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In response to the growing concern about environmental sustainability and natural resource depletion, car manufacturers are looking for ways to make their vehicles more efficient. One approach is to use turbocharging technology, which compresses the air entering the engine cylinder for a higher power density per unit of displacement volume. Although turbocharging can reduce carbon emissions by improving engine performance, it has several disadvantages, including reduced reliability and service life, increased maintenance frequency and cost, and reliance on high quality fuels and lubricants. This study investigates the possible continuous monitoring of the running time of the turbocharger (TCR) rotor shaft and the influence of various input parameters, such as oil pressure and oil temperature, on TCR wear. We hypothesize that the continuous monitoring of these variables could prevent breakdowns and optimize turbocharger operation. We created a test bench consisting of an internal combustion engine (ICE) combined with a turbocharging system which includes an independent lubrication and braking mechanism. Our experiments demonstrated a positive correlation between oil inlet temperature and wear sensitivity over time. We also revealed a close relationship between oil inlet pressure, thermal properties, initial rotor speed, and wear duration. These results indicate that continuous qualitative and quantitative monitoring of wear progression can predict impending failures and increase turbocharger efficiency. The evaluation of the TCR sensitivity to wear time can assess both the quality of the turbocharger assembly at the manufacturing stage and the adequacy of its preliminary adaptation in real conditions.

The operating parameters of present-day engines depend on many factors, including the lubrication system. The lubrication system not only reduces friction between the surfaces of moving parts, but also contributes to their cooling and cleans engine parts from carbon deposits and impurities. High environmental requirements are also imposed on the quality of the lubricants, while maintaining their specifications. To that end, various additives are added to lubricating oil. The paper studies the effect of fine powders (enamels) on the wear of friction pairs in laboratory tests. The tests are mainly carried out for all-season synthetic and mineral oils in demand. Such criteria as wear of a roller and upper and lower bearings, specific wear of friction pairs, moment load, and temperature are selected as the effectiveness evaluation criteria of the use of enamels and nanomaterials. Experimental studies show a high efficiency of using enamels as additives to reduce friction and increase the load-carrying capacity of oils. The temperature in the friction layer is reduced by 10-20%, depending on the enamel concentration in the oil. The enamel acts as a third body with a lower friction coefficient and energy to failure, which increases the actual contact area of the friction pairs. The results of the study can be applied in the development of motor oils with fine enamel as an additive.

Next generation wireless systems include battery operated devices which demand higher throughput and a better reliability in an energy efficient fashion. To fulfil these requirements, in this paper, we propose a novel scenario where we include a dynamic Wireless Power Splitting (WPS) factor for Energy Harvesting (EH) at nodes in a Drone Assisted Network Coded Cooperation (DA-NCC) system. The dynamic WPS factor used for EH in DA-NCC system is made more realistic by determining through the probability of Line-of-Sight (LoS) occurrence. Analytical framework is developed for residual Analog Network Coding (ANC) noise and variance of ANC-noise in EH scenario. We also derive the average rate and average outage probability expressions for the proposed channel model. Various algorithms are developed for deciding the Air-to-Ground (A2G) channel distributions, harvesting the energy at relay and source nodes and evaluating the performance metrics of our proposed work. Our investigations reveal that the use of EH in DA-NCC improves the lifespan of the network. Our findings play important roles in disaster management scenarios where cellular connections to base stations are disrupted due to natural calamities and battery constrained drones are deployed for assistance.

Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.

This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

With increase in the population, the number of registered vehicles has dramatically increased over all the world, and this leads to a high rate of traffic accidents on the roads. Therefore, in order to prevent such accidents, an Intelligent Transportation Systems (ITSs) is needed to be installed to notify drivers of obstacles in advance. Recently, the Internet of things (IoT) evolves the vehicular communications and covers this technology under the Internet of vehicles (IoV) application. IoV is a new field for the automotive industry and a significant part of the smart cities. However, protecting the privacy of vehicle\\\’s location is the most challenging subject in the vehicular communication, as because it threatens the personal life of drivers. This paper provides cooperation and radio silence strategy in mix zone (CRSMZ) to protect location privacy of vehicle in IoV. The strategy implements either cooperation or radio silence depending on the speed of the vehicle while it is in mix _zone. The simulation results show that CRSMZ is an efficient strategy to protect location information of vehicle drivers. CRSMZ outperforms the existing strategies in terms of mean of the number tracker confusion, continuous tracking period and max of the entropy.

Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital tools for organizing knowledge and conducting practical forms of classes. In this regard, a modern digital form of organizing and transferring knowledge to automotive service engineers in the form of virtual laboratories was developed and presented in the article. The work scenarios, functionality, and minimum technical requirements of virtual laboratories as software systems are described and reviewed in the paper. The rationale for the effectiveness of the application, based on the results of using 109 university students in training practice, is presented as a result of the research. An analysis of the distributions of the student survey results and their training progress revealed differences at the p = 0.05 significance level. This confirmed the hypothesis that the use of methods for teaching engineers special disciplines and language skills using VR technologies is much more effective than the traditional one. An increase in students’ interest in learning was revealed, and their performance improved markedly. This proves that the immersive nature of VR technology makes it possible to better assimilate the studied material, increase the level of motivation of future car service specialists, and also allow the organization of the transfer of knowledge online. The very process of knowledge transfer becomes the point of acquiring new digital competencies necessary for high-tech industries.

Vehicular network is a communication technology designed to provide comfort and improve life safety and driving efficiency on the road. In vehicular network, trustworthy communication is very important as fake applications may lead to disastrous road accidents. Several information hiding methods are used to enable vehicles to communicate secretly or to covertly report a misbehaving vehicle. The work in this paper focuses on a performance analysis based on 2-D Markov chain model for the system throughput of steganographic scheme in relation to the IEEE 802.11p standard. This model studies wireless padding (WiPad) that is used to hide data into the padding of packets at the physical layer of wireless local area networks (WLANs). The analytical study is under non-saturated conditions with non-ideal transmission channel. The study also considers the rate of packet arrival with the first order of buffer memory, back-off timer freezing, back-off phases, and short retry limit to satisfy the IEEE 802.11p specifications. It emphasizes that taking these factors into account are significant in modelling the system throughput of the steganographic channel. These factors typically provide a precise channel access estimation, yield more accurate findings of system throughput, use the channel efficiently, prevent overestimation of saturation throughput, and ensure that no packet is served indefinitely. The model is validated by comparing the numerical and simulation results under different network parameters. Analytical and simulation results stated that the values of the system throughput of the steganographic channel based on data and control frames are low as the vehicles number n, traffic arrival rate λ, packet size, and the value of Bit Error Rate (BER) increase.

Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

Abstract The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main patterns of traffic at intersections in traffic congestion and to develop an analysis technique to improve traffic management at intersections. In modern conditions, there is a sharp increase in the number of vehicles, which leads to negative consequences, such as an increase in travel time, additional fuel consumption, increased risk of traffic accidents and others. To solve the problem of improving traffic control at intersections, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The purpose of this article is to determine the most important traffic characteristics that affect the throughput of intersections. As a criterion for the cross-pass ability of the intersection, the actual number of passing cars during the permission signal of the torch light is taken. Using multivariate regression analysis, a model was developed to predict intersection throughput taking into account the most important traffic characteristics. Analysis of the throughput of intersections using the fuzzy logic method confirmed the correctness of the developed model. In addition, based on the results of processing information collected at 20 intersections and including 597 observations, a methodology was developed for determining the similarity of traffic intersections. This allows us to identify homogeneous types of intersections and to develop typical traffic management techniques in the city, instead of individually managing each node of the city’s transport network individually. The results obtained lead to a significant reduction in costs for the organization of rational traffic flows.

The urban environment near the road infrastructure is particularly affected by traffic emissions. This problem is exacerbated at road junctions. The roadside concentration of particulate (PM2.5 and PM10) emissions depends on traffic parameters, meteorological conditions, the characteristics and condition of the road surface, and urban development, which affects air flow and turbulence. Continuous changes in the structure and conditions of the traffic flow directly affect the concentration of roadside emissions, which significantly complicates monitoring and forecasting the state of ambient air. Our study presents a hybrid model to estimate the amount, concentration, and spatio-temporal forecasting of particulate emissions, accounting for dynamic changes in road traffic structure and the influence of meteorological factors. The input module of the model is based on data received from street cameras and weather stations using a trained convolutional neural network. Based on the history of emission concentration data as input data, we used a self-learning Recurrent Neural Network (RNN) for forecasting. Through micromodeling, we found that the order in which vehicles enter and exit an intersection affects the concentration of vehicle-related emissions. Preliminary experimental results showed that the proposed model provides higher accuracy in forecasting emission concentration (83–97%) than existing approaches.

In many cities of the world, the problem of traffic congestion on the roads remains relevant and unresolved. It is especially noticeable at signal-controlled intersections, since traffic signalization is among the most important factors that reduce the maximum possible value of the traffic flow rate at the exit of a street intersection. Therefore, the development of a methodology aimed at reducing transport losses when pedestrians move through signal-controlled intersections is a joint task for the research and engineering community and municipalities. This paper is a continuation of a study wherein the results produced a mathematical model of the influence of lane occupancy and traffic signalization on the traffic flow rate. These results were then experimentally confirmed. The purpose of this work is to develop a method for the practical application of the mathematical model thus obtained. Together with the obtained results of the previous study, as well as a systems approach, traffic flow theory, impulses, probabilities and mathematical statistics form the methodological basis of this work. This paper established possible areas for the practical application of the previously obtained mathematical model. To collect the initial experimental data, open-street video surveillance cameras were used as vehicle detectors, the image streams of which were processed in real time using neural network technologies. Based on the results of this work, a new method was developed that allows for the adjustment of the traffic signal cycle, considering the influence of lane occupancy. In addition, the technological, economic and environmental effects were calculated, which was achieved through the application of the proposed method.

Abstract This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data.

Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users' devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.

The insufficient development of intelligent dynamic monitoring systems, which operate with big data, obstructs the control of traffic-related air pollution in regulated urban road networks. The study introduces mathematical models and presents a practical comparative assessment of pollutant emissions at urban intersections, with two typical modes of vehicle traffic combined, i.e., freely passing an intersection when the green signal appears and uniformly accelerated passing after a full stop at the stop line. Input data on vehicle traffic at regulated intersections were collected using real-time processing of video streams by Faster R-CNN neural network. Calculation models for different traffic flow patterns at a regulated intersection for dynamic monitoring of pollutant emissions were obtained. Statistical analysis showed a good grouping of intersections into single-type clusters and factor reduction of initial variables. Analysis will further allow us to control and minimize traffic-related emissions in urban road networks. A comparative analysis of pollutant emissions in relation to the basic speed of passing at the intersection of 30 km/h was performed according to the calculations of the mathematical models. When reducing the speed to 10 km/h (similar to a traffic jam), the amount of emissions increases 3.6 times over, and when increasing the speed to 50 km/h, the amount of emissions decreases by 2.3 times. Fuzzy logic methods allow us to make a comparative prediction of the amount of emissions when changing both the speed of traffic and the capacity of the intersection lanes. The study reveals the benefits of using a real-life measurement approach and provides the foundation for continuous monitoring and emission forecasting to control urban air quality and reduce congestion in the road network.

This study addresses the problem of non-stop passage by vehicles at intersections based on special processing of data from a road camera or video detector. The basic task in this article is formulated as a forecast for the release time of a controlled intersection by non-group vehicles, taking into account their classification and determining their number in the queue. To solve the problem posed, the YOLOv3 neural network and the modified SORT object tracker were used. The work uses a heuristic region-based algorithm in classifying and measuring the parameters of the queue of vehicles. On the basis of fuzzy logic methods, a model for predicting the passage time of a queue of vehicles at controlled intersections was developed and refined. The elaborated technique allows one to reduce the forced number of stops at controlled intersections of connected vehicles by choosing the optimal speed mode. The transmission of information on the predicted delay time at a controlled intersection is locally possible due to the V2X communication of the road controller equipment, and in the horizontally scaled mode due to the interaction of HAV—the Digital Road Model.

The performance potential of the existing grain cleaning assemblies meant for nominal and stable gross grain flow in view of the agrotechnical harvest time is not realized to the full extent. It is preconditioned by the instability of the technical and technological parameters of the harvesting processes. It results in a disproportion between the harvesting and the grain cleaning process, which is manifested in accumulation of bulks of grains within open areas in uncontrolled conditions or preconditions over-estimation of the rated performance of the grain cleaning assemblies. Their required performance can be decreased and the alignment of functioning of the process system can be increased by introduction of compensating and back-up components in view of the seasonal and daily performance indicators of the combine harvesters.

This paper presents the application of simulation to assess and predict the influence of random factors of pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection during a right turn. The data were collected from the surveillance cameras of 25 signal-controlled intersections in the city of Chelyabinsk, Russia, and interpreted by a neural network. We considered the influence of both the intensity of the pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection in the stochastic approach, provided that the flow of vehicles is redundant. We used a reasonably minimized regression model as the basis for our intersection models. At the first stage, we obtained and tested a simulated continuous-stochastic intersection model that accounts for the dynamics of traffic flow. The second approach, due to the unpredictability of pedestrian flow, used a relevant method for analysing traffic flows based on the fuzzy logic theory. The second was also used as the foundation to build and graphically demonstrate a computer model in the fuzzy TECH suite for predictive visualization of the values of a traffic flow crossing a signal-controlled intersection. The results of this study can contribute to understanding the real conditions at a signal-controlled intersection and making grounded decisions on its focused control.

This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.

The possibilities of collecting the necessary information using multi-touch cameras and ways to improve road traffic data collection are considered. An increase in the number of vehicles leads to traffic jams, which in turn leads to an increase in travel time, additional fuel consumption and other negative consequences. To solve this problem, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The technology considered in the article allows taking into account pedestrians crossing the intersection. The purpose of this article is to determine the most important traffic characteristics that affect the traffic capacity of the intersection, in other words, the actual number of passing cars. Throughput is taken as a dependent variable. Based on the results of the regression analysis, a model was developed to predict the intersection throughput taking into account the most important traffic characteristics. Besides, this model is based on the fuzzy logic method and using the Fuzzy TECH 5.81d Professional Edition computer program.