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Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems.

The AHP–GTMA (analytic hierarchy process and graph theory and matrix approach) has been applied to select the best paper shredder before a company was making a bulk purchase order. However, there is a question as to whether one such relatively recent approach is effective to aid the selection decision problems in industrial/commercial practice. In this paper, a novel multi-measure, rank-based comparative research flow is proposed. The real decision problem case mentioned above is solved using the AHP–GTMA and the AHP–TOPSIS methods, respectively, with relevant datasets sourced. Several measures in the proposed flow, i.e., the arithmetical, geometrical, or even statistical ones, are multiplexed and used to validate the similarity between the rank order vectors (ROVs) (and thus between the final preferential orders determined over the alternatives) that are obtained using these two different methods. While AHP–TOPSIS is a confident multi-attribute decision-making (MADM) approach which has been successfully applied to many other fields, the similarity validated between these individual results using the proposed method is used to confirm the efficacy of the AHP–GTMA approach and to determine its applicability in practice. In addition, along with this study, some contributable points are also rendered for implementing the decision models, e.g., the optimized recursive implementation in R to compute the permanent value of a square ASAM (alternative selection attribute matrix, which is the computational basis required by AHP–GTMA) of any dimension. The proposed methodological flow to confirm the similarity based on the ordinal rank information is not only convenient in operational practice with ubiquitous tool supports (e.g., the vector-based R statistical platform), but also generalizable (to verify between another pair of results obtained using any other MADM methods). This gives options for future research.

The smart, rapid, and customizable detection of chemical warfare agents is a huge issue for taking the proper countermeasures in a timely fashion. The printing techniques have established the main pillar to develop miniaturized electrochemical biosensors for onsite and fast detection of nerve and mustard agents, allowing for a lab on a chip in the chemical warfare agent sector. In the fast growth of novel technologies, the combination of miniaturized electrochemical biosensors with flexible electronics allowed for the delivery of useful wearable sensors capable of fast detection of chemical warfare agents. The wearable microneedle sensor array for minimally invasive continuous electrochemical detection of organophosphorus nerve agents, as well as the wearable paper-based origami functionalized with nanomaterials for mustard agents in the gas phase, represent two examples of the forefront devices developed in the chemical warfare agent detection field. This review will highlight the most promising electrochemical biosensors developed by exploiting nanomaterials and cross-cutting technologies for the fabrication of smart and sensitive electrochemical biosensors for the detection of chemical warfare agents.

Reinforced concrete is the most commonly used material in urban, road, and industrial structures. Quantifying the condition of the reinforcing steel can help manage the human and financial risks that arise from unexpected reinforced concrete structure functional failure. Also, a quantitative time history of reinforcing steel condition can be used to make decisions on rehabilitation, decommissioning, or replacement. The self-magnetic behavior of ferromagnetic materials is useful for quantitative condition assessment. In this study, a ferromagnetic rebar with artificial defects was scanned by a three-dimensional (3D) laser scanner. The obtained point cloud was imported as a real geometry to a finite element software platform, its self-magnetic behavior was then simulated under the influence of Earth&rsquo, s magnetic field. The various passive magnetic parameters that can be measured were reviewed for different conditions. Statistical studies showed that 0.76% of the simulation-obtained data of the rebar surface was related to the defect locations. Additionally, acceptable coincidences were confirmed between the magnetic properties from numerical simulation and from experimental outputs, most noticeably at hole locations.

International audience; The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper

In this paper, we provide a holistic survey of multimedia content protection applications in which blockchain technology is being used. A taxonomy is developed to classify these applications with reference to the technical aspects of blockchain technology, content protection techniques, namely, encryption, digital rights management, digital watermarking and fingerprinting (or transaction tracking), and performance criteria. The study of the literature reveals that there is currently no complete and systematic taxonomy dedicated to blockchain-based copyright protection applications. Moreover, the number of successfully developed blockchain-based content protection systems is very low. This points towards a research gap. To fill this gap, we propose a taxonomy that integrates technical aspects and application knowledge and can guide the researchers towards the development of blockchain-based multimedia copyright protection systems. Furthermore, the paper discusses some technical challenges and outlines future research directions.

In this paper, the performance of a lightning protection system (LPS) on a grid-connected photovoltaic (PV) park is studied by simulating different scenarios with the use of an appropriate software tool. The aim of this paper is to highlight the importance of an LPS and optimize its design for the protection of equipment and personnel in case of a direct lightning strike. In particular, developed potential due to lightning strikes is examined considering isolated and non-isolated external LPS. Moreover, the effect of the separation distance on the lightning performance of the PV installation is investigated for different soil structures and grounding systems. The extracted results are expected to support the design and implementation of a secure PV park and endorse its uninterrupted operation considering techno-economically balanced protection measures.

Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.

This paper addresses the issue of offering a consistent 3D visual rendering of subsurface objects when databases face non-completion. Digital modelling of subsurface objects, like utility lines, underground buildings or tree roots, is a difficult task. Data available are incomplete and not precise. The in situ acquisition of existing objects to increase data quality is complex and, therefore, costly. In this paper, a methodology to obtain missing spatial and geometrical data through field or empirical means is proposed. In addition, confidence levels are assigned to existing and derived spatial and geometrical attributes. They are consolidated on a class level and visualized through a bounding shape, called secondary object.

This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion.

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented.

Pipeline inspection gauges (PIGs) are widely used for nondestructive testing of oil and natural gas pipelines, while above ground markers (AGMs) can locate and track the PIG through a variety of methods, including magnetic flux leakage signals, acoustic signals, and extremely low-frequency (ELF) magnetic signals. Traditional AGMs have the disadvantages of low positioning accuracy and only one-dimensional tracking capability. In this paper, a newly-designed PIG tracking system based on the ELF magnetic field is proposed by assembling dual receivers. Moreover, this paper develops a magnetic field sign-integration algorithm to achieve high-precision and four-dimensional (4-D) tracking of PIG. The simulation and experiment results demonstrate that the tracking system has the capability of 4-D tracking. In comparison with the previously published work, the designed tracking system improves the positioning accuracy and orientation tracking accuracy by more than 50%. The dual receivers tracking system also has the characteristic of high-robustness. Even in the state of lateral offset or tilt, it can still achieve accurate tracking of PIG. The realization of PIG’s high-precision 4-D tracking can improve the accuracy of defect location. Moreover, it can also provide the latest pipeline network layout and facilitate pipeline maintenance and pipeline surveying applications.

In order to tackle interoperability issues of large-scale automation systems, SOA (Service-Oriented Architecture) principles, where information exchange is manifested by systems providing and consuming services, have already been introduced. However, the deployment, operation, and maintenance of an extensive SoS (System of Systems) mean enormous challenges for system integrators as well as network and service operators. The existing lifecycle management approaches do not cover all aspects of SoS management, therefore, an integrated solution is required. The purpose of this paper is to introduce a new lifecycle approach, namely the SoSLM (System of Systems Lifecycle Management). This paper first provides an in-depth description and comparison of the most relevant process engineering methodologies and ITSM (Information Technology Service Management) frameworks, and how they affect various lifecycle management strategies. The paper’s novelty strives to introduce an Industry 4.0-compatible PLM (Product Lifecycle Management) model and to extend it to cover SoS management-related issues on well-known process engineering methodologies. The presented methodologies are adapted to the PLM model, thus creating the recommended SoSLM model. This is supported by demonstrations of how the IIoT (Industrial Internet of Things) applications and services can be developed and handled. Accordingly, complete implementation and integration are presented based on the proposed SoSLM model, using the Arrowhead framework that is available for IIoT SoS.

Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms.

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with 'black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments. This work was supported under projects PEAVAUTO-CM-UC3M, PID2019-104793RB-C31, and RTI2018-096036-B-C22, and by the Region of Madrid’s Excellence Program (EPUC3M17).

Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics. Compared to conventional major machine learning research areas such as image or speech recognition, machine learning-based system performance optimization fields are at the beginning stage. However, recent papers show that this approach is promising and has significant potential. This paper reviews 11 machine learning-based system performance optimization approaches from nine recent papers based on well-known machine learning models such as perceptron, LSTM, and RNN. This survey provides a detailed design and summarizes model, input, output, and prediction method of each approach. This paper covers various system performance areas from the data structure to essential system components of a computer system such as index structure, branch predictor, sort, and cache management. The result shows that machine learning-based system performance optimization has an important potential for future research. We expect that this paper shows a wide range of applicability of machine learning technology and provides a new perspective for system performance optimization.

This research received no external funding. The APC is funded by Prince Sultan University, The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication., Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques., Prince Sultan University

In this paper, we present an adaptive modeling and linearization algorithm using the weighted memory polynomial model (W-MPM) implemented in a chain involving the indirect learning approach (ILA) as a linearization technique. The main aim of this paper is to offer an alternative to correcting the undesirable effect of spectral regrowth based on modeling and linearization stages, where the 1-dB compression point (P1dB) of a nonlinear device caused by memory effects within a short time is considered. The obtained accuracy is tested for a highly nonlinear behavior power amplifier (PA) properly measured using a field-programmable gate array (FPGA) system. The adaptive modeling stage shows, for the two PAs under test, performances with accuracies of −32.72 dB normalized mean square error (NMSE) using the memory polynomial model (MPM) compared with −38.03 dB NMSE using the W-MPM for the (i) 10 W gallium nitride (GaN) high-electron-mobility transistor (HEMT) radio frequency power amplifier (RF-PA) and of −44.34 dB NMSE based on the MPM and −44.90 dB NMSE using the W-MPM for (ii) a ZHL-42W+ at 2000 MHz. The modeling stage and algorithm are suitably implemented in an FPGA testbed. Furthermore, the methodology for measuring the RF-PA under test is discussed. The whole algorithm is able to adapt both stages due to the flexibility of the W-MPM model. The results prove that the W-MPM requires less coefficients compared with a static model. The error vector magnitude (EVM) is estimated for both the static and adaptive schemes, obtaining a considerable reduction in the transmitter chain. The development of an adaptive stage such as the W-MPM is ideal for digital predistortion (DPD) systems where the devices under test vary their electrical characteristics due to use or aging degradation.

The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers, (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest, and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

With the drastically increasing traffic in the last decades, crucial environmental problems have been caused, such as greenhouse gas emission and traffic noise pollution. These problems have adversely affected our life quality and health conditions. In this paper, modelling of traffic noise employing deep learning is investigated. The goal is to identify the best machine-learning model for predicting traffic noise from real-life traffic data with multivariate traffic features as input. An extensive study on recurrent neural network (RNN) is performed in this work for modelling time series traffic data, which was collected through an experimental campaign at an inner city roundabout, including both video traffic data and audio data. The preprocessing of the data, namely how to generate the appropriate input and output for deep learning model, is detailed in this paper. A selection of different architectures of RNN, such as many-to-one, many-to-many, encoder–decoder architectures, was investigated. Moreover, gated recurrent unit (GRU) and long short-term memory (LSTM) were further discussed. The results revealed that a multivariate bi-directional GRU model with many-to-many architecture achieved the best performance with both high accuracy and computation efficiency. The trained model could be promising for a future smart city concept, with the proposed model, real-time traffic noise predictions can be potentially feasible using only traffic data collected by different sensors in the city, thanks to the generated big data by smart cities. The forecast of excessive noise exposure can help the regulation and policy makers to make early decisions, in order to mitigate the noise level.

International audience; The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. The first one adopts a block-sparse model that jointly estimates the sparse coefficients at identical or symmetrical positions around the carrier frequencies. The second is a spike-and-slab model where the spike component takes into account the symmetrical properties of the support of non-zero-coefficients in the spectrum. The resulting posterior distribution is approximated using a Gibbs sampler. Simulations show that considering the structure in the prior model yields better noise shrinkage and better reconstruction of small side-bands. Application to condition monitoring of a gearbox through CS of vibration signals highlights the good performance of the proposed models in reconstructing the signal, offering an accurate fault detection with relatively high compression rate.

The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products.

This paper deals with a complete finite-element analysis procedure for squirrel cage induction motors, including the presence of skewing and the iron losses evaluation. The machine is analyzed performing only magneto-static finite element analyses. Saturation phenomena are carefully considered in any operating condition, avoiding long time-stepping analyses. The synergy between analytical and finite element model leads to a rapid and precise estimation of the rotor induced current, saving computational time. Furthermore, the procedure proposed in this paper allows the motor performance to be directly derived, without the preliminary knowledge of the machine equivalent circuit. In order to complete the analysis, skewing effect is included, using the 2-D multi-slice technique, based on static simulations. Experimental tests are carried out and reported in order to verify analysis results.

The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer2 and 7.41 micrometer2 for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.

The research presented in this paper aims at creating a bilingual (sr-en), easily searchable, hypertext, born-digital, corpus-based terminological database of raw material terminology for dictionary production. The approach is based on linking dictionaries related to the raw material domain, both digitally born and printed, into a lexicon structure, aligning terminology from different dictionaries as much as possible. This paper presents the main features of this approach, data used for compilation of the terminological database, the procedure by which it has been generated and a mobile application for its use. Available (terminological) resources will be presented—paper dictionaries and digital resources related to the raw material domain, as well as general lexica morphological dictionaries. Resource preparation started with dictionary (retro)digitisation and corpora enlargement, followed by adding new Serbian terms to general lexica dictionaries, as well as adding bilingual terms. Dictionary development is relying on corpus analysis, details of which are also presented. Usage examples, collocations and concordances play an important role in raw material terminology, and have also been included in this research. Some important related issues discussed are collocation extraction methods, the use of domain labels, lexical and semantic relations, definitions and subentries.

This paper concerns the case of the development of a technology for increasing the efficiency of access control based on the user behavior monitoring built into a software system’s user interface. It is proposed to use the time of user reactions as individual indicators of psychological and psychophysical state. This paper presents the results and interpretation of user reactions collected during a mass web survey of students of the Russian Federation. The total number of users was equal to 22,357. To reveal the patterns in user reactions, both quantitative and qualitative approaches were applied. The analysis of the data demonstrated that the user could be characterized by their psychomotor reactions, collected during the answering of a set of questions. Those reactions reflected the personal skills of the interface interaction, the speed of reading, and the speed of answering. Thus, those observations can be used as a supplement to personal verification in information systems. The collection of the reaction times did not load the data volumes significantly nor transmit confidential information.

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous, 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

The SCOPUS and Wed of Science bibliometric databases were searched for papers related to the use of multi-criteria methods in civil engineering related disciplines. The results were analyzed for information on the reported geographical distribution of usage, the methods used, the application areas with most usage and the software tools used. There was a wide geographical distribution of usage with all northern hemisphere continents well represented. However, of the very many methods available, a small number seemed to dominate usage, with the Analytic Hierarchy Process being the most frequently used. The application areas represented in the documents found was not widely spread and mainly seemed to be focused on issues such as sustainability, environment, risk, safety and to some extent project management, with less usage on other areas. This may be due to individual engineer’s choices in relation to if and how to disseminate the results of their work and to their choice of keywords and titles that determine if their publications are selected in bibliographic searches and thus more visible to a wider readership. A comparison with more topic focused searches, relating to Bridge Design, Earthquake Engineering, Cladding, Sewage Treatment, Foundation design, Truss design, Water Supply, Building Energy, Route selection and Transport mode showed very different results. Analysis of the papers in this area indicated that the full range of supporting software available for multi-criteria decision analysis (many listed in this paper) may not be fully appreciated by potential users.

Additive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its long list of application as of late. Artificial Intelligence is currently finding its usefulness in predictive modelling to provide intelligent, efficient, customisable, high-quality and sustainable-oriented production process. This paper presents a comprehensive survey on commonly used predictive models based on heuristic algorithms and discusses their applications toward making AM “smart”. This paper summarises AM’s current trend, future opportunity, gaps, and requirements together with recommendations for technology and research for inter-industry collaboration, educational training and technology transfer in the AI perspective in-line with the Industry 4.0 developmental process. Moreover, machine learning algorithms are presented for detecting product defects in the cyber-physical system of additive manufacturing. Based on reviews on various applications, printability with multi-indicators, reduction of design complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of as prospective opportunities for further research.

The evolution of the wind turbine to generate carbon-free renewable energy is rapidly growing. Thus, performing maintenance and inspection tasks in high altitude environments or difficult to access places, and even bad weather conditions, poses a problem for the periodic inspection process of the wind turbine industry. This paper describes the design and development of a scaled-down prototype climbing robot for wind turbine maintenance to perform critical tower operations. Thus, the unique feature of this maintenance robot is the winding mechanism, which uses a tension force to grip on the tower surface without falling to the ground either in static or dynamic situations, with the locomotion to perform a straight up–down motion in a circular truncated cone and the stability to work at significant heights. The robot computer-aided design (CAD) model of the mechanical mechanism, force and structural analysis, and the testing of the prototype model, are addressed in this paper. The key hardware developments that were utilized to build a low-cost, reliable and compact climbing robot are the embedded microprocessors, brushed DC motors, stepper motors and steel rope. This paper concludes with a successful preliminary experiment of a scaled down prototype proving the functionality of the concept. The potential applications for this robot are industrial maintenance, inspection and exploration, security and surveillance, cleaning, painting, and welding at extreme height conditions.

At present, the realization of autonomous valet parking (AVP) technology does not achieve information interaction between the parking spaces and vehicles, and accurate parking spaces information perception cannot be obtained when the accuracy of the search is not precise. In addition, when using the camera vision to identify the parking spaces, traditional parking space features such as parking lines and parking angles recognition are susceptible to light and environment. Especially when the vehicle nearby partially occupies the parking space to be parked, it is not easy to determine whether it is a valid empty parking space. This paper proposes a parking space recognition method based on parking space features in the scene of AVP. By constructing the multi-dimensional features containing the parking space information, the cameras are used to extract features’ contour, locate features’ position and recognize features. In this paper, a new similarity calculation formula is proposed to recognize the stained features through template matching algorithm. According to the relative position relationship between the feature and parking space, the identification of effective empty parking spaces and their boundaries is realized. The experimental results show that compared with the recognition of traditional parking lines and parking angles, this method can identify effective empty parking spaces even when the light conditions are complex and the parking spaces are partially occupied by adjacent vehicles, which simplifies the recognition algorithm and improves the reliability of the parking spaces identification.

Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.

In this paper, the use of Fast Falling Weight Deflectometer (Fast-FWD) is analyzed as a non-destructive and quick test procedure to evaluate the efficiency of short-span bridges. The Fast-FWD is an instrument that can produce a broadband dynamic force up to an impact value of 120 KN: The impact is constant and replicable, providing accurate action measures of bridge stiffness in a truly short period (30 ms). In this paper, a single-span reinforced concrete bridge is investigated, using the Fast-FWD. The considered bridge, approximately 12.0 m long and 15.5 m wide, was in critical condition. The bridge is in a suburban principal road near to the City of Cagliari in Sardinia (Italy), with an Annual Average Daily Traffic of 13,500 vehicles/day, and was suddenly closed, creating serious problems for urban mobility. In these conditions, the investigation through other standard techniques is time-consuming and labor intensive. For this reason, it is important to introduce methods that can be rapid, accurate and cost-efficient. In this paper, bridge stiffness values obtained during the in situ experimental campaign were compared with finite element models values. The Fast-FWD has the potential to provide engineering information that can help us to better understand bridge condition, in a rapid and cost-effective procedure.

The research paper presents a novel approach toward constructing motion equations for structures with attached MTMDs (multiple tuned mass dampers). A primary system with MDOF (multiple dynamic degrees of freedom) was reduced to an equivalent system with a SDOF (single degree of freedom) through the modal approach, and equations from additional MTMDs were added to a thus-created system. Optimization based on ℌ2 and ℌ∞ for the transfer function associated with the generalized displacement of an SDOF system was applied. The research work utilized GA (genetic algorithms) and SA (simulated annealing method) optimization algorithms to determine the stiffness and damping parameters for individual TMDs. The effect of damping and stiffness (MTMD tuning) distribution depending on the number of TMDs was also analyzed. The paper also reviews the impact of primary system mass change on the efficiency of optimized MTMDs, as well as confirms the results of other authors involving greater MTMD effectiveness relative to a single TMD.

Dependability analyses in the design phase are common in IEC 60300 standards to assess the reliability, risk, maintainability, and maintenance supportability of specific physical assets. Reliability and risk assessment uses well-known methods such as failure modes, effects, and criticality analysis (FMECA), fault tree analysis (FTA), and event tree analysis (ETA)to identify critical components and failure modes based on failure rate, severity, and detectability. Monitoring technology has evolved over time, and a new method of failure mode and symptom analysis (FMSA) was introduced in ISO 13379-1 to identify the critical symptoms and descriptors of failure mechanisms. FMSA is used to estimate monitoring priority, and this helps to determine the critical monitoring specifications. However, FMSA cannot determine the effectiveness of technical specifications that are essential for predictive maintenance, such as detection techniques (capability and coverage), diagnosis (fault type, location, and severity), or prognosis (precision and predictive horizon). The paper proposes a novel predictive maintenance (PdM) assessment matrix to overcome these problems, which is tested using a case study of a centrifugal compressor and validated using empirical data provided by the case study company. The paper also demonstrates the possible enhancements introduced by Industry 4.0 technologies.

This paper proposes a new sliding surface for controlling a Semi-Bridgeless Boost Converter (SBBC) which simultaneously performs Power Factor Correction (PFC) and DC bus regulation. The proposed sliding surface is composed of three terms: First, a normalized DC voltage error term controls the DC bus and rejects DC voltage disturbances. In this case, the normalization was performed for increasing system robustness during start-up and large disturbances. Second, an AC current error term implements a PFC scheme and guarantees fast current stabilization during disturbances. Third, an integral of the AC current error term increases stability of the overall system. In addition, an Adaptive Hysteresis Band (AHB) is implemented for keeping the switching frequency constant and reducing the distortion in zero crossings. Previous papers usually include the first and/or the second terms of the proposed sliding surface, and none consider the AHB. To be best of the author’s knowledge, the proposed Sliding Mode Control (SMC) is the first control strategy for SBBCs that does not require a cascade PI or a hybrid PI-Sliding Mode Control (PI-SMC) for simultaneously controlling AC voltage and DC current, which gives the best dynamic behavior removing DC overvoltages and responding fast to DC voltage changes or DC load current perturbations. Several simulations were carried out to compare the performance of the proposed surface with a cascade PI control, a hybrid PI-SMC and the proposed SMC. Furthermore, a stability analysis of the proposed surface in start-up and under large perturbations was performed. Experimental results for PI-SMC and SMC implemented in a SBBC prototype are also presented.

The apparent advantages of Multilevel Inverter (MLI) topologies in handling medium and high power with less loss in switching and lower harmonic distortion in an output voltage waveform makes it better than the conventional inverter. However, the MLI topologies utilize a large number of DC power supplies and power semiconductor devices. They also have a higher value of total standing voltage (TSV). Moreover, capacitor voltage balancing problems, self-voltage boosting inability, and complex control techniques require a relook and improvement in their structure. More recently, Switched-Capacitor Multilevel Inverter (SCMLI) topologies have been proposed to overcome the shortcomings of MLIs. In this paper, a generalized structure for a single-phase switched capacitor multilevel inverter (SCMLI) with self-voltage boosting and self-voltage balancing capability is proposed. A detailed analysis of a general structure of SCMLI is presented. The comparative analysis of the structures is carried out with recently reported topologies to demonstrate superiority. An optimized low-frequency modulation controls the output voltage waveform. The simulation and experimental results are included in the paper for single-unit symmetric (9-level voltage) and asymmetric (17-level voltage) configurations.

The gait and the Foot Placement Estimation (FPE) has recently been extended to 3-D spaces by adopting a specific form of a spherical inverted pendulum (SIP). The approach is very attractive, as it does not involve dynamics, but it is based solely on energies and momenta, however the authors (DeHart et al.) introduced several questionable approximations, in order to reach a manageable solution. The scope of the present paper is to revisit this spherical inverted pendulum applied to biped walking, offering an exact solution to the gait and the FPE by using symbolic computation. This is facilitated by exploiting the Kane’s approach to dynamical modelling, and his software environment for symbolic manipulation, called Autolev. It generates explicit formulas describing the energies and angular momenta before/after the impact, along with the mechanics of the impact. As the resulting equations, function of (measurable) angular positions and velocities, are very compact, embedded in a numerical nonlinear solver, are suitable to be implemented in real time and used in practice to control biped robots or lower limb exoskeletons. The two main contributions of the paper are: the recovery of the balance by stepping, in the presence of a push in an arbitrary direction and omnidirectional walking. In this last respect, this specific form of SIP emphasizes the expenditure of energy in the walk. For the first time, at our knowledge, the walk of the SIP, based on energy, has been compared to the simulation of a 12 degrees of freedom biped robot tracking preview signals using the Zero Moment Point (ZMP) of the Linear Inverted Pendulum (LIPM). This quantitatively shows the inefficiency, in terms of energy, of the ZMP-based walk, and the gain due to the recovery of the collision of the flying foot. Similarity in the sagittal plane and differences in the frontal plane of the center of mass trajectories of the two approaches are shown, to open the road to an integration of fully actuated and underactuated controls, for an efficient full-dimensional robot gait to be developed in a future paper.

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.

This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMFs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMFs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMFs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.

Due to the micro-vibration of flywheels, the imaging quality of a high-resolution optical remote sensing satellite will be deteriorated, and the micro-vibration effect on the payload is complicated, so it is essential to establish a reasonable and accurate theoretical simulation model for it. This paper presents a method of full-closed-loop time-domain integrated modeling to estimate the impacts of micro-vibration generated by flywheels on optical satellites. The method consists of three parts. First, according to the satellites’ micro-vibration influence mechanism in orbit, this paper establishes a full-closed-loop model framework. The overall model input is the instructions received and the output is the image shift. Second, in order to meet the requirements of time-domain simulation, this paper proposes a time-domain vibration source subsystem model in the form of cosine harmonic superposition, and it integrates vibration source, structural, control, and optical subsystem models to create a full-closed-loop time-domain analysis model that can obtain the responses of micro-vibration in time and frequency domains. Lastly, the author designs a ground experiment and compares simulation results with experiment results. Compared with the ground experiment, frequency error is less than 0.4% at typical responses. Although the amplitude error is large at some typical responses, the mean root square error is less than 35%. Based on the data, the proposed integrated modeling method can be considered as an accurate methodology to predict the impacts of micro-vibration.

Cyber attacks bring key challenges to the system reliability of load frequency control (LFC) systems. Attackers can compromise the measured data of critical variables of the LFC system, making the data received by the defender unreliable and resulting in system frequency fluctuation or even collapse. In this paper, to detect potential attacks on measured data, we propose a novel attack detection scheme using the dual-source data (DSD) of compromised variables. First, we study the characteristics of the compromised LFC system considering potentially vulnerable variables and different types of attack templates. Second, by designing a variable observer, the relationship between the known security variables and the variables which are at risk of being compromised in the LFC system is established. The features of the data obtained by the observer can reflect those of the true data. Third, a Siamese network (SN) is designed to quantify the distance between the characteristics of measured data and that of observed data. Finally, an attack detection scheme is designed by analyzing the similarity of the DSD. Simulation results verify the feasibility of the detection scheme studied in this paper.

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.

NASA’s human spaceflight efforts are moving towards long-duration exploration missions requiring asynchronous communication between onboard crew and an increasingly remote ground support. In current missions aboard the International Space Station, there is a near real-time communication loop between Mission Control Center and astronauts. This communication is essential today to support operations, maintenance, and science requirements onboard, without which many tasks would no longer be feasible. As NASA takes the next leap into a new era of human space exploration, new methods and tools compensating for the lack of continuous, real-time communication must be explored. The Human-Computer Interaction Group at NASA Ames Research Center has been investigating emerging technologies and their applicability to increase crew autonomy in missions beyond low Earth orbit. Interactions using augmented reality and the Internet of Things have been researched as possibilities to facilitate usability within procedure execution operations. This paper outlines four research efforts that included technology demonstrations and usability studies with prototype procedure tools implementing emerging technologies. The studies address habitat feedback integration, analogous procedure testing, task completion management, and crew training. Through these technology demonstrations and usability studies, we find that low- to medium-fidelity prototypes, evaluated early in the design process, are both effective for garnering stakeholder buy-in and developing requirements for future systems. In this paper, we present the findings of the usability studies for each project and discuss ways in which these emerging technologies can be integrated into future human spaceflight operations.

In order to make robots, which are expected to play an active role in the medical and nursing care fields in the future, more practical for use in rehabilitation, it is necessary to evaluate the current status of the design of these robots. Therefore, this paper aims to investigate the existing literature on standing motion assistance robots developed and reported to date and investigate each existing design technique from the perspectives of “Functions and Effects” and “Assist form and control.” Then, we search and investigate papers written in English on standing motion assistance robots reported from 2008 to 2019 and organize the contents of the relevant papers into their different assistance modes and four categories related to design. As a result, the standing motion assistance robots are classified into three assist modes: partial assistance, total assistance, and both. The assistance forms are roughly divided into two types: a wearable type and a non-wearable type. It is also demonstrated that both the assistance forms adopt the same trends in terms of the control strategy design and system I/O relationships. On the other hand, power equipment tends to be different between the two forms.

Many bio-inspired coordination strategies have been investigated for swarm robots. Bacterial chemotaxis exhibits a certain degree of intelligence, and has been developed some optimization algorithm for robot(s), e.g., bacterial foraging optimization algorithm (BFOA) and bacterial colony chemotaxis optimization algorithm (BCC). This paper proposes a bacterial chemotaxis-inspired coordination strategy (BCCS) of swarm robotic systems for coverage and aggregation. The coverage is the problem of finding a solution to uniformly deploy robots on a given bounded space. To solve this problem, this paper uses chaotic preprocessing to generate the initial positions of the robots. After the initialization, each robot calculates the area only covered by itself as the fitness function value. Then, each robot makes an action, running or rotating, depending on coordination strategy inspired bacterial chemotaxis. Moreover, we extend this solution and introduce a random factor to overcome aggregation, which is to guide robots to rendezvous at an unspecified point. The simulation results demonstrate the superior performance of the proposed coordination strategy in both success rate and an average number of iterations than other controllers.

The weight optimization of a structure can be conducted by using fewer and downsized components, applying lighter materials in production, and removing unwanted material. Topology optimization (TO) is one of the most implemented material removal processes. In addition, when it is oriented towards additive manufacturing (AM), it increases design flexibility. The traditional optimization approach is the compliance optimization, where the material layout of a structure is optimized by minimizing its overall compliance. However, TO, in its current state of the art, is mainly used for design inspiration and not for manufacturing due to design complexities and lack of accuracy of its design solutions. The authors, in this research paper, explore the benefits and the limitations of the TO using as a case study the housings of a front and a rear brake caliper. The calipers were optimized for weight reduction by implementing the aforementioned optimization procedure. Their housings were topologically optimized, partially redesigned, prepared for 3D printing, validated, and 3D printed in titanium using selective laser melting (SLM). The weight of the optimized calipers reduced by 41.6% compared to commercial calipers. Designers interested in either TO or in automotive engineering can exploit the findings in this paper. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

In this paper, we introduce combination constraints for modeling ore bodies based on multiple implicit fields interpolation. The basic idea of the method is to define a multi-labeled implicit function that combines different sub-implicit fields by the combination operations, including intersection, union and difference operators. The contribution of this paper resides in the application of combination of more general implicit fields with combination rules for the implicit modeling of ore bodies, such that the geologist can construct constraints honoring geological relationships more flexibly. To improve the efficiency of implicit surface reconstruction, a pruning strategy is used to avoid unnecessary calculations based on the hierarchical bounding box of the operation tree. Different RBF-based methods are utilized to study the implicit modeling cases of ore bodies. The experimental results of several datasets show that the combination constraints are useful to reconstruct implicit surfaces for ore bodies with mineralization rules involving multiple fields.

This paper present recent advances in the development of local correlation based laminar–to–turbulent transition modeling relying on the Spalart–Allmaras equation. Such models are extremely important for the flow regimes involved in wind energy applications. Indeed, fully turbulent flow models are not completely reliable to predict the aerodynamic force coefficients. This is particularly significant for the wind turbine blade sections. In this paper, we focus our attention on two different transitional flow models for Reynolds–Averaged Navier–Stokes (RANS) equations. It is worth noting that this is a crucial aspect because standard RANS models assume a fully turbulent regime. Thus, our approaches couple the well–known γ– technique and logγ equation with the Spalart–Allmaras turbulence model in order to overcome the common drawbacks of standard techniques. The effectiveness, efficiency, and robustness of the above-mentioned methods are tested and discussed by computing several flow fields developing around airfoils operating at Reynolds numbers typical of wind turbine blade sections.