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36 pages, 17182 KiB

Open AccessArticle

A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot

by Omer Saleem, Ahmad Hamza and Jamshed Iqbal

Computers 2024, 13(11), 301; https://doi.org/10.3390/computers13110301 - 19 Nov 2024

Viewed by 241

Abstract

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, [...] Read more.

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, and intrinsic nonlinear dynamics associated with the aforementioned nonholonomic system. Hence, this article contributes to formulating a self-adaptive single-neuron PID control system that is driven by an extended Kalman filter (EKF) to ensure efficient learning and faster convergence speeds. The neural adaptive PID control formulation improves the controller’s design flexibility, which allows it to effectively attenuate the tracking errors and improve the system’s trajectory tracking accuracy. To supplement the controller’s robustness to exogenous disturbances, the adaptive PID control signal is modulated with an auxiliary fuzzy-immune system. The fuzzy-immune system imitates the automatic self-learning and self-tuning characteristics of the biological immune system to suppress bounded disturbances and parametric variations. The propositions above are verified by performing the tailored hardware in the loop experiments on a differentially driven lawn-mowing robot. The results of these experiments confirm the enhanced trajectory tracking precision and disturbance compensation ability of the prescribed control method. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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14 pages, 1107 KiB

Open AccessArticle

Semantic-Aware Adaptive Binary Search for Hard-Label Black-Box Attack

by Yiqing Ma, Kyle Lucke, Min Xian and Aleksandar Vakanski

Computers 2024, 13(8), 203; https://doi.org/10.3390/computers13080203 - 18 Aug 2024

Viewed by 849

Abstract

Despite the widely reported potential of deep neural networks for automated breast tumor classification and detection, these models are vulnerable to adversarial attacks, which leads to significant performance degradation on different datasets. In this paper, we introduce a novel adversarial attack approach under [...] Read more.

Despite the widely reported potential of deep neural networks for automated breast tumor classification and detection, these models are vulnerable to adversarial attacks, which leads to significant performance degradation on different datasets. In this paper, we introduce a novel adversarial attack approach under the decision-based black-box setting, where the attack does not have access to the model parameters, and the returned information from querying the target model consists of only the final class label prediction (i.e., hard-label attack). The proposed attack approach has two major components: adaptive binary search and semantic-aware search. The adaptive binary search utilizes a coarse-to-fine strategy that applies adaptive tolerance values in different searching stages to reduce unnecessary queries. The proposed semantic mask-aware search crops the search space by using breast anatomy, which significantly avoids invalid searches. We validate the proposed approach using a dataset of 3378 breast ultrasound images and compare it with another state-of-the-art method by attacking five deep learning models. The results demonstrate that the proposed approach generates imperceptible adversarial samples at a high success rate (between 99.52% and 100%), and dramatically reduces the average and median queries by 23.96% and 31.79%, respectively, compared with the state-of-the-art approach. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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26 pages, 17391 KiB

Open AccessArticle

Internet of Things-Based Robust Green Smart Grid

by Rania A. Ahmed, M. Abdelraouf, Shaimaa Ahmed Elsaid, Mohammed ElAffendi, Ahmed A. Abd El-Latif, A. A. Shaalan and Abdelhamied A. Ateya

Computers 2024, 13(7), 169; https://doi.org/10.3390/computers13070169 - 8 Jul 2024

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Abstract

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the [...] Read more.

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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17 pages, 5897 KiB

Open AccessArticle

A Contextual Model for Visual Information Processing

by Illia Khurtin and Mukesh Prasad

Computers 2024, 13(6), 155; https://doi.org/10.3390/computers13060155 - 20 Jun 2024

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Abstract

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning [...] Read more.

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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18 pages, 9307 KiB

Open AccessArticle

A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios

by Barbara Cardone, Ferdinando Di Martino and Vittorio Miraglia

Computers 2024, 13(6), 143; https://doi.org/10.3390/computers13060143 - 5 Jun 2024

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Abstract

This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting [...] Read more.

This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM₁₀, NO₂, and PM_2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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19 pages, 434 KiB

Open AccessArticle

Applying Bounding Techniques on Grammatical Evolution

by Ioannis G. Tsoulos, Alexandros Tzallas and Evangelos Karvounis

Computers 2024, 13(5), 111; https://doi.org/10.3390/computers13050111 - 23 Apr 2024

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Abstract

The Grammatical Evolution technique has been successfully applied to some datasets from various scientific fields. However, in Grammatical Evolution, the chromosomes can be initialized at wide value intervals, which can lead to a decrease in the efficiency of the underlying technique. In this [...] Read more.

The Grammatical Evolution technique has been successfully applied to some datasets from various scientific fields. However, in Grammatical Evolution, the chromosomes can be initialized at wide value intervals, which can lead to a decrease in the efficiency of the underlying technique. In this paper, a technique for discovering appropriate intervals for the initialization of chromosomes is proposed using partition rules guided by a genetic algorithm. This method has been applied to feature construction techniques used in a variety of scientific papers. After successfully finding a promising interval, the feature construction technique is applied and the chromosomes are initialized within that interval. This technique was applied to a number of known problems in the relevant literature, and the results are extremely promising. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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21 pages, 1359 KiB

Open AccessArticle

A Holistic Approach to Use Educational Robots for Supporting Computer Science Courses

by Zhumaniyaz Mamatnabiyev, Christos Chronis, Iraklis Varlamis, Yassine Himeur and Meirambek Zhaparov

Computers 2024, 13(4), 102; https://doi.org/10.3390/computers13040102 - 17 Apr 2024

Cited by 1 | Viewed by 1724

Abstract

Robots are intelligent machines that are capable of autonomously performing intricate sequences of actions, with their functionality being primarily driven by computer programs and machine learning models. Educational robots are specifically designed and used for teaching and learning purposes and attain the interest [...] Read more.

Robots are intelligent machines that are capable of autonomously performing intricate sequences of actions, with their functionality being primarily driven by computer programs and machine learning models. Educational robots are specifically designed and used for teaching and learning purposes and attain the interest of learners in gaining knowledge about science, technology, engineering, arts, and mathematics. Educational robots are widely applied in different fields of primary and secondary education, but their usage in teaching higher education subjects is limited. Even when educational robots are used in tertiary education, the use is sporadic, targets specific courses or subjects, and employs robots with narrow applicability. In this work, we propose a holistic approach to the use of educational robots in tertiary education. We demonstrate how an open source educational robot can be used by colleges, and universities in teaching multiple courses of a computer science curriculum, fostering computational and creative thinking in practice. We rely on an open-source and open design educational robot, called FOSSBot, which contains various IoT technologies for measuring data, processing it, and interacting with the physical world. Grace to its open nature, FOSSBot can be used in preparing the content and supporting learning activities for different subjects such as electronics, computer networks, artificial intelligence, computer vision, etc. To support our claim, we describe a computer science curriculum containing a wide range of computer science courses and explain how each course can be supported by providing indicative activities. The proposed one-year curriculum can be delivered at the postgraduate level, allowing computer science graduates to delve deep into Computer Science subjects. After examining related works that propose the use of robots in academic curricula we detect the gap that still exists for a curriculum that is linked to an educational robot and we present in detail each proposed course, the software libraries that can be employed for each course and the possible extensions to the open robot that will allow to further extend the curriculum with more topics or enhance it with activities. With our work, we show that by incorporating educational robots in higher education we can address this gap and provide a new ledger for boosting tertiary education. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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22 pages, 1649 KiB

Open AccessArticle

Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model

by Jorge de Andrés-Sánchez, Mario Arias-Oliva, Mar Souto-Romero and Jaume Gené-Albesa

Computers 2024, 13(3), 82; https://doi.org/10.3390/computers13030082 - 20 Mar 2024

Cited by 1 | Viewed by 1946

Abstract

Medical implantable technologies, such as cochlear implants or joint prostheses, have been commonly used since the late 20th century. By contrast, the market for this type of technology is expanding when the purpose is not medical, even though it is more marginal. This [...] Read more.

Medical implantable technologies, such as cochlear implants or joint prostheses, have been commonly used since the late 20th century. By contrast, the market for this type of technology is expanding when the purpose is not medical, even though it is more marginal. This study tests a technology acceptance model for the latter type of insideable technology based on an extension of the technology acceptance models TAM and TAM2 proposed for hedonic technologies by van del Heijden. So, the behavioral intention of insertables is explained by the perceived usefulness and perceived ease of use, as well as social influence, as proposed in the TAM2 by Venkatesh and Davis. Additionally, the perceived enjoyment, included in the extension by Van der Heijden, is added as an explanatory factor. We applied structural equation modeling to the theoretical scheme provided by the modified TAM and performed a necessary condition analysis. Statistical analysis showed that all variables considered in the model have a significantly positive influence on behavioral intention. Likewise, the model has good properties both from the point of view of the fit obtained, since it predicts 70% of behavioral intention, and from the predictive point of view. The necessary condition analysis allows us to analyze whether the presence of some of the latent variables postulated to explain the attitude toward implantables is necessary to produce the said acceptance. Therefore, its absence is a critical aspect of expansion. We observed that perceived usefulness manifests itself as a necessary condition for behavioral intention with a medium size. Perceived ease of use and enjoyment also present a significant necessity effect size, but their strength is smaller. By contrast, the subjective norm does not have the status of a necessary variable. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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13 pages, 662 KiB

Open AccessSystematic Review

The Use of Integrated Multichannel Records in Learning Studies in Higher Education: A Systematic Review of the Last 10 Years

by Irene González-Díez, Carmen Varela and María Consuelo Sáiz-Manzanares

Computers 2024, 13(4), 96; https://doi.org/10.3390/computers13040096 - 10 Apr 2024

Viewed by 1230

Abstract

Neurophysiological measures have been used in the field of education to improve our knowledge about the cognitive processes underlying learning. Furthermore, the combined use of different neuropsychological measures has deepened our understanding of these processes. The main objective of this systematic review is [...] Read more.

Neurophysiological measures have been used in the field of education to improve our knowledge about the cognitive processes underlying learning. Furthermore, the combined use of different neuropsychological measures has deepened our understanding of these processes. The main objective of this systematic review is to provide a comprehensive picture of the use of integrated multichannel records in higher education. The bibliographic sources for the review were Web of Science, PsycINFO, Scopus, and Psicodoc databases. After a screening process by two independent reviewers, 10 articles were included according to prespecified inclusion criteria. In general, integrated recording of eye tracking and electroencephalograms were the most commonly used metrics, followed by integrated recording of eye tracking and electrodermal activity. Cognitive load was the most widely investigated learning-related cognitive process using integrated multichannel records. To date, most research has focused only on one neurophysiological measure. Furthermore, to our knowledge, no study has systematically investigated the use of integrated multichannel records in higher education. This systematic review provides a comprehensive picture of the current use of integrated multichannel records in higher education. Its findings may help design innovative educational programs, particularly in the online context. The findings provide a basis for future research and decision making regarding the use of integrated multichannel records in higher education. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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