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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]

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 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]

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]

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. [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]

This paper presents the results of developing the adaptive system that provides intelligent support to the flight crews in making a decision to avoid a thunderstorm to optimize the aircraft flight route while ensuring an acceptable fuel efficiency level. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes. [ABSTRACT FROM AUTHOR]

Recommender systems appeared in the early 90s to help users deal with cognitive overload brought by the internet. From there to now, such systems have assumed many other roles like help users to explore, improve decision making, or even entertain. The system needs to look to user characteristics to accomplish such new goals. These characteristics help understand what the user task is and how to adapt the recommendation to support such task. Related research has proposed recommender systems in education. These recommender systems help learners to find the educational resources most fit for their needs. In this paper, we present an integration model between recommender and adaptive hypermedia systems. It results in a new process for educational resource recommendation, using a new algorithm of adaptive recommendation. Through a prototype and an online experiment on the educational scenario, we proved that AwARE could improve the recommendation accuracy, interaction with the system, and user satisfaction. Besides the prototype description, the paper presents a protocol to evaluate the proposed approach by both the providers' and consumers' point of view. [ABSTRACT FROM AUTHOR]

The adaptive sliding mode control (SMC) problem is addressed for singular semi-Markovian jump systems (S-MJSs) against actuator attacks, in which the transition rates rely on the random sojourn time and are not constant, and the system states are unavailable. Moreover, the vulnerability of control signals transmitted via communication network means that the actuators may receive the attacked control signals. For the sake of reducing the effect of actuator attacks, the neural network technique is used to approximate the false information injected by adversaries. Meanwhile, a sliding mode observer is introduced to estimate the unmeasured states. An adaptive SMC law is proposed to guarantee that the estimation states and errors can reach to the sliding surfaces, and the stochastic admissibility of the singular S-MJSs can be ensured. In the end, an example is applied to illustrate the method in this paper. [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]

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]

Previous research has established redirected walking as a potential answer to exploring large virtual environments via natural locomotion within a limited physical space. However, much of the previous work has either focused on investigating human perception of redirected walking illusions or developing novel redirection techniques. In this paper, we take a broader look at the problem and formalize the concept of a complete redirected walking system. This work establishes the theoretical foundations for combining multiple redirection strategies into a unified framework known as adaptive redirection. This meta-strategy adapts based on the context, switching between a suite of strategies with a priori knowledge of their performance under the various circumstances. This paper also introduces a novel static planning strategy that optimizes gain parameters for a predetermined virtual path, known as the Combinatorially Optimized Pre-Planned Exploration Redirector (COPPER). We conducted a simulation-based experiment that demonstrates how adaptation rules can be determined empirically using machine learning, which involves partitioning the spectrum of contexts into regions according to the redirection strategy that performs best. Adaptive redirection provides a foundation for making redirected walking work in practice and can be extended to improve performance in the future as new techniques are integrated into the framework. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to extend and elaborate the notion of successful organizational change to incorporate the concept of large system change (LSC), by developing a framework that brings together complexity and wicked problems theories to understand how individual organizations and change agents can better influence LSC. Design/methodology/approach – This conceptual paper integrates wicked problems and complexity theories to understand and cope with large system initiatives from the perspective of change agents in organizations, and uses the case of the electricity system as an illustrative example for these concepts. Findings – The paper provides implications for LSC and action steps for change agents in organizations, arguing that by understanding change initiatives through the lenses of complexity and wicked problems, change agents are likely to be more effective. Research limitations/implications – The integration of complexity science and wicked problems underpins the development of a comprehensive framework for creating effective LSC solutions, however, these ideas still need to be grounded in practice and empirical research. Practical implications – Using these ideas, change agents in organizations can enhance their influence and use the power of system dynamics to support positive action for sustainable change. This paper provides a foundation to help think through the cross-sectoral, inter-organizational, and change dynamics involved in LSC efforts needed to bring about a more sustainable, secure, and equitable world for all. Social implications – The world greatly needs system change; however, there is limited theory on effective LSC. This paper hopes to contribute to understanding the ways in which the difficulties of such change can be harnessed to move in positive directions with minimal disruption and greatest effectiveness. Originality/value – Theories of change management that position the organization in the context of a broader system and define its role in creating change do not yet articulate the nature of the problems at hand in relation to the large systems where they are embedded. This paper builds upon wicked problems and complexity theories to shed light on the role of change agents and organizations in effective transformational change. [ABSTRACT FROM AUTHOR]

In this paper, the problem of fault tolerant tracking control for a linear time-invariant system subject to actuator faults and saturations is addressed. An auxiliary system is developed to ensure actuators behave within amplitude and rate limits under the influence of partial loss of control effectiveness. Based on the auxiliary system, a fault tolerant compensation controller is constructed to guarantee tracking errors to converge to a small region. Some relationships among tracking errors, command signals, actuator faults, amplitude and rate limits as well as controller parameters are comprehensively studied and explicitly illustrated with formulas. An example of rudder-roll damping control for a cruise keeping ship is included to illustrate the proposed procedures and their effectiveness. [ABSTRACT FROM AUTHOR]

This paper deals with the test of a reconfigurable hardware system (RHS). The latter is a hardware device that allows to change the hardware resources at runtime in order to modify the system functions and therefore to dynamically adapt the system to its environment. The increasing functional complexity of embedded systems and the transition to the RHS make the hardware testing a challenging task, especially under the confine of providing a high quality with a low cost. Considering the fact that the hardware test represents a key cost factor in a production process, an optimal test strategy can be advantageous in the competitive industrial market. Accordingly, this paper introduces a new methodology for an efficient hardware test of RHS. For an RHS, the number of stuck-at faults can be very large, which leads to a significant slowdown in the testing process. Because of the redundancy of faults between the different circuits composing an RHS, the proposed methodology aims at minimizing the number of faults using the inter-circuits relationships and consequently at providing an optimal fault set that can be effectively used for testing. Efficient techniques for test generation and test set validation are proposed to provide the test patterns for faults reduced by inter-circuits fault collapsing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual environment named TnTest. An experimental study confirms and validates the expected findings. Note to Practitioners—This paper addresses possible challenges for future generations of adaptive embedded systems. It proposes an original methodology for an efficient reconfigurable hardware system (RHS) hardware test. The main objective is to significantly reduce time and cost needed for the testing process. For an RHS, the number of stuck-at faults can be very large, which can cause a major slowdown in the hardware test. Based on the inter-circuits relations existing between the different circuits composing an RHS, the proposed methodology decreases considerably not only the number of the faults but also the test patterns needed for testing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual software environment named TnTest, which is capable of providing the smallest fault set as well as the efficient test set that can be effectively used for testing. This environment can be applied to test any embedded device that can be deployed in any new application based on flexible technologies. It can also be useful in manufacturing industries for a required improvement of the production process in relation to time and cost. [ABSTRACT FROM AUTHOR]

This paper proposes a deep reinforcement learning-based video streaming scheme for mobility-aware vehicular networks, e.g., vehicles on the highway. We consider infrastructure-assisted and mmWave-based scenarios in which the macro base station (MBS) cannot directly provide the streaming service to vehicles due to the short range of mmWave beams so that small mmWave base stations (mBSs) along the road deliver the desired videos to users. Motivated by the fact that a video stream consists of sequential chunks and smooth streaming has to be provided while users pass through multiple mBSs at high speed, the MBS proactively pushes some video chunks of the desired contents to mBSs. This is done to support vehicles that are currently covered and/or will be by each mBS. We formulate the dynamic video delivery scheme that adaptively determines 1) which content, 2) what quality and 3) how many chunks to be proactively delivered from the MBS to mBSs using Markov decision process (MDP). Since it is difficult for the MBS to track all the channel conditions and the network states have extensive dimensions, we adopt the deep deterministic policy gradient (DDPG) algorithm for the DRL-based video delivery scheme. This paper finally shows that the DRL agentlearns a streaming policy that pursues high average quality while limiting packet drops, avoiding playback stalls, reducing quality fluctuations and saving backhaul usage. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive fault-tolerant pseudo-proportional-integral-derivative sliding-mode control (pseudo-PID-SMC) scheme for a high-speed train (HST) subject to actuator faults, asymmetric nonlinear actuator saturation (ANAS), and integral quadratic constraints (IQCs). It is worth mentioning that a pseudo-PID-SMC surface is proposed in this paper and the scheme based on this surface does not require acceleration measurement. An adaptive saturation compensation system that makes no assumption, as in existing works where nonlinear functions are used to describe the unsaturated region of ANAS as known and strictly monotonous, is developed to attenuate the adverse effects of ANAS. For the saturation-free and ANAS cases, two adaptive fault-tolerant pseudo-PID-SMC schemes with no chattering, a simple structure, and inexpensive computation are developed to guarantee the exponential convergence of all signals in the closed-loop systems. Finally, simulation results based on a real train dynamic model are presented to show the proposed schemes’ effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

During the development or deployment of discrete event simulation systems, many sudden changes in requirements emerge. To embrace the changes rapidly and reduce development costs, attaching reusable, and black-boxed components to existing systems has been regarded as one of the most effective approaches because modifications at the code level are generally costly and risky. However, since this approach requires components that are an exact fit, it may not be easy to avoid the modifications at the source code level actually. Moreover, the required components may not exist. Hence, this paper applies black-box extensibility to simulation systems in order to avoid the modifications. This paper proposes adaptive discrete event simulation systems using event control models for extending and modifying semantics through event-based simulation interface. With the proposed work, the simulation events are modulated, deleted, and generated by the event-oriented control functions in the event control model to embrace the changes of requirements. It can substitute for the modifications at the code level and extend the existing behavioral semantics. As a result, the proposed work provides a new alternative step in the development process with reusable components to avoid modifications at the source code level. The new step will lead to rapid adaptations of existing simulation systems. To support the effectiveness of this approach, this paper will describe applicable examples based on our empirical studies. [ABSTRACT FROM AUTHOR]

This paper focuses on the sliding mode control (SMC) problem of interval type-2 (IT2) fuzzy systems subject to the unmeasurable state and cyberattacks. A key issue is how to design a state observer under the constraint that only the bounds of membership functions are known. To this end, this paper introduces two weighting factors to construct a new membership function. Besides, the concept of input-to-state stability (ISS) is utilized to deal with the residual term resulting from the cyberattacks and external disturbances. The sufficient condition is established such that the sliding mode dynamics and the estimated error dynamics are input-to-state stable. Furthermore, by online estimating the unknown parameters in upper bounds of cyberattacks and external disturbances, an adaptive sliding mode controller is synthesized such that the reachability of the prescribed sliding surface can be guaranteed and the effect of cyberattacks on the system performance can be effectively attenuated. Finally, the validity of the proposed method is illustrated by a mass–spring–damper system. [ABSTRACT FROM AUTHOR]

In this paper, we develop robust adaptive nonsingular terminal sliding mode (NTSM) control methodologies to solve the position and the velocity tracking control problem of the automatic train operation (ATO) system subject to unknown parameters, model uncertainty, and external disturbances. A novel nonlinear nonsingular terminal sliding manifold is proposed by considering that its parameter is unknown, which need to be estimated via a proposed non-negative adaptive law. And a corresponding novel robust adaptive NTSM control strategy, which enables the position tracking error and the velocity tracking error of the ATO system to converge to zero, and eliminates the singularity caused by terminal sliding mode controller, is proposed. Furthermore, unknown parameters of the sliding manifold and the ATO system can be estimated online by the proposed methodology. Simulation results show the effectiveness of the proposed methodologies in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we study how to achieve network utility maximization (NUM) in flow-level wireless networks, which can be characterized by the random arrivals and departures of flow-level traffic in such networks. Existing work in this area has mainly concentrated on finding the stability region in such networks while achieving the NUM objective. In this paper, we propose a fully distributed adaptive CSMA based flow-level cross-layer resource allocation algorithm (FLRA), which achieves the NUM goal while satisfying desired delay requirement via optimized link scheduling at the MAC layer and optimal flow-level admission control at the transport layer. Detailed algorithm design is presented. We analyze the convergence property of FLRA and its optimality in terms of network utility while meeting desired delay requirement. We further reveal the relationship among maximum network utility, desired delay, and number of active flows when a network reaches stability. We conduct extensive simulations and the results validate the effectiveness of our analytical results. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs. [ABSTRACT FROM AUTHOR]

Point cloud registration, which effectively coincides the source and target point clouds, is generally implemented by geometric metrics or feature metrics. In terms of resistance to noise and outliers, feature-metric registration has less error than the traditional point-to-point corresponding geometric metric, and point cloud reconstruction can generate and reveal more potential information during the recovery process, which can further optimize the registration process. In this paper, CFNet, a correspondence-free point cloud registration framework based on feature metrics and reconstruction metrics, is proposed to learn adaptive representations, with an emphasis on optimizing the network. Considering the correlations among the paired point clouds in the registration, a feature interaction module that can perceive and strengthen the information association between point clouds in multiple stages is proposed. To clarify the fact that rotation and translation are essentially uncorrelated, they are considered different solution spaces, and the interactive features are divided into two parts to produce a dual branch regression. In addition, CFNet with its comprehensive objectives estimates the transformation matrix between two input point clouds by minimizing multiple loss metrics. The extensive experiments conducted on both synthetic and real-world datasets show that our method outperforms the existing registration methods. [ABSTRACT FROM AUTHOR]

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.Article Highlights: Biosignals are generated by the human body without any intervention during physical, mental and social activities BAS automatically adapt to user needs by continuously interpreting their biosignals in real-time Examples of BAS are presented that have demonstrated in field experiments to improve engagement, attention awareness, and attention management [ABSTRACT FROM AUTHOR]

Multiband elastic optical network (MB-EON) technology can help to sustain exponential traffic growth in the optical backbone network. However, multiband operation creates high inter-channel stimulated Raman scattering, leading to a high nonlinear impairment (NLI) that may severely affect the optical signal-to-noise ratio (OSNR) of a lightpath. Additionally, the severity of NLI on the channel of interest depends upon the choice of allocated wavelength. Hence, appropriate channel allocation may cumulatively lead to a higher network capacity. This paper proposes a quality-aware resource provisioning scheme in the context of MB-EON that selectively chooses the available channels from different bands in order to achieve the maximum network capacity in the long run. A deep neural network-assisted quality of transmission estimator is considered to estimate the OSNR of a lightpath with accuracy of 99.65% and 0.012 dB variance in estimation error. The performance of our algorithm in the proposed scheme, namely, optical signal-to-noise ratio adaptive first–last-fit (OA-FLF), is analyzed for two geographically diverse networks, namely, BT-UK and the 24-node USA network, in terms of traffic admissibility, quality of established lightpaths, and contiguous aligned available slot ratio (CAASR), and compared with four state-of-the-art baseline algorithms: first fit, last fit, route adaptive first–last-fit, and distance adaptive first–last-fit. Numerical results indicate that the proposed algorithm outperforms all of the baseline algorithms in terms of traffic admissibility. Reported results show that, compared to the baseline algorithms, consideration of the effect of NLI before resource allocation in the OA-FLF algorithm can provide a maximum gain of nearly 30% in terms of traffic admissibility for smaller networks such as BT-UK, whereas, for longer geography such as the 24-node USA network, this traffic admissibility gain becomes close to 61% till 1% blocking. [ABSTRACT FROM AUTHOR]

As wind power generation transits from centralized development mode to decentralized on-site consumption mode, microgrid (MG) can provide an efficient solution for wind power integration into the distribution network. However, the high-penetration wind power MG is the typical weak power grid system. The traditional wind turbine generator (WTG) participates in system frequency regulation through grid-following current source, which relies on the phase-locked loop for voltage phase synchronization and is unable to provide strong frequency support in weak power grid conditions. To fill this gap, this paper presents a decentralized grid-forming control strategy of high-penetration wind power MG. A wind power adaptive dynamic droop mechanism considering wind energy characteristics and rotor speed dynamic is proposed, cooperating with the implementation of wind maximum power point tracking (MPPT) for economical operation. A detailed small-signal model for voltage source wind power-based system considering electromechanical transients and adaptive droop mechanism is developed. The dominant modes are figured out and the critical control parameters are established and optimized. The proposed wind power adaptive droop mechanism can effectively provide frequency regulation and robust control performance. Theoretical analysis, time-domain simulation results and hardware-in-the-loop experiments under various scenarios verify the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

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 $\alpha $ 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Few-mode based uniform beam shaping exploiting the use of different spatial modes has been recently shown to improve the system resistance against pointing errors and link outage in indoor optical wireless communications. However, the inter-symbol interference arising from transmission of different spatial modes following different paths and mode crosstalk during the transmission becomes another important challenge. In this paper, we theoretically investigate and experimentally compare the performance of two popular adaptive equalization methods, namely least mean square and recursive least-square in both multi-equalizer and single-equalizer configurations, to improve the performance of indoor optical wireless communication system employing few-mode based uniform beam shaping. For both equalization methods, the multi-equalizer scheme achieves better equalization performance and higher convergence speed than the single-equalizer scheme for a similar cost of required computational complexity for each iteration. Moreover, recursive least-square method has a higher convergence speed than least mean square equalization while least mean square method has a lower computational complexity than recursive least-square method under the same desired equalization performance. More importantly, for a fixed reference BER threshold (KP4 FEC limit), the least mean square based single-equalizer scheme with a step size of 0.0035 and a tap number of 61 has the lowest computational complexity among the four types of equalization schemes. This kind of equalizer with relatively low computational complexity can provide guidelines for selecting an applicable adaptive equalization scheme in optical wireless communications with few-mode based beam shaping based on the comprehensive requirements of BER performance and computational complexity. [ABSTRACT FROM AUTHOR]

This paper investigates event-triggered control for saturated switched systems with switching signals subject to dual-terminal denial-of-service (DoS) attacks. The original switching signals are sampled and transmitted to sub-system and sub-controller through networks, respectively. However, the openness of network may cause switching signal transmission to be affected by possible DoS attacks. Moreover, the existing dual-terminal mismatched network delays and attacks could lead to asynchronous phenomenon. Considering the impacts of the attacked switching signals and delays, a time-delay closed-loop switched system is developed. Subsequently, by utilizing piecewise Lyapunov functional technique, various cases caused by DoS attacks and different delays are analyzed. Then, novel conditions are derived to ensure the stability of closed-loop switched system, and sufficient conditions for the desired controller gains and event-triggering parameters are presented. Finally, simulation examples are given to verify the effectiveness of the proposed method. The simulation results show that in the case of single-terminal and dual-terminal attacks, switched systems both have good anti-attack characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a reference model adaptive fuzzy Hermite neural-network controller (RAFHNC) for position tracking of a modeless magnetic bearing system (MBS). Magnetic bearings evolved from traditional bearings and can eliminate the shortcomings of traditional bearings by using electromagnetic forces to reduce friction during use. The MBS is nonlinear, for which system parameters are difficult to obtain accurately. This paper proposes the RAFHNC for precision position tracking in an MBS. The RAFHNC has three important characteristics. First, the RAFHNC does not require the MBS parameters to track a reference model accurately. Second, the RAFHNC uses Hermite functions to replace the Gaussian function, this reduces training time in the membership level. Third, the RAFHNC uses an online self-tuning fuzzy Hermite neural network estimator to resolve lumped uncertainty in the MBS. The fuzzy Hermite neural network incorporates expert knowledge, reducing training time on controller parameters. Finally, the Lyapunov stability and system adaptive laws are used to guarantee convergence. The output responses of the RAFHNC and adaptive reference model sliding-mode controller (ARSC) methods were compared. The RAFHNC method exhibited improved output responses compared with the ARSC method in terms of root mean squared error (RMSE) index. It can reduce problems associated with external loads and system lumped uncertainty in MBS. [ABSTRACT FROM AUTHOR]

This paper presents a novel rectifier array for a wide dynamic range of RF-dc conversion. The rectifier array consists of multiple rectifier cells optimized for operation at different power levels and uses an adaptive power distribution method to divide the RF input power among these rectifier cells according to the RF power levels. The adaptive power distribution takes advantage of the power-dependent input impedances of the rectifier cells and is implemented with passive devices. This method both improves the rectification efficiency and minimizes the impedance mismatch as the input power level varies, and therefore, significantly enhances the dynamic range. The rectifier array can be designed using either the same type or different types of rectifying devices and can be expanded by including more cells in the network to further increase the dynamic range. In this paper, three different implementations of such rectifier array circuits are described, including a two-diode rectifier array designed with the same type of diodes, a two-diode rectifier array designed with different types of diodes, and a three-diode rectifier array with different types of diodes. The performances of these circuits are validated with measurement and compared with each other. Significant enhancement of dynamic range over conventional rectifiers is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, the suppressing problem of the cable’s vibration is studied for a suspension cable system of helicopter with disturbances. The Hamilton’s principle is applied to obtain a distributed parameter system of the suspension cable system which includes one partial differential equation and two ordinary differential equations. Two nonlinear disturbance observers are developed to compensate the effect of boundary disturbances. The auxiliary systems are designed to eliminate the effects of input nonlinearities. Based on the designed disturbance observer and auxiliary systems, two boundary controllers are designed at the top and bottom boundaries of the suspension cable. Under the proposed controllers, the amplitude of oscillation is proven to be uniformly ultimately bounded and converges to a small neighborhood of zero by selecting proper design parameters of the antidisturbance control scheme for the suspension cable system. Meanwhile, some sufficient conditions are provided to guarantee the effectiveness of the developed control law. Finally, simulation results show that the controllers developed in this paper work well in handling disturbances for the suspension cable system of helicopter. [ABSTRACT FROM AUTHOR]