation and model-based testing is not possible without proper system model. We aim on providing better feedback from the testing process thus eliminating the unnecessary paper work.

Keywords: Model based testing, test automation, test generating, tester support.

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Authors: Jānis Zuters

Abstract:

There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.

Keywords: Learning algorithm, multi-layer perceptron, topology.

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