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Solar power satellites: Historical perspectives with a look to the future

Solar power satellites: Historical perspectives with a look ...

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AIAA Space 2007 Conference

作者： Laracy, Joseph R. Bador, Damien Adams, Danielle Weigel, Annalisa Chambers, Richard Kwon, Daniel Proudfoot, David Qu, Shen Shoepe, Ted Massachusetts Institute of Technology Cambridge MA 02139 United States Complex Systems Research Laboratory Engineering Systems Division Bldg. 33-407 AIAA Lean Aerospace Initiative Aeronautics/Astronautics and Engineering Systems Division Bldg. 41-205 Aeronautics/Astronautics and Technology and Policy Program 28 Forest Street Somerville MA 02143 United States Aeronautics/Astronautics and Engineering Systems Bldg. 33-404

ISBN: (纸本)1563479079

Since the late 1960s, there has been interest in the United States, and later in other nations, to capture solar energy in space and efficiently transmit it back to Earth. Starting with his seminal paper in 1968, Dr. Peter Glaser began architecting a prototype system that was further explored by the US Department of Energy in the Concept Development and Evaluation program. This initial study showed that the project was very ambitious and fraught with technical, social, and economic uncertainties. Energy economics and the lack of a reliable, high frequency space launch capability brought most research to a halt in the 1990s. This paper proposes a rational technical strategy to refocus Solar Power Satellite (SPS) research. It suggests a 30 year timeline for program milestones and analyzes potential technical performance. Real options analysis is used to manage uncertainty and permits the exploration of possible futures that are dependent on launch costs and electricity market prices. We propose that the U.S. can make progress toward implementing a small scale SPS system within several decades if work is begun now on technology development and on addressing societal concerns.

关键词： Satellites

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Design for self-organizing fuzzy neural networks using a novel hybrid learning algorithm

Design for self-organizing fuzzy neural networks using a nov...

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Congress on Evolutionary Computation

作者： Liang Zhao Fei-Yue Wang The Key Laboratory for Complex Systems and Intelligence cience Chinese Academy and Sciences Beijing China The Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA

A novel hybrid learning algorithm to implement automatically structure identification and parameter optimization for designing a self-organizing TSK-Type fuzzy neural network (FNN) is proposed in this paper. It includes mean shift clustering algorithm (MSC) and mean firing strength method (MFS) which are employed to identify the network structure of fuzzy neural network (FNN) and the particle swarm optimization enhancing genetic algorithm (PSO-EGA) and the modified back-propagation algorithm (MBP) which are applied to learn the free parameters of it. That is, the MSC is used to partition the input vector space to generate initial network structure. Then the MFS is used to prune the least important rule neurons of initial structure and generate optimal network structure. After the structure identification is completed, the PSO-EGA is adopted to perform a global search in free parameter space of the FNN and seek a near- optimal initial free parameters point for the next stage. Then, it is considered as the initial weights of the FNN and the MBP is used to perform the learning process until a terminal condition is satisfied. The simulation experiment has verified that the proposed hybrid learning algorithm achieves superior performance in learning accuracy than those of some traditional methods.

关键词： Algorithm design and analysis Fuzzy neural networks Clustering algorithms Neural networks Fuzzy systems Particle swarm optimization Neurons Genetic algorithms Partitioning algorithms Clustering methods

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The design of self-organizing fuzzy neural networks based on Ga-ecpso and MBP

The design of self-organizing fuzzy neural networks based on...

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IEEE International Conference on systems, Man and Cybernetics

作者： Liang Zhao Fei-Yue Wang Key Laboratory for Complex Systems and Intelligence Science Chinese Academy and Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA

A novel hybrid learning algorithm which automates the design of the FNNs is proposed in this paper. It is based on two-stage learning process. First, mean shift clustering (MSC) and mean firing strength (MFS) are combined to identify the structure. The MSC is used to generate the initial network structure and parameters of each neuron and MFS refines the initial network to produce the optimal network structure. Next, genetic algorithm enhancing chaotic particle swarm optimization (GA-ECPSO) and modified back-propagation (MBP) are proposed to learn the free parameters. The GA-ECPSO is used to seek the near-optimal parameters solution and MBP continues the learning process until the terminal condition is satisfied. The simulation experiment demonstrates the superior performance of the algorithm.

关键词： Fuzzy neural networks Fuzzy sets Clustering algorithms Fuzzy systems Training data Partitioning algorithms Algorithm design and analysis Genetic algorithms Particle swarm optimization Neural networks

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Design for recurrent fuzzy neural networks using MSC-MFS and PSO-MBP

Design for recurrent fuzzy neural networks using MSC-MFS and...

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IEEE International Conference on systems, Man and Cybernetics

A novel hybrid learning algorithm for designing a TSK-type recurrent fuzzy neural network (RFNN) is proposed in this paper. The whole designing process includes two stages, i.e., structure identification and parameter optimization. The structure identification includes mean shift clustering (MSC) and mean firing strength (MFS). The MSC is used to partition the input space and the mean firing strength (MFS) is employed to prune the redundant rule neurons. After the structure identification is performed, we adopt the PSO to adjust the free parameters of the RFNN and generate the near-optimal free parameters solution. Then, MBP is used to continue the learning process until the terminal condition is satisfied. The proposed hybrid learning algorithm achieves superior performance in learning accuracy.

关键词： Fuzzy neural networks Recurrent neural networks Neural networks Neurons Fuzzy logic Feeds Feedforward neural networks Algorithm design and analysis Clustering algorithms Partitioning algorithms

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Pose estimation of a moving humanoid using Gauss-Newton optimization on a manifold

Pose estimation of a moving humanoid using Gauss-Newton opti...

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IEEE-RAS International Conference on Humanoid Robots

作者： Michel Sarkis Klaus Diepold Knut Huper Institute for Data Processing Technische Universität München Munich Germany Systems Engineering and Complex Systems Program NICTA Canberra Australia Department of Information Engineering RSISE Australian National University Canberra ACT Australia

Extracting the motion parameters of a moving humanoid is an important issue in machine vision. This is due to the need of emerging applications such as telepresence or robot navigation. In the context of telepresence, we consider a robot to be equipped with a stereo camera the pose of which has to be determined in a fast and robust way. The key issue is to compute the robust estimates of the (3times3) compatible essential matrices of the stereo cameras since the motion can then be uniquely estimated. In this paper, a robust technique that computes these entities is proposed under the assumption that the images are calibrated. The algorithm is based on the five-point relative pose problem using an optimization technique on a manifold. It determines these matrices by intersecting three essential manifolds. Test results show that the proposed method has a quadratic convergence rate and delivers accurate results. In addition, the algorithm has shown to exhibit better performance when compared to standard algorithms.

关键词： Newton method Least squares methods Recursive estimation Robustness Robot vision systems Cameras Motion estimation Manifolds Machine vision Navigation

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Ramp metering based on adaptive critic designs

Ramp metering based on adaptive critic designs

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： Xu, Jing Yu, Wen-Sheng Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing 100080 China University of Arizonas Systems and Industrial Engineering Department Program in Advanced Research of Complex Systems Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences

ISBN: (纸本)1424400945

This paper presents the technique of adaptive critic designs (ACDs) for resolving some traffic control problems, such as urban traffic signal control, ramp metering, highway speed control and so on. ACDs are adaptive optimal methods, whose main idea is to approximate dynamic programming solutions by using a function approximation structure such as neural networks to approximate the cost function or its derivatives. They combine the positive features of both the traditional dynamic programming (e.g., optimality and feedback) and the neural networks (e.g., numerical and real-time properties). Due to these exciting merits, they are promising in handling the traffic control problems which have strong nonlinear, stochastic and real-time characteristics. In this paper, a close look at two typical adaptive critic designs is conducted. Specifically, we demonstrate the use of a specific adaptive critic design for the local on-ramp metering control problem. Simulation results indicate that the proposed controller has good performance. © 2006 IEEE.

关键词： Traffic control

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Newton-like methods for parallel independent component analysis

Newton-like methods for parallel independent component analy...

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2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

作者： Shen, Hao Hüper, Knut Systems Engineering and Complex Systems Research Program National ICT Australia Canberra ACT 2601 Australia Department of Information Engineering RSISE Australian National University Canberra ACT 0200 Australia

ISBN: (纸本)1424406560

Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear ICA problem from an algorithmic point of view. It is well known that after a pre-whitening process, linear ICA problem can be solved via an optimisation approach on a suitable manifold. FastICA is one prominent linear ICA algorithm for solving the so-called one-unit ICA problem, which was recently shown by the authors to be an approximate Newton's method on the real projective space. To extract multiple components in parallel, in this paper, we propose an approximate Newton-like ICA algorithm on the orthogonal group. The local quadratic convergence properties are discussed. The performance of the proposed algorithms is compared with several existing parallel ICA algorithms by numerical experiments. © 2006 IEEE.

关键词： Independent component analysis

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An agent-based controller for vehicular automation

An agent-based controller for vehicular automation

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： He, Feng Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

In this paper, we present a design of controller for vehicular automation based on mobile agent technology. In this approach, the control goal is conducted by coordinating active or default agents based on sensory feedback and vehicle conditions. A hosting mechanism determines which agent has the right to control the vehicle and make the decision. Those agents are divided into two groups: default agents which are developed by fuzzy rules, and active agents which are produced by neural networks. Fuzzy agents locate in the vehicle and neural network agents locate in remote server. Two types of agents have similar function but different performance, so those agents produced by neural network can transfer back to the vehicle after learning to replace default fuzzy agents according to mobile agent technology. The performance of the proposed technique is illustrated by simulation studies of a vehicle longitudinal control system. © 2006 IEEE.

关键词： Land vehicle propulsion

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Modeling and analysis of artificial transportation system based on multi-agent technology

Modeling and analysis of artificial transportation system ba...

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： He, Feng Miao, Qinghai Li, Yuantao Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

This paper presents the multi-agent architecture for artificial transportation system. In this architecture, Petri net is used as basic model to represent agents. At an intersection, the agents are divided into two groups: one for the traffic-signal and the other for the vehicle flow, which are integrated to represent the behavior of this intersection. In addition, those agents can be used as the modularity to represent urban network of more scale. To coordinate different intersection agents, game theory is used to design coordination strategy between agents. The iterated elimination of strictly dominated strategies algorithm is presented to find Nash equilibrium. © 2006 IEEE.

关键词： Transportation

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Modeling interactions in artificial transportation systems using petri net

Modeling interactions in artificial transportation systems u...

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： Zhu, Fenghua Wang, Zhixue Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

Agents and interactions between them are two key roles in modeling artificial transportation systems. This paper presents a formal theory to describe interactions between agents, which, together with the well developed models to describe components as agents, completes the first step towards establishing the formal structure for artificial transportation systems. By describing individual agents and the interactions between them as independent simple sequence processes and directive communication channels respectively, we models the whole system as one sequential communications system, which is one special type of Petri net. The theory is flexible enough to model diversified interactions between agents, and the resultant model possesses favorable properties. A case study of modeling road network has been conducted for the purpose of illustration. © 2006 IEEE.

关键词： Transportation

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