1,723,879 research outputs found
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
Hamming Distance-Based Heuristic for Flexible Job-Shop Scheduling Problems
No full textThe flexible job-shop scheduling problem (FJSSP) has gained significant attention due to its adaptability to modern manufacturing systems. However, for large-scale production chains, finding the optimal schedule to minimize the makespan quickly becomes computationally infeasible due to the complexity of the solution space, leading to the rise of heuristic and metaheuristic methods to obtain near-optimal solutions. In the same direction, this paper models the FJSSP using mixed-integer linear programming (MILP), proposing a heuristic method based on the Hamming distance between the problem's binary variables to efficiently navigate the solution space. By iteratively adjusting routing and sequencing variables, the heuristic produces intermediate solutions that gradually improve the makespan, converging to epsilon-optimal solutions with considerably reduced computation times compared to exact optimization, as demonstrated in a case study based on an instance of a well-known benchmark for FJSSP
Reducing Emission Peaks in a Port Terminal through Optimized Truck Arrivals
No full textThe reduction of pollutant emissions due to the truck operations in a maritime terminal is the objective of this work. The proposed approach is firstly based on a suitable prediction method developed by the authors and aimed at forecasting the number of trucks reaching the terminal to bring export containers in each day of a specified time interval. Once that the curve of truck arrivals is predicted, an emission model is adopted to evaluate the corresponding pollutant emissions. As a result, it is possible to verify in advance whether at certain days the overall emissions overcome a critical threshold. If this happens, it is needed to redistribute the truck arrivals in order to maintain emissions as close as possible to the threshold. This is optimally done by stating and solving an optimization problem whose solutions are tested in the case study of export flows in the PSA Genova Pra' (PSA GP) terminal
Truck Emission Forecasting and Peak Mitigation in a Port Terminal
No full textThe objective of this work is to predict the emissions generated by trucks upon their arrival at a port terminal. This prediction is based on a forecast model that predicts truck arrivals, serving as a key input for the proposed methodology. By using the curve of truck arrivals within specific time intervals, an emission model is adopted to estimate the corresponding pollutant emissions. Then, a redistribution algorithm is designed to optimize the scheduling of truck arrivals, effectively mitigating the occurrence of possible peaks in emissions. The algorithm, which takes into account constraints about the truck operations inside the terminal, operates by redistributing the arrival patterns of trucks in a smooth way in order to also consider the truck operators' reluctance to change the existing schedule. The case study of export flows in the PSA Genova Pra' (PSA GP) terminal is addressed in the paper
Model predictive control of smart greenhouses as the path towards near zero energy consumption
No full textModern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use
Non-linear MPC for Longitudinal and Lateral Control of Vehicle’s Platoon with Insert and Exit Manoeuvres
No full textVehicle platooning focuses on the problem to achieve cooperation on a shared consensus about distance and speed in a fleet of vehicles. Vehicle platooning represents a challenging concern in road management of self-driving or autonomous vehicles (AVs). Cooperation in vehicle platooning may be realized by communication and control instructions among vehicles to maintain a safe inter-vehicular distance and a specific desired velocity according to the planned path. This paper proposes a longitudinal and lateral control based on non-linear Model Predictive Control (MPC) to manage the vehicles’ safe manoeuvres to insert an external vehicle and to allow the detachment of a member in a platoon. In the proposed cooperative control model, the leader coordinates data exchange both with the followers and with the vehicle, notifying its intention to join or to leave the platoon. It is assumed that all the vehicles have the access to data related to their own position and speed by specific technological devices on board. The leader receives and elaborates the data and, by the control process, sends the optimal control decisions to the other vehicles. The proposed control algorithm minimizes the acceleration and steering wheel and the square deviations of positions and speeds in respect to reference values. The references are computed by a trajectory planner which generates a Bezier Curve in order to create the local path planning. The MPC control of the vehicle, based on a non-linear kinematic model, provides the optimal control values related to the acceleration and steering to perform safe entering and exiting manoeuvring. The simulations of the vehicle movements, demonstrate the effectiveness of the proposed control model
Impact of Age on Patello-femoral Arthroplasty Outcomes, Osteoarthritis Progression, and Survivorship: The Youngest and Oldest Achieve the Best Results
No full textBackground: Proper patient selection is essential to improve long-term outcomes in patellofemoral arthroplasty (PFA). This study aimed to assess the impact of age on functional outcomes, osteoarthritis (OA) progression, and survivorship in patients who underwent PFA using a modern implant. Methods: Patients who underwent primary isolated PFA with third-generation prosthesis were retrospectively reviewed. A total of 120 PFAs with a mean follow-up of 8.6 years were included. Patients were categorized into four age groups: Group I (< 55 years), Group II (56 to 65 years), Group III (66 to 75 years), and Group IV (> 76 years). Clinical and radiographic outcomes included knee range of motion, Knee Society scores, University of California Los Angeles Activity Score, Tegner Activity Scale, OA progression, and patient satisfaction. Survival analysis was also conducted. Results: Patients in Group I demonstrated significantly better functional outcomes, including higher Knee Society, Tegner, and University of California Los Angeles scores, compared to older groups (P < 0.01). Group I also showed the highest engagement in medium to low-impact sports (92%) and no significant OA progression. Groups I and IV had a 100% survivorship rate; in contrast, group II exhibited the lowest survivorship rate (90%) due to OA progression. Conclusions: A PFA in patients under 55 years resulted in superior clinical outcomes, minimal OA progression, and high survivorship rates. Even patients over 76 years demonstrated an excellent survival rate along with satisfactory clinical outcomes. Conversely, patients aged 56 to 65 years demonstrated the highest risk of revision. This study supports the effectiveness of PFA as a treatment option for isolated patello-femoral OA in very young and very old patients
Data-Driven EEG Model for Predict the Risk during Roundabout Maneuvers
No full textThe behaviour of drivers is significantly influenced by their perception of risk, which can have a profound impact on the transportation environment. This can potentially undermine road safety and efficiency. This study addresses this crucial concern by introducing an algorithm that forecasts driver-perceived risk using data obtained from electroencephalogram (EEG). The algorithm employs a Support Vector Machine (SVM) to develop a strong and predictive model that can forecast perceived risk levels. This model can then be used to inform the implementation of preventive safety measures. The efficacy of the algorithm was evaluated through the use of driving simulations, which involved three participants utilising the SCANeR Studio driving simulator. The simulations involved traversing a two-lane roundabout filled with vehicles and allowed the participants to make decisions during the entry and navigation stages. The results demonstrated the effectiveness of this approach even with a limited dataset with respect to a Pattern Recognition Neural Network (PRNN). This research offers valuable insights into the potential for neurobiological data-driven strategies to enhance driver safety
Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0
No full textIn recent decades, climate change and a shortage of resources have brought about the need for technology in agriculture. Farmers have been forced to use information and innovation in communication in order to enhance production efficiency and crop resilience. Systems engineering and information infrastructure based on the Internet of Things (IoT) are the main novel approaches that have generated growing interest. In agriculture, IoT solutions according to the challenges for Industry 4.0 can be applied to greenhouses. Greenhouses are protected environments in which best plant growth can be achieved. IoT for smart greenhouses relates to sensors, devices, and information and communication infrastructure for real-time monitoring and data collection and processing, in order to efficiently control indoor parameters such as exposure to light, ventilation, humidity, temperature, and carbon dioxide level. This paper presents the current state of the art in the IoT-based applications to smart greenhouses, underlining benefits and opportunities of this technology in the agriculture environment
Model predictive control for cooperative insertion or exit of a vehicle in a platoon
No full textVehicle platooning has a central role in the road management by self-driving or autonomous vehicles (AVs). The main issues in this context are the agreement of communication and control instructions among vehicles in order to maintain a safe inter vehicular distance and a specific desired speed according to the planned travel. This paper proposes a longitudinal Model Predictive Control (MPC) to carry out vehicles’ safe manoeuvres to let an external vehicle to be inserted in the platoon or alternatively to let a vehicle of the platoon to leave it. The control strategy considers a cooperative approach where the leader coordinates the exchange of information with the followers and with the vehicle which notifies its intent to enter (or to leave) the platoon. All the vehicles are equipped with technologies to monitor their own state in terms of position and speed while the leader receives, elaborates the data and, by the control process, distributes the optimal control decisions to the whole platoon. The proposed control algorithm minimizes the tractive forces and the square deviations of positions and speeds in respect to predefined references. The MPC longitudinal control of the vehicle, based on a non-linear cinematic model, provides the optimal control values related to the torques to be applied to vehicles’ acceleration or deceleration in order to perform safe entering and exiting manoeuvring. The results of the simulations demonstrate the effectiveness of the proposed approach with reduced execution time
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