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4 pages, 171 KiB

Open AccessEditorial

Artificial Intelligence and Computational Issues in Engineering Applications

by Karolina Grabowska, Jaroslaw Krzywanski, Marcin Sosnowski and Dorian Skrobek

Entropy 2023, 25(1), 5; https://doi.org/10.3390/e25010005 - 21 Dec 2022

Viewed by 1211

Abstract

High-performance supercomputers and emerging computing clusters created in research and development centres are rapidly increasing available computing power, which scientists are eager to use to implement increasingly advanced computing methods [...] Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

Research

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14 pages, 647 KiB

Open AccessArticle

A Soar-Based Space Exploration Algorithm for Mobile Robots

by Fei Luo, Qin Zhou, Joel Fuentes, Weichao Ding and Chunhua Gu

Entropy 2022, 24(3), 426; https://doi.org/10.3390/e24030426 - 19 Mar 2022

Cited by 9 | Viewed by 3235

Abstract

Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models [...] Read more.

Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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14 pages, 663 KiB

Open AccessArticle

An Edge Server Placement Method Based on Reinforcement Learning

by Fei Luo, Shuai Zheng, Weichao Ding, Joel Fuentes and Yong Li

Entropy 2022, 24(3), 317; https://doi.org/10.3390/e24030317 - 23 Feb 2022

Cited by 16 | Viewed by 3016

Abstract

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, [...] Read more.

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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9 pages, 2605 KiB

Open AccessCommunication

Thermodynamics Irreversibilities Analysis of Oxy-Fuel Diffusion Flames: The Effect of Oxygen Concentration

by Huibo Yan, Guangtong Tang, Chaoyang Wang, Lujiang Li, Yuanke Zhou, Zhongnong Zhang and Chun Lou

Entropy 2022, 24(2), 205; https://doi.org/10.3390/e24020205 - 28 Jan 2022

Cited by 5 | Viewed by 1945

Abstract

In studies on the combustion process, thermodynamic analysis can be used to evaluate the irreversibility of the combustion process and improve energy utilization efficiency. In this paper, the combustion process of a laminar oxy-fuel diffusion flame was simulated, and the entropy generation due [...] Read more.

In studies on the combustion process, thermodynamic analysis can be used to evaluate the irreversibility of the combustion process and improve energy utilization efficiency. In this paper, the combustion process of a laminar oxy-fuel diffusion flame was simulated, and the entropy generation due to the irreversibilities of the radiation process, the heat conduction and heat convection process, the mass diffusion process, and the chemical reaction process was calculated. The effect of the oxygen concentration in the oxidizer on the entropy generation was analyzed. The results indicated that, as the oxygen concentration in the oxidizer increases, the radiative entropy generation first increases and then decreases, and the convective and conductive entropy generation, the mass diffusion entropy generation, the chemical entropy generation, and the total entropy generation gradually increase. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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21 pages, 3529 KiB

Open AccessArticle

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

by Naveed Ahmad Khan, Fahad Sameer Alshammari, Carlos Andrés Tavera Romero and Muhammad Sulaiman

Entropy 2021, 23(12), 1685; https://doi.org/10.3390/e23121685 - 15 Dec 2021

Cited by 8 | Viewed by 2967

Abstract

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A [...] Read more.

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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39 pages, 1897 KiB

Open AccessArticle

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

by Muhammad Fawad Khan, Muhammad Sulaiman, Carlos Andrés Tavera Romero and Ali Alkhathlan

Entropy 2021, 23(11), 1513; https://doi.org/10.3390/e23111513 - 14 Nov 2021

Cited by 8 | Viewed by 2181

Abstract

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number [...] Read more.

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number

(E_{s})

, the electrical Reynolds number

(R_{e_{E}})

, and electrical slip number

(E_{s l})

, are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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16 pages, 5720 KiB

Open AccessArticle

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

by Shijie Wang, Guiling Sun, Bowen Zheng and Yawen Du

Entropy 2021, 23(9), 1160; https://doi.org/10.3390/e23091160 - 3 Sep 2021

Cited by 46 | Viewed by 6141

Abstract

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images [...] Read more.

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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Review

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33 pages, 2926 KiB

Open AccessReview

Recent Status and Prospects on Thermochemical Heat Storage Processes and Applications

by Tadagbe Roger Sylvanus Gbenou, Armand Fopah-Lele and Kejian Wang

Entropy 2021, 23(8), 953; https://doi.org/10.3390/e23080953 - 26 Jul 2021

Cited by 25 | Viewed by 5192

Abstract

Recent contributions to thermochemical heat storage (TCHS) technology have been reviewed and have revealed that there are four main branches whose mastery could significantly contribute to the field. These are the control of the processes to store or release heat, a perfect understanding [...] Read more.

Recent contributions to thermochemical heat storage (TCHS) technology have been reviewed and have revealed that there are four main branches whose mastery could significantly contribute to the field. These are the control of the processes to store or release heat, a perfect understanding and designing of the materials used for each storage process, the good sizing of the reactor, and the mastery of the whole system connected to design an efficient system. The above-mentioned fields constitute a very complex area of investigation, and most of the works focus on one of the branches to deepen their research. For this purpose, significant contributions have been and continue to be made. However, the technology is still not mature, and, up to now, no definitive, efficient, autonomous, practical, and commercial TCHS device is available. This paper highlights several issues that impede the maturity of the technology. These are the limited number of research works dedicated to the topic, the simulation results that are too illusory and impossible to implement in real prototypes, the incomplete analysis of the proposed works (simulation works without experimentation or experimentations without prior simulation study), and the endless problem of heat and mass transfer limitation. This paper provides insights and recommendations to better analyze and solve the problems that still challenge the technology. Full article

(This article belongs to the Special Issue Artificial Intelligence and Computational Issues in Engineering Applications)

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