A mathematical lumped parameter model of the oscillometric technique for indirect blood pressure measurement is presented. The model includes cuff compliance, pressure transmission from the cuff to the brachial artery through the soft tissue of the arm, and the biomechanics of the brachial artery both at positive and negative transmural pressure values. The main aspects of oscillometry are simulated i.e., the increase in cuff pressure pulsatility during cuff deflation maneuvers, the existence of a point of maximum pulsations (about 1.5 mmHg) at a cuff pressure close to mean arterial pressure, and the characteristic ratios for cuff pressure pulsatility at systole and diastole (0.52 and 0.70, respectively, with this model, using basal parameters and an individual set of data for the arterial pressure waveform). Subsequently, the model is used to examine how alterations in some biomechanical factors may prejudice the accuracy of pressure measurement. Numerical simulations indicate that alterations in wall viscoelastic properties and in arterial pressure pulse amplitude may significantly affect the accuracy of pressure estimates, leading to errors as great as 15-20% in the computation of diastolic and systolic arterial pressure. By contrast, changes in arterial pressure mean value and cuff compliance do not seem to have significant influence on the measurement. Evaluation of mean arterial pressure through a characteristic ratio is not robust and may lead to misleading results. Mean arterial pressure may be better evaluated as the lowest pressure at which cuff pulse amplitude reaches a plateau. The obtained results may help to explain the nature of errors which usually limit the reliability of arterial pressure measurement (for instance in the elderly).
Energy efficiency, advanced controls and renewable energy systems for operating industrial, residential and tertiary sector buildings designed to be Near-Zero Energy are investigated to explore the performance gap. The analysis involves a comparison of energy dynamic and quasi-dynamic models with data from smart monitoring systems, indoor and outdoor environment measurements, power consumption and production data. Specific issues and conclusions have been drawn as the basis for addressing the performance gap between energy efficiency prediction in the design phase and measurements' evaluation in operational phase.
Purpose
– This paper aims to deal with the problem of programming robots in industrial contexts, where the need of easy programming is increasing, while robustness and safety remain fundamental aspects.
Design/methodology/approach
– A novel approach of robot programming can be identified with the manual guidance that permits to the operator to freely move the robot through its task; the task can then be taught using Programming by Demonstration methods or simple reproduction.
Findings
– In this work, the different ways to achieve manual guidance are discussed and an implementation using a force/torque sensor is provided. Experimental results and a use case are also presented.
Practical implications
– The use case shows how this methodology can be used with an industrial robot. An implementation in industrial contexts should be adjusted accordingly to ISO safety standards as described in the paper.
Originality/value
– This paper presents a complete state-of-the-art of the problem and shows a real practical use case where the approach presented could be used to speed up the teaching process.
In this paper we present a novel methodology based on a topological entropy, the so-called persistent entropy,\ud
for addressing the comparison between discrete piecewise linear functions. The comparison is certified by the\ud
stability theorem for persistent entropy that is presented here. The theorem is used in the implementation of a\ud
new algorithm. The algorithm transforms a discrete piecewise linear function into a filtered simplicial complex\ud
that is analyzed via persistent homology and persistent entropy. Persistent entropy is used as a discriminant\ud
feature for solving the supervised classification problem of real long-length noisy signals of DC electrical motors.\ud
The quality of classification is stated in terms of the area under receiver operating characteristic curve\ud
(AUC=93.87%)
Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.
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