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Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole–proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole–proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems. [ABSTRACT FROM AUTHOR]

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 AUTHOR]

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 work envisions industrial control systems that can reliably adapt to requirements. We rely on the international standard IEC 61499 to achieve this goal. The standard allows downtimeless system evolution such that an application can be modified at runtime to satisfy the requirements. However, an IEC 61499 application consisting of multiple Function Blocks (FBs) can be modified in many different ways, such as inserting or deleting FBs, creating new FBs with their respective internal behaviours and adjusting the connections between FBs. These changes require considerable effort and cost, and there is no guarantee to satisfy the requirements. This article applies runtime enforcement techniques for supporting adaptive IEC 61499 applications. This set of techniques can modify the runtime behaviour of a system according to specific requirements. Our approach begins with specifying the requirements as a state machine-based notation called contract automaton. This automaton is then used to synthesise an enforcer as an FB. Finally, the new FB is integrated into the application to execute according to the requirements. A tool support is developed to automate the approach. Experiments were performed to evaluate the performance of enforcers by measuring the execution time of several applications before and after the integration of enforcers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Formation control is a fundamental task in the realm of autonomous multiagent systems. To drive a group of agents to maneuver continuously with the desired formation, this paper studies the finite‐time affine formation control problem with disturbances, input constraints and safety guarantee. A non‐singular terminal sliding mode control (NTSMC) is implemented to achieve finite‐time convergence of all followers to their desired positions. Additionally, an auxiliary system is deployed to address input constraints resulting from the physical properties of the affine formation system. To mitigate the impact of lumped disturbances, a finite‐time disturbance observer (FTDO) is employed to estimate the disturbances and compensate for their effects. Based on FTDO, the auxiliary system and the above NTSMC, a finite‐time robust controller is developed as the nominal controller. By modifying the nominal controllers to comply with safety constraints, control barrier functions are employed to ensure the safety of the formation system in obstacle‐filled environment. Finally, the effectiveness and feasibility of this method are validated through simulations and real‐world experiments. [ABSTRACT FROM AUTHOR]

In response to the problem of disturbance in the imaging equipment of stratospheric airships, this study designs a vibration isolation and stability system for carrying imaging equipment. The system mainly consists of microprocessor, gimbal, voice coil motor (VCM) and rubber ring. By using adaptive control law based on characteristic model to control the double closed control loop of the motor composed of position loop and speed loop, the camera deflection caused by disturbance is effectively eliminated. Furthermore, in vertical vibration suppression, the adaptive control law based on the characteristic model is also applied to the double closed control loop of the voice coil motor, which removes most of the vertical vibration and realizes active vibration isolation. The passive isolation of the system is completed by rubber rings. The experimental results show that the vibration isolation and stabilization system have good performance in complex disturbance environments, effectively stabilizing the imaging equipment. [ABSTRACT FROM AUTHOR]

The study looks into the creation of AI-powered adaptable systems to provide personalized library research help. It focuses on how AI can improve the research experience by making tools and help more accessible to each individual user. As digital libraries change, students are often swamped by the huge amount of information that is available, which makes it harder to find the sources they need. Machine learning techniques, natural language processing (NLP), and user tracking are used by this system to make personalized suggestions based on study topics, tastes, and contacts with users in the past. The system can improve ideas and search methods in real time by responding to how users are doing their research. This cuts down on search time and makes results more accurate. The framework has a mixed model that uses both controlled and unstructured learning to accurately classify data and group resources that are similar. NLP is also used by the system to understand complicated research questions and find relevant buzzwords, quotes, and reference materials from journals, digital libraries, and scholarly databases. It moves forward standard state looks conjointly permits conceptual understanding and relevant significance, which makes the relationship with the client more normal. Through fortification learning, this adaptable framework is continuously making changes to move forward its performance based on input from clients and modern consider designs. It makes a difference people from diverse scholastic areas by giving them a interesting digital library encounter and making it less demanding for them to procure data from distinctive areas. With these enhancements, the framework fathoms common issues like as well much data, look comes about that aren't valuable, and a need of get to to specialty regions. This makes the ponder handle less demanding and makes a difference make the school setting more centered and proficient. This AI-powered adaptable system changes the way library research is done by giving users better, faster, and more accurate help finding their way through the huge amount of academic information that is available. [ABSTRACT FROM AUTHOR]

In recent years, more and more attention has been paid to dynamic traffic congestion management in order to increase traffic light capacity in urban areas. To this end, a number of adaptive control algorithms have been proposed for individual traf C lamps based on the input speed. However, taking into account the road conditions passing through several intersections in real time, attention was paid to the issue of increasing the traf C light capacity. In this article, we formulate the problem of maximum throughput by considering vehicles entering in different directions. Then we present a new adaptive traffic light control algorithm that maximizes traf C and reduces the waiting time for vehicles at the intersection. The proposed algorithm adjusts the phases and duration of the traf C light signal depending on the real-time road conditions of the main and adjacent intersections. Through SUMO simulations, we demonstrate the effectiveness of the proposed algorithm in terms of bandwidth and average travel time. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The created SDE-Framework combines neutronosophic logic and fuzzy mathematics in a novel method, aiming at facilitating more informed decision outcomes in computational systems and information technology management. This method hopes to aid in determining strategic solutions by controlling the expected sophistication and ambiguity in these two technologically dynamic industries. Neutronosophic logic divides data into three components: truth, indeterminacy, and falsity, build an exhaustive technique for addressing contradiction and indeterminacy. This significantly increases the method by enabling a more complete exploration of potential options with ambiguous and inadequate data. Second, the fuzzy mathematics gives a valuable contribution. It offers a refined method for managing the levels of probability and certainty through membership features, resulting in more exact and flexible evaluations. By the usage of such compared sophisticated mathematics concepts, SDEFramework addresses potential decision-making scenarios by letting the computer formulates do the judgements for the determinable and in determinable explicit data. The subsequent crucial parameters are adopted to tolerance values: validity and responsibility, falseness foreach, indeterminacy magnitude to each, and truth value. This guarantees its combination of complexity supportive rand reading of actual surroundings. [ABSTRACT FROM AUTHOR]

Social signal recognition techniques based on nonverbal behavioral sensing allow conversational robots to understand the user’s social signals, thereby enabling them to adopt interaction strategies based on internal states inferred from the social signals. This research investigates how the online social signal recognition and adaptive dialog strategy influences the dynamic change in a user’s inner state. For this purpose, we develop a semiautonomous interview robot system with an online speaker’s willingness recognition module and an adaptive question selection module based on the willingness level. The online recognition model of speaker willingness is trained from multimodal nonverbal features extracted using a novel interview corpus, which allows appropriate interview questions to be chosen based on the estimated willingness level of the user. We conduct the experiment using the system to evaluate the effectiveness of adaptive question selection based on the willingness recognition model. First, the multimodal willingness recognition model is evaluated using the interview corpus. The best recognition accuracy of willingness level (high or low) was $72. 8\%$ 72. 8 p e r c n t ; with the random forest classifier. Second, 27 interviewees were interviewed with the two interview robot systems: (I) with the adaptive question selection module based on willingness recognition and (II) with a random question selection strategy. The proposed adaptive question strategy significantly increased the number of utterances with high willingness compared with the baseline system (II); thus, adaptive question selection with online willingness recognition elicited the speaker’s willingness even though the model cannot be estimated with near-perfect accuracy. [ABSTRACT FROM AUTHOR]

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer. [ABSTRACT FROM AUTHOR]

The original article, published in 2016, describes Emergent Learning as a "framework." In the 2024 prologue, the author argues that it is more a way of thinking and being in any situation -- and the form it takes can look different from situation to situation. Emergent Learning is about understanding the difference between an adaptive strategy and an emergent one -- what it takes to create a whole that is greater and more sustainable than the sum of its parts. Since 2016, the community of Emergent Learning practitioners has grown seven-fold and is bringing what they are doing, seeing, and learning back to the community. Over 60 community members recently came together to write a Guide to the Principles of Emergent Learning -- a material example of what can happen when these ideas are brought to life. How and when their practices do or don't result in emergence, "What does it take?" is always their first question. [ABSTRACT FROM AUTHOR]

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.

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network. [ABSTRACT FROM AUTHOR]

Optimized user experience and individualized task assistance are key factors for the success of e-business applications because they serve both, users, and service providers. In this paper we present an adaptation agent for observing and analyzing interactive behavior, emotional state and task-accomplishment of individual users while working with interactive e-business applications. The agent recognizes specific situations and can adapt the interactive application at runtime by co-operating with a model-based user interface development framework (MBUID) and by providing domain-specific guidance for the user. After introducing purpose, business context of our approach, and related work, the paper discusses the structure and functionality of the SitAdapt adaptation agent. The operation of the adaptation agent is demonstrated for a travel-booking platform that currently serves as a workbench for testing and evaluating our situation-analytic and emotion-triggered approach for creating highly competitive and individualized business applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Adaptive learning seeks to create personalized learning experiences by considering various cognitive and affective factors. However, conventional adaptive models often fall short in meeting diverse learner needs, relying heavily on single factors like learning style. In response, we present a comprehensive framework that integrates an AI-based adaptive learning model, which not only accounts for multiple factors such as prior performance, leisure interests, and learning style but also aligns with principles of green smart education. Leveraging the k-means clustering algorithm, our approach brings together learners with similar leisure interests. Predicting student performance involves a Gradient Boosting Regressor, with demographic data and past performance contributing to a performance metric. Additionally, our system incorporates sustainability practices, optimizing resource usage in computation and data storage to promote eco-friendliness in education. Artificial neural networks predict individual learning styles, and a decision tree algorithm personalizes educational content delivery to align with preferences. Our objective is twofold: to enhance the overall performance of the proposed model and to champion sustainability in education, fostering a greener and more adaptive learning ecosystem. [ABSTRACT FROM AUTHOR]

The potential of adaptive tank cleaning devices to reduce resource consumption of cleaning processes is quantified on the example of a dairy tank under production conditions. As methodological approach, a cleaning validation process was applied to obtain information on the resource consumption of the existing cleaning procedure as reference. An improved cleaning procedure with increased mechanical cleaning action was designed. It exploits the capabilities of motor‐driven jet cleaners to perform helical, zigzag, spiral and meander‐shaped nozzle movements. The nozzle movements were applied according to the location of cleaning‐critical area and the fouling deposit distribution. The configuration was supported by cleaning simulations. Both cleaning procedure were quantitatively compared. The adapted cleaning process reduced the cleaning time by 32 per cent without compromising the cleanling success. The volumetric flow rate was 55 l/min instead of 130 l/min and the energy requirement for cleaning was lowered by approx. 227,260 kJ per cleaning cycle. Practical applications: Reducing cleaning times, water consumption, and strengthening food safety are essential challenges for food production. The methodical approach for conducting a cleaning validation provides necessary information to systematically address priorities of optimization: (1) reduction of cleaning time, (2) reduction of water consumption, (3) reduction of energy consumption. The optical detection of cleaning‐critical area, fouling deposit distribution and the temporal cleaning process opens the possibility of monitoring the hygiene status and strengthening food safety. The use of adaptive, motor‐driven tank cleaning devices enables the targeted adjustment of cleaning intensity. This enables the targeted cleaning of hard‐to‐clean and easy‐to‐clean tank areas and may reduce resource consumption and cleaning time. [ABSTRACT FROM AUTHOR]

Nowadays, metaheuristic algorithms have been emerged as a potential solution in adaptive filtering applications since they offer good convergence properties. Nonetheless, most of them fall into a local minimum since their optimization is based on a single-solution technique. As a consequence, these algorithms present a high misadjustment level and require a large population to find the optimal solution. Recently, the grey wolf optimization (GWO) algorithm has emerged as a potential solution since it requires a smaller population and possesses a stronger global optimization ability with lesser control parameters. From an engineering perspective, its compactness is an attractive feature. Therefore, this opens new horizons in the implementation of this algorithm in resource-constrained devices. In this letter, we present for the first time the use of the GWO algorithm for system identification and acoustic echo canceller (AEC) and its implementation in a field programmable gate array (FPGA) device to validate its effectiveness. Our results show that the use of the GWO algorithm achieves lower steady-state mean square error (MSE) and requires less computational resources when compared with one of the most used metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

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.

Pointing tracking control of marching turret-barrel system is one of the important topics in exploration of intelligent ground combat platform. This paper focuses on an adaptive robust control scheme for pointing tracking of marching turret-barrel system driven by a motor and an electric cylinder. Three types of possibly fast time-varying but bounded uncertainty are considered: system modeling error, external disturbance and road excitation. The uncertainty bounds are not necessary to be known. First, the pointing tracking system is constructed as a coupled, nonlinear and uncertain dynamical system with two interconnected (horizontal and vertical) subsystems. Second, a tracking error e is defined as a gauge of control objective, and then the dynamical equation of the pointing tracking system is built in state-space form. Third, for uncertainty control, a comprehensive uncertainty bound α is derived to measure the most conservative influence of the uncertainty, and then an adaptive law is proposed to evaluate it in real time. Finally, for pointing tracking control, an adaptive robust control is proposed to render the pointing tracking system to be practically stable; thereout, the objective of pointing tracking is achieved. This work should be among the first ever endeavours to cast all the coupling, nonlinearity and bound-unknown uncertainty into the pointing tracking framework of marching turret-barrel system.

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.

This work develops a network of adaptive boundary observers and studies the agreement between state and parameter estimates for a single target parabolic partial differential equation (PDE) system in the presence of structured and unstructured uncertainties. It is assumed that the unknown parameters take the form of either a structured uncertainty with unknown constant parameters or an unstructured uncertainty that can be neutralized by a radial basis function neural networks with unknown weights. The proposed adaptive observers consisting of m$$ m $$ agents in the network follow the structure of adaptive identifiers for the considered target PDE systems with the insertion of a penalty term in both the state and parameter estimates. Different from earlier efforts, the proposed adaptive laws include a penalty term of the mismatch between the parameter and state estimates generated by the other adjacent agents, which helps to accelerate the estimation of uncertainties. Additionally, the effects of these modifications on the agreement amongst the state and parameter estimates are investigated. Theoretical proofs are provided to show that the proposed approach guarantees the exponential convergence of estimation errors in the case of structured uncertainties and the ultimate boundedness of estimation errors in the case of unstructured uncertainties. Finally, numerical simulations are carried out to verify the effectiveness of the design methods. [ABSTRACT FROM AUTHOR]

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research. [ABSTRACT FROM AUTHOR]

This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on "Rigorous Engineering of Collective Adaptive Systems." It presents the 11 contributions of the section categorizing them into five distinct research lines: correctness by design and synthesis, computing with bio-inspired communication, new system models, machine learning, and programming and analyzing ensembles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.