Manual Control Research Articles

Towards efficient and reliable utilization of automated data collection: Media scrapers applied to news on climate change

Abstract: Automated data collection provides tempting opportunities for social sciences and humanities studies. Abundant data accumulating in various digital archives allows more comprehensive, timely and cost-efficient ways of harvesting and processing information. While easing or even removing some of the key problems, such as laborious and time-consuming data collection and potential errors and biases related to subjective coding of materials and distortions caused by focus on small samples, automated methods also bring in new risks such as poor understanding of contexts of the data or non-recognition of underlying systematic errors or missing information. Results from testing different methods to collect data describing newspaper coverage of climate change in Finland emphasize that fully relying on automatable tools such as media scrapers has its limitations and can provide comprehensive but incomplete document acquisition for research. Many of these limitations can, however, be addressed and not all of them rely on manual control.

The Effect of a Table Tennis Exercise Program With a Task-Oriented Approach on Visual Perception and Motor Performance of Adolescents With Developmental Coordination Disorder.

In this study we investigated the effects of an 8-week table tennis exercise program with a task-oriented approach on visual perception and motor performance of 31 adolescents with developmental coordination disorder (DCD). The participants were identified by their teachers as having greater difficulty than their peers (450 students from three Korean middle schools) in physical education (PE) classes. On the Bruininks-Oseretsky Test of Motor Proficiency-2, these adolescents scored below the 15th percentile and showed difficulties in performing daily life activities due to motor performance problems; they did not have physical defects, intellectual or neurological impairments, or Attention Deficit Hyperactivity Disorder. Of 98 prospective adolescents with PE difficulties, we obtained personal assent and parents' informed consent from 54, and 31 of these met screening criteria for DCD through the Developmental Coordination Disorder Questionnaire-Korean. This final group was divided in non-random fashion (based on the proximal geographic grouping of the children's schools) between an experimental group (n = 16) and a control group (n = 15). The experimental group participated in the 8-week task-oriented table tennis training program with three 90-minute sessions per week, while the control group only participated in regular PE classes twice per week. We measured participants' visual perception and motor performance in the same environment before and after the intervention program. Participants' visual perception was significantly more improved in the experimental group than the control group, with specific improved skills in visual-motor search, visual-motor speed, figure-ground, and visual closure; copying and perceptual constancy skills did not improve significantly. In addition, total motor performance and motor sub-skills, including fine manual control, manual coordination, body coordination, strength, and agility were significantly more improved in the experimental group than in the control group. Thus, our task-oriented table tennis exercise program was of greater assistance than general PE classes for improving visual perception and motor performance in adolescents at risk of DCD.

Comparison of automated and manual control methods in minimal flow anesthesia.

New-generation anesthesia machines administer inhalation anesthetics and automatically control the fresh gas flow (FGF) rate. This study compared the administration of minimal flow anesthesia (MFA) using the automatically controlled anesthesia (ACA) module of the Mindray A9 (Shenzhen, China) anesthesia machine versus manual control by an anesthesiologist. We randomly divided 76 patients undergoing gynecological surgery into an ACA group (Group ACA) and a manually controlled anesthesia group (Group MCA). In Group MCA, induction was performed with a mixture of 40-60% O2 and air with a 4L/min FGF until the minimum alveolar concentration (MAC) reached 1. Next, MFA was initiated with 0.5L/min FGF. The target fraction of inspired oxygen (FiO2) value was 35-40%. In Group ACA, the MAC was defined as 1, and the FiO2 was adjusted to 35%. Depth of anesthesia, anesthetic agent (AA) consumption, time to achieve target end-tidal AA concentration, awakening times, and number of ventilator adjustments were analyzed. The two groups showed no statistically significant differences in depth of anesthesia or AA consumption (Group ACA: 19.1 ± 4.9ml; Group MCA: 17.2 ± 4.5; p-value = 0.076). The ACA mode achieved the MAC target of 1 significantly faster (Group ACA: 218 ± 51s; Group MCA: 314 ± 169s). The number of vaporizer adjustments was 15 in the ACA group and 217 in the MCA group. The ACA mode was more advantageous than the MCA mode, reaching target AA concentrations faster and requiring fewer adjustments to achieve a constant depth of anesthesia.

Intelligent Curtain System Based on 51 Microcontroller

In the 21st century, China's overall appearance has undergone a radical change, and people's demand for quality of life level is getting higher and higher. In view of this situation, this project designs an intelligent curtain based on 51 microcontroller control, which is opened remotely by the user, and the corresponding functions are accomplished by collecting environmental data through photosensitive materials and temperature sensors and transmitting the data to the microcontroller for the operation of the motor. The system realizes four modes of light control, temperature control, manual and remote control, and upholds the concept of green, convenient and stable when creating, in order to bring the maximum degree of safety, convenience and comfort to the user experience.

Learning Autonomous Viewpoint Adjustment from Human Demonstrations for Telemanipulation

Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, facilitating the control task. However, this design requires the coordination of multiple operators or may exhaust a single operator as s/he needs to control both the manipulator arm and the external sensors. To address this challenge, our work introduces a viewpoint prediction model, the first data-driven approach that autonomously adjusts the viewpoint of a dynamic camera to assist in telemanipulation tasks. This model is parameterized by a deep neural network and trained on a set of human demonstrations. We propose a contrastive learning scheme that leverages viewpoints in a camera trajectory as contrastive data for network training. We demonstrated the effectiveness of the proposed viewpoint prediction model by integrating it into a real-world robotic system for telemanipulation. User studies reveal that our model outperforms several camera control methods in terms of control experience and reduces the perceived task load compared to manual camera control. As an assistive module of a telemanipulation system, our method significantly reduces task completion time for users who choose to adopt its recommendation.

Is AI 3D-printed PSI an accurate option for patients with developmental dysplasia of the hip undergoing THA?

BackgroundIn traditional surgical procedures, significant discrepancies are often observed between the pre-planned templated implant sizes and the actual sizes used, particularly in patients with congenital hip dysplasia. These discrepancies arise not only in preoperative planning but also in the precision of implant placement, especially concerning the acetabular component. Our study aims to enhance the accuracy of implant placement during Total Hip Arthroplasty (THA) by integrating AI-enhanced preoperative planning with Patient-Specific Instrumentation (PSI). We also seek to assess the accuracy and clinical outcomes of the AI-PSI (AIPSI) group in comparison to a manual control group.MethodsThis study included 60 patients diagnosed with congenital hip dysplasia, randomly assigned to either the AIPSI or manual group, with 30 patients in each. No significant demographic differences between were noted the two groups. A direct anterior surgical approach was employed. Postoperative assessments included X-rays and CT scans to measure parameters such as the acetabular cup anteversion angle, acetabular cup inclination angle, femoral stem anteversion angle, femoral offset, and leg length discrepancy. Functional scores were recorded at 3 days, 1 week, 4 weeks, and 12 weeks post-surgery. Data analysis was conducted using SPSS version 22.0, with the significance level was set at α = 0.05.Results and conclusionThe AIPSI group demonstrated greater prosthesis placement accuracy. With the aid of PSI, AI-planned THA surgery provides surgeons with enhanced precision in prosthesis positioning. This approach potentially offers greater insights and guidelines for managing more complex anatomical variations or cases.

Parameter-less Pareto local search for multi-objective neural architecture search with the Interleaved Multi-start Scheme

With the emerging deployment of deep neural networks, such as in mobile devices and autonomous cars, there is a growing demand for neural architecture search (NAS) to automatically design powerful network architectures. It is more reasonable to formulate NAS as a multi-objective optimization problem. In addition to prediction performance, multi-objective NAS (MONAS) problems take into account other criteria like the number of parameters and inference latency. Multi-objective evolutionary algorithms (MOEAs) are the preferred approach for tackling MONAS due to their effectiveness in dealing with multi-objective optimization problems. Recently, local search-based NAS algorithms have demonstrated their efficiency over MOEAs for MONAS problems. However, their performance has been only verified on bi-objective NAS problems. In this article, we propose a local search algorithm for multi-objective NAS (LOMONAS), an efficient local search framework for solving not only bi-objective NAS problems but also NAS problems having more than two objectives. We additionally present a parameter-less version of LOMONAS, namely IMS-LOMONAS, by combining LOMONAS with the Interleaved Multi-start Scheme (IMS) to help NAS practitioners avoid manual control parameter settings. Experimental results from a series of benchmark problems in the CEC’23 Competition demonstrate the competitiveness of LOMONAS and IMS-LOMONAS compared to MOEAs in tackling MONAS within both small-scale and large-scale search spaces. Source code is available at: https://github.com/ELO-Lab/IMS-LOMONAS.

The relationships between motor skills and executive functions in children with and without autism spectrum disorder.

To date, information on associations between motor skills and executive functions (EF) in autistic children is limited. The purpose of this study was to compare motor skills and EF performance between autistic children and typically developing (TD) children and to examine the relationships between motor skills and EF in these two groups. Forty-eight autistic children and 48 TD children aged 6 to 12 years were recruited for this study. Motor skills were measured with the Bruininks-Oseretsky Test of Motor Proficiency-2 (BOT-2). EF was assessed with the Stroop Color and Word Test, the Wisconsin Card Sorting Task (WCST), and the Test of Attentional Performance: Go/No-go test. Independent sample t-tests were used to compare the BOT-2 scores and EF measures between autistic children and TD children. Pearson product-moment correlation and regressions were conducted to assess the relationships between the BOT-2 scores and the EF measures for each group. Results showed that autistic children scored significantly lower than TD children on all four BOT-2 composite scores and a total motor composite. Autistic children also demonstrated significantly lower levels of performance on all EF measures than TD children. Further, autistic children showed more significant associations between motor skills and EF than TD children, particularly pronounced in the domains of fine manual control and manual coordination to cognitive flexibility, as well as manual coordination and inhibitory control. Continued development of motor skills and EF in autistic children is important. The relationships between motor skills and EF were significant among autistic children, suggesting future research on promoting EF through motor skill interventions in autistic children is required.

Vision-Based Object Manipulation for Activities of Daily Living Assistance Using Assistive Robot

The increasing prevalence of upper and lower extremity (ULE) functional deficiencies presents a significant challenge, as it restricts individuals’ ability to perform daily tasks independently. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily living (ADLs). While assistive manipulators are available, manual control through traditional methods like joysticks can be cumbersome, particularly for individuals with severe hand impairments and vision limitations. Therefore, autonomous/semi-autonomous control of a robotic assistive device to perform any ADL task is open to research. This study addresses the necessity of fostering independence in ADLs by proposing a creative approach. We present a vision-based control system for a six-degrees-of-freedom (DoF) robotic manipulator designed for semi-autonomous “pick-and-place” tasks, one of the most common activities among ADLs. Our approach involves selecting and training a deep-learning-based object detection model with a dataset of 47 ADL objects, forming the base for a 3D ADL object localization algorithm. The proposed vision-based control system integrates this localization technique to identify and manipulate ADL objects (e.g., apples, oranges, capsicums, and cups) in real time, returning them to specific locations to complete the “pick-and-place” task. Experimental validation involving an xArm6 (six DoF) robot from UFACTORY in diverse settings demonstrates the system’s adaptability and effectiveness, achieving an overall 72.9% success rate in detecting, localizing, and executing ADL tasks. This research contributes to the growing field of autonomous assistive devices, enhancing independence for individuals with functional impairments.

Deep learning-based intelligent precise aeration strategy for factory recirculating aquaculture systems

Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development of aquaculture. However, the RAS nowdays still has poor self-learning and optimal decision-making capabilities, which leads to high aquaculture cost and low running efficiency. In this paper, a precise aeration strategy based on deep learning is designed for improving the healthy growth of breeding objects. Firstly, the situation perception driven by computer vision is used to detect the hypoxia behavior. Then combined with the biological energy model, it is constructed to calculate the breeding objects oxygen consumption. Finally, the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model. Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3% compared with the manual control and 12.8% compared with the threshold control. Meanwhile, stable water quality conditions accelerated breeding objects growth, and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.

Automatic Control of Nucleation and Crystal Growth Using Online Raman Analyzer

The accurate determination of crystal formation during crystallization is crucial for obtaining crystal products with consistent quality and quantity. In this study, we aimed to identify the feasibility of using Raman spectroscopy to monitor the crystal growth stage in the crystallization process using cephalosporin intermediate 7-ACT as an example molecule. By observing the changes in the characteristic peak of the 7-ACT crystal (504 cm−1) and the characteristic peak of the solvent acetonitrile (914 cm−1), a correlation between the crystal growth stage and the change in the Raman intensity of the crystal solution was discovered. The determination of the optimal starting time for the crystal growth stage through a Raman analyzer significantly improves the consistency of crystal product quality. This led to a fivefold reduction in the variation in the weight and water content of the final 7-ACT crystal products compared to those obtained via manual control. In addition, our experiments also indicated that Raman monitoring could be more efficient at enabling the chemical synthesis reaction to be completed compared to manual control. Thus, our work demonstrates the potential of Raman spectroscopy in the real-time control of chemical synthesis reactions and crystallization processes.

Automated Domesticity: Exploring the Realm of Home Automation Systems

This research paper presents the development and implementation of a comprehensive smart home automation system comprising a smart switchboard, smart smoke, flame, and gas detector, and a smart door lock system. Leveraging innovative technologies such as voice control, sensor networks, and Bluetooth Low Energy connectivity, the system offers users enhanced convenience, safety, and energy efficiency within residential environments. The smart switchboard enables intuitive voice-controlled operation alongside traditional manual control options, while the smart smoke, flame, and gas detector provide real-time monitoring and alerting capabilities. Additionally, the smart door lock system ensures secure access control through voice password authentication and manual lock and key options. Through this integration, our research demonstrates the transformative potential of smart home automation in revolutionizing household management and enhancing quality of life

On investigating drivers’ attention allocation during partially-automated driving

The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers’ ability to supervise the automated system and resume manual control of the vehicle.

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).

Anaerobic cryoEM protocols for air-sensitive nitrogenase proteins.

Single-particle cryo-electron microscopy (cryoEM) provides an attractive avenue for advancing our atomic resolution understanding of materials, molecules and living systems. However, the vast majority of published cryoEM methodologies focus on the characterization of aerobically purified samples. Air-sensitive enzymes and microorganisms represent important yet understudied systems in structural biology. We have recently demonstrated the success of an anaerobic single-particle cryoEM workflow applied to the air-sensitive nitrogenase enzymes. In this protocol, we detail the use of Schlenk lines and anaerobic chambers to prepare samples, including a protein tag for monitoring sample exposure to oxygen in air. We describe how to use a plunge freezing apparatus inside of a soft-sided vinyl chamber of the type we routinely use for anaerobic biochemistry and crystallography of oxygen-sensitive proteins. Manual control of the airlock allows for introduction of liquid cryogens into the tent. A custom vacuum port provides slow, continuous evacuation of the tent atmosphere to avoid accumulation of flammable vapors within the enclosed chamber. These methods allowed us to obtain high-resolution structures of both nitrogenase proteins using single-particle cryoEM. The procedures involved can be generally subdivided into a 4 d anaerobic sample generation procedure, and a 1 d anaerobic cryoEM sample preparation step, followed by conventional cryoEM imaging and processing steps. As nitrogen is a substrate for nitrogenase, the Schlenk lines and anaerobic chambers described in this procedure are operated under an argon atmosphere; however, the system and these procedures are compatible with other controlled gas environments.

A review of automotive intelligent and adaptive headlight beams intensity control approaches

The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

Model Predictive Control of Magnetic Helical Swimmers in Two-Dimensional Plane

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the body length which is less than 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm$</tex-math> </inline-formula> . Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

Voice Controlled Catheterisation Laboratory

Voice-controlled catheterization laboratory is future technology that would be designed to make the process of catheterization safer and more efficient. Cardiac Catheterization is a common procedure including Angiography and Angioplasty. The current method of catheterization uses manual lab, which can be cumbersome, stressful and prone to errors, especially in critical procedures. Voice-controlled catheterization laboratory utilizes voice commands and natural language processing to operate the Cath Lab. The system is programmed to respond to voice commands and execute the desired movement with accuracy and precision. This technology eliminates the need for manual control, reducing the risk of human error and enabling Cardiologists to concentrate on the procedure. In addition, voice control allows for faster, more efficient catheterization, which can save time and resources in a hospital setting. Voice-controlled catheterization laboratory is the future of cardiac procedures, providing a safer and more efficient way to perform catheterization. This article review the technology that is expected to become widely adopted in the near future and will significantly improve the quality of cardiovascular procedure.

Разработка и применение программы для автоматического нахождения невязки при пространственном контроле режимной метеорологической информации

Objective: to develop and apply software for automatic finding of discrepancies in automated meteorological stations in order to identify dubious and unreliable data without direct manual control in Fortran using PostgreSQL programming language queries. Write a program block for processing data from the archive of the database of meteorological stations of the unified network. Having provided a connection, with the help of requests, to ensure the receipt of data of the averaged values of ten-minute meteorological reports according to a special processing technique. Create a subroutine for solving the equation by the least squares matrix method, compare it to check the results with the linear regression method in a third-party application. Ensure proper recording of data before sending them to the database, prepare the necessary table for correct demonstration of data and user convenience in their use. Formulate correct queries to send the results of finding the discrepancy to the database. Formulate queries to create the necessary table, formulate queries and identify conditions for the implementation of the program for its more “flexible”functioning, that is, the ability to process data and find a discrepancy in the case of non-daily operation of the program. Methods: the methodology is similar to the principles of recommendations for the analysis of the results of spatial monitoring of regime meteorological information of the Main Geophysical Observatory named after Voeikova A. I. Methods include the translation of coordinates into a Cartesian system and the implementation of the solution of equation systems by the least squares method. Results: a program has been created that can work without the participation of an operator, performing automatic startup, data processing and data recording for further storage. Practical importance: the program allows you to receive data quickly due to the fast processing speed without errors caused by the human factor.

Smart city air quality management through leveraging drones for precision monitoring

Air pollution stands out as one of the most critical challenges impacting public health, the overall quality of life, and the pursuit of sustainable development in urban environments. Each existing air quality monitoring method carries inherent limitations alongside its advantages. This study addresses the drawbacks of both satellite-based and fixed monitoring stations by proposing a drone-based air quality monitoring solution. The entire urban area was surveyed using a multi-rotor drone equipped with sensors capable of measuring atmospheric components, resulting in the creation of high-resolution air pollution maps. The expansive urban area, exceeding the coverage capacity of a single flight, was systematically subdivided into subregions. Distinct measurements were conducted in each subregion, and a consolidated mapping was generated by combining the individual results obtained. The drone, operating autonomously, conducted precise measurements on a citywide scale, enhancing the consistency of pollutant mapping by eliminating potential errors associated with manual control. Pollution sources were identified throughout the city, and real-time data revealed how transportation-induced air pollution affected different regions. Environmental factors like wind and pressure differences were identified as influential in the varied spread of air pollutants across different parts of the city. The study underscores the pivotal role of transportation in urban air pollution, laying the groundwork for the development of targeted pollution control strategies in urban environments. Furthermore, the research validates the potential of UAVs in advancing the efficient management of smart cities, particularly through the monitoring and analysis of air pollution stemming from transportation.