1,907 research outputs found
Event-triggered Learning
The efficient exchange of information is an essential aspect of intelligent
collective behavior. Event-triggered control and estimation achieve some
efficiency by replacing continuous data exchange between agents with
intermittent, or event-triggered communication. Typically, model-based
predictions are used at times of no data transmission, and updates are sent
only when the prediction error grows too large. The effectiveness in reducing
communication thus strongly depends on the quality of the prediction model. In
this article, we propose event-triggered learning as a novel concept to reduce
communication even further and to also adapt to changing dynamics. By
monitoring the actual communication rate and comparing it to the one that is
induced by the model, we detect a mismatch between model and reality and
trigger model learning when needed. Specifically, for linear Gaussian dynamics,
we derive different classes of learning triggers solely based on a statistical
analysis of inter-communication times and formally prove their effectiveness
with the aid of concentration inequalities
Generating Correlated Ordinal Random Values
Ordinal variables appear in many field of statistical research. Since working with simulated data is an accepted technique to improve models or test results there is a need for providing correlated ordinal random values with certain properties like marginal distribution and correlation structure. The present paper describes two methods for generating such values: binary conversion and a mean mapping approach. The algorithms of the two methods are described and some examples of the outcomes are shown
Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60ā70%, which implies that two to three times more sensor nodes could be used at the same bandwidth
Does the Introduction of IFRS Change the Timeliness of Loss Recognition? Evidence from German Firms
In this paper, we re-evaluate the hypothesis that the introduction of the IFRS has an impact on the timeliness of loss recognition. We test this hypothesis in a data set of public German firms that report according to German-GAAP and IFRS, respectively. The parallel use of the two accounting standards in Germany provides a unique opportunity to contribute to the academic discussion, as well as to the current policy debate on regulatory reform in Germany. Starting from the standard time series concept of conditional conservatism that was initially proposed by Basu (1997), we implement a wide range of test specifications, including (i) a threshold unit-root test specification; (ii) a multivariate approach to outlier detection and (iii) various forms of controlling for fixed effects. We do not find evidence that IFRS and German-GAAP firms differ with respect to their timeliness of loss recognition in any of these specifications - a result that appears surprising in light of the more prudent regulation in the German-GAAP, but is consistent with some earlier findings in the literature.IFRS, German-GAAP, Timely loss recognition, Conservatism
Event-triggered Pulse Control with Model Learning (if Necessary)
In networked control systems, communication is a shared and therefore scarce
resource. Event-triggered control (ETC) can achieve high performance control
with a significantly reduced amount of samples compared to classical, periodic
control schemes. However, ETC methods usually rely on the availability of an
accurate dynamics model, which is oftentimes not readily available. In this
paper, we propose a novel event-triggered pulse control strategy that learns
dynamics models if necessary. In addition to adapting to changing dynamics, the
method also represents a suitable replacement for the integral part typically
used in periodic control.Comment: Accepted final version to appear in: Proc. of the American Control
Conference, 201
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