177,150 research outputs found
Telemedicine systems for collaborative diagnosis over the Internet: Towards virtual "collaboratories"
No full textThe working experience of the National Institute for Cancer Research (INRC) of Genova, Italy, in the use of Internet technology for second opinions and teleconsultations in histology and cytology is presented. In the first section, the current state of the art of telepathology is reviewed and the main requirements, which generally address a telepathology system, are briefly analyzed. The second section investigates the experience of using Internet technology at INRC in current telepathology consultations. The evolution from static to dynamic telepathology in relation to the speed of the current communication channel is discussed. Finally, the concept of collaboration laboratories, defined in previous works as collaboratories, is introduced
Identification of brain electrical activity related to head yaw rotations
No full textAutomatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network‐based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input‐output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects
Modeling, Design and Construction of a Zero-Energy PV Greenhouse for Applications in Mediterranean Climates
No full textThis paper concerns the design, modelling, and construction of a high-efficiency mini PV greenhouse performing as a Nearly Zero Energy Building (NZEB). The greenhouse is equipped with a semi-transparent roof-mounted photovoltaic system (3 kWhp) that feeds an air-source heat pump providing cooling and heating. The PV-generated power can be also stored in a battery. A dynamic simulation model (EPlus) of the real greenhouse is developed to predict its performance and investigate beneficial control strategies. The hourly profiles of different variables (energy, temperatures, illuminance) are deeply investigated. The energy-saving strategies, as reflective shading and controlled natural ventilation, prove to reduce the yearly energy needs by 30%. The energy storage model is developed by the Authors and coupled with hourly solar production. The PV electric model includes the temperature effect on module performance, the inverter efficiency curve, and the battery state of charge. The coupled dynamic analysis shows that the photovoltaic plant meets the air conditioning requirements for 94% of the hours of operation and that the energy surplus could feed the grid with approximately 1355 kWhel per year. The validation of the present model will be possible with future measurements and monitoring of the greenhouse once operating in place
Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison
No full textThis paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge
Optimal Control of Smart Distributed Power and Energy Systems
No full textThe increase in intermittent renewable energy resources and distributed generation has led to the need for developing new controllers and management techniques for smart grids [...
Binary Controller Based on the Electrical Activity Related to Head Yaw Rotation
No full textA human machine interface (HMI) is presented to switch on/off lights according to the head left/right yaw rotation. The HMI consists of a cap, which can acquire the brain’s electrical activity (i.e., an electroencephalogram, EEG) sampled at 500 Hz on 8 channels with electrodes that are positioned according to the standard 10–20 system. In addition, the HMI includes a controller based on an input–output function that can compute the head position (defined as left, right, and forward position with respect to yaw angle) considering short intervals (10 samples) of the signals coming from three electrodes positioned in O1, O2, and Cz. An artificial neural network (ANN) training based on a Levenberg–Marquardt backpropagation algorithm was used to identify the input–output function. The HMI controller was tested on 22 participants. The proposed classifier achieved an average accuracy of 88% with the best value of 96.85%. After calibration for each specific subject, the HMI was used as a binary controller to verify its ability to switch on/off lamps according to head turning movement. The correct prediction of the head movements was greater than 75% in 90% of the participants when performing the test with open eyes. If the subjects carried out the experiments with closed eyes, the prediction accuracy reached 75% of correctness in 11 participants out of 22. One participant controlled the light system in both experiments, open and closed eyes, with 100% success. The control results achieved in this work can be considered as an important milestone towards humanoid neck systems
Toxic Release Damage Distance Assessment Based on the Short-Cut Method: A Case Study for the Transport of Chlorine and Hydrochloric Acid in Densely Urbanized Areas in the Mediterranean Region
No full textThe transportation of dangerous goods by road is the most accident-prone mode of transportation, even if accidents involving road transportation of dangerous goods are considered as a Low Probability and High Consequence event (LPHC event). However, several dangerous goods are transported by road networks, such as petroleum products and chemicals, which can generate major dangerous consequences such as spills, explosions, fires, or toxic clouds. In this context, this article presents a method to calculate and quickly quantify the sizes of impact zones characterized by high lethality and irreversible injuries to people in the case of a hazardous materials transport accident. This method is used as a module for the analysis of the consequences of different potential accident scenarios, for the Web-GIS platform proposed by LOSE+LAB, that implements appropriate ICT tools and systems for monitoring the flow of goods that would enable a continuous monitoring system at the cross-border level and transmit data and information to the territory actors involved in the management of dangerous goods according to the ADR standard. The proposed method provides the user with a visualization of the possible outcomes of an event by reproducing the impact area for different accident scenarios, which can provide quick maps of the hazard and represents a decision support system for territorial governance in terms of intervention and response protocols for emergency management in the cases of dangerous goods accidents
Optimal Planning with Technology Selection for Wind Power Plants in Power Distribution Networks
No full textThis paper proposes a comprehensive decision framework to optimally plan wind power plants (WPPs) with technology selection in the distribution network. The proposed framework aims to maximize the net present value (NPV) associated with the WPP investment over a given planning horizon for various bus locations. The proposed design accounts for various practical cost factors, historical data of wind speeds, and WPP installation restrictions due to territorial information, environmental considerations, and work constraints, in the decision making process of optimal planning and technology selection for WPP. The planning problem, which maximizes the NPV over the potential WPP installation locations, potential technologies, and the size of WPPs, is formulated as a constrained optimization problem. The proposed design is evaluated using case studies to test its concrete practices with a radial network of 33-bus distribution system
Using CommonKADS to create a conceptual model of a guideline system for breast cancer prognosis.
No full textOne of the major aspects in breast cancer research is the identification of prognostic factors accurate enough to define different therapeutic decisions; each prognostic factor on its own is insufficient for the prediction of the biological behaviour of the tumour, but a combination of these parameters is necessary. The work described here focuses on the definition of a conceptual knowledge model of the prognosis of breast cancer. Our approach to the conceptualization of the problem follows the CommonKADS (Knowledge Acquisition and Design Structuring) Library for Expertise Modelling. The aim of this work is to provide a first conceptualization of breast cancer prognosis while evaluating the efficacy of the CommonKADS methodology in facing the problem
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