Editorial

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Open AccessEditorial

Special Issue “Applications of Artificial Intelligence Systems”

by Bruno Baruque Zanón, Jose Luis Calvo-Rolle, Santiago Porras Alfonso and Petr Dolezel

Appl. Sci. 2022, 12(8), 3886; https://doi.org/10.3390/app12083886 - 12 Apr 2022

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Artificial Intelligence, a term that was seen as an obscure subject, only to be studied by mathematicians or computer scientists, without much real-life application, less than 20 years ago, has become a pervasive term in our everyday life [...] Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

Research

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31 pages, 5688 KiB

Open AccessArticle

Neuronal Constraint-Handling Technique for the Optimal Synthesis of Closed-Chain Mechanisms in Lower Limb Rehabilitation

by José Saúl Muñoz-Reina, Miguel Gabriel Villarreal-Cervantes, Leonel Germán Corona-Ramírez and Luis Ernesto Valencia-Segura

Appl. Sci. 2022, 12(5), 2396; https://doi.org/10.3390/app12052396 - 25 Feb 2022

Cited by 3 | Viewed by 1286

Abstract

The optimal methods for the synthesis of mechanisms in rehabilitation usually require solving constrained optimization problems. Metaheuristic algorithms are frequently used to solve these problems with the inclusion of Constraint-Handling Techniques (CHTs). Nevertheless, the most used CHTs in the synthesis of mechanisms, such [...] Read more.

The optimal methods for the synthesis of mechanisms in rehabilitation usually require solving constrained optimization problems. Metaheuristic algorithms are frequently used to solve these problems with the inclusion of Constraint-Handling Techniques (CHTs). Nevertheless, the most used CHTs in the synthesis of mechanisms, such as penalty function and feasibility rules, generally prioritize the search for feasible regions over the minimization of the objective function, and it notably influences the exploration and exploitation of the algorithm such that it could induce in the premature convergence to the local minimum and thus the solution quality could deteriorate. In this work, a Neuronal Constraint-Handling (NCH) technique is proposed and its performance is studied in the solution of mechanism synthesis for rehabilitation. The NCH technique uses a neural network to search for the fittest solutions into the feasible and the infeasible region to pass them to the next generation of the evolutionary process of the Differential Evolution (DE) algorithm and consequently improve the obtained solution quality. Two synthesis problems with four–bar and cam–linkage mechanisms are the study cases for developing lower-limb rehabilitation routines. The NCH is compared with four state-of-the-art constraint-handling techniques (penalty function, feasibility rules, stochastic ranking,

ϵ

-constrained method) included into four representative metaheuristic algorithms. The irace package is used for both the algorithm settings and neuronal network training to fairly and meaningfully compare through statistics to confirm the overall performance. The statistical results confirm that, despite changes in the rehabilitation trajectories, the proposal presents the best overall performance among selected algorithms in the studied synthesis problems for rehabilitation, followed by penalty function and feasibility rule. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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14 pages, 377 KiB

Open AccessArticle

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

by Dieter De Paepe, Andy Van Yperen-De Deyne, Jan Defever and Sofie Van Hoecke

Appl. Sci. 2022, 12(2), 913; https://doi.org/10.3390/app12020913 - 17 Jan 2022

Cited by 3 | Viewed by 1398

Abstract

Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven [...] Read more.

Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven techniques are used to detect and replace poor quality welds in real-time. Statistical models are commonly used to address this problem as they use data readily available from the welding machine and require no specialized equipment. While successful in finding anomalies, these statistical models do not provide insight into the underlying process and are slow to adapt to changes in the machine’s or material’s behavior. We combine knowledge-based and data-driven techniques to create an incremental grey-box welding current prediction model for detecting anomalous welds, resulting in a powerful and interpretable model. In this work, we detail our approach and show evaluation results on industrial welding data collected over a period of 15 months containing behavioral shifts attributed to machine maintenance. Due to its incremental nature, our model resulted in two-thirds fewer rejected welds compared to statistical models, thus greatly reducing production overhead. Grey-box modeling can be applied to other welding features or domains and results in models that are more desirable for the industry. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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26 pages, 1867 KiB

Open AccessFeature PaperArticle

Automated Classification of Unstructured Bilingual Software Bug Reports: An Industrial Case Study Research

by Ömer Köksal and Bedir Tekinerdogan

Appl. Sci. 2022, 12(1), 338; https://doi.org/10.3390/app12010338 - 30 Dec 2021

Cited by 7 | Viewed by 2704

Abstract

Software bug report classification is a critical process to understand the nature, implications, and causes of software failures. Furthermore, classification enables a fast and appropriate reaction to software bugs. However, for large-scale projects, one must deal with a broad set of bugs from [...] Read more.

Software bug report classification is a critical process to understand the nature, implications, and causes of software failures. Furthermore, classification enables a fast and appropriate reaction to software bugs. However, for large-scale projects, one must deal with a broad set of bugs from multiple types. In this context, manually classifying bugs becomes cumbersome and time-consuming. Although several studies have addressed automated bug classification using machine learning techniques, they have mainly focused on academic case studies, open-source software, and unilingual text input. This paper presents our automated bug classification approach applied and validated in an industrial case study. In contrast to earlier studies, our study is applied to a commercial software system based on unstructured bilingual bug reports written in English and Turkish. The presented approach adopts and integrates machine learning (ML), text mining, and natural language processing (NLP) techniques to support the classification of software bugs. The approach has been applied within an industrial case study. Compared to manual classification, our results show that bug classification can be automated and even performs better than manual bug classification. Our study shows that the presented approach and the corresponding tools effectively reduce the manual classification time and effort. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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18 pages, 5298 KiB

Open AccessArticle

Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network

by Jan Sikora, Renata Wagnerová, Lenka Landryová, Jan Šíma and Stanislaw Wrona

Appl. Sci. 2021, 11(16), 7484; https://doi.org/10.3390/app11167484 - 14 Aug 2021

Cited by 5 | Viewed by 1724

Abstract

Testing the quality of manufactured products based on their sound expression is becoming popular nowadays. To maintain low production costs, the testing is processed at the end of the assembly line. Such measurements are affected considerably by the factory noise even though they [...] Read more.

Testing the quality of manufactured products based on their sound expression is becoming popular nowadays. To maintain low production costs, the testing is processed at the end of the assembly line. Such measurements are affected considerably by the factory noise even though they are performed in anechoic chambers. Before designing the quality control algorithm based on a convolutional neural network, we do not know the influence of the factory noise on the success rate of the algorithm that can potentially be obtained. Therefore, this contribution addresses this problem. The experiments were undertaken on a synthetic dataset of heat, ventilation, and air-conditioning devices. The results show that classification accuracy of the decision-making algorithm declines more rapidly at a high level of environmental noise. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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13 pages, 13589 KiB

Open AccessArticle

sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network

by Chao Chen, Weiyu Guo, Chenfei Ma, Yongkui Yang, Zheng Wang and Chuang Lin

Appl. Sci. 2021, 11(10), 4678; https://doi.org/10.3390/app11104678 - 20 May 2021

Cited by 16 | Viewed by 2683

Abstract

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on [...] Read more.

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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19 pages, 2830 KiB

Open AccessArticle

Development of Ground Special Vehicle PHM with Case-Based Reason Model

by Qicai Wu, Haiwen Yuan and Haibin Yuan

Appl. Sci. 2021, 11(10), 4494; https://doi.org/10.3390/app11104494 - 14 May 2021

Cited by 4 | Viewed by 1515

Abstract

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. [...] Read more.

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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24 pages, 867 KiB

Open AccessArticle

Optimization of a Depiction Procedure for an Artificial Intelligence-Based Network Protection System Using a Genetic Algorithm

by Petr Dolezel, Filip Holik, Jan Merta and Dominik Stursa

Appl. Sci. 2021, 11(5), 2012; https://doi.org/10.3390/app11052012 - 25 Feb 2021

Cited by 10 | Viewed by 2187

Abstract

The current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more [...] Read more.

The current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article’s main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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Review

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17 pages, 1025 KiB

Open AccessReview

Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

by Kok-Lim Alvin Yau, Norizan Mat Saad and Yung-Wey Chong

Appl. Sci. 2021, 11(18), 8562; https://doi.org/10.3390/app11188562 - 15 Sep 2021

Cited by 16 | Viewed by 11844

Abstract

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based [...] Read more.

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)

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