Set Of Sensors Research Articles

At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich’s Ataxia

BackgroundFriedreich ataxia (FRDA) results in progressive impairment in gait, upper extremity coordination, and speech. Currently, these symptoms are assessed through expert examination at clinical visits. Such in-clinic assessments are time-consuming, subjective, of limited sensitivity, and provide only a limited perspective of the daily disability of patients.MethodsIn this study, we recruited 39 FRDA patients and remotely monitored their physical activity and upper extremity function using a set of wearable sensors for 7 consecutive days. We compared the sensor-derived metrics of lower and upper extremity function as measured during activities of daily living with FRDA clinical measures (e.g., mFARS and FA-ADL) and biological biomarkers of disease severity (guanine-adenine-adenine (GAA) and frataxin (FXN) levels), using Spearman correlation analyses.ResultsThe results show significant correlations with moderate to high effect sizes between multiple sensor-derived metrics and the FRDA clinical and biological outcomes. In addition, we develop multiple machine learning-based models to predict disease severity in FRDA using demographic, biological, and sensor-derived metrics. When sensor-derived metrics are included, the model performance enhances 1.5-fold and 2-fold in terms of explained variance, R², for predicting FRDA clinical measures and biological biomarkers of disease severity, respectively.ConclusionsOur results establish the initial clinical validity of using wearable sensors in assessing disease severity and monitoring motor dysfunction in FRDA.

Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.

Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment. Breath samples of 126 subjects with (n=63) or withoutSARS-CoV-2 pneumonia (n=63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E-Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi-analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E-Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point-of-care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing.

Acceptability, validity and responsiveness of inertial measurement units for assessing motor recovery after gene therapy in infants with early onset spinal muscular atrophy: a prospective cohort study

BackgroundOnasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT.MethodsClinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors.Results23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001).ConclusionIMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

AbstractWe introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

Quantifying the impact of vertical greenery systems (VGS) on Mediterranean urban microclimate during heat wave events

This research investigates the potential of vertical greenery systems (VGS) to modulate the outdoor microclimate in a real-scale Mediterranean climate street canyon with respect to a non-vegetated control during four heat waves and summer conditions. The cooling effect of VGS on air temperature was assessed using an identical set of sensors in both canyons. The effect on thermal comfort was evaluated using radiation fluxes, Mr.T tool, and the ENVI-met model that was validated against measured data.The VGS cooling effect during the heat waves was up to 0.36–2.04°C, and its duration was significantly extended. The correlation (R2 = 0.75) between the cooling effect, relative humidity, and wind direction, highlights that warmer and drier conditions increase the VGS cooling effect during heat waves.VGS improved the mean radiant temperature (Tmrt) above the canyon (3.9–4.1°C) and at the pedestrian level (2.21–2.8°C), while the physiological equivalent temperature (PET) and universal thermal climate index (UTCI) decreased by an average of 0.67–1.07 °C and 0.63–0.8 °C, respectively. Shaded walls improved the average Tmrt (11.7°C), PET (3.2°C), and UTCI (2.7°C), reflecting the importance of casting shadow in the urban environment.

Development of the neutral gas diagnostic system for neutral pressure measurements and gas analysis on the SPARC tokamak.

A suite of plasma diagnostics will be installed on the SPARC tokamak to allow for real-time plasma control, an investigation of high-field tokamak physics, and to de-risk the design of ARC, a compact fusion power plant with the aim to supply electricity to the grid. Among these diagnostics is the neutral gas diagnostics system (NTGS), a set of pressure sensors and gas analyzers used to monitor neutral pressure and gas composition for plasma control, optimization of wall conditioning, and helium ash removal, among other measurement functions linked to operational and scientific research needs. While reliable measurements of neutral pressure and gas composition have been fielded on existing magnetic-confinement fusion devices, SPARC represents a step increase in challenge due to its larger power density, higher field, high vacuum vessel bake temperatures, and higher neutron flux environment, as well as a step decrease in the accessibility for maintenance of in-vessel sensors. Multiple sensor types will be employed to have defense-in-depth and mitigate common failure modes. The NTGS system is currently progressing through final design, working to close out decisions using prototyping and analysis, and then moving on to procuring sensors for assembly and installation on SPARC. This paper outlines the current status of the system design and the diagnostic requirements that motivate neutral gas measurements on SPARC, as well as highlights the planned prototyping activities.

A multispectral camera in the VIS-NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS-NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

Data Quality Based Intelligent Instrument Selection with Security Integration

We propose a novel Data Quality with Security (DQS) integrated instrumentation selection approach that facilitates aggregation of multi-modal data from heterogeneous sources. As our major contribution, we develop a framework that incorporates multiple levels of integration in finding the best DQS-based instrument selection: data fusion from multi-modal sensors embedded into heterogeneous platforms, using multiple quality and security metrics and knowledge integration. Our design addresses the security aspect in the instrumentation design, which is commonly overlooked in real applications, by aggregating it with other metrics into an integral DQS calculus. We develop DQS calculus that formalizes the problem of finding the optimal DQS value. We then propose a Genetic Algorithm–based solution to find an optimal set of sensors in terms of the DQS they provide, while maintaining the level of platform security desirable by the user. We show that our proposed algorithm demonstrates optimal real-time performance in multi-platform instrument selection. To facilitate the framework application by the instrumentation designers and users, we develop and make available multiple Android applications.

Feasibility Study of Capacitor-Based Smart Washer Sensor Systems for Highway Sign, Luminaire/Light, and Traffic Signal Structures

This paper aims to provide an improvement on the current inspection methods for anchor bolts used in highway sign, luminaire/light, and traffic signal (SLTS) support structures to reduce the risk of structural failure as well as human labor costs. A feasibility study on a capacitance-based smart washer system was conducted. The capacitor-based sensor system design combined with customized polylactic acid three-dimensional printed attachments was proposed and tested. Laboratory tests on multiple sets of sensors were performed through initial testing and tightening–loosening testing; results have shown that the performance of the sensor system follows the working principle of the capacitor and, with a less than 5% error tolerance, a capacitance–pretension relationship was developed in the laboratory. Then the sensor system was installed at a sign structure specimen for long-term evaluation; the 12-month consecutive monitoring results show that the sensor system was able to perform steadily with a proper waterproof cover applied. The change of the capacitance was found at ±5% of the reference value, and torques were applied to verify the measurement results from the sensor system, indicating that the minor capacitance change was caused by a change of temperature. The results have proven the feasibility of this capacitor-based smart washer system that detects the pretension change inside the anchor bolts and performs steadily in a controlled on-site environment with a high durability. In summary, this study provides the possibility of improving the monitoring methods for anchor bolts in highway SLTS structures.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a “diagnostic toolkit” consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, “black box” nature of the algorithms, and data storage/transfer concerns.

A spatio-temporal methodology for greenhouse microclimatic mapping

Greenhouse internal microclimate has been proven to be non-homogeneous in many aspects. However, this variability is only sometimes considered by greenhouse models, which often calculate climatic variables without any spatial reference. Farmers, on the other hand, may wish to have these differences highlighted as they could lead to aimed actions only for a specific area of the greenhouse, while at the same time, they are not willing to invest in sensors to be installed everywhere. This paper presents a data-driven methodology to generate a virtual 2D map of a greenhouse, which allows farmers to control any critical parameter they desire with minimum investment, as monitoring is done via soft sensing with only a few actual sensors. The proposed flow starts with a set of temporary sensors placed in the points of interest; then, a model for each of them is developed via linear regression and, finally, a map of the entire area can be derived by interpolating values from these models. This allows the generation of accurate models at a reduced cost as temporary sensors can be reused at other locations. The methodology has been tested on adjacent greenhouses and in two farms, where temperature and other climatic variables have been monitored. Experimental results show that the proposed methodology can reach an adjusted R2 value of 98% for predicting values in different greenhouse locations.

An Industrial Internet-of-Things (IIoT) Open Architecture for Information and Decision Support Systems in Scientific Field Campaigns.

Information and decision support systems are essential to conducting scientific field campaigns in the atmospheric sciences. However, their development is costly and time-consuming since each field campaign has its own research goals, which result in using a unique set of sensors and various analysis procedures. To reduce development costs, we present a software framework that is based on the Industrial Internet of Things (IIoT) and an implementation using well-established and newly developed open-source components. This framework architecture and these components allow developers to customize the software to a campaign's specific needs while keeping the coding to a minimum. The framework's applicability was tested in two scientific field campaigns that dealt with questions regarding air quality by developing specialized IIoT applications for each one. Each application provided the online monitoring of the acquired data and an intuitive interface for the scientific team to perform the analysis. The framework presented in this study is sufficiently robust and adaptable to meet the diverse requirements of field campaigns.

Detecting floating litter in freshwater bodies with semi-supervised deep learning

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.g., ImageNet. Fine-tuning models pre-trained on these larger datasets helps improve litter detection performances and reduces data requirements. Yet, the effectiveness of using features learned from generic datasets is limited in large-scale monitoring, where automated detection must adapt across different locations, environmental conditions, and sensor settings. To address this issue, we propose a two-stage semi-supervised learning method to detect floating litter based on the Swapping Assignments between multiple Views of the same image (SwAV). SwAV is a self-supervised learning approach that learns the underlying feature representation from unlabeled data. In the first stage, we used SwAV to pre-train a ResNet50 backbone architecture on about 100k unlabeled images. In the second stage, we added new layers to the pre-trained ResNet50 to create a Faster R-CNN architecture, and fine-tuned it with a limited number of labeled images (≈1.8k images with 2.6k annotated litter items). We developed and validated our semi-supervised floating litter detection methodology for images collected in canals and waterways of Delft (the Netherlands) and Jakarta (Indonesia). We tested for out-of-domain generalization performances in a zero-shot fashion using additional data from Ho Chi Minh City (Vietnam), Amsterdam and Groningen (the Netherlands). We benchmarked our results against the same Faster R-CNN architecture trained via supervised learning alone by fine-tuning ImageNet pre-trained weights. The findings indicate that the semi-supervised learning method matches or surpasses the supervised learning benchmark when tested on new images from the same training locations. We measured better performances when little data (≈200 images with about 300 annotated litter items) is available for fine-tuning and with respect to reducing false positive predictions. More importantly, the proposed approach demonstrates clear superiority for generalization on the unseen locations, with improvements in average precision of up to 12.7%. We attribute this superior performance to the more effective high-level feature extraction from SwAV pre-training from relevant unlabeled images. Our findings highlight a promising direction to leverage semi-supervised learning for developing foundational models, which have revolutionized artificial intelligence applications in most fields. By scaling our proposed approach with more data and compute, we can make significant strides in monitoring to address the global challenge of litter pollution in water bodies.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.

On-Site Sensor Sensitivity Adjustment Technique for a Maintenance-Free Heat Flow Monitoring in Building Systems

In this paper, an advanced solution for measuring heat flow through opaque building elements is presented. The solution is based on the implementation of a computer-aided technique for continuous monitoring of heat flow sensor performance and silent checking of their accuracy. In principle, the technique provides an ex-post compensation of potential deviations and inaccuracies detected during the measurement, which can be done without interfering with the ongoing experiment. As a consequence, traditional ‘non-smart’ sensors can be turned into advanced sensors with self-sensing or self-adjustment features at nearly zero additional costs. The high efficiency of the proposed approach was validated against experimental data obtained from an independent set of advanced high-sensitive sensors. Considering the validation results, the proposed technique brings an entirely new potential for maintenance-free applications for thermal performance monitoring in the building sector, typically for long-term experiments or measurements under dynamic environments.

FPID-RCP: A Control Method for a Swing-Type Wave Compensation Platform System

With the rapid development of marine engineering in recent years, offshore operations have become increasingly common, making wave compensation platforms crucial for safe operations at sea. This paper presents a pendulum-type wave compensation platform specifically designed for wave compensation applications. The main components of this wave compensation platform include a chassis, support base, hydraulic cylinders, telescopic rods, upper platform, three sets of balancing mechanisms, three sets of tilt angle sensors, and a control system. Firstly, to thoroughly understand the compensatory motion of the pendulum-type three-degree-of-freedom wave compensation platform, kinematic analysis of the entire system was conducted, and the motion inverse solution curves of the mechanism were obtained through simulation using motion simulation software. Secondly, to enhance the compensatory response performance of the platform, a fuzzy PID control algorithm was employed to control the system and achieve attitude control of the platform. Finally, through control system simulation, compared to PID control, fuzzy PID reduces system response delay and successfully meets the expected technical requirements and application needs.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

IMSIS: An instrumented microphysiological system with integrated sensors for monitoring cellular metabolic activities

Well plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology. Its popularity stems from its versatility to support a variety of fluorescent markers for high throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has its own challenges, namely, they can be potentially toxic to cells, and thus, may perturb their biological functions; and it is difficult to monitor multiple analytes concurrently and in real-time inside each well. This paper presents a fully instrumented microphysiological system with integrated sensors (IMSIS) with a similar well format. Each well in the microphysiological system has a set of sensors for monitoring multiple metabolic analytes in real-time. The IMSIS platform is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The system has integrated microfluidics to maintain its microphysiological environment within each well. The IMSIS platform currently incorporates O2, H2O2, and pH sensors inside each well, allowing up to six wells to perform concurrent measurements in real-time. Furthermore, the architecture is scalable to achieve an even higher level of throughput. The miniaturized design ensures portability, suitable for small offices and field applications.The IMSIS platform was successfully used to monitor in real-time the mitochondrial functions of live bovine embryos in O2 consumption, H2O2 release as an indication of ROS production, and extracellular acidity changes before and after the introduction of external substrates.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

Monitoring system of dangerous and harmful factors in the production environment

The work presents the principles of building control systems of physical factors of the production environment. The set of information sources of control of physical harmful and dangerous factors of the production environment is considered as a set of sensors of random processes. It is shown that for the construction of a system of physical control of the production environment, it is expedient to use adaptive principles of system construction. The structural scheme of the system implementing the specified principle is presented.