This paper presents a synthesis method of decision-making systems based on Agents forming multiple coalitions. The condition for coalition formation is defined by a mathematical model that solves the problem of global...
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In the Big Data Age, the company's financial management is confronted with a bigger challenge. In the face of the growing quantity of data, financial managers have difficulty providing the related information quic...
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In this paper we describe an experimentation carried out in a high school in northern Italy. The focus is to present to students new concepts of very big numbers and the infinite, studying their response, approaches, ...
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The simulation and analysis of complex spatiotemporal systems are crucial for expressing and solving chaotic dynamical systems such as those in Earth and environmental sciences. Understanding and computing physical pr...
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A wearable electroencephalographic (EEG) device-based method for the classification of the mental effort during motor imagery is presented. The solution can be used to improve the training of novice surgeons involved ...
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ISBN:
(纸本)9798350380903;9798350380910
A wearable electroencephalographic (EEG) device-based method for the classification of the mental effort during motor imagery is presented. The solution can be used to improve the training of novice surgeons involved in minimally invasive surgery. The method was validated on a public dataset comprising recordings from two participant groups: the control group (engaging in pure motor imagery tasks without feedback) and the neurofeedback group (receiving feedback during their mental tasks). In particular, a previous work on the same dataset found a higher cognitive effort for the neurofeedback group than for the control group, which was confirmed by the results of the NASA-TLX questionnaire. EEG signals were acquired with an 8-electrode dry device. The EEG features of the mental effort were identified by using the Sequential Feature Selector (SFS), in combination with different classifiers, on the EEG data of the control group. In order to classify between low and high mental effort (baseline accuracy of 50 %), four EEG features and the Multi-Layer Perceptron classifier resulted in the best combination for the mental effort assessment in the control group, achieving an average accuracy of 82.1 +/- 8.7 %. The 4 features identified were: (i) theta-to-alpha ratio on Fz channel, (ii) beta-to-delta ratio on O1 channel, (iii) theta-to-beta ratio on FP1 channel, and (iv) (theta+alpha)/beta on FP1 channel. The same pipeline was employed on the neurofeedback group, achieving an average accuracy of 84.0 +/- 6.8 %. These findings are in accordance with the results of NASA-TLX questionnaire. This work demonstrated the feasibility of assessing cognitive effort in real-time by means of wearable EEG device during motor imagery tasks. Thus, neurofeedback-supported motor imagery systems can be enriched by a new module to adapt the training to the novice surgeons and optimise learning outcomes.
The safety of software is particularly important in the safety-critical computer system which has the highest safety requirements. In order to ensure the safety quality of such systems, formal methods are recommended ...
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Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior ex...
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ISBN:
(数字)9781665479271
ISBN:
(纸本)9781665479271
Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this "neural motion planning" did not scale to complex robots in unseen 3D environments as needed for real-world applications. Here, we introduce "basis point set", well-known in computer vision, to neural motion planning as a modern compact environment encoding enabling efficient supervised training networks that generalize well over diverse 3D worlds. Combined with a new elaborate training scheme, we reach a planning success rate of 100 %. We use the network to predict an educated initial guess for an optimization-based planner (OMP), which quickly converges to a feasible solution, massively outperforming random multi-starts when tested on previously unseen environments. For the DLR humanoid Agile Justin with 19 DoF and in challenging obstacle environments, optimal paths can be generated in 200 ms using only a single CPU core. We also show a first successful real-world experiment based on a high-resolution world model from an integrated 3D sensor.
In the area of real-time systems verification, designers employ various techniques and tools for schedulability analysis. The research described in this paper introduces a novel approach for schedulability analysis ba...
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With the development and widespread application of Model-Based systems Engineering (MBSE) in the aerospace domain continue to grow, System modeling Language (SysML) has become increasingly important as the most popula...
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The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC ...
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