Application of statistical analysis of sample size:How many occupant responses are required for an indoor environmental quality(IEQ)field study

Determining required sample size is one of the critical pathways to reproducible,reliable and robust results in human-related studies.This paper aims to answer a fundamental but often overlooked question:what sample size is required in surveys of occupant responses to indoor environmental quality(IEQ).The statistical models are introduced in order to promote determining required sample size for various types of data analysis methods commonly used in IEQ field studies.The Monte Carlo simulations are performed to verify the statistical methods and to illustrate the impact of sample size on the study accuracy and reliability.Several examples are presented to illustrate how to determine the value of the parameters in the statistical models based on previous similar research or existing databases.The required sample size including“worst”and“optimal”cases in each condition is obtained by this method and references.It is indicated that 385 is a“worst case”sample size to be adequate for a subgroup analysis,while if the researcher has an estimate of the study design and outcome,the“optimal case”sample size can potentially be reduced.When the required sample size is not achievable,the uncertainty in the result can properly interpret via a confidence interval.It is hoped that this paper would fill in the gap between statistical analysis of sample size and IEQ field research,and it can provide a useful reference for researchers when planning their own studies.

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation.

Indoor environmental quality(IEQ)assessment of Nigerian university libraries:A pilot study

An indoor environmental quality(IEQ)assessment of academic libraries in Nigeria was conducted through ob-jective measurements.The selected university libraries were Abubakar Gimba Library;Awwal Ibrahim Library;and Ibrahim Badamosi Babangida Library Complex.The libraries were evaluated under operating conditions and during their peak usage periods.Measurements of the main descriptors of good IEQ,including the acoustical,visual,and thermal comfort conditions were performed using portable IEQ meters.The methodology employed for the assessment of these IEQ descriptors followed prescriptions of international standards ASHRAE standard 55 and ISO 7730.To determine the time of the day in which the library buildings can achieve optimum IEQ,measurements were taken in the morning and afternoon.The results obtained were compared with reference values found in the international Standard CIBSE Guide A for adequate IEQ compliance of academic libraries.The findings of this study are expected to help in improving the IEQ of academic libraries in Nigeria and other parts of the world.

Indoor Environment Quality Monitoring and Evaluation System Based on LoRa Communication

Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.

Investigating path loss characteristics of UWB signals in vacancy indoor environment based on time-domain technique

The path loss analysis model based on 5 rays in vacancy indoor environment is proposed. The relationship between multipath overlapping and the path loss is analyzed mathematically. Time-domain technique is introduced to compute reflection coefficient in a very short time interval. A 5 rays path loss calculation method, which is satisfactory accurate, is developed. 5 typical environments are involved to analyze and generalize the common path loss characteristics in vacancy indoor environment. The simulation result shows that the path loss can be characterized as 3 zones with different path loss exponent as distance between transmitter and receiver increasing.

An improved Gmapping algorithm based map construction method for indoor mobile robot

With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.

Specific Enthalpy Based Heat Stress Index for Indoor Environments without Radiation Effect

Over 100 human thermal indices have been developed to predict the combined thermal impact on the body.In principle,these indices based on energy thermal budget equations should not only be the most complex but also be the most accurate.However,the simple indices based on algebraic or statistical models[e.g.,the wet-bulb globe temperature(WBGT)]continue to be the most popular.A new heat stress index,the enthalpy dry-bulb temperature(EnD)for indoor environments is developed and validated in this study.The EnD index is unique in that it uses the air specific enthalpy,not the wet-bulb temperature,to measure the latent heat transfer from the skin to the surrounding environment.Theoretically,the EnD index can be treated as the equivalent temperature based on the convective heat transfer coefficient h_(c).Comparison is made between the EnD index and the widely used WBGT index based on the experimental data taken from three independent studies available in the scientific literature.The results show that the EnD index can reduce the overestimation of the dry-bulb air temperature and thus reduce heat stress in most cases,especially for hot and humid environments.It can be concluded that the EnD index has the potential to replace the WBGT index as the standard heat stress index in the future.

Hardware Implementation of STM32 Microcontroller-Based Indoor Environment Monitoring System

Microcontroller is widely used in the intelligent life of modern society. Intelligent development based on Microcontroller to solve the actual needs of people’s life, work, study and other fields is the core of Microcontroller application. Therefore, it is a task for researchers to understand the structure and performance of microcontroller, develop software, and be familiar with the method and process of intelligent development based on microcontroller. And with that in mind, this paper designs and produces a physical hardware system for indoor environment detection based on STM32 microcontroller. The system can detect the light intensity, temperature and humidity, and CO gas concentration in the indoor environment;and the data is integrated and processed by the STM32 microcontroller to display the current parameter values of each quantity in the indoor environment on a 3.5-inch resistive screen;at the same time, the PC can also log in to the OneNET cloud platform through the web page, and display the light intensity, temperature and humidity, and CO gas concentration values in the indoor environment in real time in the device created by OneNET for real-time viewing. The system can also display the light intensity, temperature and humidity, and CO gas concentration values in the indoor environment in real time. The hardware system has been tested and tested to achieve its function.

RESEARCH ON INDOOR ELECTROMAGNETIC RADIATION FIELD OF MULTIPLE ANTENNA SYSTEM

The complexity of the indoor environment brings great challenges to predict the electromagnetic radiation field of multiple antenna systems. Based on the Finite Difference Time Domain (FDTD) algorithm, using the mobile phone shielding device as the multiple antenna systems example, the mobile phone shielding device's indoor electromagnetic radiation field is researched by measurment method and simulation method. The effectivity of prediction method is verified by comparing the prediciton results with the measurment results. About 80% of the error can be controlled less than dB. The quantitative research has certain guiding significance to the prediction of the multiple antenna systems radio wave propagation.

Influence of the 60 Hz Magnetic Field on the Airborne Microbial Distribution of Indoor Environments

The aim of this work was to analyze the effect of the magnetic field generated by the household appliances on the airborne microbial surrounding these equipment located on indoor environments with particular interest in the environmental fungi.A simultaneous environmental study was carried out in locals of three different geographical places of Havana,Cuba,which have televisions,computers and an electric generator.The air samples were made by a sedimentation method using Malt Extract Agar.The concentration of total aerobic mesophilic as well as fungi and yeasts were determined in rainy and little rainy seasons by applying as factors:exposure time of dishes(5 to 60 min)and distance to the wall(0 and 1 m)at a height of 1 m above the floor.The predominant fungal genera were Cladosporium,Penicillium and Aspergillus.In the dishes that were placed at 0 and 0.5 m from the emitting sources were observed that some bacteria colonies formed inhibition halos,a great diversity of filamentous fungi and an increase in the mycelium pigmentation as well as the pigments excretion.In the rainy season,the highest amounts of fungi were obtained in all samples.In the little rain season the count of the Gram-negative bacilli increased three times the Gram-positive cocci.

An Intelligent IEQ Monitoring and Feedback System:Development and Applications

Indoor environmental quality(IEQ)significantly affects human health and wellbeing.Therefore,continuous IEQ monitoring and feedback is of great concern in both the industrial and academic communities.However,most existing studies only focus on developing sensors that cost-effectively promote IEQ measurement while ignoring interactions between the human side and IEQ monitoring.In this study,an intelligent IEQ monitoring and feedback system-the Intelligent Built Enviroment(IBEM)-is developed.Firstly,the IBEM hardware instrument integrates air temperature,relative humidity,CO_(2),particulate matter with an aerodynamic diameter no greater than _(2.5)μm(PM_(2.5)),and illuminance sensors within a small device.The accuracy of this integrated device was tested through a co-location experiment with reference sensors;the device exhibited a strong correlation with the reference sensors,with a slight deviation(R^(2)>0.97 and slopes between 1.01 and 1.05).Secondly,a wireless data transmission module,a cloud storage module,and graphical user interfaces(i.e.,a web platform and mobile interface)were built to establish a pathway for dataflow and interactive feedback with the occupants of the indoor environments.Thus,the IEQ parameters can be continuously monitored with a high spatiotemporal resolution,interactive feedback can be induced,and synchronous data collection on occupant satisfaction and objective environmental parameters can be realized.IBEM has been widely applied in 131 buildings in 18cities/areas in China,with 1188 sample locations.Among these applications,we report on the targeted IEQ diagnoses of two individual buildings and the exploration of relationships between subjective and objective IEQ data in detail here.This work demonstrates the great value of IBEM in both industrial and academic research.

AlphaMobileSensing:A virtual testbed for mobile environmental monitoring

Environmental monitoring plays a critical role in creating and maintaining a comfortable,productive,and healthy environment.Built upon the advancements of robotics and data processing,mobile sensing demonstrates its potential to address problems regarding cost,deployment,and resolution that stationary monitoring encounters,which therefore has attracted increasing research attentions recently.To facilitate mobile sensing,two key algorithms are needed:the field reconstruction algorithm and the route planning algorithm.The field reconstruction algorithm is to reconstruct the entire environment field from spatially-and temporally-discrete measurements collected by the mobile sensors.The route planning algorithm is to instruct the mobile sensors where the mobile sensor needs to move to for the next measurements.The performance of mobile sensors highly depends on these two algorithms.However,developing and testing those algorithms in the real world is expensive,challenging,and time-consuming.To address these issues,we proposed and implemented an open-source virtual testbed,AlphaMobileSensing,that can be used to develop,test,and benchmark mobile sensing algorithms.AlphaMobileSensing aims to help users more easily develop and test the field reconstruction and route planning algorithms for mobile sensing solutions,without worrying about hardware fault,test accidents(such as collision during the test),etc.The separation of concerns can significantly reduce the cost of developing software solutions for mobile sensing.For versatility and flexibility,AlphaMobileSensing was wrapped up using the standardized interface of OpenAI Gym,and it also provides an interface for loading physical fields that were generated by numerical simulations as virtual test sites to perform mobile sensing and retrieving monitoring data.We demonstrated applications of the virtual testbed by implementing and testing algorithms for physical field reconstruction in both static and dynamic indoor thermal environments.AlphaMobileSensing provides a novel and flexible platform to develop,test,and benchmark mobile sensing algorithms more easily,conveniently,and efficiently.AlphaMobileSensing is open sourced at https://github.com/kishuqizhou/AlphaMobileSensing.

Modeling Human Blockers in Millimeter Wave Radio Links

In this paper, we investigate the loss caused by multiple humans blocking millimeter wave frequencies. We model human blockers as absorbing screens of infinite height with two knife-edges, We take a physical optics approach to computing the diffraction around the absorbing screens, This approach differs to the geometric optics approach described in much of the literature. The blocking model is validated by measuring the gain from multiple-human blocking configurations on an indoor link. The blocking gains predicted using Piazzi ＇ s numerical integration method （a physical optics method） agree well with measurements taken from approximately 2.7 dB to -50 dB. Thereofre, this model is suitable for real human blockers, The mean prediction error for the method is approximately -1.2 dB, and the standard deviation is approximately 5 dB.

A Comparison of Relative Humidity between Two Swedish Buildings with Different Ventilation Solutions

This project is based on measurements of the parameter relative humidity, RH (%), in two buildings: one with natural ventilation and one with mechanical ventilation. Both buildings are located in central Sweden, which constitutes a representative climate zone with respect to Swedish conditions. An important factor for the indoor environment, which affects human health and well-being, is the level of the relative humidity, RH (%). Research studies show that the healthiest level should be in the range of 40% - 60%. Surveys have revealed that about 70% of the employees at Swedish offices, schools and kindergartens experience that the air is too dry during the winter season. Previous studies show that the level of relative humidity in the indoor environment influences the prevalence of respiratory infections and allergies. The purpose of this study is to investigate how the relative humidity differ between the two buildings, and if this may be a cause of the health problems that users are affected by. During many years, users have complained about the environment in the building with mechanical ventilation and that they suffer from health problems. The method used in the study is air measurements of the two parameters, relative humidity and air temperature in the two buildings using data loggers. The indoor environment is affected by the outdoor climate and therefore instruments are placed outdoors to record seasonal variations. The measurements were carried out during the period October 2014 to September 2015 to include all of Sweden’s four seasons with completely different climatic conditions. The results of this study show that the relative humidity in the mechanically ventilated building is consistently significantly lower than in the building with natural ventilation whatever the time of year and temperature indoors. This study shows that mechanical ventilation in buildings affects the indoor environment negatively with respect to human health during most time of the year and this fact must be taken into consideration for the existing as well as the planning of new ventilation systems.

A Study for the Influence of the Location of PCMs Assembly System on Improving Thermal Environment inside Disaster-Relief Temporary Houses

Currently,people pay more and more attention to the transitional resettlement of victims after various natural disasters.There is an urgent need for a large number of temporary houses to resettle the victims after natural disasters.Disaster-relief temporary houses(DTHs)played an important role in the post-disaster resettlement in the past,which can not only be produced on a large scale,but also can be quickly and conveniently erected,which were the main means to solve the problem of transitional resettlement.However,due to their temporary nature,there was no extra energy consuming system installed in the DTHs generally.Hence the indoor thermal environment inside the DTHs was severe in summer.In this study,combined with the field experimental tests of the DTHs in Wenchuan Earthquake and Lushan Earthquake and the experimental study of the full-size DTH,it found that the thermal environment inside the DTH was intolerably high in summer.It had negative impact on victims.In order to improve the thermal environment inside DTHs during post-disaster period which lacked of extra energy resources,this study used the method of combining phase change materials(PCMs)with walls of the DTH to explore its feasibility and effectiveness.The results showed that PCMs could effectively improve the thermal environment inside the DTH in summer.Furthermore,the difference of the composite positions between PCMs and the wall affected the improvement effect.The energy release rate of the PCMs assembly system(PAS)varied according to the positions of the PCMs.

Review for Multipath Facts in the Realm of Weak GNSS Signal

One of the most significant problem pending to be mitigated for satellite navigation at indoor environments is the multipath errors. At indoor environments, weak GNSS signals should be acquired and tracked by the GPS receivers, this paper will give a review of the facts in multipath and its main influence in the GNSS navigation systems. Investigation in this field are not new, understanding and mitigating multipath effects on GPS receivers will lead to an important level where the system can be used within a desired tolerance reducing its errors due to more accurate positioning solution.

Digital twin for healthy indoor environment:A vision for the post-pandemic era

Indoor environment has significant impacts on human health as people spend 90%of their time indoors.The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment.The advancement in emerging digital twin technologies including building information modeling(BIM),Internet of Things(IoT),data analytics,and smart control have led to new opportunities for building design and operation.Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management,very few have focused on the health of indoor environment.There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment.Therefore,this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health.Specifically,the current applications of BIM,IoT sensing,data analytics,and smart building control technologies for building digital twins are reviewed,and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health.Moreover,this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues.This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Comparison of Illuminance in Different Environments: A Case Study on Subway Stations

With urbanization and the rapid development of social economy,China’s rail transit industry has developed rapidly in recent years.In order to alleviate the pressure of road network,subways provide convenience as they are fast and space-saving.Subway stations are major energy consumers of urban power grid due to their large traffic volume and long operation time.On the premise of ensuring operation safety,reducing the energy consumption of subway helps in energy conservation and emission reduction as proposed in the 13th Five-Year Plan.According to the statistics of the energy-saving evaluation report of rail transit engineering,the lighting system accounts for 20%-30%of the total power consumption of the subway station.Due to the single lighting control mode of the lighting system in the subway station,the actual station illumination cannot be reported and adjusted in time,resulting in the waste of lighting energy and high power consumption of the system.Through in-depth research on the intelligent lighting system of subway station,this paper improves the system control,and finally summarizes the optimization scheme of subway station lighting design which can effectively save the power consumption of lighting system.The main contents of this paper are as follows:The research results of this paper can provide effective measures for energy saving of electric lighting in subway stations and reduce electric energy consumption;on the other hand,as an integral part of building lighting energy-saving system,it also has certain guiding significance for the research of building lighting energy-saving.

Partition and planning:A human-like motion decision for UAV in trap environment

This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap environments.We proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural blueprints.This method reduces the dimensionality of the path planning problem,thereby enhancing the efficiency.Then,we designed a target-switching logic for the dynamic window approach.This improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation,enhancing the efficiency of the UAV in flying to task spots indoors.Additionally,by applying human-like methods of batch distance perception and obstacle perception to this scheme,we have further enhanced the robustness and efficiency of path decisions.Finally,considering the scenario of high-rise fire rescue,we conducted simulation verification.It demonstrates that our scheme enhances the efficiency and robustness of path planning.