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Model-based testing is of high practical relevance and many model-based testing approaches have been developed during the last years. But the key question under which conditions model-based testing pays off and a related decision support procedure for its application has not been sufficiently addressed. In this paper we develop a generic decision support procedure whether to apply model-based testing...
This paper considers the model-based software reliability evaluation using the information on software testing inputs. Concretely, we define the distance between two software test cases by means of their testing inputs, and estimate the probability that the domain for a test input has already been covered by already-executed test cases. Based on the probability, we formulate the fault-detection probability...
Model based development and testing techniques have ventured diverse research directions to assure quality of the software product. Models developed during architecture and design phases are efficient tools to assess quality at an early development stage. However, testing the extra-functional or non-functional properties of software systems is not frequently practised eg. reliability. The motivation...
This paper presents a novel solution for nonquadratic controller design of nonlinear systems via Takagi-Sugeno models; it employs a first-order Levant's robust differentiator for finite-time exact estimation of the time derivatives of the membership functions, which appear as a consequence of the use of fuzzy Lyapunov functions. In contrast with most of the solutions on the subject, the proposed approach...
A new boolean compressive sensing method for solving the group-testing problem is proposed. The conventional method has the problem that the estimation performance is degraded in the case that positive elements change in the middle of tests because the results of the tests before a change-point are inconsistent with those of the tests after the change-point. To solve the problem, the proposed method...
Recently, some statistically optimal steganalyzers are proposed based on hypothesis testing theory, in which the cover pixels are supposed to be independent. However, the independent assumption is of limited interest since redundancy exists in natural images. In this paper, using a more appropriate image model considering pixel correlation, a new steganaly-sis method for the least significant bit...
This paper presents a new inlier-based outlier detection scheme for the analysis of abnormal behavior in crowded scenes. First, we segment the video into a set of cubes, and then extract the three feature descriptors from each cube, including the histogram of oriented gradients (HOG), the histogram of motion directions, and the motion magnitude descriptors. Thereafter, for each feature descriptor...
The methodology presented in this paper uses failure data from the software test and combines them with the results from mutation testing. New models based on Zipf's law are used - together with a suitable method for parameter estimation - to get interval estimates for the total number of faults. The new models have been applied to several well-known data sets and some data sets from the author. In...
Dynamic Random Testing (DRT) is a feedback-based software testing strategy, which has been proved to be more effective than the traditional Random Testing (RT) and Random-Partition Testing (RPT) strategies. The major advantage of DRT is that the test profile is dynamically adjusted based on the previous test data. Since the frequency and range of the profile adjustment are fixed during the testing...
This work proposes a new method to identify non-technical losses in typical Electrical Power Distribution Systems (EPDS). Aiming to improve the efficiency of on-site inspection for fraudulent clients, the consumers are classified in suspects and non-suspects using the Optimum-Path Forest (OPF) classifier. The authors propose an improvement to this method using results of a Distribution State Estimator...
In this paper, we proposed a region-based approach for indoor localization based on the fingerprint of radio signal strength (RSS). Unlike conventional fingerprint-based methods that based on point information to infer user's location, we used region information for indoor localization. Our results show that the proposed system has better positioning accuracy and more robustness to signal noise and...
This work aims to reduce the test time per chip, but affect the quality of the products only as little as possible compared to the conventional semiconductor test. For that purpose, a simulation software has been developed that performs adaptive test simulations based on real data and test results of conventional production testing. Product analyzes were performed and both the potential for test time...
The main contribution of this paper is a non-trivial expression, that is called dual expression, of the posterior values for a non-adaptive group testing problem. The dual expression is useful for exact bitwise MAP estimation. We assume a simplest non-adaptive group testing scenario including N-objects with binary status and M-disjunctive tests. If a group contains a positive object, the test result...
We consider a generalization of the problem of estimating the support size of a hidden subset S of a universe U from samples. This framework falls under the group testing [1] and the conditional sampling models [2, 3]. In group testing, for a query set, we are told if it intersects with the set S. We propose a generalization of this problem, where each element has a non-negative weight, and the objective...
Clock jitter is a crucial factor in high speed and high performance Analog-to-Digital Converter (ADC) testing. Random clock jitter increases the noise floor in the ADC output spectrum making it difficult to obtain the true ADC Signal to Noise Ratio (SNR). Periodic Jitter generates spurs in the ADC output spectrum. Another well-known challenge is to achieve precise coherent sampling. This paper proposes...
Estimating 3-dimensional head pose from a single 2D image is a challenging task with extensive applications. Existing approaches lack the capability to deal with multiple pose-related and — unrelated factors in a uniform way. Most of them can provide only 1-dimensional yaw estimation and suffer from limited representation ability for out-of-sample testing inputs. These drawbacks limit their performance...
This paper considers the decision-making technique through the analysis of testing knowledge results under incompleteness of initial information. This technique is notable for determination of testing system parameters as linguistic variables and the probabilistic updating algorithm of membership functions, as well as the decision-making model on the base of the classification of fuzzy situations...
This paper presents a modeling technique of sequential batch reactor (SBR) for aerobic granular sludge (AGS) using artificial neural network (ANN). A SBR fed with synthetic wastewater was operated at high temperature of 50˚C to study the formation of AGS for simultaneous organics and nutrients removal in 60 days. The feed forward neural network (FFNN) was used to model the nutrients removal process...
The paper deals with the problem of locating leaks in water distribution networks with many nodes and branches. The pressure transients measured in a few selected nodes, in response to pressure variations applied to one node, are analyzed by a detection and isolation algorithm, based on parametric estimation of faulty network models. The algorithm is tested by simulation in a realistic case, giving...
In this paper, the direction of arrival estimation for unknown number of signals is formulated as a constrained optimization based on the Maximum Likelihood criterion. For that, we propose a fitness function that depends on the number of signals, array size and the measured signal at the array output such that the correct combination of these parameters minimizes the fitness. To minimize the fitness,...
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