Research

19 pages, 5722 KiB

Open AccessArticle

Comparative Evaluation of Road Vehicle Emissions at Urban Intersections with Detailed Traffic Dynamics

by Vladimir Shepelev, Alexandr Glushkov, Olga Fadina and Aleksandr Gritsenko

Mathematics 2022, 10(11), 1887; https://doi.org/10.3390/math10111887 - 31 May 2022

Cited by 5 | Viewed by 1481

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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17 pages, 2624 KiB

Open AccessArticle

Improving the Performance of MapReduce for Small-Scale Cloud Processes Using a Dynamic Task Adjustment Mechanism

by Tzu-Chi Huang, Guo-Hao Huang and Ming-Fong Tsai

Mathematics 2022, 10(10), 1736; https://doi.org/10.3390/math10101736 - 19 May 2022

Cited by 1 | Viewed by 1738

Abstract

The MapReduce architecture can reliably distribute massive datasets to cloud worker nodes for processing. When each worker node processes the input data, the Map program generates intermediate data that are used by the Reduce program for integration. However, as the worker nodes process [...] Read more.

The MapReduce architecture can reliably distribute massive datasets to cloud worker nodes for processing. When each worker node processes the input data, the Map program generates intermediate data that are used by the Reduce program for integration. However, as the worker nodes process the MapReduce tasks, there are differences in the number of intermediate data created, due to variation in the operating-system environments and the input data, which results in the phenomenon of laggard nodes and affects the completion time for each small-scale cloud application task. In this paper, we propose a dynamic task adjustment mechanism for an intermediate-data processing cycle prediction algorithm, with the aim of improving the execution performance of small-scale cloud applications. Our mechanism dynamically adjusts the number of Map and Reduce program tasks based on the intermediate-data processing capabilities of each cloud worker node, in order to mitigate the problem of performance degradation caused by the limitations on the Google Cloud platform (Hadoop cluster) due to the phenomenon of laggards. The proposed dynamic task adjustment mechanism was compared with a simulated Hadoop system in a performance analysis, and an improvement of at least 5% in the processing efficiency was found for a small-scale cloud application. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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33 pages, 10652 KiB

Open AccessArticle

Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

by Asit Kumar Das, Debahuti Mishra, Kaberi Das, Pradeep Kumar Mallick, Sachin Kumar, Mikhail Zymbler and Hesham El-Sayed

Mathematics 2022, 10(7), 1121; https://doi.org/10.3390/math10071121 - 31 Mar 2022

Cited by 6 | Viewed by 1753

Abstract

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil [...] Read more.

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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22 pages, 7592 KiB

Open AccessArticle

Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems

by Vladimir Shepelev, Sultan Zhankaziev, Sergey Aliukov, Vitalii Varkentin, Aleksandr Marusin, Alexey Marusin and Aleksandr Gritsenko

Mathematics 2022, 10(2), 282; https://doi.org/10.3390/math10020282 - 17 Jan 2022

Cited by 23 | Viewed by 2618

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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24 pages, 1969 KiB

Open AccessArticle

Building 2D Model of Compound Eye Vision for Machine Learning

by Artem E. Starkov and Leonid B. Sokolinsky

Mathematics 2022, 10(2), 181; https://doi.org/10.3390/math10020181 - 07 Jan 2022

Cited by 1 | Viewed by 2371

Abstract

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are [...] Read more.

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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19 pages, 6314 KiB

Open AccessArticle

Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision

by Vladimir Shepelev, Alexandr Glushkov, Tatyana Bedych, Tatyana Gluchshenko and Zlata Almetova

Mathematics 2021, 9(20), 2631; https://doi.org/10.3390/math9202631 - 18 Oct 2021

Cited by 9 | Viewed by 2102

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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20 pages, 1751 KiB

Open AccessArticle

Point Cloud Registration Based on Multiparameter Functional

by Artyom Makovetskii, Sergei Voronin, Vitaly Kober and Aleksei Voronin

Mathematics 2021, 9(20), 2589; https://doi.org/10.3390/math9202589 - 15 Oct 2021

Cited by 14 | Viewed by 1435

Abstract

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem [...] Read more.

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm (

λ

-ICP) that uses a multiparameter functional (

λ

-functional). The proposed

λ

-ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the

λ

-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed

λ

-ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP). Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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21 pages, 1216 KiB

Open AccessArticle

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

by Ashwini Pradhan, Debahuti Mishra, Kaberi Das, Ganapati Panda, Sachin Kumar and Mikhail Zymbler

Mathematics 2021, 9(17), 2095; https://doi.org/10.3390/math9172095 - 30 Aug 2021

Cited by 18 | Viewed by 2131

Abstract

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of [...] Read more.

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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23 pages, 4682 KiB

Open AccessArticle

Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

by José Luis de Andrés Honrubia, José Gaviria de la Puerta, Fernando Cortés, Urko Aguirre-Larracoechea, Aitor Goti and Jone Retolaza

Mathematics 2021, 9(10), 1120; https://doi.org/10.3390/math9101120 - 15 May 2021

Cited by 4 | Viewed by 2002

Abstract

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The [...] Read more.

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity. Full article

(This article belongs to the Special Issue Intelligent Computing in Industry Applications)

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