Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owi...Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owing to interference from structurally similar compounds.In this study,we present a highly sensitive and selective gas sensor utilizing a solid-mounted film bulk acoustic resonator based on carbon nanotubes functionalized with hexafluoroisopropanol(HFiP)to enhance DMMP detection.This approach leverages the strong hydrogen bonding between HFiP and DMMP molecules to significantly improve the sensor’s adsorption capacity and selectivity.To further refine selectivity and at the same time solve the cross-sensitivity problem of sensitive membranes,we introduce a virtual sensor array design,generated by modulating the input power to the resonator,which enables the sensor to operate in multiple response modes across varying vibrational amplitudes.These multimodal responses are subjected to linear discriminant analysis,allowing precise differentiation of DMMP from other volatile organic compounds such as tributyl phosphate and dimethyl phthalate.Our results demonstrate superior performance in terms of both sensitivity and selectivity,offering a robust solution for detecting low-concentration DMMP in complex environments.展开更多
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,...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.展开更多
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)an...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.展开更多
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 m...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.展开更多
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 i...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.展开更多
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 equi...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.展开更多
The MAC protocol design for wireless sensor networks has been researched and developed for decades. SMAC protocol is a famous energy-efficient MAC protocol. Based on SMAC protocol, we find that the boundary nodes in t...The MAC protocol design for wireless sensor networks has been researched and developed for decades. SMAC protocol is a famous energy-efficient MAC protocol. Based on SMAC protocol, we find that the boundary nodes in the cluster-shaped synchronization structure bring energy consumption seriously, and provide a virtual cluster aggregation (VCA) algorithm. Because the bounder node follows multiple schedules in one cycle, it may deplete earlier and cause segmentation in wireless sensor networks. The algorithm reduces energy consumption of boundary nodes and extends the lifetime of entire sensor network by merging different virtual clusters, but increases the data transmission delay. Because the sensor nodes have the fixed duty cycle, the larger the coverage area of network is, the greater the data transmission delay increases. We propose the dynamic duty cycle (DDC) algorithm to solve this effect. When the network load and data transmission delay increase, the DDC algorithm exponentially changes the duty cycle of the node to reduce latency. The simulation results show that the performance of SMAC with the VCA and DDC algorithm obtains improvement significantly.展开更多
The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments...The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.展开更多
Clustering or connected dominating set (CDS) both approaches can establish a virtual backbone (VB) in wireless sensor networks (WSNs) or wireless mesh networks (WMNs). Each cluster consisting of a cluster head (CH) an...Clustering or connected dominating set (CDS) both approaches can establish a virtual backbone (VB) in wireless sensor networks (WSNs) or wireless mesh networks (WMNs). Each cluster consisting of a cluster head (CH) and its neighboring nodes can form a dominating set. After some bridging nodes were selected, cluster heads (CHs) connected through these bridging nodes naturally formed a CDS. Although CDS provides obvious backbone architecture, however, the number of cluster heads and bridging nodes may be too large, this may cause the loss of advantages of virtual backbone. When we effectively reduce their numbers, more effectively WCDS (Weakly Connected Dominating Set) can be fining out. Some essential topics on constructing WCDS-based VB in WSN/WMN are discussed in this paper. From the point of view of three different protocol layers, including network (NWK) layer, MAC layer, and physical (PHY) layer, we explore their cross-layer research topics and design algorithms. For NWK layer, area-based WCDS algorithms and routing strategies including via VB and not via VB are discussed. For MAC layer, a WCDS-based energy-efficient MAC protocol is presented. For PHY layer, battery-aware alternative VB selections and sensor nodes with different transmission ranges are addressed.展开更多
This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive comput...This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.展开更多
基金supported by the State Key Laboratory of Pathogens and Biosecurity(Grant No.SKLPBS2240).
文摘Accurate detection of dimethyl methylphosphonate(DMMP),a simulant for chemical warfare agents,is vital for both public safety and military defense.However,conventional detection methods suffer from low selectivity,owing to interference from structurally similar compounds.In this study,we present a highly sensitive and selective gas sensor utilizing a solid-mounted film bulk acoustic resonator based on carbon nanotubes functionalized with hexafluoroisopropanol(HFiP)to enhance DMMP detection.This approach leverages the strong hydrogen bonding between HFiP and DMMP molecules to significantly improve the sensor’s adsorption capacity and selectivity.To further refine selectivity and at the same time solve the cross-sensitivity problem of sensitive membranes,we introduce a virtual sensor array design,generated by modulating the input power to the resonator,which enables the sensor to operate in multiple response modes across varying vibrational amplitudes.These multimodal responses are subjected to linear discriminant analysis,allowing precise differentiation of DMMP from other volatile organic compounds such as tributyl phosphate and dimethyl phthalate.Our results demonstrate superior performance in terms of both sensitivity and selectivity,offering a robust solution for detecting low-concentration DMMP in complex environments.
基金supported by the Italian project POR Puglia FESR 2014-2020“Research for Innovation(REFIN)”(8473A73)the MOST-Sustainable Mobility National Research Center,receiving funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)–MISSIONE 4COMPONENTE 2,INVESTIMENTO 1.4-D.D.103317/06/2022,CN00000023)。
文摘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.
基金supported by the National Natural Science Foundation of China(22278241)the National Key R&D Program of China(2018YFA0901700)+1 种基金a grant from the Institute Guo Qiang,Tsinghua University(2021GQG1016)Department of Chemical Engineering-iBHE Joint Cooperation Fund.
文摘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.
基金Funding by Ministerium für Wirtschaft,Innovation,Digitalisierung und Energie des Landes Nordrhein-Westfalen。
文摘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.
基金supported by the National Natural Science Foundation of China (51906181)the 2021 Construction Technology Plan Project of Hubei Province (No.2021-83)the Excellent Young and Middle-aged Talent in Universities of Hubei Province,China (Q20181110).
文摘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.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2020R1A2C1099611).
文摘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.
基金Sponsored by the Important National Science and Technology Specific Projects( Grant No. 2012ZX03004003)the National Natural Science Foundation of China( Grant No. 61171110)
文摘The MAC protocol design for wireless sensor networks has been researched and developed for decades. SMAC protocol is a famous energy-efficient MAC protocol. Based on SMAC protocol, we find that the boundary nodes in the cluster-shaped synchronization structure bring energy consumption seriously, and provide a virtual cluster aggregation (VCA) algorithm. Because the bounder node follows multiple schedules in one cycle, it may deplete earlier and cause segmentation in wireless sensor networks. The algorithm reduces energy consumption of boundary nodes and extends the lifetime of entire sensor network by merging different virtual clusters, but increases the data transmission delay. Because the sensor nodes have the fixed duty cycle, the larger the coverage area of network is, the greater the data transmission delay increases. We propose the dynamic duty cycle (DDC) algorithm to solve this effect. When the network load and data transmission delay increase, the DDC algorithm exponentially changes the duty cycle of the node to reduce latency. The simulation results show that the performance of SMAC with the VCA and DDC algorithm obtains improvement significantly.
文摘The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.
文摘Clustering or connected dominating set (CDS) both approaches can establish a virtual backbone (VB) in wireless sensor networks (WSNs) or wireless mesh networks (WMNs). Each cluster consisting of a cluster head (CH) and its neighboring nodes can form a dominating set. After some bridging nodes were selected, cluster heads (CHs) connected through these bridging nodes naturally formed a CDS. Although CDS provides obvious backbone architecture, however, the number of cluster heads and bridging nodes may be too large, this may cause the loss of advantages of virtual backbone. When we effectively reduce their numbers, more effectively WCDS (Weakly Connected Dominating Set) can be fining out. Some essential topics on constructing WCDS-based VB in WSN/WMN are discussed in this paper. From the point of view of three different protocol layers, including network (NWK) layer, MAC layer, and physical (PHY) layer, we explore their cross-layer research topics and design algorithms. For NWK layer, area-based WCDS algorithms and routing strategies including via VB and not via VB are discussed. For MAC layer, a WCDS-based energy-efficient MAC protocol is presented. For PHY layer, battery-aware alternative VB selections and sensor nodes with different transmission ranges are addressed.
文摘This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.
