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Algorithms
Volume 16
Issue 6
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Algorithms, Volume 16, Issue 6 (June 2023) – 48 articles

Cover Story (view full-size image): This study focuses on the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system for a UAV with a flexible cable-connected payload, comparing it to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The primary objective of this work is to address the adverse effects caused by unknown payloads and disturbances which are inherent in the system dynamics. This means the controllers have been tuned without payload and disturbances.
Instead of rewriting the equations of the UAV dynamics and redesigning the controllers to incorporate the additional dynamics of the swinging payload, this study designs the controllers by considering the known dynamics of the UAV itself, without accounting for the payload and disturbance effects. View this paper
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17 pages, 928 KiB  
Article
Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization
by Daniyal Asif, Mairaj Bibi, Muhammad Shoaib Arif and Aiman Mukheimer
Algorithms 2023, 16(6), 308; https://doi.org/10.3390/a16060308 - 20 Jun 2023
Cited by 14 | Viewed by 5620
Abstract
Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine [...] Read more.
Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)
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20 pages, 1684 KiB  
Article
Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
by Petr Kadlec
Algorithms 2023, 16(6), 307; https://doi.org/10.3390/a16060307 - 20 Jun 2023
Viewed by 1142
Abstract
This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. [...] Read more.
This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. A novel VND MO algorithm based on the well-known particle swarm optimization (PSO) algorithm is introduced and thoroughly described in this paper. The novel VNDMOPSO algorithm is validated on a set of 21 benchmark problems with different dimensionality settings and compared with two other state-of-the-art VND MO algorithms. Then, it is applied to solve five different instances of the space robot pathfinding problem formulated as a VND MO problem where two objectives are considered: (1) the minimal distance of the selected path, and (2) the minimal energy cost (expressed as the number of turning points). VNDMOPSO shows at least comparable or better convergence on the benchmark problems and significantly better convergence properties on the VND pathfinding problems compared with other VND MO algorithms. Full article
(This article belongs to the Special Issue Applications of Evolutionary and Swarm Systems)
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16 pages, 6941 KiB  
Article
An Alternative Multi-Physics-Based Methodology for Strongly Coupled Electro-Magneto-Mechanical Problems
by Federico Maria Reato, Claudio Ricci, Jan Misfatto, Matteo Calzaferri and Simone Cinquemani
Algorithms 2023, 16(6), 306; https://doi.org/10.3390/a16060306 - 19 Jun 2023
Viewed by 1059
Abstract
The analysis of complex systems tends to be approached through a separation and a simplification of the main macro phenomena and, thus, addressed through dedicated techniques, tools, and algorithms. A smart and interesting possibility, instead, is represented by the so-called model-based design analysis, [...] Read more.
The analysis of complex systems tends to be approached through a separation and a simplification of the main macro phenomena and, thus, addressed through dedicated techniques, tools, and algorithms. A smart and interesting possibility, instead, is represented by the so-called model-based design analysis, which allows one to interface phenomena coming from interactions of different physical natures. This paper aims to propose a multi-physics Matlab/Simulink®-based architecture that allows one to integrate general and strongly non-linear coupling phenomena, taking efforts from two novel implemented bi-directional co-simulation routines based on Spice® and ESRF Radia® engines. Emphasis is dedicated to the discussion and description of the co-simulation algorithms and processes characteristic of these routines, which allow the analog electronic and the magneto dynamic domain’s integration under a single simulation environment. To highlight the reliability of the multi-domain architecture and to validate the reported co-simulation results, a comparison with the experimental measures obtained on an innovative MEMS electromagnetic actuator are proposed. Full article
(This article belongs to the Collection Feature Papers in Algorithms)
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36 pages, 6469 KiB  
Article
Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook
by Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu
Algorithms 2023, 16(6), 305; https://doi.org/10.3390/a16060305 - 17 Jun 2023
Cited by 4 | Viewed by 3409
Abstract
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key [...] Read more.
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset. Full article
(This article belongs to the Collection Feature Papers on Artificial Intelligence Algorithms and Their Applications)
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18 pages, 6442 KiB  
Article
Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
by Lijun Zhang, Yuejian Zhang and Guangfeng Li
Algorithms 2023, 16(6), 304; https://doi.org/10.3390/a16060304 - 17 Jun 2023
Cited by 2 | Viewed by 1157
Abstract
Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to [...] Read more.
Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Anomaly and Fault Detection)
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28 pages, 9566 KiB  
Article
Optimized Workflow Framework in Construction Projects to Control the Environmental Properties of Soil
by Per Lindh and Polina Lemenkova
Algorithms 2023, 16(6), 303; https://doi.org/10.3390/a16060303 - 17 Jun 2023
Cited by 1 | Viewed by 1274
Abstract
To optimize the workflow of civil engineering construction in a harbour, this paper developed a framework of the contaminant leaching assessment carried out on the stabilized/solidified dredged soil material. The specimens included the sampled sediments collected from the in situ fieldwork in Arendal [...] Read more.
To optimize the workflow of civil engineering construction in a harbour, this paper developed a framework of the contaminant leaching assessment carried out on the stabilized/solidified dredged soil material. The specimens included the sampled sediments collected from the in situ fieldwork in Arendal and Kongshavn. The background levels of the concentration of pollutants were evaluated to assess the cumulative surface leaching of substances from samples over two months. The contamination of soil was assessed using a structured workflow scheme on the following toxic substances, heavy metals—As, Pb, Cd, Cr, Hg, Ni, and Zn; organic compounds—PAH-16 and PCB; and organotin compounds—TBT. The numerical computation and data analysis were applied to the results of geochemical testing creating computerised solutions to soil quality evaluation in civil engineering. Data modelling enabled the estimation of leaching of the contaminants in one year. The estimated leaching of As is 0.9153 mg/m2, for Ni—2.8178 mg/m2, for total PAH-16 as 0.0507 mg/m2, and for TBT—0.00061 mg/m2 per year. The performance of the sediments was examined with regard to permeability through a series of the controlled experiments. The environmental engineering tests were implemented in the Swedish Geotechnical Institute (SGI) in a triplicate mode over 64 days. The results were compared for several sites and showed that the amount of As is slightly higher in Kongshavn than for Arendal, while the content of Cd, Cr, and Ni is lower. For TBT, the levels are significantly lower than for those at Arendal. The algorithm of permeability tests evaluated the safety of foundation soil for construction of embankments and structures. The optimized assessment methods were applied for monitoring coastal areas through the evaluated permeability of soil and estimated leaching rates of heavy metals, PHB, PACs, and TBT in selected test sites in harbours of southern Norway. Full article
(This article belongs to the Collection Feature Papers in Algorithms for Multidisciplinary Applications)
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14 pages, 4156 KiB  
Article
Identification of Highlighted Cells in Low-Variance Raster Data Application to Digital Elevation Models
by Manuel Antonio Ureña-Cámara and Antonio Tomás Mozas-Calvache
Algorithms 2023, 16(6), 302; https://doi.org/10.3390/a16060302 - 16 Jun 2023
Cited by 1 | Viewed by 838
Abstract
This study describes a new algorithm developed to detect local cells of minimum or maximum heights in grid Digital Elevation Models (DEMs). DEMs have a low variance in digital levels due to the spatial continuity of the data. Traditional algorithms, such as SIFT, [...] Read more.
This study describes a new algorithm developed to detect local cells of minimum or maximum heights in grid Digital Elevation Models (DEMs). DEMs have a low variance in digital levels due to the spatial continuity of the data. Traditional algorithms, such as SIFT, are based on statistical variance, which present issues to determine these highlighted cells. However, one of the main purposes of this identification is the use of these points (cells) to assess the positional accuracy of these products by comparing those extracted from the DEM with those obtained from a more accurate source. In this sense, we developed an algorithm based on a moveable window composed of variable sizes, which is displaced along the image to characterize each set of cells. The determination of highlighted cells is based on the absolute differences of digital levels in the same DEM and compared to those obtained from other DEMs. The application has been carried out using a great number of data, considering four zones, two spatial resolutions, and different definitions of height surfaces. The results have demonstrated the feasibility of the algorithm for the identification of these cells. Thus, this approach expects an improvement in traditional procedures. The algorithm can be used to contrast DEMs obtained from different sources or DEMs from the same source that have been affected by generalization procedures. Full article
(This article belongs to the Collection Feature Papers in Algorithms)
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20 pages, 9775 KiB  
Article
Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
by Elliot Pachniak, Yongzhen Fan, Wei Li and Knut Stamnes
Algorithms 2023, 16(6), 301; https://doi.org/10.3390/a16060301 - 16 Jun 2023
Cited by 1 | Viewed by 1263
Abstract
The Ocean Color—Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (Rrs estimates), aerosol optical depths, and inherent optical [...] Read more.
The Ocean Color—Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (Rrs estimates), aerosol optical depths, and inherent optical properties. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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12 pages, 482 KiB  
Article
Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution
by Evangelos D. Spyrou, Chrysostomos Stylios and Ioannis Tsoulos
Algorithms 2023, 16(6), 300; https://doi.org/10.3390/a16060300 - 15 Jun 2023
Viewed by 1341
Abstract
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data [...] Read more.
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data transmission over the Internet. Artificial intelligence (AI) and machine learning techniques play a crucial role in classifying patterns derived from sensor data. Environmental stations offer a multitude of parameters that can be obtained to uncover hidden patterns showcasing the impact of pollution on the surrounding environment. This paper focuses on utilizing the CO parameter as an indicator of pollution in two datasets collected from wireless environmental monitoring devices in the greater Port area and the Town Hall of Igoumenitsa City in Greece. The datasets are normalized to facilitate their utilization in classification algorithms. The k-means algorithm is applied, and the elbow method is used to determine the optimal number of clusters. Subsequently, the datasets are introduced to the grammatical evolution algorithm to calculate the percentage fault. This method constructs classification programs in a human-readable format, making it suitable for analysis. Finally, the proposed method is compared against four state-of-the-art models: the Adam optimizer for optimizing artificial neural network parameters, a genetic algorithm for training an artificial neural network, the Bayes model, and the limited-memory BFGS method applied to a neural network. The comparison reveals that the GenClass method outperforms the other approaches in terms of classification error. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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10 pages, 327 KiB  
Article
A Multithreaded Algorithm for the Computation of Sample Entropy
by George Manis, Dimitrios Bakalis and Roberto Sassi
Algorithms 2023, 16(6), 299; https://doi.org/10.3390/a16060299 - 15 Jun 2023
Viewed by 997
Abstract
Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample [...] Read more.
Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample entropy, the high computational cost is a severe limitation. Especially when large amounts of data are processed, or when parameter tuning is employed premising a large number of executions, the necessity of fast computation algorithms becomes urgent. In the past, our research team proposed fast algorithms for sample, approximate and bubble entropy. In the general case, the bucket-assisted algorithm was the one presenting the lowest execution times. In this paper, we exploit the opportunities given by the multithreading technology to further reduce the computation time. Without special requirements in hardware, since today even our cost-effective home computers support multithreading, the computation of entropy definitions can be significantly accelerated. The aim of this paper is threefold: (a) to extend the bucket-assisted algorithm for multithreaded processors, (b) to present updated execution times for the bucket-assisted algorithm since the achievements in hardware and compiler technology affect both execution times and gain, and (c) to provide a Python library which wraps fast C implementations capable of running in parallel on multithreaded processors. Full article
(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)
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12 pages, 2004 KiB  
Article
Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
by Yiming Zhao and Jing Dong-O’Brien
Algorithms 2023, 16(6), 298; https://doi.org/10.3390/a16060298 - 15 Jun 2023
Cited by 1 | Viewed by 1130
Abstract
Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic [...] Read more.
Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic breakdown. Therefore, ramp meters have been used to regulate the traffic flow from the ramps to maintain stable traffic flow on the mainline. However, existing traffic breakdown prediction models do not consider on-ramp traffic flow. In this paper, an algorithm based on artificial neural networks (ANN) is developed to predict the probability of a traffic breakdown occurrence on freeway segments with merging traffic, considering temporal and spatial correlations of the traffic conditions from the location of interest, the ramp, and the upstream and downstream segments. The feature selection analysis reveals that the traffic condition of the ramps has a significant impact on the occurrence of traffic breakdown on the mainline. Therefore, the traffic flow characteristics of the on-ramp, along with other significant features, are used to build the ANN model. The proposed ANN algorithm can predict the occurrence of traffic breakdowns on freeway segments with merging traffic with an accuracy of 96%. Furthermore, the model has been deployed at a different location, which yields a predictive accuracy of 97%. In traffic operations, the high probability of the occurrence of a traffic breakdown can be used as a trigger for the ramp meters. Full article
(This article belongs to the Special Issue Neural Network for Traffic Forecasting)
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13 pages, 958 KiB  
Communication
On a Class of Orthogonal Polynomials as Corrections in Lienard Differential System: Applications
by Vesselin Kyurkchiev, Anton Iliev, Asen Rahnev and Nikolay Kyurkchiev
Algorithms 2023, 16(6), 297; https://doi.org/10.3390/a16060297 - 12 Jun 2023
Cited by 11 | Viewed by 1009
Abstract
In this paper we demonstrate some specialized modules for investigating the dynamics of differential models, an integral part of a planned much more general Web-based application for scientific computing. As “corrections” in the Lienard differential system is presented a class of orthogonal polynomials [...] Read more.
In this paper we demonstrate some specialized modules for investigating the dynamics of differential models, an integral part of a planned much more general Web-based application for scientific computing. As “corrections” in the Lienard differential system is presented a class of orthogonal polynomials (also known as “shell polynomials”). We will note that some specifics of the amplitudes of these polynomials open up the possibility of modeling signals from the field of antenna-feeder techniques. Algorithms and modules have been consistently used for: automatic generation of a theorem on the number and type of limit cycles (in the light of Melnikov’s considerations); study of the Hamiltonian of the system and “level curves”; for the study of catastrophic surfaces (in the light of Zeeman’s considerations), etc. Similar studies have been carried out for associated polynomials. Numerical examples, illustrating our results using CAS MATHEMATICA are given. Full article
(This article belongs to the Collection Feature Papers in Algorithms)
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14 pages, 5723 KiB  
Article
Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
by Miaomiao Yu, Hongyong Yuan, Kaiyuan Li and Lizheng Deng
Algorithms 2023, 16(6), 296; https://doi.org/10.3390/a16060296 - 12 Jun 2023
Cited by 1 | Viewed by 1008
Abstract
To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, [...] Read more.
To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold ξ and B-spline order n, were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (CCKur) constituted the objective function. Initially, the objective function CCKur was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely CC, Kur, MI, EE, EEMI, and CCKur, revealed that the synthetic measurement index CCKur and objective function fitness were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method. Full article
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60 pages, 11060 KiB  
Article
An Experimental Outlook on Quality Metrics for Process Modelling: A Systematic Review and Meta Analysis
by Ashish T. S. Ireddy and Sergey V. Kovalchuk
Algorithms 2023, 16(6), 295; https://doi.org/10.3390/a16060295 - 10 Jun 2023
Viewed by 2001
Abstract
The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics [...] Read more.
The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics measure various characteristics of process models and describe their qualities. Over the years, vast amounts of metrics have been proposed in the community, making it difficult to find and select the appropriate ones for implementation. This paper presents a state-of-the-art meta-review that lists and summarises all the evaluation metrics proposed to date. We have studied the behaviour of the four most widely used metrics in process mining with an experiment. Further, we have used seven healthcare domain datasets of varying natures to analyse the behaviour of these metrics under different threshold conditions. Our work aims to propose and demonstrate the capabilities to use our selected metrics as a standard of measurement for the process mining domain. Full article
(This article belongs to the Special Issue Process Mining and Its Applications)
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25 pages, 8359 KiB  
Article
Generalized Algorithm Based on Equivalent Circuits for Evaluating Shielding Effectiveness of Electronic Equipment Enclosures
by Anton A. Ivanov, Aleksey A. Kvasnikov, Alexander V. Demakov, Maxim E. Komnatnov, Sergei P. Kuksenko and Talgat R. Gazizov
Algorithms 2023, 16(6), 294; https://doi.org/10.3390/a16060294 - 08 Jun 2023
Viewed by 1412
Abstract
The article proposes a generalized algorithm for evaluating the shielding effectiveness (SE) of electronic equipment enclosures. The algorithm is based on a number of analytical models that use equivalent circuits to obtain SE values. The article begins with a brief review and interpretation [...] Read more.
The article proposes a generalized algorithm for evaluating the shielding effectiveness (SE) of electronic equipment enclosures. The algorithm is based on a number of analytical models that use equivalent circuits to obtain SE values. The article begins with a brief review and interpretation of the mathematical formulation used in the algorithm. Then, we describe the proposed algorithm using flowcharts, and we perform its validation. The validation results show that the proposed algorithm has acceptable accuracy and gives SE values comparable to numerical methods or measurements, with much less time costs. The last part of the article presents the software developed to evaluate SE based on analytical models. Full article
(This article belongs to the Collection Feature Papers in Algorithms for Multidisciplinary Applications)
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16 pages, 2287 KiB  
Article
Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
by José-Luis Solorio-Ramírez, Raúl Jiménez-Cruz, Yenny Villuendas-Rey and Cornelio Yáñez-Márquez
Algorithms 2023, 16(6), 293; https://doi.org/10.3390/a16060293 - 08 Jun 2023
Viewed by 1873
Abstract
Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn [...] Read more.
Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations. Full article
(This article belongs to the Collection Feature Papers in Algorithms for Multidisciplinary Applications)
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24 pages, 1564 KiB  
Article
Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques
by Damilola Akingbesote, Ying Zhan, Rytis Maskeliūnas and Robertas Damaševičius
Algorithms 2023, 16(6), 292; https://doi.org/10.3390/a16060292 - 05 Jun 2023
Cited by 1 | Viewed by 2182
Abstract
The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition [...] Read more.
The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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23 pages, 1351 KiB  
Article
An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem
by Ioannis X. Tassopoulos, Christina A. Iliopoulou, Iosif V. Katsaragakis and Grigorios N. Beligiannis
Algorithms 2023, 16(6), 291; https://doi.org/10.3390/a16060291 - 04 Jun 2023
Viewed by 1329
Abstract
This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances [...] Read more.
This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed. Full article
(This article belongs to the Collection Feature Paper in Metaheuristic Algorithms and Applications)
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21 pages, 2828 KiB  
Article
Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
by Sebastiano Fichera, Gregorio Mariggiò, Mauro Corrado and Giulio Ventura
Algorithms 2023, 16(6), 290; https://doi.org/10.3390/a16060290 - 04 Jun 2023
Viewed by 1118
Abstract
The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable [...] Read more.
The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable results. In this scope, equivalent polynomials represent an effective method to circumvent the problem while exploiting the standard Gauss quadrature rule to exactly integrate polynomials times step function. Certain scenarios, however, might require the integration of polynomials times two step functions (i.e., problems in which branching cracks, kinking cracks or crack junctions within a single finite element occur). In this context, the use of equivalent polynomials has been investigated by the authors, and an algorithm to exactly integrate arbitrary polynomials times two Heaviside step functions in quadrilateral domains has been developed and is presented in this paper. Moreover, the algorithm has also been implemented into a software library (DD_EQP) to prove its precision and effectiveness and also the proposed method’s ease of implementation into any existing computational software or framework. The presented algorithm is the first step towards the numerical integration of an arbitrary number of discontinuities in quadrilateral domains. Both the algorithm and the library have a wide application range, in addition to fracture mechanics, from mathematical computing of complex geometric regions, to computer graphics and computational mechanics. Full article
(This article belongs to the Special Issue Computational Methods and Optimization for Numerical Analysis)
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19 pages, 402 KiB  
Article
Adding a Tail in Classes of Perfect Graphs
by Anna Mpanti, Stavros D. Nikolopoulos and Leonidas Palios
Algorithms 2023, 16(6), 289; https://doi.org/10.3390/a16060289 - 03 Jun 2023
Viewed by 988
Abstract
Consider a graph G which belongs to a graph class C. We are interested in connecting a node w∉V(G) to G by a single edge uw where u∈V(G); we call [...] Read more.
Consider a graph G which belongs to a graph class C. We are interested in connecting a node w∉V(G) to G by a single edge uw where u∈V(G); we call such an edge a tail. As the graph resulting from G after the addition of the tail, denoted G+uw, need not belong to the class C, we want to compute the number of non-edges of G in a minimum C-completion of G+uw, i.e., the minimum number of non-edges (excluding the tail uw) to be added to G+uw so that the resulting graph belongs to C. In this paper, we study this problem for the classes of split, quasi-threshold, threshold and P4-sparse graphs and we present linear-time algorithms by exploiting the structure of split graphs and the tree representation of quasi-threshold, threshold and P4-sparse graphs. Full article
(This article belongs to the Special Issue Graph Theoretic Methods in Scientific Computing & Industrial Applications)
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18 pages, 651 KiB  
Article
An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids
by Xue Jun Li, Maode Ma and Yihan Sun
Algorithms 2023, 16(6), 288; https://doi.org/10.3390/a16060288 - 02 Jun 2023
Cited by 2 | Viewed by 1793
Abstract
Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various [...] Read more.
Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids. Full article
(This article belongs to the Special Issue AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection)
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25 pages, 8154 KiB  
Article
A Fast Hybrid Pressure-Correction Algorithm for Simulating Incompressible Flows by Projection Methods
by Jiannong Fang
Algorithms 2023, 16(6), 287; https://doi.org/10.3390/a16060287 - 02 Jun 2023
Viewed by 1328
Abstract
To enforce the conservation of mass principle, a pressure Poisson equation arises in the numerical solution of incompressible fluid flow using the pressure-based segregated algorithms such as projection methods. For unsteady flows, the pressure Poisson equation is solved at each time step usually [...] Read more.
To enforce the conservation of mass principle, a pressure Poisson equation arises in the numerical solution of incompressible fluid flow using the pressure-based segregated algorithms such as projection methods. For unsteady flows, the pressure Poisson equation is solved at each time step usually in physical space using iterative solvers, and the resulting pressure gradient is then applied to make the velocity field divergence-free. It is generally accepted that this pressure-correction stage is the most time-consuming part of the flow solver and any meaningful acceleration would contribute significantly to the overall computational efficiency. The objective of the present work was to develop a fast hybrid pressure-correction algorithm for numerical simulation of incompressible flows around obstacles in the context of projection methods. The key idea is to adopt different numerical methods/discretisations in the sub-steps of projection methods. Here, a classical second-order time-marching projection method, which consists of two sub-steps, was chosen for the purposes of demonstration. In the first sub-step, the momentum equations were discretised on unstructured grids and solved by conventional numerical methods, here a meshless method. In the second sub-step (pressure-correction), the proposed algorithm adopts a double-discretisation system and combines the weighted least-squares approximation with the essence of immersed boundary methods. Such a design allowed us to develop an FFT-based solver to speed up the solution of the pressure Poisson equation for flow cases with obstacles, while keeping the implementation of the boundary conditions for the momentum equations as easy as conventional numerical methods do with unstructured grids. The numerical experiments of five test cases were performed to verify and validate the proposed hybrid algorithm and evaluate its computational performance. The results showed that the new FFT-based hybrid algorithm works and is robust, and it was significantly faster than the multigrid-based reference method. The hybrid algorithm opens an avenue for the development of next-generation high-performance parallel computational fluid dynamics solvers for incompressible flows. Full article
(This article belongs to the Collection Feature Papers in Algorithms)
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21 pages, 3991 KiB  
Article
Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning
by Yang-Kuei Lin, Chien-Fu Chen and Tien-Yin Chou
Algorithms 2023, 16(6), 286; https://doi.org/10.3390/a16060286 - 01 Jun 2023
Viewed by 1053
Abstract
Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has [...] Read more.
Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes. Full article
(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)
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23 pages, 1093 KiB  
Article
Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
by Domagoj Jakobović, Marko Đurasević, Karla Brkić, Juraj Fosin, Tonči Carić and Davor Davidović
Algorithms 2023, 16(6), 285; https://doi.org/10.3390/a16060285 - 01 Jun 2023
Cited by 4 | Viewed by 1423
Abstract
Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which [...] Read more.
Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives. Full article
(This article belongs to the Special Issue Algorithms for Natural Computing Models)
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16 pages, 1679 KiB  
Article
Fully Parallel Homological Region Adjacency Graph via Frontier Recognition
by Fernando Díaz-del-Río, Pablo Sanchez-Cuevas, María José Moron-Fernández, Daniel Cascado-Caballero, Helena Molina-Abril and Pedro Real
Algorithms 2023, 16(6), 284; https://doi.org/10.3390/a16060284 - 31 May 2023
Viewed by 1266
Abstract
Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of [...] Read more.
Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O(log(M×N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field. Full article
(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)
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27 pages, 476 KiB  
Article
The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms
by Christiaan Scheepers and Andries Engelbrecht
Algorithms 2023, 16(6), 283; https://doi.org/10.3390/a16060283 - 31 May 2023
Cited by 1 | Viewed by 1406
Abstract
In spite of being introduced over twenty-five years ago, Fonseca and Fleming’s attainment surfaces have not been widely used. This article investigates some of the shortcomings that may have led to the lack of adoption of this performance measure. The quantitative measure based [...] Read more.
In spite of being introduced over twenty-five years ago, Fonseca and Fleming’s attainment surfaces have not been widely used. This article investigates some of the shortcomings that may have led to the lack of adoption of this performance measure. The quantitative measure based on attainment surfaces, introduced by Knowles and Corne, is analyzed. The analysis shows that the results obtained by the Knowles and Corne approach are influenced (biased) by the shape of the attainment surface. Improvements to the Knowles and Corne approach for bi-objective Pareto-optimal front (POF) comparisons are proposed. Furthermore, assuming M objective functions, an M-dimensional attainment-surface-based quantitative measure, named the porcupine measure, is proposed for comparing the performance of multi-objective optimization algorithms. A computationally optimized version of the porcupine measure is presented and empirically analyzed. Full article
(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications)
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24 pages, 3664 KiB  
Article
Iterative Oblique Decision Trees Deliver Explainable RL Models
by Raphael C. Engelhardt, Marc Oedingen, Moritz Lange, Laurenz Wiskott and Wolfgang Konen
Algorithms 2023, 16(6), 282; https://doi.org/10.3390/a16060282 - 31 May 2023
Cited by 2 | Viewed by 1602
Abstract
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate [...] Read more.
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents (“oracles”) and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models. Full article
(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)
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13 pages, 305 KiB  
Article
Folding Every Point on a Polygon Boundary to a Point
by Nattawut Phetmak and Jittat Fakcharoenphol
Algorithms 2023, 16(6), 281; https://doi.org/10.3390/a16060281 - 31 May 2023
Viewed by 1256
Abstract
We consider a problem in computational origami. Given a piece of paper as a convex polygon P and a point f located within, we fold every point on a boundary of P to f and compute a region that is safe from folding, [...] Read more.
We consider a problem in computational origami. Given a piece of paper as a convex polygon P and a point f located within, we fold every point on a boundary of P to f and compute a region that is safe from folding, i.e., the region with no creases. This problem is an extended version of a problem by Akitaya, Ballinger, Demaine, Hull, and Schmidt that only folds corners of the polygon. To find the region, we prove structural properties of intersections of parabola-bounded regions and use them to devise a linear-time algorithm. We also prove a structural result regarding the complexity of the safe region as a variable of the location of point f, i.e., the number of arcs of the safe region can be determined using the straight skeleton of the polygon P. Full article
(This article belongs to the Special Issue Machine Learning in Computational Geometry)
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27 pages, 431 KiB  
Article
Reinforcement Learning in a New Keynesian Model
by Szabolcs Deák, Paul Levine, Joseph Pearlman and Bo Yang
Algorithms 2023, 16(6), 280; https://doi.org/10.3390/a16060280 - 31 May 2023
Cited by 1 | Viewed by 1459
Abstract
We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational [...] Read more.
We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE) and BR agents. The latter are anticipated utility learners, given their beliefs of aggregate states, and they use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. In the most general form of the model, RE and BR agents learn from their forecasting errors by observing and comparing them with each other, making the composition of the two types endogenous. This reinforcement learning is then at the core of the heterogeneous expectations model and leads to the striking result that increasing the volatility of exogenous shocks, by assisting the learning process, increases the proportion of RE agents and is welfare-increasing. Full article
(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)
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17 pages, 4102 KiB  
Article
A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution
by Patricia Ochoa, Cinthia Peraza, Oscar Castillo and Zong Woo Geem
Algorithms 2023, 16(6), 279; https://doi.org/10.3390/a16060279 - 31 May 2023
Viewed by 1453
Abstract
The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to [...] Read more.
The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2023)
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