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Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our...
Face recognition is a popular technique in identifying human features. In certain application such as recognizing criminals from video surveillance, where no other physical trait is available, face recognition is the most practical and assessable human recognition method. For this reason, face recognition continue to attract large research interest among image processing community. In this paper,...
In ubiquitous environment, too much information exist, and it is not easy to obtain the well classified data from the information. Therefore an algorithm which should be fast and deduce good result is needed. About it, a decision tree algorithm is much useful in the field of data mining or machine learning system for the problem of classification. However sometimes according to several reasons, a...
Data, especially in large item sets, hide a wealth of information on the processes that have created and modified them. Often, a data-field or a set of data-fields are not modified only through well-defined processes, but also through latent processes; without the knowledge of the second type of processes, testing cannot be considered exhaustive. As a matter of fact, changes in the data deriving from...
Two damage anomalous filters which were set up by BP neural network have been used to alarm the damage in structural members. After dealing with eigenparameter extracted from damaged and intact structure, different input data is considered for setting up different damage warning anomalous filters. Filter □: the first eight natural frequencies are chosen as input data of network. Filter □: one mode...
This paper studied the robustness of ARX model by varying the noise sequence adapted to the output of a heating process. The ARX model is fitted from output data which is obtained from a pilot plant of essential oil extraction by applying steam distillation technique. The plant is supplied by 1.5kW power delivered to the heating element which then is converted into PRBS signal as input and produced...
The concept of differential privacy as a rigorous definition of privacy has emerged from the cryptographic community. However, further careful evaluation is needed before we can apply these theoretical results to privacy preservation in everyday data mining and statistical analysis. In this paper we demonstrate how to integrate a differential privacy framework with the classical statistical hypothesis...
Micro gas chromatograph (Micro GC) converts the content of separated gas to the electronic signal with two thermo conductive detector (TCD), one is for the reference gas, and another is for the sample gas. The signal conditioning circuit directly determines the performance of Micro GC. In this paper, we designed a signal conditioning circuit to restrain the high common voltage of 10 V and keep a very...
3D reconstruction based on high-level features, such as line and plane, is an important development trend in Digital Photogrammetry and Computer Vision. A novel method for extracting stratight line is presented, which can be illustrated as follows. Firstly, image is preprocessed by Wallis filtering that is used to enhance the image contrast and reduce the noise, so it is easy to extract more lines...
The original electrical signals in Crassula portulacea were tested by a touching test used platinum sensors in a system of self-made double shields. Tested data of the electrical signals were denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting system was set up to forecast the...
This paper presents a new approach to image restoration based on ANN, considering the learning of the inverse process using a standard image for training under a multiscale approach. Different models of ANN were tested and compared with the traditional techniques. The standard image was artificially degraded to simulate some types of frequent degradation problems. Due to the huge amount of data generated...
Many cellular processes exhibit cyclic behaviors. Hence, one important task in gene expression data analysis is to detect subset of genes that exhibit periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, unevenly sampled, and are highly contaminated with noise. This makes detection of...
Performance of some suboptimal detectors can be improved by adding independent noise to their observations. In this paper, the effects of adding independent noise to observations of a detector are investigated for binary composite hypothesis-testing problems in a generalized Neyman-Pearson framework. Sufficient conditions are derived to determine when performance of a detector can or cannot be improved...
A common method for real-time video detection in image sequences involves ??background subtraction??. The numerous approaches to this problem differ in the type of background model. This paper discuss a new background updating method based on classification. A growth template that can detect the target and interference, like noise, has put forward. This template can choose the growth direction automatically...
Modern industrial systems can't exist without fault detection and diagnostics subsystem. Creation of such subsystem becomes a challenging task. Often it's more difficult than creation of the rest system's parts. This paper provides an approach for building fault detection and diagnostics system based on artificial neural networks, automatic training method for such systems and investigates different...
In this paper it is shown that the effective number of bits (ENOB) of an analog-to-digital converter (ADC) can be accurately estimated by means of the three-parameter sine-fit algorithm, when the normalized frequency is a priori estimated by the interpolated discrete Fourier transform (IpDFT) method with maximum sidelobe decay windows. A criterion for optimal window choice is proposed. In addition,...
The Uniformly Most Powerful (UMP) test, which is one with the highest probability of detection over all unknown parameters in a composite hypothesis test, does not exist in the most practical problems. It is common to derive the suboptimal Generalize Likelihood Ratio (GLR) detector, that is shown to perform close to optimal invariant tests in some problems. The Uniformly Most Powerful Invariant (UMPI)...
An inversion algorithm for the reconstruction of natural crack shape from eddy current testing signals is developed by using an artificial neural network based forward model and particle swarm optimization algorithm. Eddy current inspections are performed to measure signals caused by fatigue cracks introduced into plate specimens. The preprocessed ECT signals and the true crack shapes are used in...
We propose to improve the spatial resolution of electroencephalography (EEG) using a differential recording methodology. Conventional EEG (CEEG) systems independently amplify and digitize the signal from each electrode. The Differential EEG (DEEG) approach amplifies the minute difference signal between neighboring electrodes which greatly eases the burden on the subsequent amplification and data conversion...
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