53,548 research outputs found
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Emotional Brain-Computer Interfaces
Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud
These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs
Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems
There is a trend towards using wireless technologies in networked control
systems. However, the adverse properties of the radio channels make it
difficult to design and implement control systems in wireless environments. To
attack the uncertainty in available communication resources in wireless control
systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS)
scheme is developed, which takes advantage of the co-design of control and
wireless communications. By exploiting cross-layer design, CLAFS adjusts the
sampling periods of control systems at the application layer based on
information about deadline miss ratio and transmission rate from the physical
layer. Within the framework of feedback scheduling, the control performance is
maximized through controlling the deadline miss ratio. Key design parameters of
the feedback scheduler are adapted to dynamic changes in the channel condition.
An event-driven invocation mechanism for the feedback scheduler is also
developed. Simulation results show that the proposed approach is efficient in
dealing with channel capacity variations and noise interference, thus providing
an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at
http://www.mdpi.org/sensors/papers/s8074265.pd
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
The role of simulations in consumer experiences and behavior: insights from the grounded cognition theory of desire
What are the mechanisms by which extrinsic and environmental cues affect consumer experiences, desires, and choices? Based on the recent grounded cognition theory of desire, we argue that consumption and reward simulations constitute a central mechanism in these phenomena. Specifically, we argue that appetitive stimuli, such as specific product cues, can activate simulations of consuming and enjoying the respective products, based on previous learning experiences. These consumption and reward simulations can lead to motivated behavior, and can be modulated by state and trait individual differences, situational factors, and product-extrinsic cues. We outline the role of simulations within the grounded theory of desire, offering a theoretical framework for understanding motivational processes in consumer behavior. Then we illustrate the theory with behavioral, physiological, and neuroimaging findings on simulations in appetitive behavior and sensory marketing. Finally, we outline important issues for further research and applications for stimulating healthy, prosocial, and sustainable consumer choices
Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration
Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship
Analysis, filtering, and control for Takagi-Sugeno fuzzy models in networked systems
Copyright © 2015 Sunjie Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 11301118 and 61174136, the Natural Science Foundation of Jiangsu Province of China under Grant BK20130017, the Fundamental Research Funds for the Central Universities of China under Grant CUSF-DH-D-2013061, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Resource-aware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in
large-scale networks. When the 'things' coordinate decisions and act
collectively on shared information, feedback is introduced between them.
Multiple feedback loops are thus closed over a shared, general-purpose network.
Traditional feedback control is unsuitable for design of IoT control because it
relies on high-rate periodic communication and is ignorant of the shared
network resource. Therefore, recent event-based estimation methods are applied
herein for resource-aware IoT control allowing agents to decide online whether
communication with other agents is needed, or not. While this can reduce
network traffic significantly, a severe limitation of typical event-based
approaches is the need for instantaneous triggering decisions that leave no
time to reallocate freed resources (e.g., communication slots), which hence
remain unused. To address this problem, novel predictive and self triggering
protocols are proposed herein. From a unified Bayesian decision framework, two
schemes are developed: self triggers that predict, at the current triggering
instant, the next one; and predictive triggers that check at every time step,
whether communication will be needed at a given prediction horizon. The
suitability of these triggers for feedback control is demonstrated in hardware
experiments on a cart-pole, and scalability is discussed with a multi-vehicle
simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of
Things Journal. arXiv admin note: text overlap with arXiv:1609.0753
Temporal structure in neuronal activity during working memory in Macaque parietal cortex
A number of cortical structures are reported to have elevated single unit
firing rates sustained throughout the memory period of a working memory task.
How the nervous system forms and maintains these memories is unknown but
reverberating neuronal network activity is thought to be important. We studied
the temporal structure of single unit (SU) activity and simultaneously recorded
local field potential (LFP) activity from area LIP in the inferior parietal
lobe of two awake macaques during a memory-saccade task. Using multitaper
techniques for spectral analysis, which play an important role in obtaining the
present results, we find elevations in spectral power in a 50--90 Hz (gamma)
frequency band during the memory period in both SU and LFP activity. The
activity is tuned to the direction of the saccade providing evidence for
temporal structure that codes for movement plans during working memory. We also
find SU and LFP activity are coherent during the memory period in the 50--90 Hz
gamma band and no consistent relation is present during simple fixation.
Finally, we find organized LFP activity in a 15--25 Hz frequency band that may
be related to movement execution and preparatory aspects of the task. Neuronal
activity could be used to control a neural prosthesis but SU activity can be
hard to isolate with cortical implants. As the LFP is easier to acquire than SU
activity, our finding of rich temporal structure in LFP activity related to
movement planning and execution may accelerate the development of this medical
application.Comment: Originally submitted to the neuro-sys archive which was never
publicly announced (was 0005002
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