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This paper examines the problem of generating testing actions for electronic industry test systems designed for verification of electronic packages of UHF band. This paper shows complex problems of setting amplitude and time parameters of multichannel generators of test signals. The problems of multichannel wide range signal generation and frequency control, rise and fall time control, pulse time...
On the example of operations for manufacturing the “Axis” detail, the effectiveness of statistical control of the technological production process for the “Nalchik plant of highvoltage equipment” is demonstrated. Specific recommendations on the effective quality assurance of parts are given.
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
As the demand and deployment of renewables get better, there comes a growing interest in improved techniques of forecasting the energy generation from those sources. This paper aims at testing and suggesting techniques for input parameter selection and accuracy enhancement in forecasting power output of a PV system. The PV system under study is an operational system in Goldwind smart microgrid in...
An Android application (app), called Real Time Range Tracker, was developed as part of a Naval Innovation Science and Engineering program and provides near real-time “line of sight” range between mobile smart devices. In order to properly design tests and analyze test results, the accuracy of the data generated by the app must be fully understood along with any associated measurement uncertainties...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize...
Even if the Vector Space Model used for document representation in information retrieval systems integrates a small quantity of knowledge it continues to be used due to its computational cost, speed execution and simplicity. We try to improve this document representation by adding some syntactic information such as the parts of speech. In this paper, we have evaluated three different tagging algorithms...
The Phasor Measurement Units (PMUs) are becoming an important element for the measurement systems of the electrical grid. To assure the high penetration of these measurement devices, the interoperability of the PMUs from different vendors must be ensured. The IEEE Standard C37.118.1-2011, with its amendments of 2014, defines two accuracy classes, P and M, and provides the steady state and the dynamic...
The comparison of two classifiers, the Extreme Learning Machine (ELM) and the Support Vector Machine (SVM) is considered for performance, resources used (neurons or support vector kernels) and computational complexity (speed). Both implementations are of similar type (C++ compiled as Octave .mex files) to have a better evaluation of speed and computational complexity. Our results indicate that ELM...
Effective prediction of unobservable degradation can assist to schedule preventive maintenance and reduce unexpected downtime for realistic industrial systems. In this paper, an extended time-/condition-based framework is proposed for the Probability Density Function (PDF) prediction of unobservable industrial wear. Furthering our earlier work of unobservable degradation estimation, a stage-based...
This paper provides a discussion on the coming technological changes in process automation of networked production systems, which will change the testing procedure. In the smart factory of the future there will be no possibility to reach a test coverage of 100%, assuming a flexible automation with continuous reconfiguration and dynamic changes during runtime. Consequently, large amounts of test cases...
The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems...
Starting from the last century, animals identification became important for several purposes, e.g. tracking, controlling livestock transaction, and illness control. Invasive and traditional ways used to achieve such animal identification in farms or laboratories. To avoid such invasiveness and to get more accurate identification results, biometric identification methods have appeared. This paper presents...
Machine-learning test strategy has been developed in the last decade as an alternative to costly specification-driven tests for Analog, Mixed-Signal and RF circuits (AMS-RF). The concept is simple: powerful algorithms are used to map simple measurements onto specifications. But the proper execution requires an information-rich input space. This paper presents an efficient hybrid algorithm to select...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
Document classification is usually more challenging than numerical data classification, because it is much more difficult to effectively represent documents than numerical data for classification purposes. Vector space model (VSM) has been widely used for document representation for classification, in which a document is represented by a vector of feature values based on a bag of words. This paper...
In this paper a new Taylor series-based weighted least squares (TWLS) algorithm for synchrophasor measurements is proposed in the case of electrical signals affected by decaying dc offset. The proposed procedure is based on an algorithm recently published in the scientific literature, called TWLS-DC(1) algorithm in the paper. The procedure, called TWLS-DC(2) algorithm, requires two iterations. In...
We present generalized hybrid surface-integral-equation formulations for three-dimensional conductors with arbitrary shapes. The proposed formulations are based on flexible applications of the electric-field integral equation and the magnetic-field integral equation with varying combinations on different regions of the given objects. As a proof of concept, we demonstrate hybrid formulations using...
An online recognition system must analyze the changes in the sensing data and at any significant detection; it has to decide if there is a change in the activity performed by the person. Such a system can use the previous sensor readings for decision-making (decide which activity is performed), without the need to wait for future ones. This paper proposes an approach of human activity recognition...
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