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Number of results: 83
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Abstract

Considering the low efficiency during the process of traditional calibration for digital-display vibrometers, an automatic calibration system for vibrometers based on machine vision is developed. First, an automatic vibration control system is established on the basis of a personal computer, and the output of a vibration exciter on which a digital-display vibrometer to be calibrated is installed, is automatically adjusted to vibrate at a preset vibration level and a preset frequency. Then the display of the vibrometer is captured by a digital camera and identified by means of image recognition. According to the vibration level of the exciter measured by a laser interferometer and the recognized display of the vibrometer, the properties of the vibrometer are calculated and output by the computer. Image recognition algorithms for the display of the vibrometer with a high recognition rate are presented, and the recognition for vibrating digits and alternating digits is especially analyzed in detail. Experimental results on the built-up system show that the prposed image recognition methods are very effective and the system could liberate operators from boring and intense calibration work for digital-display vibrometers
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Abstract

This article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector Machine (SVM) classifier and co-training method adapted for the standard SVM are involved in genre classification. Also, some additional experiments are performed using reduced feature vectors, which improved the overall result. Finally, results and conclusions drawn from the study are presented, and suggestions for further work are outlined.
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Abstract

Similarity assessment between 3D models is an important problem in many fields including medicine, biology and industry. As there is no direct method to compare 3D geometries, different model representations (shape signatures) are developed to enable shape description, indexing and clustering. Even though some of those descriptors proved to achieve high classification precision, their application is often limited. In this work, a different approach to similarity assessment of 3D CAD models was presented. Instead of focusing on one specific shape signature, 45 easy-to-extract shape signatures were considered simultaneously. The vector of those features constituted an input for 3 machine learning algorithms: the random forest classifier, the support vector classifier and the fully connected neural network. The usefulness of the proposed approach was evaluated with a dataset consisting of over 1600 CAD models belonging to 9 separate classes. Different values of hyperparameters, as well as neural network configurations, were considered. Retrieval accuracy exceeding 99% was achieved on the test dataset.
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Abstract

Development of complex lubrication systems in the Oil&Gas industry has reached high levels of competitiveness in terms of requested performances and reliability. In particular, the use of HazOp (acronym of Hazard and Operability) analysis represents a decisive factor to evaluate safety and reliability of plants. The HazOp analysis is a structured and systematic examination of a planned or existing operation in order to identify and evaluate problems that may represent risks to personnel or equipment. In particular, P&ID schemes (acronym of Piping and Instrument Diagram according to regulation in force ISO 14617) are used to evaluate the design of the plant in order to increase its safety and reliability in different operating conditions. The use of a simulation tool can drastically increase speed, efficiency and reliability of the design process. In this work, a tool, called TTH lib (acronym of Transient Thermal Hydraulic Library) for the 1-D simulation of thermal hydraulic plants is presented. The proposed tool is applied to the analysis of safety relevant components of compressor and pumping units, such as lubrication circuits. Opposed to the known commercial products, TTH lib has been customized in order to ease simulation of complex interactions with digital logic components and plant controllers including their sensors and measurement systems. In particular, the proposed tool is optimized for fixed step execution and fast prototyping of Real Time code both for testing and production purposes. TTH lib can be used as a standard SimScape-Simulink library of components optimized and specifically designed in accordance with the P&ID definitions. Finally, an automatic code generation procedure has been developed, so TTH simulation models can be directly assembled from the P&ID schemes and technical documentation including detailed informations of sensor and measurement system.
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Abstract

Homogeneity of die castings is influenced by wide range of technological parameters as piston velocity in filling chamber of die casting machine, filling time of mould cavity, temperature of cast alloy, temperature of the mould, temperature of filling chamber, surface pressure on alloy during mould filling, final pressure and others. Based on stated parameters it is clear, that main parameters of die casting are filling time of die mould cavity and velocity of the melt in the ingates. Filling time must ensure the complete filling of the mould cavity before solidification process can negatively influence it. Among technological parameters also belong the returning material, which ratio in charge must be constrained according to requirement on final homogeneity of die castings. With the ratio of returning material influenced are the mechanical properties of castings, inner homogeneity and chemical composition.
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Abstract

There were two aims of the research. One was to enable more or less automatic confirmation of the known associations – either quantitative or qualitative – between technological data and selected properties of concrete materials. Even more important is the second aim – demonstration of expected possibility of automatic identification of new such relationships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial Intelligence, (AI), and are to be based on actual results from experiments on concrete materials. The reason of applying the AI tools is that in Civil Engineering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different AI methods in a one system, aimed at estimation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different AI techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different AI methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and – at the same time – possibly more consistent, in case of ML algorithms).
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Abstract

The genesis of both coherent structures and reactive flow control strategies is explored. Futuristic control systems that utilize mi-crosensors and microactuators together with artificial intelligence to target specific coherent structures in a transitional or turbulent flow are considered. Of possible interest to the readers of this journal is the concept of smart wings, to be briefly discussed early in the article.
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