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52nd IEEE Conference on Decision and control, CDC 2013

作者： Thomasson, Andreas Eriksson, Lars Lindell, Tobias Peyton Jones, James C. Spelina, Jill Frey, Jesse Division of Vehicular Systems Department of Electrical Engineering Linkoping University Linkoping SE-581 83 Sweden Center for Nonlinear Dynamics and Control Department of Electrical and Computer Engineering Villanova University Villanova PA 19085 United States Center for Nonlinear Dynamics and Control Villanova University Villanova PA 19085 United States Science Technology Engineering and Mathematics Division Delaware County Community College Media PA 19063 United States Department of Mathematics and Statistics Villanova University Villanova PA 19085 United States

ISBN: (纸本)9781467357173

A new likelihood-based stochastic knock controller, that achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response is presented. Up until now it has only been evaluated using simulations and the main contribution here is the implementation and validation of the knock controller on a five cylinder engine with variable compression ratio. Furthermore, an extension of the fast response strategy and a re-tuning of the controller is shown to improve performance. The controller is validated with respect to its robustness to changes in engine operating condition as well as compression ratio. The likelihood-based controller is demonstrated in engine tests and compared to a conventional controller and it is shown that it is able to operate closer to the knock limit with less variations in control action without increasing the risk of engine damage. © 2013 IEEE.

关键词： controllers

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Corrigendum: A limit formula for the quantum fidelity (2013 J. Phys. A: Math. Theor. 46 025304)

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Journal of Physics A: Mathematical and Theoretical 2014年第32期47卷

作者： Gaetana Spedalieri Christian Weedbrook Stefano Pirandola Department of Computer Science University of York York YO10 5GH UK Center for Quantum Information and Quantum Control Department of Electrical and Computer Engineering and Department of Physics University of Toronto Toronto M5S 3G4 Canada

The full text of this article is available in the PDF provided.

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Solutions to finite horizon cost problems using actor-critic reinforcement learning

Solutions to finite horizon cost problems using actor-critic...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ivo Grondman Hao Xu Sarangapani Jagannathan Robert Babuška Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla USA

Actor-critic reinforcement learning algorithms have shown to be a successful tool in learning the optimal control for a range of (repetitive) tasks on systems with (partially) unknown dynamics, which may or may not be nonlinear. Most of the reinforcement learning literature published up to this point only deals with modeling the task at hand as a Markov decision process with an infinite horizon cost function. In practice, however, it is sometimes desired to have a solution for the case where the cost function is defined over a finite horizon, which means that the optimal control problem will be time-varying and thus harder to solve. This paper adapts two previously introduced actor-critic algorithms from the infinite horizon setting to the finite horizon setting and applies them to learning a task on a nonlinear system, without needing any assumptions or knowledge about the system dynamics, using radial basis function networks. Simulations on a typical nonlinear motion control problem are carried out, showing that actor-critic algorithms are capable of solving the difficult problem of time-varying optimal control. Moreover, the benefit of using a model learning technique is shown.

关键词： Mathematical model Equations Optimal control Learning (artificial intelligence) Approximation algorithms Heuristic algorithms Markov processes

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Model Predictive control with Sequential Linearization Approach for a Nonlinear Time Varying Biological System

Model Predictive Control with Sequential Linearization Appro...

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第25届中国控制与决策会议

作者： Mojtaba Zaare Mehrjerdi Behzad Moshiri Ghassem Amoabediny Arman Ashktorab Babak Nadjar Araabi Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Research Center for New Technologies in Life Science Engineering University of Tehran Department of Life Science Engineering Faculty of New Technologies University of Tehran Department of Chemical Engineering Faculty of Engineering University of Tehran

ISBN: (纸本)9781467355339

As life science progress and its consequences provide human life necessities, controlling these kinds of processes has recently become very important. Unfortunately because of including delay and nonlinear behavior of micro-organisms these processes have nonlinear time varying model and so controlling them is too complicated. These nonlinear dynamics are too slow and so it is possible to linearize the model step by step and apply control signal to the local linearized models. In this paper we design a model predictive controller for each linearized model at time unit and as a result a nonlinear time varying system has been controlled properly. As these localized models are open loop unstable, we have to use close loop paradigm also due to its numerically robustness. Finally result has validated with experimental values and reliability of this approach has been exposed.

关键词： MPC, Sequential Linearization, Biological System control, Aerated Flask

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Model Free Predictive control for a Nonlinear Biological System with Intelligent Optimization Approach

Model Free Predictive Control for a Nonlinear Biological Sys...

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第25届中国控制与决策会议

作者： Mojtaba Zaare Mehrjerdi Ghassem Amoabediny Behzad Moshiri Arman Ashktorab Babak Nadjar Araabi Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Research Center for New Technologies in Life Science Engineering University of Tehran Department of Life Science Engineering Faculty of New Technologies University of Tehran Department of Chemical Engineering Faculty of Engineering University of Tehran

ISBN: (纸本)9781467355339

Today the importance of life science and its related processes are undeniable. Modeling and control of these kind of processes are too complicate because of existence of delay in growth and also nonlinear behavior of micro-organisms. Model predictive control is one of the most popular advanced controlling strategies in this industry, however its dependence on accurate model for predicting future input and output values is limitating. If there is a way that could predict the future values of the process properly, it is possible to overcome to the existing challenges. In this paper we design a model free predictive controller by using a trained recurrent neural network as a predictor for prediction stage at MPC and using GA for solving the associated optimization problem that result the optimal control signal sequence.

关键词： NNMPC GA Biological System Aerated Flask

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Evaluating task scheduling in hadoop-based cloud systems

Evaluating task scheduling in hadoop-based cloud systems

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IEEE International Conference on Big Data

作者： Shengyuan Liu Jungang Xu Zongzhen Liu Xu Liu College of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China Department of Computer Science Rice University Houston America

ISBN: (纸本)9781479912940

Nowadays, private clouds are widely used for resource sharing. Hadoop-based clusters are the most popular implementations for private clouds. However, because workload traces are not publicly available, few previous work compares and evaluates different cloud solutions with publicly available benchmarks. In this paper, we use a recently-released Cloud benchmarks suite - CloudRank-D to quantitatively evaluate five different Hadoop task schedulers, including FIFO, capacity, naïve fair sharing, fair sharing with delay, and HOD (Hadoop On Demand) scheduling. Our experiments show that with an appropriate scheduler, the throughput of a private cloud can be improved by 20%.

关键词： Cloud computing Delays Benchmark testing Scheduling Data mining Throughput Data warehouses

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Postprocessing for quantum random-number generators: Entropy evaluation and randomness extraction

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Physical Review A 2013年第6期87卷 062327-062327页

作者： Xiongfeng Ma Feihu Xu He Xu Xiaoqing Tan Bing Qi Hoi-Kwong Lo Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Center for Quantum Information and Quantum Control Department of Physics and Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Mathematics College of Information Science and Technology Jinan University Guangzhou Guangdong China

Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with classical randomness due to classical noises. To distill this quantum randomness, one needs to quantify the randomness of the source and apply a randomness extractor. Here, we propose a generic framework for evaluating quantum randomness of real-life QRNGs by min-entropy, and apply it to two different existing quantum random-number systems in the literature. Moreover, we provide a guideline of QRNG data postprocessing for which we implement two information-theoretically provable randomness extractors: Toeplitz-hashing extractor and Trevisan's extractor.

关键词： Entropy

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A driver face monitoring system for fatigue and distraction detection

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International Journal of Vehicular Technology 2013年第1期2013卷

作者： Sigari, Mohamad-Hoseyn Fathy, Mahmood Soryani, Mohsen Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran 14399 Iran Computer Engineering Department Iran University of Science and Technology Tehran 16846 Iran

Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection;the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction. © 2013 Mohamad-Hoseyn Sigari et al.

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Quantum hacking of a continuous-variable quantum-key-distribution system using a wavelength attack

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Physical Review A 2013年第6期87卷 062329-062329页

作者： Jing-Zheng Huang Christian Weedbrook Zhen-Qiang Yin Shuang Wang Hong-Wei Li Wei Chen Guang-Can Guo Zheng-Fu Han Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China Center for Quantum Information and Quantum Control Department of Electrical and Computer Engineering and Department of Physics University of Toronto Toronto Ontario M5S 3G4 Canada

The security proofs of continuous-variable quantum key distribution are based on the assumptions that the eavesdropper can neither act on the local oscillator nor control Bob's beam splitter. These assumptions may be invalid in practice due to potential imperfections in the implementations of such protocols. In this paper, we consider the problem of transmitting the local oscillator in a public channel and propose a wavelength attack which allows the eavesdropper to control the intensity transmission of Bob's beam splitter by switching the wavelength of the input light. Specifically we target continuous-variable quantum key distribution systems that use the heterodyne detection protocol using either direct or reverse reconciliation. Our attack is proved to be feasible and renders all of the final keys shared between the legitimate parties insecure, even if they have monitored the intensity of the local oscillator. To prevent our attack on commercial systems, a simple wavelength filter should be randomly added before performing monitoring detection.

关键词： Quantum cryptography

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