Accessible network of performance index tables

In each of the information processing apparatuses connected to each other via a network, there is arranged a quality of service (QOS) table to which functions and performance thereof are registered. When an information processing apparatus is additionally linked with the network, a QOS table thereof is automatically registered to a local directory of the network such that an agent converts the contents of the QOS table into service information to be supplied via a user interface to the user. Thanks to the operation, information of functions and performance of each information processing apparatus connected to the network is converted into service information for the user. Consequently, the user can much more directly receive necessary services.     

Algebraic conditions of stability for Hopfield neural network

Using the relationship between the resistance, capacitance and current in Hopfield neural network, and the properties of sigmoid function, this paper gives the terse, explicit algebraical criteria of global exponential stability, global asymptotical stability and instability. Then this paper makes clear the essence of the stability that Hopfield defined, and provides a theoretical foundation for the design of a network.     

Characteristics of YouTube network traffic at a campus network – Measurements,models, and implications

User-Generated Content has become very popular since new web services such as YouTube allow for the distribution of user-produced media content. YouTube-like services are different from existing traditional VoD services in that the service provider has only limited control over the creation of new content. We analyze how content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. Based on these measurements, we analyzed the duration and the data rate of streaming sessions, the popularity of videos, and access patterns for video clips from the clients in the campus network. The analysis of the traffic shows that trace statistics are relatively stable over short-term periods while long-term trends can be observed. We demonstrate how synthetic traces can be generated from the measured traces and show how these synthetic traces can be used as inputs to trace-driven simulations. We also analyze the benefits of alternative distribution infrastructures to improve the performance of a YouTube-like VoD service. The results of these simulations show that P2P-based distribution and proxy caching can reduce network traffic significantly and allow for faster access to video clips.     

network网络

现在每次打开QQ时，它都会提示未安装KB936181系统补丁，我已经将其安装到系统中了，但是第二次启动后，它依旧会弹出该提示。请问这是什么原因？     

network网络

无法显示验证码图片 Q为了做系统备份,我使用优化软件对系统进行了一遍优化操作,再使用杀毒软件对系统进行了病毒全面检测。现在登录任何一个带验证码的论坛,验证码都显示为一个红叉。请问这是什么原因？     

network网络

禁止“QQ医生”检查木马；无法阅读RTF格式的邮件；不显示我所在城市；让IE再次提示密码保存；修改《迅雷》网吧版的下载参数。     

Stabilization of stochastic Hopfield neural network with distributed parameters

In this paper, the stability of stochastic Hopfield neural network with distributed parameters is studied. To discuss the stability of systems, the main idea is to integrate the solution to systems in the space variable. Then, the integration is considered as the solution process of corresponding neural networks described by stochastic ordinary differential equations. A Lyapunov function is constructed and Ito formula is employed to compute the derivative of the mean Lyapunov function along the systems, with respect to the space variable. It is difficult to treat stochastic systems with distributed parameters since there is no corresponding Ito formula for this kind of system. Our method can overcome this difficulty. Till now, the research of stability and stabilization of stochastic neural networks with distributed parameters has not been considered.     

network网络

《百度Hi》无法拖搜索,双网页同屏浏览,更改Antivir自动更新时间,将Foxmail中的邮件导入到Outlook中,《飞信》中实现回车发送消息,     

Anomaly detection of network traffic based on autocorrelation principle

Network anomalies caused by network attacks can significantly degrade or even terminate network services. A Real-time and reliable detection of anomalies is essential to rapid anomaly diagnosis, anomaly mitigation, and malfunction recovering. Unlike most detection methods based on the statistical analysis of the packet headers （Such as IP addresses and ports）, a new approach only using network traffic volumes is proposed to detect anomalies reliably. Our method is based on autocorrelation function to judge whether anomalies have happened. In details, the correlation coefficients of normal and anomaly data fluctuate slightly respectively, while those of the overlapped data composed of them fluctuate greatly. Experimental results on network traffic volumes transformed from 1999 DARPA intrusion evaluation data set show that this method can effectively detect network anomalies, while avoiding the high false alarms rate.     

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

Control of robotic manipulators—a neural network approach

A neural-network-based scheme is used for the control of a robotic manipulator. The main idea is that, by using a neural network to learn the characteristics of the robot system (or specifically its inverse dynamics), accurate trajectory following and good performance results are obtained. However, the traditional back-propagation algorithm commonly used for control and identification of nonlinear systems suffers from a slow rate of convergence. We investigate the effect of adusting the slope of the activation function (the node nonlinearity) on the performance of a back-propagation algorithm. It is shown that learning speed is increased significantly by making the slope of non-linearity adaptive. The results demonstrate that the proposed method gives better error minimization and faster convergence. The suggested method is applied to a two-link robotic manipulator. The resulting controller is sufficiently robust with respect to the changing conditions.     

Effects of network communications on a class of learning controlled non-linear systems†

In this article, an iterative learning control approach is proposed for a class of sampled-data non-linear systems over network communication channels. The effects of constant time delays and stochastic packet loss are discussed and demonstrated by simulation results. The focus of this article is to study the remote control problems when the environment is periodic or repeatable over iterations in a fixed finite interval. Because of the existence of time delays and packet loss in input and output signal transmissions, it is not trivial to accomplish the remote stabilisation task of any system. Moreover, to track a desired trajectory through a remote controller is even more difficult. Previous cycle-based learning method is incorporated into the network-based control for a class of non-linear systems which satisfies a global Lipschitz condition. The convergence property of this approach is proven. Furthermore, the convergence in the iteration domain is also discussed when there exists packet loss in both transmission channels of the system. Finally, one single-link rigid robot is given as an example to show the effectiveness of the proposed approach.     

Neural network’s selection of color in UI design of social software

Neural Computing and Applications - In recent years, the design of social software UI has become a design research focus in the field of design. Color affects many factors in UI design. However,...     

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.     

An OSCA™ architecture characterization of network functionality and data

This article demonstrates that the Bellcore OSCA architecture can serve as a basis for the metaarchitecture for software architectures that must support interoperability among functionality that traditionally has resided in operation systems and functionality that traditionally has resided in network elements (NE) (i.e., interoperability among operations functionality and network functionality). The need for this interoperability is driven by such telephone company business needs, as the need for customers to access operations capabilities spanning operations systems and NEs, the need for new service offerings to span operations systems and NEs, the need for a flexible environment for service development, and the need to manage all corporate data as a company resource. As a result, it is becoming beneficial to apply interoperability requirements to the network functionality that interfaces with operations systems. Therefore, it is reasonable to apply the OSCA architecture to network functionality. This article applies the OSCA architecture interoperability principles of separation of concerns to current and emerging network functionality. It demonstrates that this functionality can be partitioned among the three OSCA architecture layers of corporate data, processing, and user, and that there are a number of benefits to applying the OSCA interoperability principles to network functionality.