1,720,958 research outputs found
Optimal control of networked systems using reinforcement learning
No full textThe trend of using wireless communication channel in network control system increases a lot, because of its flexibility and mobility. Improving system performance with simple devices, such as low storage capacity sensors and low transmission power channel, is very important to ensure long life time. Hence, there is interest in system communication and controller design to optimize the information used by devices, so as to maintain overall system performance. This thesis explores an approach to co-design of communication and control. First of all, the design of encoder and controller pair for feedback control systems over binary symmetric channels is concerned. An iterative design method based on Q-learning is proposed to obtain a pair of encoder and controller that can optimize a finite-horizon linear quadratic cost function. Three encoder strategies, memoryless encoder, memory encoder and predictive encoder, are considered. The proposed design can be implemented online, and has the potential to provide better performance. Compared with traditional control optimization method, the proposed design method is model-free, only data measured along with the system trajectories is utilized. Simulations are provided to show the effectiveness and the merits of the proposed method. Only finite channel inputs and finite outputs is considered in previous work, while there are some infinite channel output models in practical. Hence, we studies how the generalization to infinite-output channels affected the optimization of the encoder-controller, theoretically and practically, by studying one special type of infinite output channels, namely, Gaussian channel. Since the infinite-channel outputs mainly affect the controller design, we devote to controller design, which are soft controller design, hard controller design and the combination. From above considerations, all the research works are based on iterative design method, which means the encoder is optimized with fixed controller and the controller is optimized with fixed encoder. However, only local optimal solutions can be got by iterative design. Therefore, distributed encoder and controller design is proposed. Both encoder and controller learn independently with their own local information, and both of them can be optimized simultaneously. Obviously, the system performance is better than iterative design. In addition, distributed Qlearning can be applied into complex networked control systems
Artificial Neural Network based Iterative Learning Control for Stroke Rehabilitation
No full textAn artificial neural network (ANN) is combined with gradient descent to form a model-free iterative learning control (ILC) approach than can be applied to a wide range of nonlinear discrete-time systems. The ANN is recursively trained on the entire set of past data collected from the system and uses a passivity condition to determine when the ANN can be used to compute the next ILC update, or if an identification test is needed. Convergence properties are established alongside design selections that ensure the passivity condition is fulfilled. By minimizing the reliance on identification tests, this methodology is substantially faster than existing model-free ILC algorithms. It is tested on a key stroke rehabilitation problem using functional electrical stimulation (FES) for hand/wrist tracking.Experimental results using the new ILC approach with eight participants show that three hand/wrist references can be tracked using an average of 56% fewer experimental inputs compared with the most accurate previous approach. As the first approach to combine ILC and machine learning in upper limb rehabilitation, the results demonstrate how their combination addresses their individual deficiencies
Neural network based ILC with application to FES electrode arrays
No full textFunctional electrical stimulation (FES) is a technology that can help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movement. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To addressthis problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It is based on iterative learning control (ILC), a technique that exploits the repeated nature of rehabilitation training. The method uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by conventional ILC algorithms. The approach is then applied experimentally to four unimpaired subjects using a realistic rehabilitation scenario, with results showing mean tracking accuracy within 5, while requiring only between 25% and 64:9% of the experimental tests of conventional ILC
Parametrised Function ILC with application to FES Electrode Arrays
No full textFunctional electrical stimulation (FES) is an effective approach to regain lost movement in paralysed or impaired subjects. FES arrays can achieve functional multi-joint angular motion by activating a large number of FES elements. However, their control is challenging due to the need for high precision but the lack of a model or available identification time in a clinical or home setting. This paper develops an approach to deliver high accuracy with minimal identification overhead. It is based on iterative learning control (ILC), a technique that exploits the repeated nature of rehabilitation training. It uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Results show that 4 references can be tracked using only 10.8% of the experimental tests required by conventional ILC approaches
Parameterised function ILC with application to stroke rehabilitation
No full textFunctional electrical stimulation (FES) is a popular assistive technology that uses electrical impulses to artificially stimulate muscles to help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movements. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To date, by far the highest accuracy has been achieved using iterative learning control (ILC), a technique that mirrors the repeated nature of rehabilitation task practice. In particular, high accuracy has been achieved using a well-known ILC law for a general class of nonlinear systems which computes the updated control input using a linearised plant model. Since a global system model is unavailable, this is identified on every ILC trial by running an identification test. This adds many time-consuming identification tests, making it infeasible for clinical deployment. To solve this problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It introduces a parameterised plant model that is updated in parallel with the ILC using all available data, and then applied to replace identification tests. Rigorous conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by standard ILC algorithms. The approach is then applied experimentally to six unimpaired subjects using a realistic rehabilitation scenario. In particular, a novel stereo camera system is used to measure hand joint angles in a manner that can transfer to home use. Results show mean joint angle tracking accuracy within 5°, while requiring only between 25% and 64.9% of the experimental tests of standard ILC.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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