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Epilepsy is the most common neurological disorder in the world, second only to stroke. There are nearly 15 million patients suffer from refractory epilepsy, with no available therapy. Although most seizures are not life threatening, they are an unpredictable source of annoyance and embarrassment, which will result in unconfident and fear. Prediction of epileptic seizures has a profound effect in understanding the mechanism of seizure, improving the rehabilitation possibilities and thereby the quality of life for epilepsy patients. A seizure prediction system can help refractory patients rehabilitate psychologically. In this paper, we introduce an epilepsy seizure prediction algorithm from scalp EEG based on morphological filter and Kolmogorov complexity. Firstly, a complex filter is constructed to remove the artifacts in scalp EEG, in which a morphological filter with optimized structure elements is proposed to eliminate the ocular artifact. Then, the improved Kolmogorov complexity is applied to describe the non-linear dynamic transition of brains. Results show that only the Kolmogorov complexity of electrodes near the epileptogenic focus reduces significantly before seizures. Through the analysis of 7 long-term scalp EEG recordings from 5 epilepsy patients, the average prediction time is 8.5 minutes, the mean sensitivity is 74.0% and specificity is 33.6%. [ABSTRACT FROM AUTHOR]

The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group's previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a multiple swarms technique(FMSO) based on fast particle swarm optimization(FPSO) algorithm is proposed by bringing crossover operation. FPSO is a global search algorithm witch can prevent PSO from trapping into local optima by introducing Cauchy mutation. Though it can get high optimizing precision, the convergence rate is not satisfied, FMSO not only can find satisfied solutions ,but also speeds up the search. By proposing a new information exchanging and sharing mechanism among swarms. By comparing the results on a set of benchmark test functions, FMSO shows a competitive performance with the improved convergence speed and high optimizing precision. [ABSTRACT FROM AUTHOR]

With ever-improving information technologies and high performance computational power, recent techniques in granular computing, soft computing and cognitive science have allowed an increasing understanding of normal and abnormal brain functions, especially in the research of human's pattern recognition by means of computational intelligence. It is well understood that normal brains have high intelligence to recognize different geometrical patterns, but a systematic framework of biological neural network has not yet be established. In this paper, we propose the genetic granular cognitive fuzzy neural networks (GGCFNN) in order to efficiently testify artificial neural networks' learning capability on human's pattern recognition in term of symmetric and similar geometry patterns. In contrast to other information systems, the GGCFNN is a highly hybrid intelligent system integrating the techniques of genetic algorithms, granular computing, and fuzzy neural networks with cognitive science for pattern recognition. Our ability to simulate biological neural networks makes it possible a more comprehensive quantitative analysis on the pattern recognition of human brains, and our preliminary experiment results would shed lights on the future research of cognitive science and brain informatics. [ABSTRACT FROM AUTHOR]

Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve (AUC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUCs. Our experiments show that AUC-based genetic fuzzy SVM fusion model produces not only better AUC but also better accuracy than individual SVM classifiers. [ABSTRACT FROM AUTHOR]

In this paper, a Type-II fuzzy and neural network system is introduced to car auxiliary control system for inexperienced drivers. The approach applies Type-II fuzzy logic to generate the training data set for the neural network. The Type-II fuzzy logic is launched by the component called the fuzzy controller. With Type-II fuzzy logic and neural network system, the auxiliary system can manipulate more driving conditions so that enables the neural network to predict more reliably. The results of the approach is compared with Type-I fuzzy/neural system and discussed in detail. [ABSTRACT FROM AUTHOR]

A hybrid of type-1 and type-2 fuzzy model is proposed, which is applied in controlling the surface roughness of mechanical workpiece in metal cutting manufacturing. There are dozens of factors that affect the quality of surface roughness. The factors can be divided into two groups that are controlled and uncontrolled factors, e.g. feed rate can be setup. Therefore it is controlled factor while tool wear is an example of uncontrolled factor. There are two kinds of factors respectively correspond to type-1 and type-2 solutions because type-1 is suitable for controlled factors and type-2 fuzzy logic can handle uncontrolled or uncertain inputs. The proposed study will use genetic algorithm to identify the significant factors during the cutting process and a mathematical model that can predict the surface roughness under process variations. A fuzzy set based model for metal cutting operations can be used to reliably predict surface roughness under variations so that a continuous control of surface roughness can be affirmed. Two main factors (feed rate and tool wear) which affect the quality of surface roughness are investigated and simulated. The result of simulation shows that hybrid fuzzy logic system has improved precision of output. [ABSTRACT FROM AUTHOR]

In this paper, we propose an identities(ID) based watermarking scheme for Java programs. In our scheme, the watermark is generated by participants’ identities, embedded via the watermarked opaque predicates, and verified using zero-knowledge proof. We also present a construction of a family of opaque predicates by Legendre symbol, which is resilient, cheap, and stealthy. The order of the watermark is encoded and embedded into the watermarked opaque predicates, and the watermarked opaque predicates are treated as threads of a Java program. Thus, the embedded watermark is dynamic and secure against all usual types of watermarks algorithms attacks and watermarks protocols attacks, and also secure against static and dynamic attacks. Keywords: Java programs, watermarked opaque predicates, ID, aggregate signature, watermarking scheme. [ABSTRACT FROM AUTHOR]

Information management of equipments is a crucial component in enterprise resource plan (ERP) and data acquisition of equipment condition is the basis of information management system in enterprise. For most medium and small equipments, route inspection is the main method of condition information achievement. In this paper, condition information is divided into measurement information, display information and observation information. A data collector based on Palm handheld is developed to attain display and observation information. Acquired data is gathered to the route workstation in the spot. Then, each workstation uploads the data to the higher database server automatically, to share all the data in the range of whole enterprise. Results show that this data acquisition system enhances the efficiency of route inspection, provides support to discover the abnormity of machines and helps technicians to master the current condition of equipments timely. [ABSTRACT FROM AUTHOR]

Medium and small equipments in manufacturing are numerous, various and dispersive, so route inspection is the best method to acquire their condition information for its flexibility and low costs. In this paper, an integrated information system for route inspection is constructed to achieve distributed information acquisition on a large scale and centralized information management in a well order. Firstly, the information transfer model of route inspection is erected to describe information demands of route inspection participant. Then, several key problems, such as the instrument for information acquiring, information administration mode, interaction mode and security, are resolved respectively. Finally, three units, workstation, server and client are developed to acquire, manage and utilize the information. They are integrated into a whole system called MTREE, which has been applied into several steel enterprises successfully. [ABSTRACT FROM AUTHOR]