State Key Laboratory of Management and Control for Complex Systems

Laboratory of, China

State Key Laboratory of Management and Control for Complex Systems

Laboratory of, China
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News Article | May 16, 2017
Site: www.eurekalert.org

Smart homes need smart batteries. Current systems overindulge on power, which can shorten the life of batteries and the devices they power. Future batteries may get an intelligence boost, though. A collaborative research team based in Beijing, China, has proposed a novel programming solution to optimize power consumption in batteries. The scientists, from the Institute of Automation, the Chinese Academy of Sciences, and the School of Automation and Electrical Engineering at the University of Science and Technology Beijing, published their results in IEEE/CAA Journal of Automatica Sinica (JAS), a joint publication of the IEEE and the Chinese Association of Automation. "In smart home energy management systems, the intelligent optimal control of [the] battery is a key technology for saving power consumption," Prof. Qinglai Wei, with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, wrote in the paper. To develop a system in which batteries can learn and optimize their power consumption, Wei and his team turned to adaptive dynamic programming. This method breaks down one big problem - how to best use batteries in smart home systems - into smaller problems. The answer to each small problem builds into the answer to the big problem, and, as the circumstances of the question change, the system can examine all the small answers to see if and how the big answer adapts. Wei and his team are the first to use this method while also considering the physical charging and discharging constraints of the battery. The algorithm learns which inputs, such as the demand for power from a device, lead to which outputs, such as providing power. By continually questioning the link between input and output, the algorithm learns more about the best times to charge and to discharge to limit power consumed from the grid. To extend the battery life, every iteration of learning is constrained by the understanding that the battery can only charge and discharge to certain limits. Anything more, and the battery could experience excessive wear. "The battery [makes] decisions to meet the demand of the home load according to the real-time electricity rate," Wei wrote, noting that the objective of optimal control is to find the ideal balance for each battery state (charging, discharging, and idle) within the battery's constraints, while still minimizing the power needed from the grid. To further extend the lifetime of batteries in smart home systems, Wei and his team will next examine how the damage caused by frequently switching between charging and discharging modes may be avoided. Fulltext of the paper is available: http://ieeexplore. http://html. IEEE/CAA Journal of Automatica Sinica (JAS) is a joint publication of the Institute of Electrical and Electronics Engineers, Inc (IEEE) and the Chinese Association of Automation. JAS publishes papers on original theoretical and experimental research and development in all areas of automation. The coverage of JAS includes but is not limited to: Automatic control/Artificial intelligence and intelligent control/Systems theory and engineering/Pattern recognition and intelligent systems/Automation engineering and applications/Information processing and information systems/Network based automation/Robotics/Computer-aided technologies for automation systems/Sensing and measurement/Navigation, guidance, and control. To learn more about JAS, please visit: http://ieeexplore.


Kang M.,State Key Laboratory of Management and Control for Complex Systems | Kang M.,Qingdao Academy of Intelligent science | Hua J.,Qingdao Academy of Intelligent science | De Reffye P.,CIRAD - Agricultural Research for Development | And 2 more authors.
Proceedings - 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, FSPMA 2016 | Year: 2016

Plant architectures generally display structural variations among individuals. Stochastic FSPMs have been developed to capture such feature, but calibrating such models is a challenging issue. For GreenLab model, parameter identification has been achieved on several crops and trees, but the estimation of functional parameters is mostly limited to plants with deterministic development. In this work, we propose a methodological framework allowing the efficient FSPM parameter estimation for stochastic ramified plants. We focus on the randomness in three kinds of meristem activities in plant development: Growth, death and branching. Concepts of organic series and potential structure are introduced to build the fitting target as well as corresponding model output. We show that, with a limited set of sampled plants (here from simulation), using a few organic series, the inverse method retrieves well the parameter values (the original parameter set being known here). Requiring the concept of physiological age and the assumption of common biomass pool, the proposed approach provides a solution of solving source-sink functions of complex plant architectures, with a novel simplified way of plant sampling. The proposed parameter estimation frame is promising, since this in silico process mimics the procedure of calibrating model for real plants in a stand. Estimating parameters on stochastic plant architectures opens a new range of coming applications. © 2016 IEEE.


Zhang Z.,State Key Laboratory of Management and Control for Complex Systems | Zheng X.,State Key Laboratory of Management and Control for Complex Systems | Zeng D.D.,State Key Laboratory of Management and Control for Complex Systems | Zeng D.D.,University of Arizona | Leischow S.J.,Mayo Medical School
PLoS ONE | Year: 2015

This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms "tobacco" and "lung cancer" from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use. © 2015 Zhang et al.


Wang F.-Y.,State Key Laboratory of Management and Control for Complex Systems
IEEE Intelligent Systems | Year: 2012

Some observers raise the spectre of a world after a "singularity" in which machine intelligence exceeds human intelligence. That may be unlikely, but the threat of technological capacity still exists. Perhaps the idea of an "open society" combined with cyberspace and intelligent systems could produce a "computational society" that is open, impartial, and fair. © 2012 IEEE.


Shi C.,State Key Laboratory of Management and Control for Complex Systems | Wang C.,State Key Laboratory of Management and Control for Complex Systems | Xiao B.,State Key Laboratory of Management and Control for Complex Systems | Zhang Y.,State Key Laboratory of Management and Control for Complex Systems | And 2 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2013

Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific structure information, we use part-based tree-structure to model each type of character so as to detect and recognize the characters at the same time. While for word recognition, we build a Conditional Random Field model on the potential character locations to incorporate the detection scores, spatial constraints and linguistic knowledge into one framework. The final word recognition result is obtained by minimizing the cost function defined on the random field. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods significantly both for character detection and word recognition. © 2013 IEEE.


Zhang Z.,State Key Laboratory of Management and Control for Complex Systems | Wang C.,State Key Laboratory of Management and Control for Complex Systems | Xiao B.,State Key Laboratory of Management and Control for Complex Systems | Zhou W.,State Key Laboratory of Management and Control for Complex Systems | And 2 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2013

In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtual views are concatenated into an infinite-dimensional feature to characterize continuous changes from the source to the target view. However, these infinite-dimensional features cannot be used directly. Thus, we propose a virtual view kernel to compute the value of similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, there are a lot of unlabeled samples from the target view, which can be utilized to improve the performance of classifiers. Thus, we present a constraint strategy to explore the information contained in the unlabeled samples. The rationality behind the constraint is that any action video belongs to only one class. Our method is verified on the IXMAS dataset, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods. © 2013 IEEE.


Wang F.-Y.,State Key Laboratory of Management and Control for Complex Systems
IEEE Intelligent Systems | Year: 2012

The flood of big data in cyberspace will require immediate actions from the AI and intelligent systems community to address how we manage knowledge. Besides new methods and systems, we need a total knowledge-management approach that willl require a new perspective on AI. We need Merton's systems in which machine intelligence and human intelligence work in tandem. This should become a normal mode of operation for the next generation of AI and intelligent systems. © 2001-2011 IEEE.


Li L.,State Key Laboratory of Management and Control for Complex Systems | Li L.,Harbin Institute of Technology | Li L.,University of Chinese Academy of Sciences | Zeng D.,State Key Laboratory of Management and Control for Complex Systems | And 3 more authors.
Journal of the Association of Information Systems | Year: 2012

Despite the tremendous commercial success of generalized second-price (GSP) keyword auctions, it still remains a big challenge for an advertiser to formulate an effective bidding strategy. In this paper, we strive to bridge this gap by proposing a framework for studying pure-strategy Nash equilibria in GSP auctions. We first analyze the equilibrium bidding behaviors by investigating the properties and distribution of all pure-strategy Nash equilibria. Our analysis shows that the set of all pure-strategy Nash equilibria of a GSP auction can be partitioned into separate convex polyhedra based on the order of bids if the valuations of all advertisers are distinct. We further show that only the polyhedron that allocates slots efficiently is weakly stable, thus allowing all inefficient equilibria to be weeded out. We then propose a novel refinement method for identifying a set of equilibria named the stable Nash equilibrium set (STNE) and prove that STNE is either the same as or a proper subset of the set of the well-known symmetrical Nash equilibria. These findings free both auctioneers and advertisers from complicated strategic thinking. The revenue of a GSP auction on STNE is at least the same as that of the classical Vickrey-Clarke-Groves mechanism and can be used as a benchmark for evaluating other mechanisms. At the same time, STNE provides advertisers a simple yet effective and stable bidding strategy.


Xu X.,National University of Defense Technology | Shen D.,CAS Institute of Automation | Shen D.,State Key Laboratory of Management and Control for Complex Systems | Gao Y.-Q.,State Key Laboratory of Management and Control for Complex Systems | And 3 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2012

Learning control of dynamical systems based on Markov decision processes (MDPs) is an interdisciplinary research area of machine learning, control theory, and operations research. The main objective in this research area is to realize data-driven multi-stage optimal control for complex or uncertain dynamical systems. This paper presents a comprehensive survey on the theory, algorithms, and applications of MDP-based learning control of dynamical systems. Emphases are put on recent advances in the theory and methods of reinforcement learning (RL) and adaptive/approximate dynamic programming (ADP), including temporal-difference learning theory, value function approximation for continuous state and action spaces, direct policy search, approximate policy iteration, and adaptive critic designs. Applications and the trends for future research and developments in related fields are also discussed. Copyright © 2012 Acta Automatica Sinica. All rights reserved.


Zhao Y.,State Key Laboratory of Management and Control for Complex Systems | Wang J.,State Key Laboratory of Management and Control for Complex Systems | Wang F.,State Key Laboratory of Management and Control for Complex Systems
Proceedings - 2015 Chinese Automation Congress, CAC 2015 | Year: 2015

Similar cases recommendation is more and more popular in the internet inquiry. There have been lots of cases which have been solved perfectly, and recommending them to similar inquiries can not only save the patients' waiting time, but also giving more good references. However, the inquiry platform cannot understand the diversity of description, i.e.The same meaning with different description. This may shield some cases with very high quality answers. In this paper, based on deep learning, we proposed a retrieval model combining word embedding with language models. We use word embedding to solve the problem of description diversity, and then recommend the similar cases for the inquiries. The experiments are based on the data from ask.39.net, and the results show that our methods outperform the state-of-Art methods. © 2015 IEEE.

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