Algorithms

21 pages, 8146 KiB

Open AccessArticle

Inverse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution and Jacobian Pseudoinverse Approach

by Jesus Hernandez-Barragan, Josue Plascencia-Lopez, Michel Lopez-Franco, Nancy Arana-Daniel and Carlos Lopez-Franco

Algorithms 2024, 17(10), 454; https://doi.org/10.3390/a17100454 (registering DOI) - 12 Oct 2024

Abstract

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, [...] Read more.

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor generalization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipulators using a metaheuristic algorithm enhanced with a Jacobian step. Our method overcomes those limitations by selectively applying the Jacobian step to the differential evolution (DE) algorithm. The effectiveness and versatility of the proposed approach are demonstrated through simulations and real-world experimentation on a 5 DOF KUKA robotic arm. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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18 pages, 1114 KiB

Open AccessArticle

Performance Analysis and Parallel Scalability of Numerical Methods for Fractional-in-Space Diffusion Problems with Adaptive Time Stepping

by Svetozar Margenov and Dimitar Slavchev

Algorithms 2024, 17(10), 453; https://doi.org/10.3390/a17100453 (registering DOI) - 12 Oct 2024

Abstract

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian

{(- Δ)}^{α}

,

0 < α < 1

, following the integral definition. Fractional diffusion is non-local, and the [...] Read more.

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian

{(- Δ)}^{α}

,

0 < α < 1

, following the integral definition. Fractional diffusion is non-local, and the finite element method (FEM) discretization in space leads to a dense stiffness matrix. It is well known that numerically solving such non-local boundary value problems is expensive. Difficulties increase significantly when the problem is time-dependent. The aim of the article is to develop computationally efficient methods and algorithms. There are two main features of our approach. Hierarchical semi-separable (HSS) compression is applied for an approximate solution of the arising linear systems. For time discretization, we use an adaptive forward–backward Euler scheme. The properties of the composite algorithm thus obtained are investigated. In particular, the block representation of HSS compression allowed us to upgrade the HSS solver to efficiently handle varying diagonally perturbed transition matrices corresponding to changing time steps. The contribution of the paper is threefold. The methods are completely constructive, which allows for a clearly structured description of the algorithms. A theoretical estimate of the computational complexity is presented. It shows the advantages of the adaptive time stepping in combination with the HSS solver. Theoretical results are supported by representative numerical experiments. Both sequential and parallel scalability and efficiency are analyzed. The presented results provide convincing proof of the concept of the proposed methods and algorithms. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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26 pages, 15828 KiB

Open AccessArticle

Optimization of Offshore Saline Aquifer CO₂ Storage in Smeaheia Using Surrogate Reservoir Models

by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca and Cuthbert Shang Wui Ng

Algorithms 2024, 17(10), 452; https://doi.org/10.3390/a17100452 - 11 Oct 2024

Abstract

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage [...] Read more.

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO₂ storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO₂ saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO₂ injection, which maximizes the volume of injected CO₂ while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO₂. Full article

(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))

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20 pages, 13981 KiB

Open AccessArticle

An Algorithm for Creating a Synaptic Cleft Digital Phantom Suitable for Further Numerical Modeling

by Olga A. Zagubnaya and Yaroslav R. Nartsissov

Algorithms 2024, 17(10), 451; https://doi.org/10.3390/a17100451 - 11 Oct 2024

Abstract

One of the most significant applications of mathematical numerical methods in biology is the theoretical description of the convectional reaction–diffusion of chemical compounds. Initial biological objects must be appropriately mimicked by digital domains that are suitable for further use in computational modeling. In [...] Read more.

One of the most significant applications of mathematical numerical methods in biology is the theoretical description of the convectional reaction–diffusion of chemical compounds. Initial biological objects must be appropriately mimicked by digital domains that are suitable for further use in computational modeling. In the present study, an algorithm for the creation of a digital phantom describing a local part of nervous tissue—namely, a synaptic contact—is established. All essential elements of the synapse are determined using a set of consistent Boolean operations within the COMSOL Multiphysics software 6.1. The formalization of the algorithm involves a sequence of procedures and logical operations applied to a combination of 3D Voronoi diagrams, an experimentally defined inner synapse area, and a simple ellipsoid under different sets of biological parameters. The obtained digital phantom is universal and may be applied to different types of neuronal synapses. The clear separation of the designed domains reveals that the boundary’s conditions and internal flux dysconnectivity functions can be set up explicitly. Digital domains corresponding to the parts of a synapse are appropriate for further application of the derived numeric meshes, with various capacities of the included elements. Thus, the obtained digital phantom can be effectively used for further modeling of the convectional reaction–diffusion of chemical compounds in nervous tissue. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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20 pages, 7027 KiB

Open AccessArticle

Empirical Studies on Merging Characteristics in Temporary Highway Work Zones with Different Traffic States and Layouts

by Yanli Jiao, Xiaoyue Zhu, Xinkai Ji, Long Zhang and Zhibin Li

Algorithms 2024, 17(10), 450; https://doi.org/10.3390/a17100450 - 10 Oct 2024

Abstract

Vehicles conduct mandatory lane changes to merge into the open lanes in temporary highway work zones, and the merging characteristics have been widely studied over the past decades. Unlike research on other traffic facilities, the data and scenarios of highway work zones are [...] Read more.

Vehicles conduct mandatory lane changes to merge into the open lanes in temporary highway work zones, and the merging characteristics have been widely studied over the past decades. Unlike research on other traffic facilities, the data and scenarios of highway work zones are often lacking and monotonous. This study presented a vehicle trajectory dataset in highway work zones. The dataset contains ten work zone sites and three different layouts. After transforming raw videos into high-resolution trajectories, we selected eight areas and divided them into three scenarios to investigate the merging characteristics in highway work zones with different traffic states and layouts. In work zones with varying traffic conditions, we found opposite merging locations and lane speed distribution trends. In work zones with varying layouts, we found different merging location distributions, lane speed distributions, and impacts on the following vehicle’s speed. The relationship between the gap and merge speed was consistent with previous studies. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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27 pages, 466 KiB

Open AccessArticle

Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem

by Rim Ben Jelloun, Khalid Jebari and Abdelaziz El Moujahid

Algorithms 2024, 17(10), 449; https://doi.org/10.3390/a17100449 - 9 Oct 2024

Abstract

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and [...] Read more.

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and requires a great deal of time. In these cases, researchers use approximate solutions offered by metaheuristics. This work involved the design of a new metaheuristic called Open Competency Optimization (OCO), which was inspired by human behavior during the learning process and based on the competency approach. The aim is the construction of solutions that represent learners’ ideas in the context of an open problem. The candidate solutions in OCO evolve over three steps. Concerning the first step, each learner builds a path of learning (finding the solution to the problem) through self-learning, which depends on their abilities. In the second step, each learner responds positively to the best ideas in their group (the construction of each group is based on the competency of the learners or the neighbor principle). In the last step, the learners interact with the best one in the group and with the leader. For the sake of proving the relevance of the proposed algorithm, OCO was tested in dynamic vehicle-routing problems along with the Generalized Dynamic Benchmark Generator (GDBG). Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

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44 pages, 909 KiB

Open AccessSystematic Review

Niching Global Optimisation: Systematic Literature Review

by Yves Matanga, Pius Owolawi, Chunling Du and Etienne van Wyk

Algorithms 2024, 17(10), 448; https://doi.org/10.3390/a17100448 - 9 Oct 2024

Abstract

Niching in global optimisation refers to a set of techniques designed to identify multiple optimal solutions within a nonlinear, multimodal landscape. These algorithms enhance the exploratory capabilities of conventional metaheuristics by maintaining diversity and supporting coexisting subpopulations across a search space, thereby allowing [...] Read more.

Niching in global optimisation refers to a set of techniques designed to identify multiple optimal solutions within a nonlinear, multimodal landscape. These algorithms enhance the exploratory capabilities of conventional metaheuristics by maintaining diversity and supporting coexisting subpopulations across a search space, thereby allowing a more deterministic approach to the true global optimum. Niching algorithms can be categorised into three primary subfamilies: sequential or temporal niching, parallel or spatial niching, and hybrid models which integrate various niching subparadigms. This research paper aims to explore the effectiveness and limitations of different niching algorithms by providing a systematic literature review of the theoretical frameworks within these subfamilies. Eleven major niching native subparadigms have been identified: fitness sharing, crowding, clearing, speciation, restricted tournament selection, clustering, multiobjectivisation, embedded hybrid methods, ensemble hybrid methods, and other hybrid approaches. This study offers a detailed examination of each paradigm’s theoretical foundation, including template algorithmic layouts, and delineates the unique elements of each approach. Research contributions from the inception of niching to 2024 have been aggregated from the SCOPUS database and systematically classified. Data aggregation included journal articles, conference papers, review papers, and research reports published in English only following the PRISMA framework. Application papers with novel theoretical ideas were also taken into account. In all, 203 research works were retained under the inclusion and exclusion criteria. This study concludes with overarching high-level recommendations for future research in modern niching optimisation, emphasising the development of space and time-scalable methods to enhance the adaptability and efficiency of optimisation algorithms in diverse, increasingly multivariable multimodal problems. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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25 pages, 5210 KiB

Open AccessArticle

Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

by Alina I. Stepanova, Alexandra I. Khalyasmaa, Pavel V. Matrenin and Stanislav A. Eroshenko

Algorithms 2024, 17(10), 447; https://doi.org/10.3390/a17100447 - 8 Oct 2024

Abstract

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment [...] Read more.

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)

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26 pages, 793 KiB

Open AccessArticle

Improving the Generalization Abilities of Constructed Neural Networks with the Addition of Local Optimization Techniques

by Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis and Alexandros Tzallas

Algorithms 2024, 17(10), 446; https://doi.org/10.3390/a17100446 - 6 Oct 2024

Abstract

Constructed neural networks with the assistance of grammatical evolution have been widely used in a series of classification and data-fitting problems recently. Application areas of this innovative machine learning technique include solving differential equations, autism screening, and measuring motor function in Parkinson’s disease. [...] Read more.

Constructed neural networks with the assistance of grammatical evolution have been widely used in a series of classification and data-fitting problems recently. Application areas of this innovative machine learning technique include solving differential equations, autism screening, and measuring motor function in Parkinson’s disease. Although this technique has given excellent results, in many cases, it is trapped in local minimum and cannot perform satisfactorily in many problems. For this purpose, it is considered necessary to find techniques to avoid local minima, and one technique is the periodic application of local minimization techniques that will adjust the parameters of the constructed artificial neural network while maintaining the already existing architecture created by grammatical evolution. The periodic application of local minimization techniques has shown a significant reduction in both classification and data-fitting problems found in the relevant literature. Full article

(This article belongs to the Section Databases and Data Structures)

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23 pages, 1456 KiB

Open AccessArticle

Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model

by Sebastián Dormido-Canto, Joaquín Rohland, Matías López, Gonzalo Garcia, Ernesto Fabregas and Gonzalo Farias

Algorithms 2024, 17(10), 445; https://doi.org/10.3390/a17100445 - 5 Oct 2024

Abstract

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the [...] Read more.

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption. Full article

(This article belongs to the Special Issue Artificial Intelligence for More Efficient Renewable Energy Systems)

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34 pages, 2104 KiB

Open AccessArticle

Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains

by Vitor Santos

Algorithms 2024, 17(10), 444; https://doi.org/10.3390/a17100444 (registering DOI) - 5 Oct 2024

Abstract

This paper presents a general analytical solution for the problem of locating points in planar regions with an arbitrary geometry at the boundary. The proposed methodology overcomes the traditional solutions used for polygonal regions. The method originated from the explicit evaluation of the [...] Read more.

This paper presents a general analytical solution for the problem of locating points in planar regions with an arbitrary geometry at the boundary. The proposed methodology overcomes the traditional solutions used for polygonal regions. The method originated from the explicit evaluation of the contour integral using the Residue and Cauchy theorems, which then evolved toward a technique very similar to the winding number and, finally, simplified into a variant of ray-crossing approach slightly more informed and more universal than the classic approach, which had been used for decades. The very close relation of both techniques also emerges during the derivation of the solution. The resulting algorithm becomes simpler and potentially faster than the current state of the art for point locations in arbitrary polygons because it uses fewer operations. For polygonal regions, it is also applicable without further processing for special cases of degeneracy, and it is possible to use in fully integer arithmetic; it can also be vectorized for parallel computation. The major novelty, however, is the extension of the technique to virtually any shape or segment delimiting a planar domain, be it linear, a circular arc, or a higher order curve. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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17 pages, 904 KiB

Open AccessArticle

Explainable Machine Learning Model for Chronic Kidney Disease Prediction

by Muhammad Shoaib Arif, Ateeq Ur Rehman and Daniyal Asif

Algorithms 2024, 17(10), 443; https://doi.org/10.3390/a17100443 - 3 Oct 2024

Abstract

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has [...] Read more.

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)

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Graphical abstract

28 pages, 1554 KiB

Open AccessArticle

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

by José Antonio Cárdenas-Haro, Mohamed Salem, Abraham N. Aldaco-Gastélum, Roberto López-Avitia and Maurice Dawson

Algorithms 2024, 17(10), 442; https://doi.org/10.3390/a17100442 - 3 Oct 2024

Abstract

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct [...] Read more.

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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22 pages, 6490 KiB

Open AccessArticle

Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking

by Harry Hoa Huynh and Cheol-Hong Min

Algorithms 2024, 17(10), 441; https://doi.org/10.3390/a17100441 - 2 Oct 2024

Abstract

Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to [...] Read more.

Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time. Full article

(This article belongs to the Collection Feature Papers on Artificial Intelligence Algorithms and Their Applications)

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14 pages, 15389 KiB

Open AccessArticle

Impact of Sliding Window Variation and Neuronal Time Constants on Acoustic Anomaly Detection Using Recurrent Spiking Neural Networks in Automotive Environment

by Shreya Kshirasagar, Andre Guntoro and Christian Mayr

Algorithms 2024, 17(10), 440; https://doi.org/10.3390/a17100440 - 1 Oct 2024

Abstract

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in [...] Read more.

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows’ variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples. Full article

(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)

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14 pages, 2535 KiB

Open AccessReview

Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets

by Reza Khoshkbary Rezayiye, Clemente Ibarra-Castanedo and Xavier Maldague

Algorithms 2024, 17(10), 439; https://doi.org/10.3390/a17100439 - 1 Oct 2024

Abstract

This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data [...] Read more.

This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications. Full article

(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)

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25 pages, 3831 KiB

Open AccessArticle

Solving Optimal Power Flow Using New Efficient Hybrid Jellyfish Search and Moth Flame Optimization Algorithms

by Chiva Mayouf, Ahmed Salhi, Fanta Haidara, Fatima Zahra Aroua, Ragab A. El-Sehiemy, Djemai Naimi, Chouaib Aya and Cheikh Sidi Ethmane Kane

Algorithms 2024, 17(10), 438; https://doi.org/10.3390/a17100438 - 1 Oct 2024

Abstract

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its [...] Read more.

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its exploitation mechanism. To improve its efficiency, we combined it with the Moth Flame Optimizer, which has proven its ability to exploit good solutions in the search area. This hybrid algorithm combines the advantages of both algorithms. The performance and precision of the hybrid optimization approach (JS-MFO) were investigated by minimizing well-known mathematical benchmark functions and by solving the complex OPF problem. The OPF problem was solved by optimizing non-convex objective functions such as total fuel cost, total active transmission losses, total gas emission, total voltage deviation, and the voltage stability index. Two test systems, the IEEE 30-bus network and the Mauritanian RIM 27-bus transmission network, were considered for implementing the JS-MFO approach. Experimental tests of the JS, MFO, and JS-MFO algorithms on eight well-known benchmark functions, the IEEE 30-bus, and the Mauritanian RIM 27-bus system were conducted. For the IEEE 30-bus test system, the proposed hybrid approach provides a percent cost saving of 11.4028%, a percent gas emission reduction of 14.38%, and a percent loss saving of 50.60% with respect to the base case. For the RIM 27-bus system, JS-MFO achieved a loss percent saving of 50.67% and percent voltage reduction of 62.44% with reference to the base case. The simulation results using JS-MFO and obtained with the MATLAB 2009b software were compared with those of JS, MFO, and other well-known meta-heuristics cited in the literature. The comparison report proves the superiority of the JS-MFO method over JS, MFO, and other competing meta-heuristics in solving difficult OPF problems. Full article

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33 pages, 8616 KiB

Open AccessArticle

Theoretical Study of the Wear of a Reduced-Diameter Wheel for Freight Wagons, Based on Its Diameter

by David S. Pellicer and Emilio Larrodé

Algorithms 2024, 17(10), 437; https://doi.org/10.3390/a17100437 - 1 Oct 2024

Abstract

This paper presents the development of a numerical analysis model designed to estimate the lifespan of reduced-diameter wheels used in freight wagons based on their diameter, under quasi-static conditions. These wheels are increasingly being used in combined transport applications, where they are installed [...] Read more.

This paper presents the development of a numerical analysis model designed to estimate the lifespan of reduced-diameter wheels used in freight wagons based on their diameter, under quasi-static conditions. These wheels are increasingly being used in combined transport applications, where they are installed in various bogie configurations and subjected to different operational environments. However, due to the unique characteristics of reduced-diameter wheels, their lifespan has been scarcely studied. To accurately build this model, an in-depth investigation of the rolling phenomenon was required, addressing key issues in the track–vehicle interaction and establishing relationships between these factors. After constructing the rail–wheel interaction model, it was applied to calculate the lifespan of wheels with standard, medium, and reduced diameters under identical conditions for comparison. This approach makes it possible to determine the lifespan of reduced-diameter wheels relative to standard ones, as well as to observe how lifespan changes with wheel diameter, and it is observed how lifespan diminishes non-linearly with decreasing diameters. The underlying reasons for this variation are explained through a comprehensive understanding of the rolling phenomenon, enabled by the full analysis. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (2nd Edition))

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16 pages, 1790 KiB

Open AccessArticle

Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning

by Tan-Hanh Pham, Godwyll Aikins, Tri Truong and Kim-Doang Nguyen

Algorithms 2024, 17(10), 436; https://doi.org/10.3390/a17100436 - 1 Oct 2024

Abstract

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a [...] Read more.

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a nonfunctional joint during task execution. Our experimental platform is the Franka robot with seven degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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