The Brain Computer Interface technology allows the communication between people and mechanical devices controlled by microprocessors [1] [8]. It translates the human mental activity into device commands. The kernel of...
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ISBN:
(纸本)0889865787
The Brain Computer Interface technology allows the communication between people and mechanical devices controlled by microprocessors [1] [8]. It translates the human mental activity into device commands. The kernel of this technology is an algorithm that takes samples, filters and classifies the electro-encephalographic signal [2] [3] [4] [5]. In this paper, different types of filtering windows are considered, the main objective is to determine the type of window with best results in the discrimination stage, when the user is thinking about different activities;as secondary result the most relevant features are extracted. With an earlier and better discrimination the classifier would be easier to implement, faster and more reliable[14].
作者：
Brščić, DraženUniversity of Zagreb
Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation Unska 3 ZagrebHR-10000 Croatia
This paper presents the current work on the development of a tool for remote control of mobile robots. The tool consists of a local controller on the robot side and a user interface for simulation and remote control t...
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Many historians and even some scientists argue that we are living in the Anthropocene, a new epoch characterized by the human control of the biosphere. Next year the International Geological Congress will consider rec...
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Many historians and even some scientists argue that we are living in the Anthropocene, a new epoch characterized by the human control of the biosphere. Next year the International Geological Congress will consider recognizing this name as the latest addition to the standard geological time scales. My reaction, echoing the Romans: Festina lente. Make haste slowly.
In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystem...
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In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystems under the influence of actuator faults. First, distributed finite-time observers are proposed for all subsystems to observe the state of the exosystem. Then, a novel fault-tolerant controller is designed to compensate for the influence of matched system uncertainties and actuator faults. By using the linear matrix inequality technique, a sufficient condition is provided to guarantee the solvability of the considered problem in the presence of mismatched coupling uncertainties. Moreover, it is shown that the system in closed-loop with the developed controller can achieve output regulation by using the Lyapunov stability theory and cyclic-small-gain ***, a numerical example is given to illustrate the effectiveness of the obtained result.
Facial paresis, temporary or permanent loss of facial muscle mobility, seriously affects the patient’s physical and mental health. Patients suffer from difficulties with eating, salivating or speaking. Facial rehabil...
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A microrobot-based nanohandling station, which is able to handle objects in the micrometer range and below, is introduced. Following a short description of the microro-bot's mobile platform the station's contr...
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In traditional Chinese medicine(TCM) diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimen...
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In traditional Chinese medicine(TCM) diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional *** is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis,which affects the modeling of diagnostic *** selection entails choosing the smallest feature subset of relevant symptoms,and maximizing the generalization performance of the *** present there are rare researches on feature selection on multi-label data.A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper,and modeling is made by means of four multi-label learning algorithms like k nearest neighbors,*** compare the performance of the algorithm with the current popular dimension reduction algorithms like MEFS(embedded feature selection for multi-Label learning),MDDM(multi-label dimensionality reduction via dependence maximization) on the UCI Yeast gene functional data set and an inquiry diagnosis dataset of coronary heart disease(CHD).Experimental results show that the algorithm we present has significantly improved the *** particular,the improvement on the average precision for the classifier is up to 10.62% and 14.54%.Syndrome inquiry modeling of CHD in TCM is realized in this paper,providing effective reference for the diagnosis of CHD and analysis of other multi-label data.
In this article, we present a case study of pipeline leakage detection via an adaptive novelty detection algorithm. The detection is based on an affine projection filter applied to a sound recording of the pipeline ou...
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This paper analyzes some basic issues involving the application of discontinuous control techniques for controlling fractional order systems (FOS). With reference to a simple class of SISO perturbed processes, we comp...
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The authors propose a novel reinforcement learning(RL)framework,where agent behaviour is governed by traditional control *** integrated approach,called time-in-action RL,enables RL to be applicable to many real-world ...
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The authors propose a novel reinforcement learning(RL)framework,where agent behaviour is governed by traditional control *** integrated approach,called time-in-action RL,enables RL to be applicable to many real-world systems,where underlying dynamics are known in their control theoretical *** key insight to facilitate this integration is to model the explicit time function,mapping the state-action pair to the time accomplishing the action by its underlying *** their framework,they describe an action by its value(action value),and the time that it takes to perform(action time).An action-value results from the policy of RL regarding a *** time is estimated by an explicit time model learnt from the measured activities of the underlying *** value network is then trained with embedded time model to predict action *** approach is tested using a variant of Atari Pong and proved to be convergent.
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