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Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper...
Repair records constitute an invaluable source of information for early detection of systematic failures, despite issues such as inherent noise and missing data. In this paper, we present methodology and algorithms for mining repair records to discover root causes of system failure. We employ both domain-driven and data-driven clustering approaches to reduce data noise and to consider system failures...
Advantages and disadvantages of two common algorithms frequently used in the moving target detection: background subtraction method and frame difference method are analyzed and compared in this paper. Then based on the background subtraction method, a moving target detection algorithm is proposed. The background image used to process the next frame image is generated through superposition of the current...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
We construct prediction intervals for the linear regression model with IID errors with a known distribution, not necessarily Gaussian. The coverage probability of our prediction intervals is equal to the nominal confidence level not only unconditionally but also conditionally given a natural sigma-algebra of invariant events. This implies, in particular, the perfect calibration of our prediction intervals...
In this paper, a modular design approach of adaptive tracking is proposed for a class of stochastic nonlinear systems with standard Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and input-to-state stable (ISS) controller of global stabilization in probability are designed separately to ensure control module can be achieved. According to passive theorems, passive...
A novel adaptive nonlinear controller is presented for nonlinear active noise control systems, which is expanded by memory function mapping on the basis of a single neuron structure, and a generalized filtered-X gradient descent algorithm is developed to attenuate the nonlinear, non-Gaussian noises, which defines the weighted sum of Renyi's quadratic error entropy and the mean square error as the...
In this paper, we propose image enhancement of microarray images using histogram specification method. The proposed approach consists of system model that discuss about finding the type of noise present in the image and enhancing image by removing the noise present in the image. The proposed method is very efficient as it enhances image by revealing most of the microarray spots which is used for subsequent...
In this paper, we present a grid-based fault-tolerant event detection scheme for wireless sensor networks. The sensor field is divided into square-shaped cells, called virtual grids here, where sensor nodes in each grid form a cluster. Each grid is further divided into subgrids of equal size. Each cluster head receives sensor readings of its member nodes and counts the number of sensor nodes reporting...
A modified Sobel edge detection is proposed in this paper. Dempster-Shafer theory, also known as the theory of belief function, is applied to improve the drawbacks of the conventional Sobel operator, for instance, the thick edge and sensitive to noise. The reason is that by selecting the mass function, Dempster-Shafer theory can distinguish the edge pixels from the uncertain edge pixels correctly,...
Biometric authentication has been extensively studied for many years and attracted much attention due to its large potential security application. Finger vein is more stable and more difficult to fake than on-touch feature such as fingerprint, palm print and face. In this paper, we proposed a novel finger vein pattern extraction method based on low quality vein image, which is generated by common...
This paper deals with the problem of probabilistic detection of weak sine waves immersed in noise. The analysis is performed in the frequency domain after a discrete Fourier transform, and it is suited for real-time applications. The detection and false alarm probabilities are provided in analytical form as functions of the sampling parameters and the properties of the time window. In particular,...
In this paper, we study the reverse link (RL) capacity of a co-channel macrocell-femtocell network, where the macrocell and femtocell networks share the same carrier. The analysis is done based on an outage probability criterion, and we use this analysis further to compare the capacities of different practical multi-carrier macrocell-femtocell deployment strategies.
In this paper, the detection performance of OSGO and OSSO CFAR detectors embedded in heavy-tailed Pearson distributed clutter is analyzed. We derive the closed mathematic form expressions of probability of false alarm rate and detection probability of the two CFAR schemes. While the processing speed of OSGO and OSSO CFAR is two times of that of OS CFAR, simulation results indicate that OSGO CFAR detector's...
This paper presents a robust interval type-2 possibilistic C-means (IT2PCM) clustering algorithm which is actually alternating cluster estimation, but membership functions are selected with interval type-2 fuzzy sets by the users. The cluster prototypes are calculated by type reduction combined with defuzzification; consequently they could be directly extracted to generate interval type-2 fuzzy rules...
Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive...
In this work, a technique addressed to the reliable identification of very similar filled-in forms, with a reject option, is proposed. The method is based on the automatic detection of the most discriminant regions at the image level, to be used by a distance-based classifier. Experiments included multi-page form images and the results suggest that a very high accuracy can be achieved when identifying...
The bounds on the rate of uniform convergence of learning processes play an important role in the Statistical Learning Theory. They provide theoretical bases for the application of support vector machine and reflect the generalization ability of the learning machines. This paper mainly deals with the bounds on the rate of uniform convergence of learning processes when samples are corrupted by equality-expect...
The traditional weighting schemes used in text categorization for the vector space model (VSM) cannot exploit information intrinsic to texts obtained through online handwriting recognition or any OCR process. Especially, top n (n > 1) recognition candidates could not be used without flooding the resulting text with false occurrences of spurious terms. In this paper, an improved weighting scheme...
For the radiotherapy, the tumor inside thorax or abdomen keep varying with respiration motion. Current technologies, e.g., respiratory gating and beam tracking, face great challenges in predicting the respiratory tumor motion. Whereas respiratory motion is changeful, traditional prediction model such as Linear Model, Kalman Filter, and so on, can not imitate the motion accurately. In this article,...
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