Coverage Modeling and Reliability Analysis Using Multi-state Function

Fault tree analysis is an effective method for predicting the reliability of a system.It gives a pictorial representation and logical framework for analyzing the reliability.Also,it has been used for a long time as an effective method for the quantitative and qualitative analysis of the failure modes of critical systems.In this paper,we propose a new general coverage model (GCM) based on hardware independent faults.Using this model,an effective software tool can be constructed to detect,locate and recover fault from the faulty system.This model can be applied to identify the key component that can cause the failure of the system using failure mode effect analysis (FMEA).     

Function Approximation with Complex Neuro-Fuzzy System Using Complex Fuzzy Sets �C A New Approach

A new neuro-fuzzy computing paradigm using complex fuzzy sets is proposed in this paper. The novel computing paradigm is applied to the problem of function approximation to test its nonlinear mapping ability. A complex fuzzy set (CFS) is an extension of traditional type-1 fuzzy set whose membership is within the unit real-valued interval. For a CFS, the membership is extended to complex-valued state within the unit disc of the complex plane. For self-adaption of the proposed complex neuro-fuzzy system (CNFS), the Particle Swarm Optimization (PSO) algorithm and Recursive Least Squares Estimator (RLSE) algorithm are used in a hybrid way to adjust the free parameters of the CNFS. With the novel PSO-RLSE hybrid learning method, the CNFS parameters can be converged efficiently and quickly. By the PSO-RLSE method for the CNFS, fast learning convergence is observed and great performance in accuracy is shown. In the experimental results, the CNFS shows much better performance than its traditional neuro-fuzzy counterpart and other compared approaches. Excellent performance by the proposed approach has been shown.     

Improving Phoneme Classification Performance Using Observation Context–Dependent Segment Models

This article describes a novel method that models the correlation among acoustic observations in contiguous speech segments. The basic idea behind the method is that acoustic observations are conditioned not only on the phonetic context but also on the preceding acoustic segment observation. The correlation between consecutive acoustic observations is modeled by mean trajectory polynomial segment models (PSM). This method is an extension of conventional segment modeling approaches in that it describes the correlation of acoustic observations not only inside segments but also between contiguous segments. It is also a generalization of phonetic context (e.g., triphone) modeling approaches because it can model acoustic context and phonetic context at the same time. Using the proposed method in a speaker-independent phoneme classification test resulted in a 7 to 9% relative reduction of error rate as compared with the traditional triphone segmental model system and a 31% reduction as compared with a similar triphone hidden Markov model (HMM) system.     

Testing Aesthetic and Function Design Preference for Touch Screen and Non–Touch Screen Mobile Phones Using Interactive Genetic Algorithms

Rapid mobile phone market growth has drastically shortened design cycle times forcing designers to identify user preferences quickly. Two studies test a novel method to quickly determine design preferences for touch screen and non–touch screen mobile phones. Interactive Genetic Algorithms (IGAs) are tested as an option for preference identification. IGAs efficiently explore design spaces, finding user preferences via an iterative system mimicking evolution. Each study tested six independent variables: horizontal and vertical button spacing; horizontal and vertical screen dimensions; corner radius; and user’s goal between aesthetically pleasing, functional, and both aesthetic and functional (first experiment), and aesthetic preference differences between touch screen and non–touch screen mobile phones for dialing phone numbers only (second experiment). Results showed IGAs to be a viable method; participant preference varied by goal (horizontal button spacing and screen width were most effected). Designers can employ IGAs to quickly determine aesthetic and other user preferences.     

Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning

ABSTRACT

This work is part of an effort to develop of a knowledge–vision integration platform for hazard control in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment. The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting experiences from a new scenario, querying the user, and using their feedback to retrain the system through a hierarchical recognition structure using convolutional neural network (CNN). Thereafter, in contrast to the random sampling, the concept of active learning based on pruning of redundant points is tested. Results obtained from both random sampling and active learning are compared with a rigid systems that is not capable to aggregate new experiences as it runs. From the results obtained, it can be concluded that the classification accuracy improves greatly by adding new experiences, which makes it possible to customize the service according to each scenario and application as it functions. In addition, the active learning approach was able to reduce the user query and slightly improve the overall classification performance, when compared with random sampling.     

Approximation property of partition of unity and its applications

The linear combination of certain partition of unity, subordinate to certain open covering of a compact set, is proved to be capable of approximating to a continuous function at arbitrarily precision. By using proper open covering and partition of unity, the robust nonlinear controllers and adaptive laws are designed for a class of nonlinear systems with uncertainties. The states and parameters of the closed-loop systems can be stabilized in the meaning of UUB (uniformly ultimately bounded) via the robus tnonlinear controllers and adaptive laws. Finally, an example shows the validity of method in this paper.     

Classification and counting on multi-continued fractions and its application to multi-sequences

In the light of multi-continued fraction theories, we make a classification and counting for multi-strict continued fractions, which are corresponding to multi-sequences of multiplicity m and length n. Based on the above counting, we develop an iterative formula for computing fast the linear complexity distribution of multi-sequences. As an application, we obtain the linear complexity distributions and expectations of multi-sequences of any given length n and multiplicity m less than 12 by a personal computer. But only results of m=3 and 4 are given in this paper.     

A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.     

Fixed-Parameter Approximation: Conceptual Framework and Approximability Results

The notion of fixed-parameter approximation is introduced to investigate the approximability of optimization problems within the framework of fixed-parameter computation. This work partially aims at enhancing the world of fixed-parameter computation in parallel with the conventional theory of computation that includes both exact and approximate computations. In particular, it is proved that fixed-parameter approximability is closely related to the approximation of small-cost solutions in polynomial time. It is also demonstrated that many fixed-parameter intractable problems are not fixed-parameter approximable. On the other hand, fixed-parameter approximation appears to be a viable approach to solving some inapproximable yet important optimization problems. For instance, all problems in the class MAX SNP admit fixed-parameter approximation schemes in time O(2 ^{O((1−ε/O(1))k)} p(n)) for any small ε>0.     

Approximation Schemes for Degree-Restricted MST and Red–Blue Separation Problems

We develop a quasi-polynomial time approximation scheme for the Euclidean version of the Degree-Restricted MST Problem by adapting techniques used previously by Arora for approximating TSP. Given n points in the plane, d = 3 or 4, and > 0, the scheme finds an approximation with cost within 1 + of the lowest cost spanning tree with the property that all nodes have degree at most d. We also develop a polynomial time approximation scheme for the Euclidean version of the Red–Blue Separation Problem, again extending Aroras techniques. Given > 0, the scheme finds an approximation with cost within 1+ of the cost of the optimum separating polygon of the input nodes, in nearly linear time.     

Knowledge Based Pipeline Network Classification and Recognition Method of Maps

Map recognition is an essential data input means of Geographic Information System (GIS). How to solve the problems in the procedure, such as recognition of maps with crisscross pipeline networks, classification of buildings and roads, and processing of connected text, is a critical step for GIS keeping high-speed development. In this paper, a new recognition method of pipeline maps is presented, and some common patterns of pipeline connection and component labels are established. Through pattern matching, pipelines and component labels are recognized and peeled off from maps. After this approach, maps simply consist of buildings and roads, which are recognized and classified with fuzzy classification method. In addition, the Double Sides Scan (DSS) technique is also described, through which the effect of connected text can be eliminated.     

A New Supervised Clustering Framework Using Multi Discriminative Parts and Expectation–Maximization Approach for a Fine-Grained Animal Breed Classification (SC-MPEM)

Neural Processing Letters - Fine-grained image classification is active research in the field of computer vision. Specifically, animal breed classification is an arduous task due to the challenges...     

Classification and prediction of β–turn types by neural network

Although a β–turn consists of only four amino acids, it assumes many different types in proteins. Is this basically dependent on the tetrapeptide sequence alone or as a result of variety of interactions with the other part of a protein? To answer this question, T. Kohonen's self–organization Model which is one of the typical neural networks is proposed that can reflect the sequence–coupling effect of a tetrapeptide is not only a β–turn or non–β–turn, but also different types of a β–turn. There are 6028 β–turn tetrapeptides of β–turn types I (1227), I′(125), II(405), II′(89), VI(55), VIII(320), and non–β–turns (3807) in the training database as constructed recently by Chou and Blinn (1997). Using these training data the rate of correct prediction by the neural network for a given protein: rubredoxin (54 residues, 51 tetrapeptides) which includes 12 β–turn types I tetrapeptides, 1 β–turn types II tetrapeptides and 38 non–β–turns reaches 90.2%. The high quality of prediction of the neural network model implies that the formation of different β–turn types or non–β–turns is considerably correlated with the sequence of a tetrapeptide.     

Uncertainty Quantification in Image Segmentation Using the Ambrosio–Tortorelli Approximation of the Mumford–Shah Energy

The quantification of uncertainties in image segmentation based on the Mumford–Shah model is studied. The aim is to address the error propagation of noise and other error types in the original image to the restoration result and especially the reconstructed edges (sharp image contrasts). Analytically, we rely on the Ambrosio–Tortorelli approximation and discuss the existence of measurable selections of its solutions as well as sampling-based methods and the limitations of other popular methods. Numerical examples illustrate the theoretical findings.

     

Better and Simpler Approximation Algorithms for?the?Stable Marriage Problem

We first consider the problem of finding a maximum size stable matching if incomplete lists and ties are both allowed, but ties are on one side only. For this problem we give a simple, linear time 3/2-approximation algorithm, improving on the best known approximation factor 5/3 of Irving and Manlove (J. Comb. Optim., doi:10.1007/s10878-007-9133-x, 2007). Next, we show how this extends to the Hospitals/Residents problem with the same ratio if the residents have strict orders. We also give a simple linear time algorithm for the general problem with approximation factor 5/3, improving the best known 15/8-approximation algorithm of Iwama, Miyazaki and Yamauchi (SODA ??07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.?288?C297, 2007). For the cases considered in this paper it is NP-hard to approximate within a factor of 21/19 by the result of Halldórsson et?al. (ACM Transactions on Algorithms 3(3):30, 2007). Our algorithms not only give better approximation ratios than the cited ones, but are much simpler and run significantly faster. Also we may drop a restriction used in (J. Comb. Optim., doi:10.1007/s10878-007-9133-x, 2007) and the analysis is substantially more moderate. Preliminary versions of this paper appeared in (Király, Egres Technical Report TR-2008-04, www.cs.elte.hu/egres/, 2008; Király in Proceedings of MATCH-UP 2008: Matching Under Preferences??Algorithms and Complexity, Satellite Workshop of ICALP, July 6, 2008, Reykjavík, Iceland, pp.?36?C45, 2008; Király in ESA 2008, Lecture Notes in Computer Science, vol.?5193, pp.?623?C634, 2008). For the related results obtained thenceforth see Sect.?5.     

Optimizing Gabor Filter Design for Texture Edge Detection and Classification

An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.