Method and system for adaptive network security using intelligent packet analysis

<美国专利>6,499,107Gleichauf, et a1.December 24,2002<发明人>Gleichauf;Robert E.(San Antonio,TX);TealDaniel M.(San Antonio,TX);Wiley;Kevin L(Elgin,TX)<代理人>Cisco Technology,Inc.(San Jose,CA)<公开号>223071<公开日>December 29,1998<美国分类>713/20]<国际分类>G06F 01l/30<检索号>713/200,201 709/223,224,229,225,100,102,103,104,226 705/8,9<摘要>A method and system for adaptlye net-work security using intelligent packet analysisare provided.The method comprises monitoringnetwork data traff…     

Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction

In human–robot interaction scenarios, an intelligent robot should be able to synthesize an appropriate behavior adapted to human profile (i.e., personality). Recent research studies discussed the effect of personality traits on human verbal and nonverbal behaviors. The dynamic characteristics of the generated gestures and postures during the nonverbal communication can differ according to personality traits, which similarly can influence the verbal content of human speech. This research tries to map human verbal behavior to a corresponding verbal and nonverbal combined robot behavior based on the extraversion–introversion personality dimension. We explore the human–robot personality matching aspect and the similarity attraction principle, in addition to the different effects of the adapted combined robot behavior expressed through speech and gestures, and the adapted speech-only robot behavior, on interaction. Experiments with the humanoid NAO robot are reported.     

SEDRET—an intelligent system for the diagnosis and prediction of events in power plants

Artificial Intelligence applications in large-scale industry, such as fossil power plants, require the ability to manage uncertainty and time. In this paper, we present an intelligent system to assist an operator of a power plant. This system, called SEDRET, is based on a novel knowledge representation of uncertainty and time, called Temporal Nodes Bayesian Networks (TNBN), a type of Probabilistic Temporal Network. A set of temporal nodes and a set of edge define a TNBN, each temporal node is defined by a value of a variable and a time interval associate to the change of variable value. A TNBN generates a formal and systematic structure for modeling the temporal evolution of a process under uncertainty. The inference mechanism is based on probabilistic reasoning. A TNBN can be used to recognize events and state variables with respect to current plant conditions and predict the future propagation of disturbances. SEDRET was validated with the diagnosis and prediction of events in a steam generator with a power plant training simulator. The results performed in this work indicate that SEDRET can potentially improve plant availability through early diagnosis and prediction of disturbances that could lead to plant shutdown.     

Diffusivity and atomic mobility for fcc Ni–Cu–Ti alloy: Measurements and an intelligent modeling

The diffusivities of fcc Ni-rich Ni–Cu–Ti alloy have been systematically investigated by means of experimental measurements and an intelligent modeling. Utilizing two bulk diffusion couples without intersection points in diffusion paths together with electron probe microanalysis technique, the interdiffusion coefficients along the whole diffusion path in fcc Ni–Cu–Ti alloy at 1273, 1373 and 1473 K are determined by a novel numerical inverse method. Three new diffusion couples together with two initial diffusion couples are used to obtain the diffusivities by Matano-Kirkaldy method. Two sets of atomic mobilities are established based on the interdiffusion coefficients obtained using numerical inverse method and Matano-Kirkaldy method, respectively, through an intelligent modeling in CALTPP (CALculation of ThermoPhysical Properties) program. The reliability of present two sets of atomic mobilities is verified by comprehensive comparisons between the calculated and experimental properties, including diffusivities, concentration profiles and diffusion paths. A preferable agreement is found in the comparisons where the diffusivities are calculated using numerical inverse method. The present investigation of diffusivities for fcc Ni–Cu–Ti alloy demonstrates that two diffusion couples combined with the numerical inverse method can establish a reliable atomic mobility for the targeted alloys.     

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

This paper proposed a hybrid genetic based functional link artificial neural network (HFLANN) with simultaneous optimization of input features for the purpose of solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded of selected features, HFLANN overcomes the nonlinearity nature of problems, which is commonly encountered in single-layer neural networks. The features like simplicity of the architecture and low computational complexity of the network encourage us to use it in classification task of data mining. Further, the issue of statistical tests for comparison of algorithms on multiple datasets, which is even more essential to typical machine learning and data mining studies, has been all but ignored. In this work, we recommend a set of simple, yet safe and robust parametric and nonparametric tests for statistical comparisons of HFLANN with FLANN and RBF classifiers over multiple datasets by an extensive simulation studies.     

An analysis of students�� gaming behaviors in an intelligent tutoring system: predictors and impacts

Students who exploit properties of an instructional system to make progress while avoiding learning are said to be ??gaming?? the system. In order to investigate what causes gaming and how it impacts students, we analyzed log data from two Intelligent Tutoring Systems (ITS). The primary analyses focused on six college physics classes using the Andes ITS for homework and test preparation, starting with the research question: What is a better predictor of gaming, problem or student? To address this question, we developed a computational gaming detector for automatically labeling the Andes data, and applied several data mining techniques, including machine learning of Bayesian network parameters. Contrary to some prior findings, the analyses indicated that student was a better predictor of gaming than problem. This result was surprising, so we tested and confirmed it with log data from a second ITS (the Algebra Cognitive Tutor) and population (high school students). Given that student was more predictive of gaming than problem, subsequent analyses focused on how students gamed and in turn benefited (or not) from instructional features of the environment, as well as how gaming in general influenced problem solving and learning outcomes.     

Design and architecture of an interactive eTextbook – The OpenDSA system

The OpenDSA Project seeks to provide complete instructional materials for data structures and algorithms (DSA) courses. Our vision for a highly interactive eTextbook involves the use of many algorithm visualizations (AVs) and a wide range of interactive exercises with automated assessment. To realize this vision we require a mix of third-party and custom software components that make up a client/server-based web application. The massive amount content development required compels us to adopt an appropriate mix of open-source practices that will encourage broad contribution to the project. In this paper we describe the OpenDSA system architecture and the design goals that led to the present version of the system.     

Robotic batting system — an architecture for learning and dynamic pattern generation

The robot map-making of a scene containing unknown objects of arbitrary shapes is considered. A specific formulation of the terrain acquisition problem due to Lumelsky et al. is investigated. The aim is to develop efficient new algorithms and to understand the basics of the problem. A new, generalized algorithm-GenTer-is developed. GenTer produces a family of algorithms through the variation of a scalar parameter . A version, Terl, of the generalized algorithm may offer a better performance, on the average, than the existing algorithm, the Sightseer Strategy. Terl offers a new feature called hierarchical map-making, through which a good appropriate map can be efficiently created.     

Synchronization of delayed reaction–diffusion neural networks via an adaptive learning control approach

An adaptive learning control strategy is utilized to investigate the synchronization problem for delayed reaction–diffusion neural networks (RDNNs) with unknown time-varying coupling strengths. A novel adaptive synchronization approach is proposed, which is consisted of differential–difference type updating law and feedback control law. By constructing a Lyapunov–Krasovskii-like composite energy functional (CEF), based on the LaSalle invariant principle of functional differential equations, a sufficient condition for the adaptive synchronization of such a system is obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.     

Analysis and solution of a predator–protector–prey multi-robot system by a high-level reinforcement learning architecture and the adaptive systems theory

The area of competitive robotic systems usually leads to highly complicated strategies that must be achieved by complex learning architectures since analytic solutions are unpractical or completely unfeasible. In this work we design an experiment in order to study and validate a model about the complex phenomena of adaptation. In particular, we study a reinforcement learning problem that comprises a complex predator–protector–prey system composed by three different robots: a pure bio-mimetic reactive (in Brook’s sense, i.e. without reasoning and representation) predator-like robot, a protector-like robot with reinforcement learning capabilities and a pure bio-mimetic reactive prey-like robot. From the high-level point of view, we are interested in studying whether the Law of Adaptation is useful enough to model and explain the whole learning process occurring in this multi-robot system. From a low-level point of view, our interest is in the design of a learning system capable of solving such a complex competitive predator–protector–prey system optimally. We show how this learning problem can be addressed and solved effectively by means of a reinforcement learning setup that uses abstract actions to select a goal or target towards which a pure bio-mimetic reactive robot must navigate. The experimental results clearly show how the Law of Adaptation fits this complex learning system and that the proposed Reinforcement Learning setup is able to find an optimal policy to control the defender robot in its role of protecting the prey against the predator robot.     

Simulation-based learning: The learning–forgetting–relearning process and impact of learning history

The results of empirical experiments evaluating the effectiveness and efficiency of the learning–forgetting–relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a teaching tool called the project management trainer (PMT) that simulates a generic dynamic, stochastic project management environment. In this research, we focused on the effect of history recording mechanism on the learning forgetting process. Manual or automatic history recording mechanisms were used by the experimental group, while the control group did not use any history recording mechanism. The findings indicate that for the initial learning phase, the manual mechanism is better than the automatic mechanism. However, for the relearning phase, the break period length influenced the performance after the break. When the break period is short, the manual history keeping mechanism is better, but for a long period break, there is no significant difference. A comparison between the experimental group and the control group revealed that using any history recording mechanism reduced forgetting. Based on the findings, some practical implications of using simulators to improve the learning–forgetting process are discussed.     

Psychophysiological assessment of an adaptive asynchronous human–machine system with the dual-task method

Human error is one of the leading causes of industrial accidents at modern manufacturing production sites. An adaptive asynchronous human–machine system (Async-HMS) was previously proposed as a new mechanism for preventing the reduction of cognitive level by controlling the actual operation period of machines that lead to monotonous behavior. The objective of this paper is to assess Async-HMS from the aspects of person’s current capacity for performing processing resources. A dual-task method consisting of a synchronization task with three periodic fluctuations and four levels of difficulties of mental arithmetic tasks was conducted with Async-HMS on a PC. The three periodic fluctuations are constant periodic fluctuation (Cnst), perceptible periodic fluctuation (Supraliminal), and imperceptible periodic fluctuation (Subliminal). The task performance and the psychophysiological state were assessed through the dual-task method. In an ANOVA analysis, the time shared fraction, which is an index of processing resources devoted to an arithmetic task, had no significant effect on the synchronization task. The mean blood pressure in Subliminal had a significant increase compared with that in Cnst and Supraliminal. These results indicate that subliminal fluctuation evokes sympathetic hyperactivity without affecting processing resources. Implementation of subliminal operation period into industrial machines performing periodic behavior might inhibit monotony and, therefore, prevent human error.     

A teaching–learning based optimization approach for economic design of X-bar control chart

Being simple to use X-bar control chart has been most widely used in industry for monitoring and controlling manufacturing processes. Measurements of a quality characteristic in terms of samples are taken from the production process at regular interval and the sample means are plotted on this chart. Design of a control chart involves the selection of three parameters, namely the sample size (n), the sampling interval (h) and the width of control limits (k). In case of economic design, these three control chart parameters are selected in such a manner that the total cost of controlling the process is the least. The effectiveness of this design depends on the accuracy of determination of these three parameters. In this paper, a new efficient and effective optimization technique named as teaching–learning based optimization (TLBO) has been used for the global minimization of a loss cost function expressed as a function of three variables n, h and k in an economic model of X-bar chart based on unified approach. In this work, the TLBO algorithm has been modified to simplify the tuning of teaching factor. A MATLAB computer program has been developed for this purpose. A numerical example has been solved and the results are found to be better than the earlier published results. Further, the sensitivity analysis using fractional factorial design and analysis of variance have been carried out to identify the critical process and cost parameters affecting the economic design.     

Development and application of a model for analysis and design phases of Web-based system development

The major characteristics of Web systems that shall be taken into account can be summarized as follows: First, the implementation of Web systems shall have a beneficial effect. To meet the requirements of Internet business system, the Web systems shall enable businesses to provide customers with something valuable through the Internet and profit from this process in return[1]. Second, the way to express the contents on the Web matters. To express the contents, Web systems shall introduce mu…     

Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.     

Design and evaluation of a device worn for fall detection and localization: Application for the continuous monitoring of risks incurred by dependents in an Alzheimer’s care unit

The Homecare project, which is part of a research project funded by the French National Research Agency (ANR), aims to define a new multi-sensor monitoring system for the elderly with cognitive disabilities in a care unit. Two subjects were recruited to participate to experimental trials. The main objective of this project is to design and test a complete monitoring system at a real site. It is a new clinical and technical approach which is complex to implement: Homecare is intended to propose a possible technical solution, demonstrate its feasibility and illustrate its use working at a protected site. The system consists of a motion sensor network deployed on the ceiling to monitor motion and an electronic patch worn by the subjects to identify them and detect falls. In order to locate tagged subjects inside the care unit, a network of anchor points is used. From these positions and movement data, an analysis algorithm detects an abnormal situation and alerts the nursing staff in real time. A Web application allows the medical staff to access movements and alarms. The complete monitoring system has been functioning for several months and continuously monitors two patients around the clock. In this paper, we present the implementation of the system, the method of localization inside the care unit, and the characterization of the fall detector, and we show certain results relating to activity data.     

Design and performance evaluation of a biomimetic microrobot for the father–son underwater intervention robotic system

Underwater intervention is a favorite and difficult task for AUVs. To realize the underwater manipulation for the small size spherical underwater robot SUR-II, a father–son underwater intervention robotic system (FUIRS) is proposed in our group. The FUIRS employs a novel biomimetic microrobot to realize an underwater manipulation task. This paper describes the biomimetic microrobot which is inspired by an octopus. The son robot can realize basic underwater motion, i.e. grasping motion, object detection and swimming motion. To enhance the payload, a novel buoyancy force adjustment method was proposed which can provides 11.8 mN additional buoyancy force to overcome the weight of the object in water. Finally, three underwater manipulation experiments are carried out to verify the performance of the son robot. One is carried by swimming motion and buoyancy adjustment; the other two are only carried by buoyancy adjustment. And the experimental results show that the son robot can realize the underwater manipulation of different shape and size objects successfully. The swimming motion can reduce the time cost of underwater manipulation remarkably.