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18 pages, 2068 KiB

Open AccessArticle

Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

by Patricia Gamboa, Rui Varandas, João Rodrigues, Cátia Cepeda, Cláudia Quaresma and Hugo Gamboa

Computers 2022, 11(4), 49; https://doi.org/10.3390/computers11040049 - 24 Mar 2022

Cited by 6 | Viewed by 3699

Abstract

Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and [...] Read more.

Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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17 pages, 878 KiB

Open AccessArticle

Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison

by Diogo Pereira and Rodolfo Oliveira

Computers 2022, 11(2), 27; https://doi.org/10.3390/computers11020027 - 17 Feb 2022

Cited by 5 | Viewed by 3166

Abstract

This paper investigates the detection of abnormal sequences of signaling packets purposely generated to perpetuate signaling-based attacks in computer networks. The problem is studied for the Session Initiation Protocol (SIP) using a dataset of signaling packets exchanged by multiple end-users. A sequence of [...] Read more.

This paper investigates the detection of abnormal sequences of signaling packets purposely generated to perpetuate signaling-based attacks in computer networks. The problem is studied for the Session Initiation Protocol (SIP) using a dataset of signaling packets exchanged by multiple end-users. A sequence of SIP messages never observed before can indicate possible exploitation of a vulnerability and its detection or prediction is of high importance to avoid security attacks due to unknown abnormal SIP dialogs. The paper starts to briefly characterize the adopted dataset and introduces multiple definitions to detail how the deep learning-based approach is adopted to detect possible attacks. The proposed solution is based on a convolutional neural network capable of exploring the definition of an orthogonal space representing the SIP dialogs. The space is then used to train the neural network model to classify the type of SIP dialog according to a sequence of SIP packets prior observed. The classifier of unknown SIP dialogs relies on the statistical properties of the supervised learning of known SIP dialogs. Experimental results are presented to assess the solution in terms of SIP dialogs prediction, unknown SIP dialogs detection, and computational performance, demonstrating the usefulness of the proposed methodology to rapidly detect signaling-based attacks. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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23 pages, 1011 KiB

Open AccessArticle

Meta-Governance Framework to Guide the Establishment of Mass Collaborative Learning Communities

by Majid Zamiri, Luis M. Camarinha-Matos and João Sarraipa

Computers 2022, 11(1), 12; https://doi.org/10.3390/computers11010012 - 8 Jan 2022

Cited by 4 | Viewed by 2856

Abstract

The application of mass collaboration in different areas of study and work has been increasing over the last few decades. For example, in the education context, this emerging paradigm has opened new opportunities for participatory learning, namely, “mass collaborative learning (MCL)”. The development [...] Read more.

The application of mass collaboration in different areas of study and work has been increasing over the last few decades. For example, in the education context, this emerging paradigm has opened new opportunities for participatory learning, namely, “mass collaborative learning (MCL)”. The development of such an innovative and complementary method of learning, which can lead to the creation of knowledge-based communities, has helped to reap the benefits of diversity and inclusion in the creation and development of knowledge. In other words, MCL allows for enhanced connectivity among the people involved, providing them with the opportunity to practice learning collectively. Despite recent advances, this area still faces many challenges, such as a lack of common agreement about the main concepts, components, applicable structures, relationships among the participants, as well as applicable assessment systems. From this perspective, this study proposes a meta-governance framework that benefits from various other related ideas, models, and methods that together can better support the implementation, execution, and development of mass collaborative learning communities. The proposed framework was applied to two case-study projects in which vocational education and training respond to the needs of collaborative education–enterprise approaches. It was also further used in an illustration of the MCL community called the “community of cooks”. Results from these application cases are discussed. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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15 pages, 3156 KiB

Open AccessArticle

Application of Unsupervised Multivariate Analysis Methods to Raman Spectroscopic Assessment of Human Dental Enamel

by Iulian Otel, Joao Silveira, Valentina Vassilenko, António Mata, Maria Luísa Carvalho, José Paulo Santos and Sofia Pessanha

Computers 2022, 11(1), 5; https://doi.org/10.3390/computers11010005 - 28 Dec 2021

Cited by 1 | Viewed by 2988

Abstract

This work explores the suitability of data treatment methodologies for Raman spectra of teeth using multivariate analysis methods. Raman spectra were measured in our laboratory and obtained from control enamel samples and samples with a protective treatment before and after an erosive attack. [...] Read more.

This work explores the suitability of data treatment methodologies for Raman spectra of teeth using multivariate analysis methods. Raman spectra were measured in our laboratory and obtained from control enamel samples and samples with a protective treatment before and after an erosive attack. Three different approaches for data treatment were undertaken in order to evaluate the aptitude of distinguishing between groups: A—Principal Component Analysis of the numerical parameters derived from deconvoluted spectra; B—PCA of average Raman spectra after baseline correction; and C—PCA of average raw Raman spectra. Additionally, Hierarchical Cluster Analysis were applied to Raman spectra of enamel measured with different laser wavelengths (638 nm or 785 nm) to evaluate the most suitable choice of illumination. According to the different approaches, PC₁ scores obtained between control and treatment group were A—50.5%, B—97.1% and C—83.0% before the erosive attack and A—55.2%, B—93.2% and C—87.8% after an erosive attack. The obtained results showed that performing PCA analysis of raw or baseline corrected Raman spectra of enamel was not as efficient in the evaluation of samples with different treatments. Moreover, acquiring Raman spectra with a 785 nm laser increases precision in the data treatment methodologies. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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16 pages, 9185 KiB

Open AccessArticle

Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

by Eduardo Guzman, Beatriz Andres and Raul Poler

Computers 2022, 11(1), 1; https://doi.org/10.3390/computers11010001 - 22 Dec 2021

Cited by 10 | Viewed by 4560

Abstract

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become [...] Read more.

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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18 pages, 13765 KiB

Open AccessArticle

Assessment of Sustainable Collaboration in Collaborative Business Ecosystems

by Paula Graça and Luis M. Camarinha-Matos

Computers 2021, 10(12), 167; https://doi.org/10.3390/computers10120167 - 6 Dec 2021

Cited by 4 | Viewed by 3129

Abstract

Advances in information and communication technologies and, more specifically, in artificial intelligence resulted in more intelligent systems, which, in the business world, particularly in collaborative business ecosystems, can lead to a more streamlined, effective, and sustainable processes. Following the design science research method, [...] Read more.

Advances in information and communication technologies and, more specifically, in artificial intelligence resulted in more intelligent systems, which, in the business world, particularly in collaborative business ecosystems, can lead to a more streamlined, effective, and sustainable processes. Following the design science research method, this article presents a simulation model, which includes a performance assessment and influence mechanism to evaluate and influence the collaboration of the organisations in a business ecosystem. The establishment of adequate performance indicators to assess the organisations can act as an influencing factor of their behaviour, contributing to enhancing their performance and improving the ecosystem collaboration sustainability. As such, several scenarios are presented shaping the simulation model with actual data gathered from three IT industry organisations running in the same business ecosystem, assessed by a set of proposed performance indicators. The resulting outcomes show that the collaboration can be measured, and the organisations’ behaviour can be influenced by varying the weights of the performance indicators adopted by the CBE manager. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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20 pages, 924 KiB

Open AccessArticle

A Systematic Modelling Procedure to Design Agent-Oriented Control to Coalition of Capabilities—In the Context of I4.0 as Virtual Assets (AAS)

by Jackson T. Veiga, Marcosiris A. O. Pessoa, Fabrício Junqueira, Paulo E. Miyagi and Diolino J. dos Santos Filho

Computers 2021, 10(12), 161; https://doi.org/10.3390/computers10120161 - 28 Nov 2021

Cited by 2 | Viewed by 3119

Abstract

Manufacturing systems need to meet Industry 4.0 (I4.0) guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. [...] Read more.

Manufacturing systems need to meet Industry 4.0 (I4.0) guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements’ behavior. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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15 pages, 4120 KiB

Open AccessArticle

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

by Filipa Esgalhado, Beatriz Fernandes, Valentina Vassilenko, Arnaldo Batista and Sara Russo

Computers 2021, 10(12), 158; https://doi.org/10.3390/computers10120158 - 25 Nov 2021

Cited by 26 | Viewed by 9259

Abstract

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need [...] Read more.

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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24 pages, 2140 KiB

Open AccessArticle

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

by Julio C. Serrano-Ruiz, Josefa Mula and Raúl Poler

Computers 2021, 10(12), 156; https://doi.org/10.3390/computers10120156 - 23 Nov 2021

Cited by 18 | Viewed by 7070

Abstract

Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to [...] Read more.

Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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24 pages, 1311 KiB

Open AccessArticle

Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

by David Lionel Bernal-Romero, Oscar Danilo Montoya and Andres Arias-Londoño

Computers 2021, 10(11), 151; https://doi.org/10.3390/computers10110151 - 12 Nov 2021

Cited by 9 | Viewed by 2943

Abstract

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as [...] Read more.

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were

32.33

s,

49.45

s, and

138.88

s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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17 pages, 667 KiB

Open AccessArticle

Black-Hole Optimization Applied to the Parametric Estimation in Distribution Transformers Considering Voltage and Current Measures

by Camilo Andres Arenas-Acuña, Jonathan Andres Rodriguez-Contreras, Oscar Danilo Montoya and Edwin Rivas-Trujillo

Computers 2021, 10(10), 124; https://doi.org/10.3390/computers10100124 - 9 Oct 2021

Cited by 13 | Viewed by 2451

Abstract

The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters [...] Read more.

The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization and genetic algorithms, among others. All the simulations were carried out in the MATLAB programming environment. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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12 pages, 282 KiB

Open AccessArticle

Approximated Mixed-Integer Convex Model for Phase Balancing in Three-Phase Electric Networks

by Oscar Danilo Montoya, Luis Fernando Grisales-Noreña and Edwin Rivas-Trujillo

Computers 2021, 10(9), 109; https://doi.org/10.3390/computers10090109 - 31 Aug 2021

Cited by 7 | Viewed by 1971

Abstract

With this study, we address the optimal phase balancing problem in three-phase networks with asymmetric loads in reference to a mixed-integer quadratic convex (MIQC) model. The objective function considers the minimization of the sum of the square currents through the distribution lines multiplied [...] Read more.

With this study, we address the optimal phase balancing problem in three-phase networks with asymmetric loads in reference to a mixed-integer quadratic convex (MIQC) model. The objective function considers the minimization of the sum of the square currents through the distribution lines multiplied by the average resistance value of the line. As constraints are considered for the active and reactive power redistribution in all the nodes considering a

3 \times 3

binary decision variable having six possible combinations, the branch and nodal current relations are related to an extended upper-triangular matrix. The solution offered by the proposed MIQC model is evaluated using the triangular-based three-phase power flow method in order to determine the final steady state of the network with respect to the number of power loss upon the application of the phase balancing approach. The numerical results in three radial test feeders composed of 8, 15, and 25 nodes demonstrated the effectiveness of the proposed MIQC model as compared to metaheuristic optimizers such as the genetic algorithm, black hole optimizer, sine–cosine algorithm, and vortex search algorithm. All simulations were carried out in MATLAB 2020a using the CVX tool and the Gurobi solver. Full article

(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2021)

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