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In anti-lock brake systems (ABS), the primary goal of the controller is to maximize vehicle deceleration by maintaining the slip ratio at an optimal level. This work presents a fresh approach that enhances ABS performance by integrating a sliding mode controller with a barrier function. This method combines integral sliding mode control with adaptive laws informed by barrier functions, effectively managing external disturbances and uncertainties in inertia. A significant benefit of this approach is that it does not require prior knowledge of the upper limits of these uncertainties and disturbances, thanks to the barrier function-based sliding mode control. The system state is initially aligned with the switching manifold, ensuring robust compensation for any uncertainties and disturbances right from the start of braking. During the sliding mode phase, dynamic properties are finely tuned to ensure that the system’s performance remains consistent. The effectiveness and reliability of the proposed controller have been demonstrated through numerical simulations conducted in MATLAB/Simulink, proving its capability across a range of road conditions.

This paper introduces SmartBlendEd, an innovative platform designed to enhance blended learning through advanced AI-driven scheduling. SmartBlendEd optimizes educational logistics by dynamically adapting to the individual needs of students and educators, significantly reducing administrative overhead and enhancing learning outcomes. The platform integrates artificial intelligence to personalize educational experiences and streamline the scheduling process, enabling educators to focus more on pedagogy and less on coordination tasks. Empirical evidence showcasing the platform’s efficacy in real-world settings is discussed, along with challenges such as technological disparities, data security, and user adoption. This study contributes to the discourse on leveraging technology to improve blended learning environments, offering insights into future technological advancements in education.

Abstract Reconfigurable intelligent surfaces (RISs) have attracted a great deal of interest due to the potential contributions to the next‐generation wireless networks. This letter proposes an enhancement to the physical layer security (PLS) of a multi‐hop RIS‐assisted underwater optical wireless communication (UOWC) system. Owing to the complexity of the underwater environment, a security‐based adaptive RIS (SA‐RIS) clustering strategy, which aims to reflect optical signals among clusters to improve the performance of the overall system, is evaluated. By combining the underwater channel model, the closed‐form expressions of probability density function (PDF) and cumulative distribution function (CDF) are derived. Moreover, by increasing the numbers of RIS clusters, the performance metrics such as secrecy outage probability (SOP) and average secrecy capacity (ASC) are evaluated under different scenarios. The obtained results demonstrated that, in contrast to the case without preventing the eavesdropper, the proposed strategy in evasion scenarios could improve the SOP significantly. It can be concluded that the system secrecy performances are further improved by assigning different RIS clusters with proper channel quality.

Abstract For liquid crystal display‐based devices, the memory sub‐system and backlight consume a notable amount of energy, posing a critical concern, especially for portable devices. This work introduces an integrated display system (IDS) aimed at combining technologies for energy‐efficient memory usage and backlight power optimization, which have been addressed independently until now in the existing literature. Two modifications of approximated memory are proposed to find an optimal scheme for energy‐efficient memory usage as part of the proposed IDS schemes. Additionally, three distinct IDS schemes are proposed and compared to identify an optimum solution, ensuring energy efficiency maximization while maintaining image quality for each image. Experimental results demonstrate that 42.19% of energy consumption in memory access and 37.57% of the backlight power consumption can be reduced, while maintaining the image quality in terms of two different metrics.

The present paper proposes the application of global and exact differentiators with dynamic gains based on higher-order sliding modes (HOSM) to the design of adaptive backstepping control for nonlinear uncertain systems of strict-feedback type. The use of this kind of differentiator in the closed-loop system allow us to guarantee global uniform stability (for any initial conditions) due to the variable nature of the dynamic gain. The dynamic gain can grow or decrease with the unmeasured state. In addition, asymptotic output tracking is also assured. In order to illustrate the results of the new theorem, the proposed controller is applied to a high-performance aircraft system, suppressing the wing-rock phenomenon usually observed for fast-speed flight conditions. Comparison results with a linear-inexact differentiator, a local HOSM differentiator with fixed gains and the proposed global and exact HOSM differentiator with dynamic gain shows the superiority of the latter over the former approaches. To demonstrate the practical efficacy of the proposed approach, we conclude with an experimental test featuring a DC motor and the novel differentiator-based backstepping control scheme.

Abstract In the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone‐based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high‐dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC‐based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score.

The article discusses the problem of adaptive system synthesis to provide the detection, estimation, and elimination of the negative consequences of faults in the thrusters of underwater robots and deviations of their parameters. For this purpose, an approach consisting of three stages is proposed. At the first stage, based on a bank of diagnostic observers, deviations of the thruster parameters from their nominal values or the occurrence of errors in the readings of its sensors are detected. At the second stage, the magnitudes of the detected deviations and errors are estimated using sliding mode observers. To estimate not only single but also multiple faults, the additional sliding mode observers are used. At the third stage, a control signal is generated to retain the basic dynamic properties of the thruster based on the estimated magnitudes of deviations and errors. Thus, the main contribution of this paper is designing the systems compensating for the consequences of faults occurring in the thrusters of the UR and errors in the readings of the angular velocity sensors. This avoids reducing the accuracy of movement along specified trajectories, as well as preventing accidents that could lead to the loss of the vehicle. The operability and efficiency of the proposed systems were tested using computer simulations and experimental studies on an electro-mechanical stand.

This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision applications, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure the maximum symmetry of real objects with objects/segments in their digital representations. However, clustering method performances can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms—including K-Means, K-Medoids, Fuzzy C-Means, Possibilistic C-Means, Gustafson–Kessel, Entropy-based Fuzzy, Ridler–Calvard, Kohonen Self-Organizing Maps and MeanShift—across images captured under different illumination conditions. Additionally, we develop an adaptive image segmentation system utilizing empirical data. Conducted experiments highlight varied performances among clustering methods under different luminosity conditions. This research enhances a better understanding of luminosity’s impact on image segmentation and aids the method selection for diverse lighting scenarios.

The implementation of morphing wing mechanisms shows significant potential for improving aircraft performance, as highlighted in the recent literature. The Clean Sky 2 AirGreen 2 European project team is currently performing ground and wind tunnel tests to validate improvements in morphing wing structures. The project aims to demonstrate the effectiveness of these morphing designs on a full-scale flying prototype. This article describes the design methodology and structural testing of a scaled morphing-flap structure, which can adapt to three different morphing modes for various flight conditions: low-speed (take-off and landing) and high-speed (cruise). A scale factor of 1:3 was selected for the wind tunnel test campaign. Due to challenges in scaling the embedded mechanisms and actuators necessary for shape-changing, a full geometrical scale of the real flap prototype was not feasible. Static analyses were performed using the finite element method to address critical load conditions determined through three-dimensional computational fluid dynamic (CFD) analysis. The finite element (FE) analysis was conducted and the results were compared with the empirical data from the structural test. Good correlations were found between the structural testing results and numerical predictions, including static deflections and elastic deformations under applied loads. This indicates that the modeling approaches used during the design and testing phases were highly successful. Based on simulations for the ultimate load conditions tested during the wind tunnel tests, the scaled flap prototype has been deemed suitable for further testing.

Abstract From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches.

Objective. This article is a continuation of the topic raised in the article “Systems with optimal geometric shapes”, published in the previous issue of this journal. The purpose of the study is to develop design diagrams and explanations for them, outline the principles of their operation, design features that provide the necessary seismic resistance.Method. To ensure adaptability, various design techniques are used: switching connections, switching connections, switching connections, buildings and structures consisting of various structural blocks having different frequencies of natural oscillations. Thus, the blocks of the building dampen each other during vibrations, thereby providing the necessary seismic resistance during earthquakes.Result. Structural systems with adaptive properties that impart high seismic resistance to buildings and structures are presented.Conclusion. The design techniques outlined in the article ensure high seismic resistance of buildings and structures, which can be used in the practice of designing and constructing earthquake-resistant systems.

Abstract In order to solve the problems of difficult control, poor stability, and low control precision in complex autonomous non‐linear systems, and some sensors have non‐linear errors in special environments. Based on the PSO (Particle Swarm Optimization) algorithm, an PSO‐BP‐PID (Particle Swarm Optimization Back Propagation neural network PID) control method and a sensor error compensation algorithm based on BP (Back Propagation) neural network are designed for optimal temperature and humidity control and sensor error compensation in the autonomous greenhouse system. The error between the average temperature value and the target value after steady state is 0.5°C, and the error between the average humidity value and the target value is 1% RH. The results show that the control method can effectively compensate the non‐linear error of the sensor and improve the performance of the control system in a complex environment, which is suitable for the stable and control of actuators in autonomous systems. The error of temperature and humidity sensor is compensated by BP neural network; PSO (Particle Swarm Optimization) was used to optimize the BP‐PID parameters of the automatic greenhouse system.

Abstract Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

Abstract This paper presents a perspective on Biosignal-Adaptive Systems (BAS) which automatically adapt to user needs by continuously interpreting their biosignals and by providing transparent feedback, thereby keeping the user in the loop. The major hallmark of the described BAS is the low latency with which biosignals are processed, interpreted, and applied to perform rapid system adaptation, providing the user in the loop with immediate feedback on the BAS’s understanding of his or her condition. In contrast to explicit user input or the interpretation of observable behavior, the rapid system adaptation relies on biosignals, which in context of a concrete application can be interpreted as implicit signals of user needs. Recently, great advances have been made in sensor integration into smart devices, making it possible to collect vasts amounts of multimodal biosignal data. Furthermore, powerful machine learning methods enable rapidly processing and learning from such data. We argue that the time has come to harness the full spectrum of low-latency processing of biosignals to understand user needs and to apply this information to deliver adaptive systems accordingly. However, this will just be the beginning: real-time signal processing in combination with ubiquitous devices that are always connected to huge processing and storage capacities allow systems to provide users (and bystanders) with instant and transparent feedback and adaptations for the recognized needs. In the future, such systems could run 24/7 to assist users @home, @work, and @play from the cradle to the grave. Thus, BAS must be human-centric to curate data, archive information, learn from experience, and adapt to individual users throughout their lifespan. In this position paper, we present the concept of BAS with its key building blocks, provide selected examples of BAS from our research, and articulate selected challenges for future research.

The purpose of the study is to develop the architecture and working algorithm of intelligent relay protection of an electrical network with an isolated neutral. “Intellectualization” of relay protection is necessary to increase its sensitivity and reliability. This problem is relevant due to the accident rate of overhead power lines and incorrect operation of traditional relays. This is due to the fact that the set point is not adjusted according to the deviation of the line parameters with changing environmental and load conditions. The purpose of the study is achieved by solving the following problems: the concept of supplementing an electronic relay with a mechanism for correcting the operation set point is proposed. The use of the digital shadow of the electrical network is substantiated. The digital shadow interrogates the sensor system. The advantage of using a digital shadow as part of relay protection has been proven. This allows emulating faults and estimating currents to correct the setpoint. The most significant result of the study is an example of the implementation of intelligent relay protection using a digital shadow in the form of an artificial neural network. The application of a neural network for approximating the current function of a two-phase short circuit is demonstrated. The significance of the results lies in the improvement of the sensitivity and selectivity of current protection due to the digital shadow for working out emergency modes. The convenience of operating and reproducing a digital shadow in the form of a neural network is noted.

Abstract Corona Virus disease 2019 (COVID‐19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non‐pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID‐19 in some countries, it has been criticized as inefficient or ineffective, because of the time‐delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA‐ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID‐19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID‐19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state‐of‐the‐art methods.

The paper presents the results of studies of the congestion of a regulated intersection of the city based on the use of quadcopter technologies for recording the dynamics of vehicle flows during times of peak traffic, when it can be problematic to determine the parameters of traffic delays due to the limited visibility of stationary means of video observation. The selected calculation method for the adequacy of transport delay calculations requires precise geo-positioning data of a number of cars at the intersection, which is fully provided by quadcopter surveying. The results of the calculations can be used to adjust the parameters of the intersection regulation, or to assess its environmental friendliness and economic losses at times of the highest traffic loads. Transport delays at regulated intersections of large and even medium-sized cities lead to the accumulation of significant concentrations of harmful emissions, which can many times exceed the maximum permissible concentrations, and this exacerbates already existing environmental problems. It should be noted that the use of quadcopter surveying technologies is a method of the transition period to the introduction of modern geo-information technologies and intelligent electronic systems based on satellite navigation data, which can be adaptive traffic control systems. The transition to adaptive traffic control systems is a promising direction of optimal traffic management, which provides local adaptive control of the most complex and important intersections of the transport network.

Technology-aided learning is one of the important aspects of cognitive computing where education is provided through the means of digital media. This paper presents a comprehensive review of trends and the development of technology-based adaptive language learning systems. It also strives to highlight challenges and opportunities in the field of Technology-Assisted Language Learning (TALL). Articles from various electronic databases and reputed journals in the related field have been reviewed from 2011to 2021 to get an insight into state-of-the-art adaptive systems for TALL. To analyze results, the authors have proposed three dimensions viz. spatial and temporal aspects, system and targeted learners’ characteristics, and adaptation provided. The findings of the review indicate that research in this field has been gaining popularity since 2015, especially in the Asian region. English is the most frequently investigated language in TALL systems which have been employed majorly for university students. The study also analyzes various methods and sources of adaptation provided in such systems. Challenges, opportunities, limitations, and implications of research in this direction have also been discussed. The study also proposes new ways and ideas that can be taken up and implemented by language educators and local governments to make the research in the field of TALL commercially feasible and penetrable to various levels of society.

Abstract To address the problems that the UAV (Unmanned Aerial Vehicle) is vulnerable to distance limitation and environmental interference when tracking and landing on a moving platform autonomously, the accuracy of position estimation relying only on visual odometry in the point‐featureless environment is insufficient, and the traditional linear path planning solvers and controllers cannot meet the fast and safe requirements under the non‐linear strong coupling characteristics of the cooperative landing system, an nonlinear model predictive control (NMPC)‐based multi‐sensor fusion method for autonomous landing of UAVs on motion platforms is proposed. The UAV combines the position information obtained by the RTK‐GPS and the image information obtained by the camera and uses the special identification codes placed in the landing area of the UAV to carry out cooperative planning and navigation while using UKF (Unscented Kalman Filter) to estimate the position of the moving platform and using the interference‐resistant NMPC algorithm to optimise the UAV tracking trajectory based on the precise positioning of the two platforms to achieve the autonomous landing control of the UAV. The simulation and practical experimental results show the feasibility and effectiveness of the proposed algorithm and the autonomous landing control method and provide an effective solution for the autonomous landing of quadrotors on arbitrarily moving platforms.

Abstract The upcoming generation of power systems would be outlined as a system of systems in which many AC and DC microgrids are interconnected to exchange power in a controlled manner. In this work, a new platform for the interconnection of multiple microgrids using a unified interphase power controller (UIPC) is presented. The networked microgrids operate in an islanded mode where voltage control in DC microgrids, and voltage and frequency control in AC microgrids are demanding. Therefore, we develop a new structure for the UIPC according to a current‐controlled four‐leg power converter configuration which can satisfy voltage and frequency support at the AC side. Due to many power and voltage oscillations, UIPC control is challenging in this heterogeneous environment. Therefore, the UIPC is equipped with a new affine projection‐like optimal control (APLOC) strategy which enables the microgrids to safely exchange power when necessary. Further, each AC or DC microgrid contains a new controllable load model which is commanded by the UIPC according to the power management paradigm. Simulation results proved the sufficiency of the proposed networked microgrids model in both controlling the exchanged power among microgrids and improving power quality problems.

Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations.

Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact.

Educational Recommender Systems (ERSs), intelligent tutoring systems that adapt their pedagogical recommendations to each student, are becoming increasingly common. Context-Sensitive Affective Educational Recommender Systems (CSAERSs) personalize the recommendations according to a learning context with multiple dimensions, including the affective dimension and the personality traits of the user. To date, in the field of educational technology, there is little or no research that focuses on offering context-sensitive, personalized, psycho-pedagogical affective support to distance-learning students in real time. Nor do there seem to be any proposals for approaches to the knowledge engineering (term which encompasses knowledge acquisition and knowledge representation) of these systems, in which the relation between the user and his or her context is crucial. There is little work on a systematic approach to the requirements-elicitation phase and to the use of ontologies in the development and validation of ERSs, in general, and CSAERSs, in particular. In this article, we report on a student-centred requirements-elicitation methodology that uses psycho-pedagogical theatre in combination with student surveys. We then illustrate its application in the design and validation of an ontology, together with a semantic-similarity function, that could serve as the nucleus of a CSAERS.

Situation-awareness-based decision-making (SABDM) models constructed using cognitive maps and goal-direct task analysis techniques have been successfully used in decision support systems in safety-critical and mission-critical environments such as air traffic control and electrical energy distribution. Reinforcement learning (RL) and other machine learning techniques are used to automate situational awareness mental model parameter adjustments, reducing expert work on the initial configuration and long-term maintenance without affecting the mental model’s structure and maintaining the situation-awareness-based decision-making model’s cognitive and explainability characteristics. Real-world models should evolve to cope with changes in the environmental conditions. This study evaluates the application of reinforcement learning as an online adaptive technique to adjust the situation-awareness mental model parameters under evolving conditions, a technique we named SABDM/RL. We conducted evaluation experiments using real-world public datasets to compare the performance of the SABDM/RL technique with that of other adaptive machine learning methods under distinct concept drift-evolving conditions. We measured the overall and dynamic performances of these techniques to understand how well they adapt to evolving environmental conditions. The experiments show that the SABDM/RL performs similarly to modern online adaptive machine learning classification methods with the support of concept drift detection techniques while maintaining the mental model strength of the situation-awareness-based decision-making systems.

Abstract Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian‐sum in this paper. Firstly, based on the traditional CKF algorithm, we introduced a Gaussian‐sum method to approximate non‐Gaussian noise to get more accurate filtering results in view of the problem of reduced filtering accuracy caused by the inherent non‐Gaussian nature of the noise and the system non‐linearity. Secondly, the maximum correntropy criterion is introduced to solve further the problem of improving the filtering accuracy of the system in the case of non‐linearity. Simulation results and actual data verification showed that the Square‐root cubature Kalman filter algorithm based on the maximum correntropy and Gaussian‐sum has higher accuracy than traditional filtering algorithms, which verified the algorithm's effectiveness in the application.

Electromyography-based wearable biosensors are used for prosthetic control. Machine learning prosthetic controllers are based on classification and regression models. The advantage of the regression approach is that it permits us to obtain a smoother and more natural controller. However, the existing training methods for regression-based solutions is the same as the training protocol used in the classification approach, where only a finite set of movements are trained. In this paper, we present a novel training protocol for myoelectric regression-based solutions that include a feedback term that allows us to explore more than a finite set of movements and is automatically adjusted according to real-time performance of the subject during the training session. Consequently, the algorithm distributes the training time efficiently, focusing on the movements where the performance is worse and optimizing the training for each user. We tested and compared the existing and new training strategies in 20 able-bodied participants and 4 amputees. The results show that the novel training procedure autonomously produces a better training session. As a result, the new controller outperforms the one trained with the existing method: for the able-bodied participants, the average number of targets hit is increased from 86% to 95% and the path efficiency from 40% to 84%, while for the subjects with limb deficiencies, the completion rate is increased from 58% to 69% and the path efficiency from 24% to 56%.

Augmentative and alternative communication techniques (AAC) are essential to assist individuals facing communication difficulties. (1) Background: It is acknowledged that dynamic solutions that adjust to the changing needs of patients are necessary in the context of neuromuscular diseases. (2) Methods: In order address this concern, a differential approach was suggested that entailed the prior identification of the disease state. This approach employs fuzzy logic to ascertain the disease stage by analyzing intuitive patterns; it is contrasted with two intelligent systems. (3) Results: The results indicate that the AAC system’s adaptability enhances with the progression of the disease’s phases, thereby ensuring its utility throughout the lifespan of the individual. Although the adaptive AAC system exhibits signs of improvement, an expanded assessment involving a greater number of patients is required. (4) Conclusions: Qualitative assessments of comparative studies shed light on the difficulties associated with enhancing accuracy and adaptability. This research highlights the significance of investigating the use of fuzzy logic or artificial intelligence methods in order to solve the issue of symptom variability in disease staging.

Abstract Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non‐deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting‐based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non‐robust system's performance by up to 13.8 times in a real‐world setting.

Personality is considered as the internal factor that defines a person’s behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners’ personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners’ behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners’ navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners’ personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners’ personalities.

Objective. The aim of the study is to solve the problems of constructing logicallinguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects.Method. To build logical-linguistic (fuzzy) control models, theories of fuzzy sets and algorithms of L. Zadeh were used.Result. The process of constructing logical-linguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects using the theory of fuzzy sets and fuzzy algorithms is given. A marine mobile object (MMO) operating in an uncertain environment was used as a control object in the work. It is shown that logical-linguistic (fuzzy) MPO control models can be built taking into account the rich practical experience of a navigator who does not have special knowledge, expressed by them in a qualitative form for building fuzzy control models.Conclusion. Logicallinguistic control models make it possible to ensure the movement of a marine moving object along a given trajectory with a quality of the controlled process sufficient for practical purposes under conditions of various external disturbances and drift of the parameters of the controlled object. The use of fuzzy control algorithms can significantly reduce the cost of computer time compared to the optimal search algorithm with an acceptable deterioration in the quality of the process and ensure the implementation of the control process in real time.

The article presents a certain investment strategy based on the difference between two moving averages, modified to allow the extraction of patterns. The strategy concept dropped the traditionally considered intersections of two averages and opening positions just after those intersections. Based on the observation of changes happening in the moving averages difference, it has been noticed that for some values of this difference and some values of additional strategy parameters, an interesting pattern appears that allows short-term prediction. These patterns also depended on the first derivative of the moving averages difference and the location of the current price relative to certain thresholds of the difference. Therefore, the strategy uses five parameters, including Stop Loss, adapted to the properties of the time series through machine learning. The importance of machine learning is highlighted by comparing simulation results with and without it. The strategy effectiveness was tested in the Matlab environment on the time series of the WIG20 (primary index of the Warsaw Stock Exchange) historical data. Satisfactory results were obtained considered in terms of minimizing investment risk measured by the Calmar indicator.

The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Runtime validation techniques are required because complex self-adaptive systems must consistently offer acceptable behavior for important services. The runtime testing can offer further confidence that a self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for self-adaptive systems. The objective is to ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm facilitates both the execution and adaptation of runtime testing operations.

Adaptive Neural Fuzzy Inference Systems ANFIS have an increasing tendency to be used in scientific research and practical applications. The digitization of production and the emergence of Industry 4.0 enabled the development of this trend, primarily due to the ability to adapt to the task by integrating artificial neural networks and fuzzy logic, which can potentially use the advantages of both techniques in unique frameworks. This approach facilitated the modeling, data analysis, classification and control processes. The advantage of the ANFIS, compared to conventional methods, is reflected in the ability to predict the output based on a set of inputs and on the rule base. Also, these systems are suitable, because they provide the possibility to adjust the parameters of the control system. This paper presents the structure of the ANFIS system and gives a detailed review of the achievements so far, through a comparative analysis, where some possible spheres of interdisciplinary application are highlighted. Possibilities for variations, improvements and innovations of the algorithm, as well as reducing the complexity of the network architecture itself, are discussed. Proposals for some new, as yet unused combinations with metaheuristic optimization methods are presented. Finally, important guidelines are provided on when and where it is useful to apply ANFIS systems.

The design, development, deployment, and operation of a distributed Video Analytics Pipeline (VAP) at the edge of the network is highly complex. In the domain of adaptive systems, several solutions are proposed in literature to optimize either one particular performance aspect of a VAP, e.g., execution time or latency, or focus on minimal energy consumption, or calculate a trade-off including some of those aspects. However, nowadays, most systems utilizing a VAP that records personally identifiable data have to adhere to some form of data protection regulation, such as the GDPR. Still, adaptations to increase data protection requirements are often second to previously mentioned performance or energy consumption characteristics of a VAP. While there is state of the art literature dealing with data protection related adaptations, most of them solely focus on increasing certain security or privacy aspects of a system, leaving previously mentioned performance or energy consumption characteristics out of scope. To the best of our knowledge, there is no solution that covers all of these aspects. In this paper, we present a data protection focused adaptation engine that leverages the application- and infrastructure based adaptation space of a distributed VAP. The engine employs an extended system model and adaptation rules that are based on previous research. It features an optimization algorithm to improve data protection, performance and energy consumption characteristics of a distributed VAP.

The need for adaptive e-learning environments that respond to learning variability is now a fundamental requirement in education, as it helps to ensure that students learn and pass their courses within a set time frame. Although guidelines, techniques and methods have been established in recent years to contribute to the development of accessible and adaptable e-learning environments that promote digital inclusion, their implementation is challenging due to the lack of knowledge of an adequate way to do it and because it is considered more of a technological competence for scholars in the area. In this context, automated support for adapting material that responds to the correct use of accessibility metadata not only provides a way to improve the description of adapted educational resources, but also facilitates their search according to the needs and preferences of students, particularly those with disabilities. In this article, we carry out a multilevel methodological proposal for the automatic adaptation of open educational resources, in order to provide a tool that contributes to the accessibility and correct use of their metadata in e-learning environments. A research is conducted with students with disabilities to establish their real needs and preferences, highlighting the need to strengthen the adequate description and coherent alternative text in images, the correct subtitling in videos and the conversion of audio to text, data that are relevant to our proposal. The research conducted aims to contribute with an automated support tool in the generation of accessible educational resources that are correctly labeled for search and reuse. This research also aims to support researchers in artificial intelligence applications to address challenges and opportunities in the field of virtual education, in addition to providing an overview that could help those who generate educational resources and maintain their interest in making them accessible.

The advent of the Internet of Things (IoT) has triggered an increased demand for sensing devices with multiple integrated wireless transceivers. These platforms often support the advantageous use of multiple radio technologies to exploit their differing characteristics. Intelligent radio selection techniques allow these systems to become highly adaptive, ensuring more robust and reliable communications under dynamic channel conditions. In this paper, we focus on the wireless links between devices equipped by deployed operating personnel and intermediary access-point infrastructure. We use multi-radio platforms and wireless devices with multiple and diverse transceiver technologies to produce robust and reliable links through the adaptive control of available transceivers. In this work, the term ‘robust’ refers to communications that can be maintained despite changes in the environmental and radio conditions, i.e., during periods of interference caused by non-cooperative actors or multi-path or fading conditions in the physical environment. In this paper, a multi-objective reinforcement learning (MORL) framework is applied to address a multi-radio selection and power control problem. We propose independent reward functions to manage the trade-off between the conflicting objectives of minimised power consumption and maximised bit rate. We also adopt an adaptive exploration strategy for learning a robust behaviour policy and compare its online performance to conventional methods. An extension to the multi-objective state–action–reward–state–action (SARSA) algorithm is proposed to implement this adaptive exploration strategy. When applying adaptive exploration to the extended multi-objective SARSA algorithm, we achieve a 20% increase in the F1 score in comparison to one with decayed exploration policies.

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.

In this paper, a robust and safety distributed formation control with unknown external disturbances is researched. For a multi-quadrotors system, a novel nonsingular terminal sliding mode control strategy is studied to realize the formation control with collision avoidance and inter-quadrotor avoidance. The formation controller is redesigned once the system reaches the small field of sliding surface to solve the existence of singular of quadrotor formation and realize the fixed time convergence of singularity region. Then, the position controller and attitude controller are designed to maintain formation configuration with collision avoidance and track the desired angular rate with disturbances, respectively. The global fixed time convergence of quadrotor formation is verified by Lyapunov theory with the fixed time convergence characteristic of singularity region and nonsingular region. At last, simulation results are presented to demonstrate the efficiency of the developed algorithm.

This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to an appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc.

In this paper, the algorithm design problem of intelligent ship autopilot with an unmeasured yaw rate is investigated based on adaptive fuzzy control. The fuzzy logic system (FLS) is employed to approximate the unknown function, and a fuzzy state observer is designed to estimate the unmeasured state. To achieve the specified performance constraints, the error transformed method is introduced. Based on adaptive backstepping method, an observer-based adaptive fuzzy output-feedback control scheme is developed. The proposed control scheme can reduce the conservativeness and the complexity of controller. It is proved that all the signals in the closed-loop system are bounded via Lyapunov theory. The efficacy of theoretical results is demonstrated by the simulations.

Modern breeding, especially when fruit plants cultivated for decades at the same location are concerned, requires a new strategy to develop cultivars resistant to abiotic limfactors of the environment. A more in-depth analysis of genotype–environment interaction phenomena is needed, as modern studies have shown that the level of plant productivity and yields is determined not by specific “genes of quantitative traits”, but mainly by the emergent (newly occurring when a lim-factor of the environment changes at the ontogenetic and phytocenotic levels) effects of the genotype–environment interaction (GEI). New knowledge is needed about the values of contributions to the productivity of a cultivar made by each of the genetic–physiological systems of plant adaptability (GPS-ad) when exposed to a particular lim-factor of the environment at a particular phase of ontogenesis. For the first time, aiming at finding promising peach genotypes for further breeding, we studied their adaptability to low temperatures in different horticulture zones of Krasnodar Territory.Shifts in the effects of low-temperature environmental stressors in the developmental phases of cultivars (produced by climate change) were analyzed in the time interval of 1985–2018.The presence of hereditary adaptive reserves for increasing peach productivity for each phase of development in the process of studying the phenomena of GEI was disclosed. Recommendations are given to breeders on phase-to-phase breeding of future cultivars: how to protect their production process at each developmental phase from negative effects of low temperatures.

Relevance.The comprehensive assessment of cardiorespiratory coupling (CRC) for the diagnosis of functional adaptation in pulmonological patients is presented.The objective of the study was to test the complex criteria of CRC for objective differential diagnosis of individual adaptive reactions of pulmonological patients.Methods and materials. 30 volunteers without verified pathology and 31 patients with restrictive and obstructive disorders were examined. All subjects underwent physical examination with traditional routine method using spiroarteriocardiorhythmography (SACR) at rest and during performing functional tests with hypercapnia and moderate physical exercise.Results. We proposed the methodology of integrated assessment of multicomponent system of CRC, which increases the diagnostic precision of SACR in the tasks of predicting the adaptive defects of the organism at different pulmonary syndromes.Conclusion. Breathing with a fixed frequency normalizes the regulation of CRC, both in healthy and pulmonological patients. Moderate physical activity (80 W) modifies the indicators of CRC in pulmonological patients in comparison with the control group statistically due to disorders of the heart rate and circulation. During moderate physical activity, stroke volume (SV) statistically decreases in patients differently: SV were significantly lower in the group with obstructive disorders than in the group with non-obstructive disorders.

Educational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms® educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students' learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students' performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students' learning process.

Internet-Delivered Psychological Treatments (IDPT) are based on evidence-based psychological treatment models adjusted for interaction through the Internet. The use of Internet technologies has the potential to increase the availability of evidence-based mental health services for a far-reaching population with the use of fewer resources. Despite evidence that Internet Interventions can be effective means in mental health morbidities, most current IDPT systems are tunnel-based, inflexible, and non-interoperable. Hence it becomes essential to understand which elements of an Internet intervention contribute to effectiveness and treatment outcomes. By analogy, adaptation is a central aspect of successful face-to-face mental health therapy. Adaptability to patient needs can be regarded as an essential outcome factor in online systems for mental health interventions as well. While some aspects of rule-based and machine-learning-based adaptation have attracted attention in recent IDPT development, systematic reporting of core components, dimensions of adaptiveness, information architecture, and strategies for adaptation in the IDPT system are still lacking. To bridge this gap, we propose a model that shows how adaptive systems are represented in classical control theory and discuss how the model can be used to specify adaptive IDPT systems. Concerning the reference model, we outline the core components of adaptive IDPT systems, the main adaptive elements, dimensions of adaptiveness, information architecture applied to adaptive systems, and strategies used in the adaptation process. We also provide comprehensive guidelines on how to develop an adaptive IDPT system based on the Person-Based Approach.

Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny's superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application's circumstances requires additional preprocessing, which increases the algorithm's computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection's performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.

The design of a vessel path-following control system based on a full, realistic, nonlinear model is considered. The control objective is to force a surface, course-unstable vessel to track a predefined geometric path. We study an underactuated ship characterized only by a surge control force and yaw control moment, typical of many supply vessels. The assumption is made that the ship's model parameters are unknown, while significant external disturbances and unmodeled dynamics exist. Therefore, the design procedures make use of robust and adaptive control techniques. The controller synthesis uses adaptive output feedback linearization and H∞ optimal control techniques. In this way, the proposed control scheme assures position tracking despite various uncertainties. Because the considered design method leads to a nonminimum phase system, the problem of how to stabilize unstable zero dynamics arises. The presented simulations are based on a realistic ship model in terms of the structure and experimentally identified parameters. The simulations illustrate the effectiveness of the proposed algorithms.

The development of vision-based navigation algorithms using a camera is becoming more important. The vision-based navigation can be categorized into two types. The first is to use sequential camera images as relative navigation. The second is to estimate the absolute navigation solution using a camera image and database. In absolute navigation, the difference between the database and the camera image is a major obstacle to image registration. One of the factors that make a lot of difference is the shadow effect. This shadow increases the inconsistency between the two images and eventually degrades the localization accuracy. This means that shadows have a significant impact when measuring the similarity of the two templates. To mitigate this effect, we inherited and developed the Monte Carlo Localization (MCL) algorithm based on a new similarity cost function, which is a key contribution to this article. We have established the importance of information with information reallocation logic that considers shadow areas. The proposed algorithm allocates the importance of the information considering a portion of the shaded area in the camera image. First of all, we analyzed the effects of shadows on the camera. To compare the performance of the algorithm, we used not only the shadow restoration algorithm but also various template-based matching algorithms. The proposed algorithm is validated through various simulations and real flight experiments as well.

This article contains the design and development of an Adaptive Virtual Learning Environment (AdaptiveVLE) framework to assist educators of all disciplines with creating adaptive VLEs tailored to their needs and to contribute towards the creation of a more generic framework for adaptive systems. Fully online education is a major trend in education technology of our times. However, it has been criticised for its lack of personalisation and therefore not adequately addressing individual students' needs. Adaptivity and intelligence are elements that could substantially improve the student experience and enhance the learning taking place. There are several attempts in academia and in industry to provide adaptive VLEs and therefore personalise educational provision. All these attempts require a multiple-domain (multi-disciplinary) approach from education professionals, software developers, data scientists to cover all aspects of the system. An integrated environment that can be used by all the multiple-domain users mentioned above and will allow for quick experimentation of different approaches is currently missing. Specifically, a transparent approach that will enable the educator to configure the data collected and the way it is processed without any knowledge of software development and/or data science algorithms implementation details is required. In our proposed work, we developed a new language/framework using MPS JetBrains Domain-Specific Language (DSL) development environment to address this problem. Our work consists of the following stages: data collection configuration by the educator, implementation of the adaptive VLE, data processing, adaptation of the learning path. These stages correspond to the adaptivity stages of all adaptive systems such as monitoring, processing and adaptation. The extension of our framework to include other application areas such as business analytics, health analytics, etc. so that it becomes a generic framework for adaptive systems as well as more usability testing for all applications will be part of our future work.