The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor env...The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor environments and radio signal interference caused by multiwall environments,which collectively lead to significant positioning errors,vision-based positioning has emerged as a crucial method in indoor positioning research.This paper introduces a scale hierarchical matching model to tackle challenges associated with large visual databases and high scene similarity,both of which will compromise matching accuracy and lead to prolonged positioning delays.The proposed model establishes an image feature database using GIST features and speeded up robust feature(SURF)in the offline stage.In the online stage,a positioning navigating algorithm is constructed based on Dijkstra’s path planning.Additionally,a corresponding Android application has been developed to facilitate visual positioning and navigation in indoor environments.Experimental results obtained in real indoor environments demonstrate that the proposed method significantly enhances positioning accuracy compared with similar algorithms,while effectively reducing time overhead.This improvement caters to the requirements for indoor positioning and navigation,thereby meeting user needs.展开更多
Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples w...Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.展开更多
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of mult...Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of multi-sensor fusion localization algorithms for smart plastering robots,and an analysis of path planning and navigation functions for smart plastering robots.It is hoped that through this analysis,a reference is provided for the path planning and navigation design of such robots to meet their practical application needs.展开更多
Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an in...Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.展开更多
Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance ...Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.展开更多
This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was t...This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was to understand the impact of external and internal sources on indoor air quality(IAQ)in school environments.Monitoring campaigns were carried out in different indoor spaces,including classrooms,the gym,and the canteen,and the results were compared with PM_(10) levels outside the building.At Primary School No.5,indoor PM10 concentrations were consistently higher than the outdoor values measured on Avenida do Bocage,with an average Indoor/Outdoor(I/O)ratio of 2.2,indicating a significant impact of indoor activities on particle levels.Similarly,at the D.Luís Mendonça Furtado Basic School,there was an increase in PM_(10) and PM_(2:5) concentrations during school hours,with the highest I/O ratio(3.04)recorded on school days.In the evenings and at weekends,when the spaces were unoccupied,particle concentrations dropped considerably,reaching an I/O ratio of 0.70.Said results suggest that indoor activities are a determining factor for particle levels in indoor air,emphasizing the need for ventilation and pollution control strategies in schools to protect the health of students and staff.展开更多
As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to...As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorg...Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorganisms,bacteria and viruses.This study investigates the common ionizers with different module types,and the mechanism of NAIs for enhancing indoor air quality,as well as the positive and negative impacts on human health.The association between NAI concentrations and human health outcomes is examined,and alternative measures to balance beneficial and unavailing effects are investigated.While NAIs demonstrate efficacy in removing particulate pollutants,alleviating depression,enhancing cognitive function and even stimulating sympathetic activity,it is pertinent to acknowledge the presence of contradictory findings concerning their effects on cardiac autonomic function and respiratory physiology.To address this complexity,it is imperative to consider alternative measures that strike a balance between the beneficial and unavailing effects of NAIs.These measures can encompass a general assessment of the characteristics of particulate pollutants,a strategic selection of ionizer technologies,and adherence to the recommended optimal concentration thresholds of NAIs.展开更多
To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflectio...To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.展开更多
Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the rece...Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.展开更多
Objective We aimed to analyze the current indoor radon level and estimate the population risk of radon-induced lung cancer in urban areas of China.Methods Using the passive monitoring method,a new survey on indoor rad...Objective We aimed to analyze the current indoor radon level and estimate the population risk of radon-induced lung cancer in urban areas of China.Methods Using the passive monitoring method,a new survey on indoor radon concentrations was conducted in 2,875 dwellings across 31 provincial capital cities in Chinese mainland from 2018 to 2023.The attributable risk of lung cancer induced by indoor radon exposure was estimated based on the risk assessment model.Results The arithmetic mean(AM)and geometric mean(GM)of indoor radon concentrations were 65 Bq/m^(3)and 55 Bq/m^(3),respectively,with 13.6%of measured dwellings exceeding 100 Bq/m^(3)and 0.6%exceeding 300 Bq/m^(3).The estimated number of lung cancer deaths induced by indoor radon exposure was 150,795,accounting for 20.30%(95%CI:20.21%-20.49%)of the lung cancer death toll.Conclusion This study provided the most recent data on national indoor radon levels in urban areas and the attributable risk of lung cancer.These results served as an important foundation for further research on the disease burden of indoor radon exposure and radon mitigation efforts.展开更多
In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine ...In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.展开更多
This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetwork...This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.展开更多
Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)sig...Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.展开更多
The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination s...The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure distortion.Here,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient IPVs.The hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline order.Simultaneously,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier mobility.As a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.展开更多
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+1 种基金the Open Research Fund of National Mobile Communications Research Laboratory in Southeast University(No.2023D07)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘The impact of location services on people’s lives has grown significantly in the era of widespread smart device usage.Due to global navigation satellite system(GNSS)signal rejection,weak signal strength in indoor environments and radio signal interference caused by multiwall environments,which collectively lead to significant positioning errors,vision-based positioning has emerged as a crucial method in indoor positioning research.This paper introduces a scale hierarchical matching model to tackle challenges associated with large visual databases and high scene similarity,both of which will compromise matching accuracy and lead to prolonged positioning delays.The proposed model establishes an image feature database using GIST features and speeded up robust feature(SURF)in the offline stage.In the online stage,a positioning navigating algorithm is constructed based on Dijkstra’s path planning.Additionally,a corresponding Android application has been developed to facilitate visual positioning and navigation in indoor environments.Experimental results obtained in real indoor environments demonstrate that the proposed method significantly enhances positioning accuracy compared with similar algorithms,while effectively reducing time overhead.This improvement caters to the requirements for indoor positioning and navigation,thereby meeting user needs.
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
基金supported by the National Key Research and Development Program of China(No.2023YFC3706602)the National Natural Science Foundation of China(Nos.22225605 and 22193051)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750200).
文摘Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金Science and Technology Research Project of Chongqing Education Commission(Project No.:KJQN202401902)Chongqing Construction Science and Technology Plan Project(Project No.:Chinese Society For Urban Studies,2024:3-24)+1 种基金cientific Research Fund Project of Chongqing Institute of Engineering(Project No.:2022gcky01)College Student Innovation and Entrepreneurship Training Program Project of Chongqing Institute of Engineering(Project No.:CXCY2024018)。
文摘Taking modern indoor building construction as an example,this study analyzes the path planning and navigation of a smart plastering robot.It includes a basic introduction to smart plastering robots,an analysis of multi-sensor fusion localization algorithms for smart plastering robots,and an analysis of path planning and navigation functions for smart plastering robots.It is hoped that through this analysis,a reference is provided for the path planning and navigation design of such robots to meet their practical application needs.
基金Funding Statement:This research was conducted as part of the Tech4You Project“Technologies for climate change adaptation and quality of life improvement”,n.ECS0000009,CUP H23C22000370006,Italian PNRR,Mission 4,Component 2,Investment 1.5 funded by the European Union-NextGenerationEU.
文摘Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
文摘Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.
文摘This article analyses the relationship between PM_(10) concentrations inside and outside two schools in Barreiro,Portugal:Primary School No.5 and D.Luís Mendonça Furtado Basic School.The main objective was to understand the impact of external and internal sources on indoor air quality(IAQ)in school environments.Monitoring campaigns were carried out in different indoor spaces,including classrooms,the gym,and the canteen,and the results were compared with PM_(10) levels outside the building.At Primary School No.5,indoor PM10 concentrations were consistently higher than the outdoor values measured on Avenida do Bocage,with an average Indoor/Outdoor(I/O)ratio of 2.2,indicating a significant impact of indoor activities on particle levels.Similarly,at the D.Luís Mendonça Furtado Basic School,there was an increase in PM_(10) and PM_(2:5) concentrations during school hours,with the highest I/O ratio(3.04)recorded on school days.In the evenings and at weekends,when the spaces were unoccupied,particle concentrations dropped considerably,reaching an I/O ratio of 0.70.Said results suggest that indoor activities are a determining factor for particle levels in indoor air,emphasizing the need for ventilation and pollution control strategies in schools to protect the health of students and staff.
基金supported in part by the Natural Science Basic Research Plan in Shaanxi Province under Grant 2024JC-ZDXM-36in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-255+2 种基金in part by the Excellent Youth Science Foundation of Xi’an University of Science and Technology under Grant 2019YQ3-13in part by the Xi’an Key Laboratory of Network Convergence Communications under Grant 2022NCC-K102in part by the Fundamental Research Funds for the Central Universities under Grant QTZX23029。
文摘As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金Research Project of Marine Design and Research Institute of China。
文摘Negative air ions(NAIs)in indoor environments have been suggested to positively impact human health by effectively reducing particulate contamination and gaseous pollutants,as well as inhibiting the growth of microorganisms,bacteria and viruses.This study investigates the common ionizers with different module types,and the mechanism of NAIs for enhancing indoor air quality,as well as the positive and negative impacts on human health.The association between NAI concentrations and human health outcomes is examined,and alternative measures to balance beneficial and unavailing effects are investigated.While NAIs demonstrate efficacy in removing particulate pollutants,alleviating depression,enhancing cognitive function and even stimulating sympathetic activity,it is pertinent to acknowledge the presence of contradictory findings concerning their effects on cardiac autonomic function and respiratory physiology.To address this complexity,it is imperative to consider alternative measures that strike a balance between the beneficial and unavailing effects of NAIs.These measures can encompass a general assessment of the characteristics of particulate pollutants,a strategic selection of ionizer technologies,and adherence to the recommended optimal concentration thresholds of NAIs.
基金supported by the National Natural Science Foundation of China(No.62071365)the Key Research and Development Program of Shaanxi Province(No.2017ZDCXL-GY-06-02).
文摘To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.
基金supported in part by the National Natural Science Foundation of China(U2001213 and 61971191)in part by the Beijing Natural Science Foundation under Grant L182018 and L201011+2 种基金in part by National Key Research and Development Project(2020YFB1807204)in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006)in part by the Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2020-S321)。
文摘Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.
基金supported by National Natural Science Foundation(NO.12105274),China.
文摘Objective We aimed to analyze the current indoor radon level and estimate the population risk of radon-induced lung cancer in urban areas of China.Methods Using the passive monitoring method,a new survey on indoor radon concentrations was conducted in 2,875 dwellings across 31 provincial capital cities in Chinese mainland from 2018 to 2023.The attributable risk of lung cancer induced by indoor radon exposure was estimated based on the risk assessment model.Results The arithmetic mean(AM)and geometric mean(GM)of indoor radon concentrations were 65 Bq/m^(3)and 55 Bq/m^(3),respectively,with 13.6%of measured dwellings exceeding 100 Bq/m^(3)and 0.6%exceeding 300 Bq/m^(3).The estimated number of lung cancer deaths induced by indoor radon exposure was 150,795,accounting for 20.30%(95%CI:20.21%-20.49%)of the lung cancer death toll.Conclusion This study provided the most recent data on national indoor radon levels in urban areas and the attributable risk of lung cancer.These results served as an important foundation for further research on the disease burden of indoor radon exposure and radon mitigation efforts.
基金The research will be funded by the Multimedia University,Department of Information Technology,Persiaran Multimedia,63100,Cyberjaya,Selangor,Malaysia.
文摘In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.
基金the Fundamental Research Grant Scheme-FRGS/1/2021/ICT09/MMU/02/1,Ministry of Higher Education,Malaysia.
文摘This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.
文摘Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.
基金financial support from the Natural Science Foundation of Guizhou Province (Grant No. ZK 2024-087)Natural Science Foundation of China (no. 22005071)。
文摘The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure distortion.Here,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient IPVs.The hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline order.Simultaneously,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier mobility.As a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.
