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The Automated Systems of Technical Diagnostics (ASTD) of the dynamic distributed information systems (DIS) need the operational reconfiguration corresponding to changes of DIS. As a result, support of ASTD also requires the up-dating, advancing changes of DIS. In this work, development of the network model of testing is provided, allowing to reduce time of synthesis of tests for DIS. The Model is...
In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data...
Following the Service-Oriented Architecture, a large number of diversified Cloud services are exposed as Web APIs (Application Program Interface), which serve as the contracts between the service providers and service consumers. Due to their massive and broad applications, any flaw in the cloud APIs may lead to serious consequences. API testing is thus necessary to ensure the availability, reliability,...
The objective of this paper is to extend the applicability of the GLR method to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Kernel partial least squares (KPLS) models have been widely used to represent such systems. Therefore, in this paper, kernel PLS-based GLR method will be utilized in practice to improve...
The advancement of information technology and research in finance have recently led to flash decision making and actions by computer algorithms in order to respond to fast events occurring in the stock markets. This new area of technology involves the implementation of high-speed trading strategies which have generated significant amount of activity and information for financial research. In this...
The User Requirements Notation is a standard published by the International Telecommunication Union that contains two complementary notations for goal and scenario/workflow modeling. Use Case Maps (UCM) – the workflow notation – focuses on the causal relationships of the steps in a workflow without requiring the specification of detailed message exchanges and data. A UCM model captures the interactions...
Large-scale software systems like Amazon and healthcare.gov are used by thousands or millions of people every day. To ensure the quality of these systems, load testing is a required testing procedure in addition to the conventional functional testing techniques like unit and system integration testing. One of the important requirements of load testing is to create a field-like test environment. Unfortunately,...
We present SimCoTest, a tool to generate small test suites with high fault revealing ability for Simulink/Stateflow controllers. SimCoTest uses meta-heuristic search to (1) maximize the likelihood of presence of specific failure patterns in output signals (failure-based test generation), and to (2) maximize diversity of output signal shapes (output diversity test generation). SimCoTest has been evaluated...
Big data and cloud computing are the two top IT initiatives that are in the mind for industries across the globe. Both innovations keep on evolving. As a delivery model for IT services, cloud computing has the potential to enhance agility and productivity while enabling greater efficiencies and reducing costs. As a result a number of enterprises are building efficient and agile cloud environments,...
This paper addresses the problem of identifying signals of interest from discrete-time sequences contaminated by erroneous segments, which we define as the part of time series whose dynamic patterns are inconsistent with that of the signals. Assuming the signals of interest consist of consecutive samples with arbitrary starting point, duration and following a stationary dynamic pattern, we propose...
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale...
Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting the behavior of complex models, or asserting that certain problematic attributes...
In this paper, we proposed an innovative approachfor feature selection and model updating in big data machinelearning. Since hard drive access is the biggest barrier for bigdata problems, it is therefore nature to reduce disk I/O operationswhen evaluating different combinations of features, or updatinga learning machine. Particularly, we are interested in discoveringif small enough matrices exist...
The testability of equipment has become the key factor affecting equipment availability, and detracts from readiness and mission success. To overcome the current problems associated with the analysis of equipment testability, such as non-comprehensive failure mode coverage, low fault detection rate, and low fault location accuracy, this paper presents a system testability modeling and analysis method...
Today's highly increasing product diversity and decreasing product life cycles, also in the automotive industry lead to fast changing production systems with a high ratio of mechatronic components and (control) software. That again leads to ever increasing use of Virtual Commissioning during the development process of automated manufacturing plants. Paired with the still increasing request towards...
Zero shot learning (ZSL) provides a solution to recognising unseen classes without class labelled data for model learning. Most ZSL methods aim to learn a mapping from a visual feature space to a semantic embedding space, e.g. attribute or word vector spaces. The use of word vector space is particularly attractive as compared to attribute, it offers vast auxiliary classes with free parts embedding...
With one class outnumbering another, many real classification tasks show imbalanced class distributions, which brings big trouble to standard classification models: they usually intend to recognize a minority instance as a majority one. The data gravitation based classification (DGC) model, a newly developed physical-inspired supervised learning model, has been proven effective for standard supervised...
Satellite simulation and test platform is used for testing and verifying function and information flow of each satellite subsystem. Time management problems involving the instruction execution time, the simulation speed, the system logic sequence, etc. This paper proposes a method to realize the time management, include changing simulation speed in real time, correcting time of subsystem, broadcasting...
The use of multivariate pattern analysis (MVPA) has grown substantially over the past few years. Many studies using MVPA estimate the response of individual trial activity and perform hypothesis testing using a non-parametric approach. Here we show that the default auto regression model of order 1 used for temporal whitening of BOLD data is problematic in that it leads to biased permutation tests...
In this paper, the distributed detection problem of linear and nonlinear signals embedded in white Gaussian noise (WGN) is considered. First, the asymptotically optimal generalized likelihood ratio test (GLRT) detector is derived for both signal models. It is found that the GLRT detector requires the submission of all observed data to the central processor which is practically infeasible. Thus, several...
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