Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets

About 60%of emissions into the earth’s atmosphere are produced by the transport sector,caused by exhaust gases from conventional internal combustion engines.An effective solution to this problem is electric mobility,which significantly reduces the rate of urban pollution.The use of electric vehicles(EVs)has to be encouraged and facilitated by new information and communication technology(ICT)tools.To help achieve this goal,this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience.The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle’s current position,battery type,state of charge,nearby charge point availability,and compatibility.In particular,the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points(CPs).To this purpose,two virtual sensors(VSs)are designed,modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking.In particular,the first VS is devoted to locate and find available CPs in a preferred area,whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase.A UML activity diagram describes VSs operations and cooperation,while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors(CP operator,EV manufacturer,etc.).Furthermore,two timed Petri Nets(TPNs)are designed to model the proposed VSs,functioning and interactions as discrete event systems.The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Virtual speed sensors based algorithm for expressway traffic state estimation

The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers. The speed is one of the most representative parameter of the traffic state. So the expressway speed spatial distribution can be taken as the expressway traffic state equivalent. In this paper, an algorithm based on virtual speed sensors (VSS) is presented to estimate the expressway traffic state (the speed spatial distribution). To gain the spatial distribution of expressway traffic state, virtual speed sensors are defined between adjacent traffic flow sensors. Then, the speed data extracted from traffic flow sensors in time series are mapped to space series to design virtual speed sensors. Then the speed of virtual speed sensors can be calculated with the weight matrix which is related with the speed of virtual speed sensors and the speed data extracted from traffic flow sensors and the speed data extracted from traffic flow sensors in time series. Finally, the expressway traffic state (the speed spatial distribution) can be gained. The acquisition of average travel speed of the expressway is taken for application of this traffic state estimation algorithm. One typical expressway in Beijing is adopted for the experiment analysis. The results prove that this traffic state estimation approach based on VSS is feasible and can achieve a high accuracy.

PULSE CUMULATING METHOD FOR MULTI-TARGETS' JOINT AZIMUTH-ELEVATION FREQUENCY-DELAY ESTIMATION

The Directions of Arrivals （DOAs）, speeds and distances of targets are all required for array signal processing. Based on the periodic phase shift of coherent pulse sequence waveform, a new estimation of multi-targets＇ 2-Dimentional （2-D） DOA angle, Doppler frequency shift and relative time-delay is proposed. Based on a virtual sensor array constructed by pulse cumulating, the estinaations of azimuth, elevation, Doppler frequency shift and time-delay can be obtained simultaneously, and the least number of pulses could be two. This method is computationally efficient even in heavier noised environment, and all estimations are automatically paired in calculation process with no used to any plane sensor array and deal with many spectrum searching. Further more, this algorithm can be targets at the same time only by few sensors. The targets number that can deal with simultaneously is several times to the sensor number, which is the upper limit for normal algorithms such as ESPRIT and MUSIC. These characteristics would be very useful, especially, for aerial systems. Simulations demonstrate the capabilities of this method efficiently.

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.

Energy-efficient virtual sensor-based deep reinforcement learning control of indoor CO_(2)in a kindergarten

High concentrations of indoor CO_(2)pose severe health risks to building occupants.Often,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO_(2)concentrations.However,such equipment consumes large amounts of energy,substantially increasing building energy consumption.In the end,the issue becomes an optimization problem that revolves around maintaining CO_(2)levels below a certain threshold while utilizing the minimum amount of energy possible.To that end,we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning(DRL)-based control to efficiently control indoor CO_(2)while utilizing the minimum amount of energy possible.The data used to train and test the DRL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery ventilator.The results show that,unlike the manual control initially employed at the kindergarten,the DRL agent could always maintain the CO_(2)concentrations below sufficient levels.Furthermore,a 58%reduction in the energy consumption of the ventilator under the DRL control compared to the manual control was estimated.The demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.

A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs

Angle of Attack(AOA) is a crucial parameter which directly affects the aerodynamic forces of an aircraft.The measurement of AOA is required to ensure a safe flight within its designed flight envelop.This paper intends to summarise a comprehensive survey on the measurement techniques and estimation methods for AOA, specifically in Unmanned Aerial Vehicle(UAV) applications.In the case of UAVs, weight constraint plays a major role as far as sensor suites are concerned.This results in selecting a suitable estimation method to extract AOA using the available data from the autopilot.The most feasible and widely employed AOA measurement technique is by using the Multi-Hole Probes(MHPs).The MHP measures the AOA regarding the pressure variations between the ports.Due to the importance of MHP in AOA measurement, the calibration methods for the MHP are also included in this paper.This paper discusses the AOA measurement using virtual AOA sensors, their importance and the operation.