Model reduction by trimming for a class of semi-Markov reliability models and the corresponding error bound

Semi-Markov processes have proved to be an effective and convenient tool to construct models of systems that achieve reliability by redundancy and reconfiguration. These models are able to depict complex system architectures and to capture the dynamics of fault arrival and system recovery. A disadvantage of this approach is that the models can be extremely large, which poses both a model and a computational problem. Techniques are needed to reduce the model size. Because these systems are used in critical applications where failure can be expensive, there must be an analytically derived bound for the error produced by the model reduction technique. A model reduction technique called trimming is presented that can be applied to a popular class of systems. Automatic model generation programs were written to help the reliability analyst produce models of complex systems. This method, trimming, is easy to implement and the error bound easy to compute. Hence, the method lends itself to inclusion in an automatic model generator

Automatic Model Generation Strategies for Model Transformation Testing

International audienceTesting model transformations requires input models which are graphs of inter-connected objects that must conform to a meta-model and meta-constraints from heterogeneous sources such as well-formedness rules, transformation pre-conditions, and test strategies. Manually specifying such models is tedious since models must simultaneously conform to several meta-constraints. We propose automatic model generation via constraint satisfaction using our tool Cartier for model transformation testing. Due to the virtually infinite number of models in the input domain we compare strategies based on input domain partitioning to guide model generation. We qualify the effectiveness of these strategies by performing mutation analysis on the transformation using generated sets of models. The test sets obtained using partitioning strategies gives mutation scores of up to 87\% vs. 72\% in the case of unguided/random generation. These scores are based on analysis of 360 automatically generated test models for the representative transformation of UML class diagram models to RDBMS models

The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor is categorized as a low-cost sensor that is widely used in various applications. This sensor has a significant deviation that leads to significant errors in the measurement result. The error produced by this sensor tends to increase proportionally to the measured distance. The implementation of a particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The length of the usage of the sensor leads to sensor fatigue. Sensor fatigue is one of the causes of model state changes. If the drift is still within the tolerance limit, the sensor performance can still be restored using the calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model-making process is not an easy task, time, and cost required, then the Newton polynomial-based (Automatic Model Generation (AMG) has been implemented in this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of a polynomial-based AMG algorithm for sensor calibration has been succeeded in improving the calibrated sensor’s accuracy by 96.4% and reducing the MSE level from 25.6 to 0.914The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor is categorized as a low-cost sensor that is widely used in various applications. This sensor has a significant deviation that leads to significant errors in the measurement result. The error produced by this sensor tends to increase proportionally to the measured distance. The implementation of a particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The length of the usage of the sensor leads to sensor fatigue. Sensor fatigue is one of the causes of model state changes. If the drift is still within the tolerance limit, the sensor performance can still be restored using the calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model-making process is not an easy task, time, and cost required, then the Newton polynomial-based (Automatic Model Generation (AMG) has been implemented in this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of a polynomial-based AMG algorithm for sensor calibration has been succeeded in improving the calibrated sensor’s accuracy by 96.4% and reducing the MSE level from 25.6 to 0.91

Model Transformations from Requirements to Web System Design

Requirements models are used to specify system functionalities from the customer viewpoint and are the starting point of software development. However, most Web engineering approaches do not provide a systematic method to build design models from requirements specification. We propose an approach using model transformations to close this gap. Our transformation rules are defined in the QVT language – a forthcoming OMG standard, which makes automatic model generation possible. This way design is kept consistent with the customer requirements.Deutsche Forschungsgemeinschaft (DFG) WI841/7-1EC 6th Framework project SENSORIA IST 01600

Advances in contact algorithms and their application to tires

Currently used techniques for tire contact analysis are reviewed. Discussion focuses on the different techniques used in modeling frictional forces and the treatment of contact conditions. A status report is presented on a new computational strategy for the modeling and analysis of tires, including the solution of the contact problem. The key elements of the proposed strategy are: (1) use of semianalytic mixed finite elements in which the shell variables are represented by Fourier series in the circumferential direction and piecewise polynomials in the meridional direction; (2) use of perturbed Lagrangian formulation for the determination of the contact area and pressure; and (3) application of multilevel iterative procedures and reduction techniques to generate the response of the tire. Numerical results are presented to demonstrate the effectiveness of a proposed procedure for generating the tire response associated with different Fourier harmonics

Matching and Clustering: Two Steps Towards Automatic Model Generation in Computer Vision

International audienceIn this paper, we present a general frame for a system of automatic modelling and recognition of 3D polyhedral objects. Such a system has many applications for robotics : recognition, localization, grasping,...Here we focus upon one main aspect of the system : when many images of one 3D object are taken from different unknown viewpoints, how to recognize those of them which represent the same aspect of the object ? Briefly, it is possible to determine automatically if two images are similar or not ? The two stages detailed in the paper are the matching of two images and the clustering of a set of images. Matching consists in finding the common features of two images while no information is known about the image contents, the motion or the calibration of the camera. Clustering consists in regrouping into sets the images representing a same aspect of the modeled objects. For both stages, expermiental results on real images are shown

Использование оптимизационных моделей в аграрном образовании

Использование оптимизационных моделей развивает у студентов способности логически мыслить, анализировать хозяйственные связи и принимать обоснованные решения. Задания должны быть индивидуализированы. Предлагается применять автоматическую генерацию моделей для проверки заданий. The use of optimization models develops students' ability to think logically, analyze business relationships and make informed decisions. Tasks should be individualized. It is proposed to use automatic model generation to check tasks

Combining scripting and commercial simulation software to simulate in-plant logistics

In this paper we describe the use of a commercial discrete event simulation package (Siemens 2008) combined with a custom program, written in the programming language Python (Martelli 2006). Combining these two makes it possible to automatically generate a model for assembly line logistics simulation. The different stations of the assembly line, their connections and the storage near the assembly line were generated within seconds. A huge amount of time was saved compared with manual generation