Corrupted data

Johnston, L., and Kramer, M. A. (1998). Estimating state probability distributions from noisy and corrupted data. AIChE J. 44, 591-602. 

Autoregressive (AR) model-based Click Detection. In this method ([Vaseghi and Rayner, 1988, Vaseghi, 1988, Vaseghi and Rayner, 1990]) the underlying audio data. v n is assumed to be drawn from a short-term stationary autoregressive (AR) process (see equation (4.1)). The AR model parameters a and the excitation variance <52e are estimated from the corrupted data x[n using some procedure robust to impulsive noise, such as the M-estimator (see section 4.2). 

The corrupted data x[ri is filtered using the prediction error filter H(z) = (1 -... 

For a validated data set, the measured real part and transformed imaginary part, Equation C.l, will match. Similarly, the measured imaginary dispersion and the transformed real part, Equation C.2, will match. In contrast, for a corrupted data set neither the measured real part and transformed imaginary part, nor the measured imaginary dispersion and the transformed real part will match. For systems that are not completely stable, significant deviations between the experimental and transformed data are usually apparent at low frequencies due to the longer acquisition time. 

Challenge software functionality for incorrect user input, corrupted data, and an incorrect decision. 

Client Interrupt Testing The system was tested to determine what would happen in the case of a sudden breakdown of a client s Personal Computer (PC). The acceptance criterion was that the database would not be corrupted. Data not saved properly should thus be rolled back automatically to the latest secure version of the data. 

Nonlinear PCA To address the nonlinearity in the identity mapping of multivariate data, a nonlinear counterpart of the PCA can be used (see Section 3.6.1). As the versions of NLPCA make use of the neural network (NN) concept to address the nonlinearity, they suffer from the known overparameterization problem in the case of noise corrupted data. Data with small SNR will also give rise to extensive computations during the training of the network. Shao et al. [266] used wavelet filtering to pre-process the data followed by IT-net to detect the non-conforming trends in an industrial spray drier. 

This section examines some of the spectrometer procedures that relate to the collection, digitisation and computational manipulation of NMR data, including some of the fundamental parameters that define the way in which data is acquired. Such technicalities may not seem relevant to anyone who does not consider themselves a spectroscopist, but the importance of understanding a few basic relationships between experimental parameters comes fi-om the need to recognise spectmm artefacts or corrupted data that can result from inappropriate parameter settings and to appreciate the limitations inherent in NMR measurements. Only then can one make full and appropriate use of the spectroscopic information at hand. 

Both developers and testers use a wide variety of tools to help them test their software. Debugging versions of a product typically contain tens of thousands of checked assertion statements. Various program analysis tools have been developed to detect such things as the use of uninitialized variables. Debugging versions also typically contain code that checks for memory allocation errors and corrupted data structures. Yet another technique used is fault injection. Code is added to a system to artificially cause faults to occur in various subsystems and to produce incorrect input parameters to and output results from called functions. 

ANN to memorize that case. The test set should also contain a representative sampling of cases to realistically assess how the ANN responds to new situations. A word about autoassociation problems is in order here. If your goal is to use an ANN to simply store patterns or to compress data, you really do not need a test set because all you care about are the cases with which you train the network. If you want to pass corrupt data through the ANN to see if the network will output a clean version of the input, you may want to construct a test set to see how well the network can do this training set construction is presumably trivial here you know what data you want to store or compress, and this data is the training set. 

The method was tested on evenly or randomly distributed experimental error-free and error-corrupted data, and the results show that even rather higher experimental errors do not influence significantly the prediction power and correctness of ANN results. The ED-ANN approach can provide accurate prediction of the stability constants, whereas the computation is very robust. 

In 1999 an incident at Uljin Nuclear Power Station Unit 3 in Korea corrupted data on the performance net of the plant control computer [2]. The incident was caused by the failure of an application-specific integrated circuit (ASIC) chip on a rehostable module, which is part of a network interface module. Several non-operational pumps started without any demand, some closed valves opened and other open valves closed, and some circuit breakers switched on or off. There was also some relay chattering. Due to the response of the operators and because of diverse systems, the incident was mitigated without adverse consequences. 

Fuzzy logic uses information efficiently all available evidence is used and propagated until final defuzzification is robust to uncertain, missing or corrupted data. 

The aim of every image recovery procedure is to obtain enough good information to fill all the locations of -space (i.e. spatial frequency space) so that when the data processing has been completed there are no artefacts in the image due to missing or corrupt data. 

We are currently investigating other experiments. The first one consists in abusing the update of data in the flash of the P4080. Thus process is performed thanks to the cooperation between two user applications. We plan to check whether the corruption of one of these applications could provoke the flashing of purposely corrupted data. A second attack consists in using a core to execute malicious code. Actually, only one core is currently used in our experimental platform but we want to test the level of difficulty that is required to activate... 

Information/Technology Any disruption due to a breakdown in the iirformation or technology systems. This could come from such factors as a system crash, corrupted data, or a computer virus. 

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