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检索条件"主题词=Network Programming"

共 295 条记录，以下是11-20 订阅

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OSDF: A Framework For Software Defined network programming 15

OSDF: A Framework For Software Defined Network Programming

引用

15th IEEE Annual Consumer Communications and networking Conference (CCNC)

作者： Comer, Douglas Rastegarnia, Adib Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781538647905

Using SDN to configure and control a multi-site network involves writing code that handles low-level details. We describe preliminary work on a framework that takes a network description and set of policies as input, and handles all the details of deriving routes and installing flow rules in switches. The paper describes key software components and reports preliminary results.

关键词： Software Defined network network programming OpenFlow network Operating System

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Two-Stage Reinforcement Learning Based on Genetic network programming for Mobile Robot

Two-Stage Reinforcement Learning Based on Genetic Network Pr...

引用

Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Sendari, Siti Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9784907764401

This paper studies the adaptability of Two-Stage Reinforcement Learning based on Genetic network programming for a mobile robot to cope with sudden changes in the environments, i.e., sensors break suddenly in the implementation. Two-Stage Reinforcement Learning (TSRL) uses two kinds of learning, that is, (1) sub node selection proposed in the conventional Genetic network programming with Reinforcement Learning and (2) branch connection selection. As a result, when the sudden changes occur in the environments, the proposed method can determine the actions more appropriately.

关键词： network programming Mobile robots learning (artificial intelligence) Learning Changing environments Environments

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Comparative Association Rules Mining using Genetic network programming(GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems

Comparative Association Rules Mining using Genetic Network P...

引用

IEEE Congress on Evolutionary Computation

作者： Wei, Wei Zhou, Huiyu Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424418220

In this paper, we present a method of comparative association rules mining using Genetic network programming (GNP) with attributes accumulation mechanism in order to uncover association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining approach to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute difference of confidences among different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. For the large attributes case, the calculation is very time-consuming, when the conventional GNP based data mining is used. So, we have proposed an attribute accumulation mechanism to improve the performance. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.

关键词： traffic systems mining Association rules economic indicators data mining network programming RECOVERY CATEGORY absolute difference

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A Multitasks Learning Approach to Autonomous Agent based on Genetic network programming

A Multitasks Learning Approach to Autonomous Agent based on ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI)

作者： Yang, Yang Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781467315098

The standard methodology in machine learning is to learn one problem at a time. But, many real-world problems are complex and have multitasks, and it is a bit hard to learn them well by one machine learning approach. So, the simultaneous learning of several tasks has been considered, that is, so-called multitask learning. This paper describes a new approach to the autonomous agent problem using the multitask learning scheme based on Genetic network programming (GNP), called ML-GNP, where each GNP is used to learn one corresponding task. ML-GNP has some charateristics, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results illustrate that ML-GNP can give much better performance than learning all the tasks of the problem by one GNP algorithm.

关键词： Multitasks Learning Approach Genetic network programming network programming Intelligent agents PARALLEL PROCESSING (COMPUTERS) economic indicators Religious Missions Learning Autonomy

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Continuous Traveling Time Prediction using Genetic network programming-based Data Mining and Neural networks

Continuous Traveling Time Prediction using Genetic Network P...

引用

Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Zhang, Qin Zhou, Huiyu Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9784907764401

In this paper, a method combining Genetic network programming-based class association rule mining and Neural networks is proposed for continuous traveling time prediction. Genetic network programming (GNP)[1], as an extended algorithm of GP[2], shows its advantage because of its graph structures. GNP is used to generate class association rules[3]. Then, the average matching degree of the data with the rules is calculated. Lastly, the back propagation algorithm of Neural networks[4] is utilized in order to acquire the concrete prediction of the traveling time.

关键词： Neural network network programming data mining GP2 gene

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A network programming language based on concurrent processes and regular expressions

A network programming language based on concurrent processes...

引用

International Conference on Software Engineering

作者： Hatori, Kenta Hiraki, Kei ISP Res Inst Syst Planning Inc Syst Dev Div Tokyo Japan

ISBN: (纸本)9780889866416

We propose a new programming language Preccs for networking and implementing communication protocols. Preccs is based on ideas of concurrent processes and regular expressions. We designed the language for simple and intuitive description of communication protocols. Then, we have developed a Preccs compiler that generates C code from a Preccs source program. The generation of C code with the Preccs compiler will make it possible for reliable communication programs development in a short period. In order to evaluate the effectiveness of our approach, we have implemented a simple HTTP server and a simple VoIP application in Preccs. Both of them are implemented in a few lines of code.

关键词： network programming concurrent processes regular expressions domain specific languages

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Elevator Group Supervisory Control System using Genetic network programming with reinforcement learning

Elevator Group Supervisory Control System using Genetic Netw...

引用

IEEE Congress on Evolutionary Computation

作者： Zhou, J Eguchi, T Hirasawa, K Hu, JL Markon, S Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka Japan

ISBN: (纸本)0780393635

Since Genetic network programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world systems like Elevator Group Supervisory Control System (EGSCS) which is a very large scale stochastic dynamic optimization problem. From those researches, most of the significant features of GNP have been verified comparing to Genetic Algorithm (GA) and Genetic programming (GP). Especially, the improvement of the performances on EGSCS using GNP showed an interesting and promising prospect in this field. On the other hand, some studies based on GNP with Reinforcement Learning (RL) revealed a better performance over conventional GNP on some problems such as tile-world models. As a basic study, Reinforcement Learning is introduced in this paper expecting to enhance EGSCS controller using GNP.

关键词： control system analysis computing controllers directed graphs dynamic programming genetic algorithms learning (artificial intelligence) lifts stochastic programming elevator group supervisory control system controller evolutionary computation genetic net control system analysis computing Elevators network programming Genetic Algorithm (GA) evolutionary algorithm directed graphs lifts Controllers dynamic programming Learning Supervisory control stochastic programming Stochastic models

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Securing the deluge network programming system

Securing the deluge network programming system

引用

5th International Conference on Informational Processing in Sensor networks

作者： Dutta, Prabal K. Hui, Jonathan W. Chu, David C. Culler, David E. Univ Calif Berkeley Dept Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)1595933344

A number of multi-hop, wireless, network programming systems have emerged for sensor network retasking but none of these systems support a cryptographically-strong, public-key-based system for source authentication and integrity verification. The traditional technique for authenticating a program binary, namely a digital signature of the program hash, is poorly suited to resource-contrained sensor nodes. Our solution to the secure programming problem leverages authenticated streams, is consistent with the limited resources of a typical sensor node, and can be used to secure existing network programming systems. Under our scheme, a program binary consists of several code and data segments that are mapped to a series of messages for transmission over the network. An advertisement, consisting of the program name, version number, and a hash of the very first message, is digitally signed and transmitted first. The advertisement authenticates the first message, which in turn contains a hash of the second message. Similarly, the second message contains a hash of the third message, and so on, binding each message to the one logically preceding it in the series through the hash chain. We augmented the Deluge network programming system with our protocol and evaluated the resulting system performance.

关键词： wireless sensor networks dissemination protocols network programming authenticated broadcast security

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Java network programming and Distributed Computing

引用

2002年

作者： David Reilly Michael Reilly

ISBN: (纸本)0201710374;9780201710373

Java network programming and Distributed Computing is an accessible introduction to the changing face of networking theory, Java technology, and the fundamental elements of the Java networking API. With the explosive growth of the Internet, Web applications, and Web services, the majority of today's programs and applications require some form of networking. Because it was created with extensive networking features, the Java programming language is uniquely suited for network programming and distributed computing.

关键词： network programming Parallel Distributed and Supercomputing Java Web programming

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Traffic flow prediction with Genetic network programming

Traffic flow prediction with Genetic Network Programming

引用

SICE Annual Conference

作者： Wei Wei Huiyu Zhou Manoj Kanta Mainali Kaoru Shimada Shingo Mabu Kotaro Hirasawa Graduate School of Information Waseda University Fukuoka Japan

ISBN: (纸本)9784907764302

In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using genetic network programming(GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of graphic structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models for N-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.

关键词： Traffic flow forecasting network programming economic indicators Memory Traffic data penalty function optimal solution

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