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ردیف | عنوان | نوع |
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1 |
LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring
پیاده سازی سیستم اینترنت اشیاء مبتنی بر LoRaWAN برای نظارت بر کیفیت هوای خارج از منزل در محدوده بلند-2022 This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality in-
formation and update it on the cloud. The developed long-range wide area network (LoRaWAN)-
based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-
IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness.
The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and hu-
midity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network
(TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a
rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable
operation. Our system simultaneously gathers the considered air quality information by using the
smart sensing unit. Then, the system transmits the information through the gateway to the TTN
platform, which is integrated with the ThingSpeak IoT server. This action updates the collected
data and displays these data on a developed Web-based dashboard and a Graphical User Interface
(GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily
accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-
AQMS are validated by comparing them with experimental results based on the high-
technology Aeroqual air quality monitoring devices. Our system can reliably monitor various
air quality indicators and efficiently transmit the information in real time over the Internet. keywords: پایش کیفیت هوا | Air quality monitoring | Iot lora lorawan | TTN ThingSpeak Virtuino |
مقاله انگلیسی |
2 |
Knowledge Management Process for Air Quality Systems based on Data Warehouse Specification
فرآیند مدیریت دانش برای سیستم های کیفیت هوا بر اساس مشخصات انبار داده-2021 Even though several systems for Air Quality (AQ) monitoring have been in existence for over a decade, a research model for
Knowledge Management (KM) of AQ data has to be created in order to enhance the decision-making and organize the air quality
data collected from the Internet of Things (IoT) consumer devices. This model should be made more performant by ensuring greater
flexibility and interoperability between devices and emerging technologies. In this context, we propose an approach for representing
Data WareHouse (DWH) schema based on an ontology that captures the multidimensional knowledge of tools, techniques, and
technologies used for novel AQ systems. This enhances decision-making by coping with potential problems such as data sources
heterogeneity and covering the various phases of the decision-making life cycle.
Keywords: Knowledge Management | Air Quality | Data Warehouse | Conceptual Data Model | Multidimensional Design | Ontology. |
مقاله انگلیسی |
3 |
The services field: A cornucopia filled with potential management topics
زمینه خدمات: قرنیه پر از موضوعات بالقوه مدیریت-2021 A primary aim of management research is to develop knowledge about the management of organizations. Many
countries’ long-trend shift towards a service economy and the concomitant increase in the number of service
organizations have stimulated advancements in service management research. However, some blind spots
remain. In this article, we focus on product market stakeholders and organizational stakeholders and discuss
research questions (or blind spots) that map to different areas of management literature, especially those of
service management literature. We believe that attaining answers to those research questions can yield important
insights that have the potential to advance management research. Keywords: Air quality | e-commerce | Service communication | Servicescape safety and healthiness | Sustainability |
مقاله انگلیسی |
4 |
A stochastic model of particulate matters with AI-enabled technique-based IoT gas detectors for air quality assessment
مدل تصادفی ذرات معلق با ردیاب های گاز IoT مبتنی بر تکنیک هوش مصنوعی برای ارزیابی کیفیت هوا-2020 Monitoring air quality in urban and industrial environments and estimating exposure to particulate matter (PM)
pollution concentrations are critical issues that affect human health. Because of aerosols (suspended particles),
PM is mostly observed near the surface and thus can be inhaled. To predict the modeling of micro-to-nano-sized
particle suspensions, this study presents a stochastic model in environmental dynamics with internet of things
(IoT) gas detectors based on an artificial intelligence (AI)-enabled technique; the model can determine floating
fine PM dispersion in a city to assess and monitor air quality. The factors that influence the prediction are
weather- and air pollution-related data, such as humidity, temperature, wind, PM2.5, and PM10. In this study,
these factors have been considered at 7 measuring stations across the urban region in Taipei City, Taiwan, from
2013 to 2018. A nonlinear autoregressive network with exogenous inputs model is constructed using estimated
states to investigate approaches for identifying PM; the model can be a state–space self-tuning stochastic model
for predicting unknown nonlinear sampled data. The results indicate that a satisfactory agreement was obtained
using a normalized root mean square deviation, with small values of 0.0504 and 0.0802 for PM2.5 and PM10,
respectively. Accordingly, this study presents that the time-domain causality between PM and the atmospheric
environment can be constructed using discrete-time models that can be satisfactorily implemented in developing
different air quality monitoring systems for the long-term prediction of air pollution. Keywords: Particulate matter | Micro-to-nano-sized particle suspensions | Modeling | Micropollutants | Artificial intelligence | Atmospheric environment |
مقاله انگلیسی |
5 |
Identification of high impact factors of air quality on a national scale using big data and machine learning techniques
شناسایی عوامل تأثیر زیاد کیفیت هوا در مقیاس ملی با استفاده از داده های بزرگ و تکنیک های یادگیری ماشین-2020 To effectively control and prevent air pollution, it is necessary to study the influential factors of air
quality. A number of previous studies have explored the relationships between air pollution and related
factors. However, the methods currently used either cannot well address the multicollinearity problem
or fail to explain the importance of the influential factors. Moreover, most of the existing literature
limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of
studies that analyze the influential factors from the perspective of a country or take into consideration
multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national
scale to investigate the most important factors of air quality. In order to study as much influential factors
as possible, 171 features ranging from environmental, demographical, economic, meteorological, and
energy, were collected and analyzed. To tackle such a “big data” problem, a non-linear machine learning
algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and
measure the variable importance. Geographical Information System (GIS) is employed to preprocess the
diversified variables and visualize the results. Performance of XGBoost is compared with other models
and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S.
show that our methodology framework can effectively uncover the important factors of air quality. Six
kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed
from the six aspects to control and prevent air pollution. Keywords: Air quality index | Big data | GIS | National scale | Variable importance | XGBoost |
مقاله انگلیسی |
6 |
PRAISE-HK: A personalized real-time air quality informatics system for citizen participation in exposure and health risk management
PRAISE-HK: یک سیستم انفورماتیک شخصی با کیفیت هوا در زمان واقعی برای مشارکت شهروندان در معرض خطر و مدیریت ریسک سلامت-2020 Exposure to air pollutants causes a range of adverse health effects. These harmful effects occur whenever and
wherever people come into direct contact with air pollution. Therefore, individual actions that reduce the frequency,
duration, and severity of personal contact with air pollution can reduce health risks. We developed a
system that empowers the public with personalized information on air quality and exposure health risk. This
system, the Personalised Real-Time Air Quality Informatics System for Exposure – Hong Kong (PRAISE-HK,
http://praise.ust.hk/), is embodied in an interactive mobile application. PRAISE-HK is based on real-time data
on emissions, high resolution urban morphology, meteorology, physical and chemical processes affecting pollutant
transport and transformations, extensive measurements of air pollution concentrations in typical locations
such as homes, schools, offices, and transportation, and big data integration of sensor monitoring to accurately
estimate current and short-term forecasted street-level air quality. The street-level air quality simulation has
been validated against reference monitoring data. Ongoing and planned future enhancements to PRAISE-HK
include prediction of personal exposure and health response. PRAISE-HK is an example of the use of collective
intelligence in a smart city to engage citizens in learning about and managing their own exposure to air pollution. Keywords: Air pollution | Personalized exposure | Individual health sensitivity | Citizen engagement |
مقاله انگلیسی |
7 |
Design of Low Cost, Energy Efficient, IoT Enabled, Air Quality Monitoring System with Cloud Based Data Logging, Analytics and AI
طراحی کم هزینه ، انرژی کارآمد ، اینترنت اشیا ، سیستم نظارت بر کیفیت هوا با ثبت داده مبتنی بر ابر ، تجزیه و تحلیل و هوش مصنوعی-2020 This paper presents a design of real-time Air Quality
Monitoring System (AQMS) which incorporates Internet of Things
(IoT) and cloud computing. AQMS utilizes solar panel and battery
pack for independent and autonomous operation, thus, making it
self-powered and sustainable. AQMS is based on AVR
Microcontroller (Atmega32) and GSM modem (Sim900) for
connectivity with the cloud application. The design is made low cost
and scalable so that around 50nos. of such systems can be installed on
roundabouts of market places, residential and industrial areas. The
AQMS monitors the air quality with the help of a miniature suction
pump (5volt DC) which establishes a controlled and constant stream
of air-flow through a manifold that encapsulates electromechanical
sensors, thus measuring the concentration of O2, CO, CO2, SO / SO2
(SOx), NO/ NO2 (NOx), Hydrocarbon (CxHx), temperature, humidity
and noise. By default, the air sampling is carried out once in an hour
which may be changed depending on the change in air quality, i.e.
making it adoptive for energy conservation and extending the sensor’s
life. The data collected at the cloud application will be processed using
data analytics and Artificial Intelligence (AI) for getting insights of
data (data mining) regarding the potential locations where the
emissions are critical and disastrous for environmental, thus, leading
to prevent any mishap. The design is mapped over a metropolitan city
of Pakistan, i.e. Karachi, thus initiating the transformation of Karachi
to a smart city. Keywords: Air Quality Monitoring System | Internet of Things | Cloud Computing | Data Analytics | Artificial Intelligence |
مقاله انگلیسی |
8 |
Neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality
روش های الکتروانسفالوگرام سیگنال عصبی (EEG) برای بهبود تعامل انسان سازی با کیفیت هوای متفاوت داخلی-2019 In this study, neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality conditions were investigated. Experiment was conducted to study cor- relations between EEG frequency bands and subjective perception as well as task performance. Machine learning-based EEG pattern recognition methods as feedback mechanisms were also investigated. Results showed that EEG theta band (4–8 Hz) correlated with subjective perceptions, and EEG alpha band (8–13 Hz) correlated with task performance. These EEG indices could be utilized as more objective metrics in addition to questionnaire and task-based metrics. For the machine learning-based EEG pattern recog- nition methods, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers can classify mental states under different indoor air quality conditions with high accuracy. In general, the EEG theta and alpha bands as more objective indices and the machine learning-based EEG pattern recog- nition methods as real-time feedback mechanisms have good potential to improve the human-building interaction. Keywords: Electroencephalogram (EEG) | Machine learning | Human-building interaction | Indoor air quality | Short-term performance |
مقاله انگلیسی |
9 |
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
بهبود دقت پیش بینی کیفیت هوا در وضوح زمانی بزرگتر با استفاده از تکنیک های یادگیری عمیق و انتقال یادگیری-2019 As air pollution becomes more and more severe, air quality prediction has become an important approach for air
pollution management and prevention. In recent years, a number of methods have been proposed to predict air
quality, such as deterministic methods, statistical methods as well as machine learning methods. However, these
methods have some limitations. Deterministic methods require expensive computations and specific knowledge
for parameter identification, while the forecasting performance of statistical methods is limited due to the linear
assumption and the multicollinearity problem. Most of the machine learning methods, on the other hand, cannot
capture the time series patterns or learn from the long-term dependencies of air pollutant concentrations.
Furthermore, there is a lack of methods that could generate high prediction accuracy for air quality forecasting
at larger temporal resolutions, such as daily and weekly or even monthly. This paper, therefore, proposes a deep
learning-based method namely transferred bi-directional long short-term memory (TL-BLSTM) model for air
quality prediction. The methodology framework utilizes the bi-directional LSTM model to learn from the longterm
dependencies of PM2.5, and applies transfer learning to transfer the knowledge learned from smaller temporal
resolutions to larger temporal resolutions. A case study is conducted in Guangdong, China to test the
proposed methodology framework. The performance of the framework is compared with other commonly seen
machine learning algorithms, and the results show that the proposed TL-BLSTM model has smaller errors,
especially for larger temporal resolutions Keywords: Air quality prediction | Large temporal resolution | Deep learning | Long short-term memory | Transfer learning |
مقاله انگلیسی |
10 |
Advancing the prediction accuracy of satellite-based PM2:5 concentration mapping: A perspective of data mining through in situ PM2:5 measurements
پیشبرد دقت پیش بینی نقشه برداری غلظت PM2:5 ماهواره ای مبتنی بر ماهواره: چشم انداز کاوی داده از طریق اندازه گیری PM2:5 درجا-2019 Ground-measured PM2.5 concentration data are oftentimes used as a response variable in various
satellite-based PM2.5 mapping practices, yet few studies have attempted to incorporate groundmeasured
PM2.5 data collected from nearby stations or previous days as a priori information to
improve the accuracy of gridded PM2.5 mapping. In this study, Gaussian kernel-based interpolators were
developed to estimate prior PM2.5 information at each grid using neighboring PM2.5 observations in
space and time. The estimated prior PM2.5 information and other factors such as aerosol optical depth
(AOD) and meteorological conditions were incorporated into random forest regression models as
essential predictor variables for more accurate PM2.5 mapping. The results of our case study in eastern
China indicate that the inclusion of ground-based PM2.5 neighborhood information can significantly
improve PM2.5 concentration mapping accuracy, yielding an increase of out-of-sample cross validation R2
by 0.23 (from 0.63 to 0.86) and a reduction of RMSE by 7.72 (from 19.63 to 11.91) mg/m3. In terms of the
estimated relative importance of predictors, the PM2.5 neighborhood information played a more critical
role than AOD in PM2.5 predictions. Compared with the temporal PM2.5 neighborhood term, the spatially
neighboring PM2.5 term has an even larger potential to improve the final PM2.5 prediction accuracy.
Additionally, a more robust and straightforward PM2.5 predictive framework was established by
screening and removing the least important predictor stepwise from each modeling trial toward the final
optimization. Overall, our results fully confirmed the positive effects of ground-based PM2.5 information
over spatiotemporally neighboring space on the holistic PM2.5 mapping accuracy. Keywords: PM2.5 | Aerosol optical depth | Spatiotemporal interpolation | Random forest | Air quality |
مقاله انگلیسی |