Real-time Mode Research Articles

A Hopular based weighting scheme for improving kinematic GNSS positioning in deep urban canyon

Global navigation satellite system (GNSS) positioning performance in the urban dense environment experiences significant deterioration due to frequent non-line-of-sight (NLOS) and multipath errors. An accurate weighting scheme is critical for positioning, especially in urban environment. Traditional methods for determining the weights of observations typically rely on the carrier-to-noise density ratio (C/N0) and the elevations from satellites to receivers. Nevertheless, the performance of these methods is degraded in the dense urban settings, as C/N0 and elevation measurements fail to fully capture the intricacies of NLOS and multipath errors. In this paper, a novel GNSS observations weighting scheme based on Hopular GNSS signal classifier, which can accurately identify the LOS/NLOS signals using medium-sized training dataset, is proposed to improve the urban kinematic navigation solution in real-time kinematic positioning mode. Four GNSS features: C/N0, time-differenced code-minus-carrier, loss of lock indicator and satellite’s elevation, are employed in the training of the Hopular based signal classifier. The performance of the new method is validated using two urban kinematic datasets collected by a U-blox F9P receiver with a low-cost antenna, in downtown Calgary. For the first testing dataset, the results show that the Hopular based weighting scheme outperforms the three most commonly used GNSS observations weighting schemes: C/N0, elevation, and a combined C/N0-elevation approach. Approximately 10.089 m of horizontal root-mean-squared (RMS) positioning error and 12.592 m of vertical RMS error are achieved using the proposed method; with improvements of 78.83%, 46.82% and 43.27% on horizontal positioning accuracy and 54.00%, 47.51% and 49.69% on vertical positioning accuracy, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively. For the second testing dataset, a similar performance is achieved with nearly 11.631 m of horizontal RMS error and 10.158 m of vertical RMS error; improvements of 64.58%, 32.90% and 22.40% on horizontal positioning accuracy and 71.99%, 65.24% and 55.88% on vertical positioning accuracy are achieved, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively.

APPLICATION OF OBJECT DETECTION TECHNOLOGY IN AORTOGRAPHY KEYPOINT TRACKING

HighlightsThe practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during transcatheter aortic valve implantation interventions, supporting real-time operation mode. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the system, allowing specialists to achieve the best result. AbstractAim. The aim of this study is to develop a visual assistance system for transcatheter aortic valve implantation procedures.Methods. To address the stated objective, our own dataset consisting of 35 videos of the intervention was used. The visualization system is based on the approach of detecting key points in aortography, utilizing “Object detection” technology with the application of YOLO family artificial neural networks. To achieve the best result, we proposed a method to enhance the quality of input data using convolutional neural networks, specifically the «Autoencoder» technology.Results. The results of the study revealed that the convolutional autoencoder model is capable of restoring the informativeness of noisy input images from 40 to 75%, thereby increasing the accuracy of object detection in images. The presented real-time tracking system for facilitating TAVI procedures achieves a final accuracy of 51.9% according to the Mean Average Precision (MAP) quality metric. The visual assistance system can recognize and track key points indicating the location of the aortic root, delivery system, and heart valve prosthesis during surgery. The practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during interventions, supporting real-time operation mode.Conclusion. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the visualization system, allowing specialists to achieve the best results.

An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes

Objectives Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. Methods This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. Results Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. Conclusions The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

Modeling Approach Estimates Temperatures in Growing High-Temperature Wells

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214836, “Downhole Temperature Estimation of a Growing High-Temperature Wellbore Using a Modified Drift-Flux Modeling Approach,” by Ningyu Wang, SPE, Mohamed S. Khaled, and Anthony Luu, The University of Texas at Austin, et al. The paper has not been peer reviewed. _ Downhole temperature (DHT) estimation is very important for heat management while drilling high-pressure/high-temperature (HP/HT) and geothermal wells. Existing transient models neglect the effect of wellbore growth or deepening on the downhole temperature. The complete paper provides a new modeling tool to estimate and manage the DHT in higher-temperature oil, gas, and geothermal wells. The model, which has the potential to run in real time and thereby digitally twin the drilling operation, may contribute to preventing premature temperature-related failures of bits and downhole tools while drilling future wells in high-temperature environments. Introduction Analytical and numerical modeling are the two main approaches discussed in the literature to estimate the mud temperature inside the drillstring and the annulus. Although these models provide valuable insights on selecting optimal heat-management techniques during circulation, none consider the effect of drilling rate of penetration (ROP) on downhole temperature, and all consider the wellbore as having fixed and static dimensions. On the other hand, the effect of ROP in an arbitrarily growing wellbore on other aspects of drilling has been modeled and studied. In this work, the authors present a validated thermohydraulic model based on the drift-flux approach to simulate the transient behavior of downhole temperature during drilling. This model can predict the effect of different cooling-strategy techniques based on real-time operational changes and can predict temperature-sensor measurements. It thereby establishes a foundation for active managed-temperature drilling of HP/HT and geothermal wells. Methodology The drift-flux model is a well-studied model for 1D flow in a flow channel with static geometry. Conservation equations, and their discretization, are detailed in the complete paper, as are equations regarding heat transfer in the solid. Assumptions. To lay the foundation for a thermohydraulics model that considers the effect of the growth of the wellbore, the following key assumptions are made: - The ROP is much slower than the mud-flow velocity. - The cuttings are assumed to be perfectly integrated into the mud and to share the physical properties of the drilling mud. Therefore, the added mud volume equals the volume of the cuttings. The complete paper focuses on laying the foundation for a solver that accounts for the wellbore growth. - The transient temperature distribution in the drillstring is neglected because the authors are primarily interested in temperature behavior on larger time scales. - The growth of the wellbore trajectory is an external input and is to be updated in a timely manner if the solver is run in real-time mode. - The drillpipe entering the well has constant cross-sectional geometry. - The formation temperature ahead of the bit is considered to not yet be disturbed as drilling proceeds.

Определение местоположения людей в горных выработках угольной шахты — реальность и мифы

The study considers the systems of location of workers in coalmine excavations; the application of such systems is regulated by the Safety rules in coalmines. It has been shown that in Russian coalmines, zonal-type systems and continuous systems are operated. The compliance of each type of system with the requirements of the Safety rules in terms of design, functionality, and technical parameters has been assessed. The features of building a zonal-type system as to the methodology of placing tag readers in excavations have been determined. The continuous system functionality has been analyzed. It has been shown that in addition to the main function of personnel’s location, the system acquires other useful properties, i.e., the use of gas analyzers installed in the headlamp to transmit measurements in real-time mode indicating a measurement location; the implementation of two-way voice communication between a mining dispatcher and workers in excavations via individual headlamps. By the results of the study, the concluding provisions have been formulated: in zonal-type location systems, it is possible to meet the regulatory requirement for a five-second data update period on the condition that the maximum distance between readers does not exceed 60 meters; in continuous personnel’s location systems, due to their design features, additional functions to ensure industrial safety and organizational properties are implemented, expanding the practical value of such devices.

S2Cloud: a novel cloud-based precision health system for smart and secure IoT big data harnessing

This study aims to demonstrate a novel cloud big data system, s2Cloud, to enabled both smart and secure transmission and management of patient big data from Internet of Things (IoT), especially in the new common of long covid. More specifically, s2Cloud allows doctors to manage patient records effectively through the interactive web server. It further enables real-time, long-term, and continuous streaming and management of biomechanical dynamics from IoT phones/wearables. The big data can be visualized in both real-time and historical modes. Besides, s2Cloud achieves big data security through secure sign up and log in, as well as data transmission protection. It also allows doctor-patient interactions through instantaneous chat. s2Cloud has been evaluated on the real-world application, in which human IoT biomechanical dynamics are streamed, managed, stored, and visualized to demonstrate the effectiveness. Overall, the proposed system, empowered by smart and secure design innovations, has demonstrated the feasibility and potential for IoT big data-driven precision health.

Development of a system for controlling a diamond drilling mode in terms of rock fault resistance

Relevance. Resistance to rock-cutting elements in rock destruction largely determines the effectiveness of drilling results. Information about the drag coefficient allows you to correctly and timely adjust the intensity of a drilling tool impact on the bottom of the well. To control the force contact of the drilling tool cutters with the bottom of the well, it is necessary to have a methodological apparatus that allows determining the rock destruction mechanism, taking into account the resistance forces. The existing methods for selecting combinations of diamond drilling mode parameters according to the known RPI index or according to the criterion of energy intensity of rock destruction N/vm , where N is the bottomhole power to overcome the resistance during rock destruction at the bottom hole; vm – mechanical drilling speed, have a number of disadvantages. They consist, for example, in occurrence of tool wear or the need to obtain a bottomhole power value in the current time mode, which is difficult under modern drilling conditions. Therefore, the study of rock resistance to destruction by a diamond cutter, based on application of the full factorial experiment method with obtaining mathematical models of factors and their graphical interpretation in combination with process control systems using computer technology in the current time mode, is a relevant topic. The disclosure of this topic will determine the direction of increasing technical and economic indicators of drilling. Aim. To develop a methodological apparatus that allows determining the mechanism of rock destruction taking into account the resistance forces to ensure optimal control of diamond drilling. Objects. Mechanism of rock resistance to destruction by a diamond cutter. Methods. Analysis, analytical research, full factorial experiment. Results. The paper presents an analytical analysis of the possibility of controlling drilling modes in order to achieve the greatest effect from drilling by estimating the drag coefficient as a function of the intensity of destruction or deepening per one revolution. Based on the analytical studies and the mode of drilling control by a computer in the real time mode, the authors have proposed a method for selecting the parameters of the drilling mode according to a given optimal value of the resistance index during drilling, and a way for its automatic implementation.

Flexible multimode sensors based on hierarchical microstructures enable non-destructive grading of fruits in cold chain logistics

In practical applications, flexible pressure sensors must demonstrate adequate sensitivity, durability, and the ability to detect both dynamic and static forces across a wide range. The objective of this study is to develop a flexible dual-mechanism piezoelectric/piezoresistive sensor (FDMPS) based on layered microstructures to enable versatile detection in real fruit sorting operations. The FDMPS consists of an upper layer featuring planar MXene electrodes on a PDMS film, a mid-layer comprising microstructured Ag electrodes on a PVDF-TrFE/Silica gel/ZnO film, and a lower layer with microstructured MXene electrodes on a PDMS film composition. To ensure a secure fit, the three-layer structure is treated with APTES and plasma. All electrodes are produced using a pneumatic direct-write process, while the PDMS and piezoelectric films are created via a spin-coating process, making them suitable for large-scale production. The flexible FDMPS, with a 10 wt% ZnO content, achieves an optimal piezoelectric output of 3.6 V. Additionally, the FDMPS demonstrates excellent linearity (0.997), resistive sensitivity (23.65 kpa−1), and stability (5000 cycles). The incorporation of microstructures significantly enhances the performance of piezoelectric/piezoresistive sensing. Moreover, the FDMPS can accurately measure bending strain rate and angle within the ranges of 0–90°/s (with a sensitivity of 0.014 V/(°·s−1)) and 0–110° (with a sensitivity of 0.216/°), respectively. In a wireless, real-time mode, the FDMPS proves effective in monitoring the reciprocating motion of a robotic arm and assessing fruit ripeness during the grading process. This advancement promotes the development and application of precision agriculture and wearable sensing technologies.

An Intelligent Automatic Sea Forecasting System Targeting Specific Areas on Sailing Routes

Sailing vessel navigators always want to receive state-of-the-art prompt and accurate marine weather-forecasting services. However, the weather-routing services by private sectors are expensive. Further, forecasting results from public institutes are usually free, and they are not in real-time or numerical modes, so they are not quite suitable for small-size or offshore vessels. In this study, an intelligent system was constructed for delivering sea forecasting at specific areas according to the navigator’s order. The system can automatically obtain web-based forecasting charts issued from multi-source meteorological agencies and convert the regional information into numerical text at requested points. During this step, several intelligent algorithms, like the OpenCV digital image processing algorithm and the YOLO wind vector deep learning recognition method, were applied. By applying this state-of-the-art system, navigators on board do not need to download different institutional graphics (usually with large stream bytes) to explore the future states of the sea surface in a specific area in the sailing route but can obtain the multi-source text forecasting information just by sending the area coordinates to a designated email address. The field tests confirmed that this auto-intelligent system could assist the navigator within a few minutes and thus greatly enhance the navigation safety with minor text-based communication costs. It is expected that by improving the efficiency of marine services and bringing in more artificial intelligence technology, maritime security would be more sustainable.

DIGITALIZATION OF RISK-ORIENTED MANAGEMENT OF FINANCIAL AND ECONOMIC SECURITY OF THE BANK

The scientific results of the study are to determine the current trends of digitalization of risk-oriented management of banking activities to justify the directions of increasing the level of financial and economic security of Ukrainian banks in the context of the Russian-Ukrainian war and considering the needs of economic recovery. The expediency of using indicators of financial stability, which are published by the National Bank of Ukraine under martial law, has been proven, namely: the capital of the first level to the assets weighted by risk; the ratio of the capital of the first level to assets; non-performing loans excluding provisions for capital; non-performing loans to aggregate gross loans; rate of return on assets; net open position in foreign currency to capital. It is determined that modern banks introduce innovative digital solutions aimed at risk-oriented management of financial and economic security in a continuous real-time mode. Some procedures are performed automatically, and it is possible to use artificial intelligence programs, but these innovations must be managed carefully because we are talking about the financial security of millions of bank customers and the financial and economic security of banks, as well as the financial stability of the banking system of Ukraine. It has been substantiated that prospects for further research should be developed in the areas of economic recovery in Ukraine, by strengthening the banking system and developing digitalization of risk-based management of financial and economic security of banks using best practices and international experience. For a more objective assessment of the level of financial and economic security of Ukrainian banks, it is advisable to apply economic standards, regarding banking risks, indicators of financial stability, and the financial stress index, which together reflect the bank’s propensity for force majeure in the financial system, the ability to resist and adapt to adverse changes, and various economic challenges.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Effect of medium renewal mode on the degradation behavior of Mg alloys for biomedical applications during the long-term in vitro test

To build a suitable corrosion environment for testing the degradation behavior of magnesium alloy in vitro, the medium and test environment are constantly optimized. However, the applicability of medium renewal mode is still vague. In this study, based on three representative test protocols, the influence of the specific ratio of media volume to surface area and the disposable medium real-time renewal mode (flow-through) on Mg corrosion were investigated. It is found that in flow-through medium renewal mode, the composition of the medium is maintained quasi-constant which is essential for corrosion tests. The selection suggestion on medium renewal mode was also proposed. This work is beneficial for ultimately establishing the representative in vitro testing protocols for Mg bioabsorbable materials.

Diffusive and displacive phase transitions in Fe-Ga alloys

Phase transitions between structurally ordered phases in Fe–Ga alloys have been studied upon their heating to 850 °C and subsequent cooling to room temperature by neutron diffraction in real-time mode. The transitions between four types of phases have been analyzed: D03 → L12 (both cubic phases), L12 ↔ D019 (cubic – hexagonal), D019 ↔ A2 (hexagonal – cubic). It has been established that all phase transitions include stage of the formation of a disordered state and have a combined, diffusive-displacive nature. The diffusion stages are necessary for the disordering and ordering of the structure, the displacive stage provides a change in the type of the crystal lattice. It can be concluded that the formation of an intermediate disordered state followed by a transition to the final equilibrium state is less energy-consuming than the direct transition to the final state. During the observed transformations, no traces of predicted intermediate structurally ordered tetragonal phases were found. These results may provide new insight into the microscopic basis for the formation of enhanced magnetostriction in Fe–Ga alloys.

Udderly accurate: A deep learning based modelling for determination of dairyness of Sahiwal cow using computer vision

Dairy farming is a crucial agricultural practice for food and nutritional security. Selection of the best milching animals has always been a challenging task for optimising production efficiency. The efficacy of milk yield production is contingent upon a number of factors including inherent linear traits. Linear traits refer to the quantifiable physical characteristics that are associated with the production and reproduction capabilities of dairy animals. This article presents an innovative approach for the classification of Sahiwal cows into high, medium and low yielder categories based on images featuring linear traits through emerging deep learning and computer vision techniques. A large dataset of 4110 images highlighting important linear traits such as udder size, shape, and texture of different categories of Sahiwal cows has been created for training, validation and testing of the model. Images were collected from the herd of Sahiwal cows maintained at the National Dairy Research Institute, Karnal. The dataset was pre-processed using image augmentation techniques to enhance the model’s robustness. Different architectures of CNN models namely InceptionV3, ResNet50 and GoogleNet were trained and optimised. The Inception V3 model demonstrated the best result with 85.64% testing accuracy among all these models in classifying the cow. The developed model can be used under field conditions to determine the dairyness of a cow in real time mode in place of human experts. Additionally, the model’s interpretability is evaluated through feature visualisation, showcasing the importance of different udder features in milk yield prediction.

ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ФІНАНСОВО-ЕКОНОМІЧНОМУ АНАЛІЗІ ДІЯЛЬНОСТІ ПІДПРИЄМСТВА

The article examines the peculiarities of artificial intelligence development and the using its achievements for the future development of Ukrainian economy, in particular in accordance with the Concept of Artificial Intelligence Development in Ukraine. The major tasks of AI using in economical spare of Ukraine are: stimulating the development of entrepreneurship in the field of artificial intelligence; motivation of business to introduce artificial intelligence technologies to increase their own efficiency; development of the Roadmap to retrain people whose work can be automated in the nearest future; introduction of the state order for artificial intelligence systems, IT specialists and data researchers; stimulation of partnership between the state and business in the field of innovative projects, as well as improvement of legislation in the relevant field. Using of information technologies allows to monitor business projects, influence the results of business operations, which enables to warn possible risks and make effective decisions. AI is a modern tool capable to increase business efficiency through rapid analysis of available data, that lets choose the best development strategy. The main advantages of using AI include: forecasting financial and market-commercial risks; optimization of development strategies; creation the system for monitoring business results in real time; automation of accounting and financial and economic analysis; analysis of market and economic trends. The key problems in the economic sector that affect the development of AI in Ukraine are separated, special attention is paid to the advantages and risks of using artificial intelligence technologies in business activities, among them low-quality data for analysis, opacity of the obtained results, as well as the risk of privacy loss. In order to minimize the possible risks connected with the loss of confidential data, it is proposed to use of software that would allow, taking into account the industry specifics of each enterprise's activity, to maintain, monitor and analyze significant data. That would allow to monitor the effectiveness of business processes in the real time mode and make efficient management decisions without fear of leakage of important confidential information.

Features of the functioning of microbiocenoses of cultivated soddy-podzolic soil under conditions of oil pollution

The study goal was to explore the characteristics of the functioning of microbiocenoses of well-cultivated soddy-podzolic soil, contaminated with doses of oil under the conditions of a field experiment. The level of initial pollution with petroleum products was in percentage: 0, 0.5, 1.0, 1.5 and 3.0. Sampling for laboratory research was carried out 14 days, 6 and 18 months after the oil spill. To assess the activity of the microbiota under conditions of soil self-purification from oil pollution, integral indicators were chosen. 1. The amount of carbon dioxide released from the soil, the so-called basal respiration, corresponds to the complete destruction of oil (to CO2 and H2O) by soil microorganisms. 2. The medium-regulating (regulatory) microorganisms’ activity, which is defined as the biological response of the soil to a disruption of chemical balance, was assessed by the quantity of CO2 release after 1% glucose addition. The oil products content was defined in real time mode. Soil microbiocenoses were highly perseverant to the inhibitory effects of oil pollution. Disturbances in the normal functioning of the microbiota as a result of oil pollution were non-critical and reversible. Half-year after the oil spill, at all levels of pollution, there was a sharp increase in basal respiration. Its level exceeded the respiration rate of clean soil several-fold. This indicated the high biochemical activity of hydrocarbon-oxidizing resident microbiota and as a result an active self-cleaning of soil from oil pollution. The negative dynamics of the petroleum products content proved the microbial character of the oil destruction processes in the soil.

Real-Time Continuous Locomotion Mode Recognition and Transition Prediction for Human With Lower Limb Exoskeleton.

Real-time continuous locomotion mode recognition and seamless timely transition detection is critical for the exoskeleton robot. This study aims to present a comprehensive and innovative framework for locomotion mode recognition and transition prediction, exclusively utilizing inertial measurement unit (IMU) signals from the exoskeleton. In this framework, a CNN-BiLSTM model was developed and trained to be the classifier and a novel majority filter was designed to reduce the transition misjudgment rate. Moreover, a comprehensive evaluation system encompassing eight dimensions for the classifier, incorporating evaluation metrics specifically for transition misjudgment, was proposed. We collected locomotion motion data from six subjects wearing a rigid exoskeleton robot using six IMU sensors on the exoskeleton. The proposed method achieves a high level of recognition accuracy, with an overall average of 99.58% for the five steady locomotion modes (level ground walking (LG), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)) across six subjects following the transition decision. All transitions are recognizable, and the majority can be predicted in advance, with an average prediction time of 353ms. Furthermore, the implementation of majority filter resulted in an average 87.04% reduction in the transition misjudgment rate among six subjects, thereby decreasing the average transition misjudgment rate to 4.82%. Finally, the model was tested on a Jetson Nano to verify its real-time performance. The results presented above were obtained under the condition where either leg could function as the first transition leg and revealed that the developed system was capable of achieving precise locomotion mode recognition and timely transition prediction, with high real-time performance.

Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis

This research introduces an algorithmic framework for enhancing the security of Internet of Things (IoT) networks. The Enhanced Anomaly Detection (EAD) algorithm initiates the process by detecting anomalies in real-time IoT data, serving as the foundational layer. The Behavior Analysis for Profiling (BAP) algorithm builds upon EAD, adding behavior analysis for profiling and adaptive identification of abnormal behavior. Signature-Based Detection (SBD) involves pre-identified attack signatures, which supports detection of known attacks and provides proactive defense measures against documented threats. The MLID, or the Machine Learning-Based Intrusion Detection, algorithm uses trained machine learning models in order to detect anomalies and the adaptability to changing security risks. The Real-Time Threat Intelligence Integration (RTI) algorithm integrates updated threat intelligence feeds, which improves the framework's responsiveness to emerging threats. The visual representations illustrate once again the idea of the new framework being very accurate at intergration, applicability, and overal security effectiveness. The research makes a standard solution which proves to be a smart and responsive way guarding the IoT networks reducing and even fighting known and potential threats in a real-time mode.

Experimental and Simulated Investigation of a Single-Phase Transformerless H5 Inverter Topology

This paper aims to simulate and experimentally apply a single-phase transformerless H5 inverter topology. The H5 inverter topology is simulated in MATLAB/Simulink environment as both off-grid and grid-connected. The simulation results of the grid-connected system are evaluated in terms of output voltage/current, total harmonic distortion (THD), and injected active and reactive powers into the grid. The off-grid system is evaluated regarding load current, inverter output voltage, efficiency, and THD. Despite the variable output load, the results offer good THD values that comply with the IEEE 1547 standard. Additionally, experimental studies are conducted on the single-phase off-grid transformerless H5 inverter. A digital processing unit is utilized to control and display the output voltage of the H5 inverter system in real-time mode. PI and Fuzzy PI controllers are used for output voltage control. The obtained results reveals that the investigated topology outperforms its counterparts.

АНАЛИЗ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ УПРАВЛЕНИЯ ПРИ ПЕРЕВОЗКАХ ОПАСНЫХ ГРУЗОВ В РОССИИ И ЗА РУБЕЖОМ

The research work examines the issues of managing the transportation of dangerous goods using information technologies that can improve efficiency and reliability. Monitoring the movement of special rolling stock during the transportation of dangerous goods is a key element of effective management. The monitoring system provides a real-time mode or a specified frequency to obtain information about the location, status and condition of the vehicle, which allows you to make optimal management decisions.