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Least squares support vector machines (LSSVM) has been carried out in order to obtain a statistically meaningful analysis of the extended set of molecules. The combined HF with LSSVM correction approach (LSSVM/HF) has been applied to evaluate the transition energies of organic molecules. After LSSVM correction, the RMS deviations of the calculated transition energies reduce from 0.91 to 0.26 eV for...
A novel design-for-testability approach is proposed, which is derived from the aggressive probabilistic targets set forth for the yield and quality to be achieved in the massproduction of high-volume low-cost transceiver SoCs, thus requiring solutions that are fundamentally different from the traditional approaches. Statistical analysis is presented as the basis for the proposed approach, and specific...
Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training text and images...
In this paper, a selection sort method of test cases is presented that is according to historical datum from the test cases pools, statistical analysis of test cases based on defect severities and choose the order of test cases and then measure the efficiency by the formula to find the choice of test cases in testing process in order. Finally, the test case is given overall selection method in regression...
Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper....
The Fast Fourier Transform is the standard approach for spectral testing. However, its correct application to sinusoidal signals requires either strict coherent sampling, or careful windowing, or other methods that are not computationally efficient. This paper introduces an improved method for achieving accurate and robust spectral testing for sinusoidal signals without the need for coherent sampling...
In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent...
To diagnose compound faults of rotating machine, this paper presents a novel hybrid intelligent fault diagnosis model based on adaptive lifting wavelet and multi-class support vector machine. First of all, the adaptive lifting wavelet is constructed to mach the signal local characteristics. The original signal is decomposed into approximation signal and detail signal. Secondly, 32 time-domain statistical...
Testing and debugging account for at least 30% of the project effort. Scientific advancements in individual activities or their integration may bring significant impacts to the practice of software development. Fault localization is the foremost debugging sub-activity. Any effective integration between testing and debugging should address how well testing and fault localization can be worked together...
We continue studying a new context-free computationally simple stylometry-based text homogeneity test: the sliced conditional compression complexity (sCCC or simply CCC) of literary texts introduced and inspired by the incomputable Kolmogorov conditional complexity. Other stylometry tools can occasionally almost coincide for different authors. Our CCC-attributor is asymptotically strictly minimal...
In response to problems surrounding measuring ice sheet thickness in high attenuation areas of Greenland and the Antarctic, the Center for the Remote Sensing of Ice Sheets (CReSIS) created a Multi-Channel RADAR Depth Sounder (MCRDS). The MCRDS system was used to measure ice thicknesses of up to five kilometers in depth. This system produced large datasets, which required greater processing capabilities...
A modified learning algorithm of Artificial Neural Networks (ANN) is introduced in this paper to solve imbalanced data set problems. In solving imbalanced data set, it is critical to predict the minority class due to their imbalanced nature. In order to improve the standard ANN classifier prediction performance, this paper focuses on optimizing the decision boundary of the step function at the output...
Classification of mental tasks from electroencephalogram (EEG) signals has important applications in brain-computer interfacing (BCI). However, classification of the highly redundant and high-dimensional EEG signal, with high spatial and spectral correlations, is quite challenging. Therefore, the discriminant information, especially that of the first and second data moments, need to be extracted in...
A mixture of Student t-distributions (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach by modeling the data sets using mixture of Pearson type VII distributions (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. An outlier...
This paper presents a methodology to perform passive testing of behavioural conformance for the web services based on the security rule. The proposed methodology can be used either to check a trace (offline checking) or to runtime verification (online checking) with timing constraints, including future and past time. In order to perform this: firstly, we use the Nomad language to define the security...
Accurate statistical models for hyperspectral imaging (HSI) data are fundamental for many subsequent applications including detection, classification, and estimation. Suppose the whole nonhomogeneous HSI data is well classified into homogeneous unimodal clutters, we find that the family of elliptically contoured distributions (ECDs) is capable of providing sufficiently accurate model for each clutter...
Each stage is influence on product quality in multistage manufacturing process. When product have some problem, the traditional statistical process control method was not detected the exact errant reason timely due to the interaction of each stage. On the basis of the variation transmission model, the conception of virtual stage is proposed. Then, by the hypothesis testing, the stage having errant...
We address the task of detecting surprising patterns in large textual data streams. These can reveal events in the real world when the data streams are generated by online news media, emails, Twitter feeds, movie subtitles, scientific publications, and more. The volume of interest in such text streams often exceeds human capacity for analysis, such that automatic pattern recognition tools are indispensable...
The transmission transformer represent a significant asset in the electrical network. The transformer is expensive to manufacture and it is costly to replace. The cost of the transformer replacement is approximately 4 million EURO which is larger than the average component replacement activity. Therefore it is desired to make the replacement both timely and smooth to reduce unnecessary costs. Life...
A comparison among three linear methodologies, a novel auto-adjusted fuzzy quadruple TSK model (QA-TSK) and two evolutionary decision tree representations, is presented in this paper. The three architectures make use of a vast number of primitives utilised to reconfigure and evolve their internal structures of the classifier models so that to discriminate among spatial physical activities. Such primitives...
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