Dynamic Adaptive System Research Articles

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

An adaptive dynamical system model for development of schizophrenia: Epigenetics and false memories

This paper introduces a fifth-order adaptive dynamical system model represented as a self-modelling network model to understand the role of epigenetic factors in developing schizophrenia, particularly about the occurrence of false memories. Schizophrenia, affecting a significant portion of the global population, presents complex challenges due to its symptoms across cognitive, emotional, and social domains. Central to this paper is the exploration of epigenetic influences on the development of schizophrenia, with a focus on how these epigenetic factors contribute to the generation of false memories, a notable symptom of the disorder. The model assumes that early adverse experiences significantly affect hippocampal development, which is crucial for memory processes. By employing a fifth-order adaptive dynamical system model represented as a self-modelling network, this paper examines the intricate dynamics between genetic predispositions, environmental factors, and epigenetic modifications in schizophrenia. The model’s sophistication allows for a nuanced understanding of the gene-environment interactions, particularly highlighting the role of early-life trauma in shaping lifelong cognitive outcomes. This approach offers a comprehensive framework for analyzing the multifaceted nature of schizophrenia, contributing to a deeper understanding of its etiology and progression.

Reinforcement Learning-Based Adaptive Optimal Control Model for Signal Light in Intelligent Transportation Systems

Day-to-day mobility among the population has increased with economic growth. Smart cities are renovated with advanced technologies to admire modern life in which intelligent transportation becomes highly focused. Because the traffic signal control systems are fixed at a constant time. They split the traffic signal into predetermined intervals and function inefficiently; they result in long wait times, waste fuel and increased carbon emissions. This research study introduces a novel technique for traffic light management to reduce the uncertainties in the system. A dynamic and intelligent traffic light adaptive optimal management system (DITLAOCS) is implemented in this research. It does this by modifying the traffic signal duration in run time and using real-time traffic data as input. Furthermore, the proposed DITLAOCS executes based on three modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness mode (FM), all vehicles are prioritized equally, while vehicles in different categories receive varying priority levels. Emergency vehicles, on the other hand, receive the highest priority. Furthermore, a fuzzy inference method based on traffic data is shown to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement learning to switch traffic lights in three different phases (red, green and yellow). We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms on several performance measures.

Engineering Supramolecular Hydrogels via Reversible Photoswitching of Cucurbit[8]uril-Spiropyran Complexation Stoichiometry.

The integration of photoswitchable supramolecular units into hydrogels allows for spatiotemporal control over their nanoscale topological network and macroscale properties using light. Nevertheless, the current availability of photoswitchable supramolecular interactions for the development of such materials remains limited. Here, the molecular design of a novel photoswitchable cucurbit[8]uril-spiropyran host-guest complex exhibiting fast and reversible switching of binding ratios between 1:2 and 1:1 is reported. Photoswitchable complexation stoichiometries are rationally exploited as (de)crosslinking units in multiple polymers for the design of supramolecular hydrogels displaying highly dynamic and switchable features that are spatiotemporally controlled by light. The hydrogels exhibit rapid reversible mechanical softening-hardening upon alternating irradiation with blue and UV light, which is used to significantly accelerate and improve the efficiency of self-healing and shape-remolding of hydrogels. Furthermore, spiropyran endows such materials with unique reversible photochromic properties for reproducible patterning/erasing and information storage. Using a dual-light-assisted extrusion process, meter-scale hydrogel fibers with enhanced structural integrity and photoswitchable ionic conductivity are constructed and woven into various slidable knots and fluorescent shapes. This work represents an innovative molecular design strategy for advancing the development of spatiotemporally engineered supramolecular hydrogels using light and opens avenues for their prospective applications in dynamic materials and adaptive systems.

The coevolution of corporate capabilities in intellectual property management and technological innovation

PurposeIn this study, we investigate how capability in managing intellectual property may be treated as a type of “dynamic capability,” and we seek to understand how, when it is linked to the new technology development capability of a complex technological organization, these two types of dynamic capabilities may coevolve.Design/methodology/approachWe conducted a longitudinal empirical case study of Airbus, incorporating an abductive research methodology that required investigating theory and empirical data concurrently and iteratively. The data, ranging over a period of two decades from 2000 to 2021, was collected from four different sources, including interviews, internal company documents, publicly available information and patent data.FindingsOur main findings are that the capabilities and roles of the Intellectual Property function in Airbus and their interaction with the company's Technology function have indeed influenced the overall innovation strategy of the organization, and that three coevolutionary phases may be identified in the interactive development of the two functions.Research limitations/implicationsOur investigation into how new technology development and intellectual property (IP) management capabilities coevolve within complex technological organizations, exemplified by Airbus, provides significant theoretical and practical implications. Theoretically, it enhances understanding of capability co-development dynamics in complex organizations, particularly in strategic IP management. Practically, it suggests aligning IP strategy with overall corporate objectives and optimizing organizational structures to promote collaboration and efficiency across IP and technology teams. This alignment may foster innovation, maximize the value of intellectual assets and strengthen collaborations, positioning organizations for long-term success in competitive landscapes.Originality/valueThis study makes a fresh contribution to the innovation studies literature by showing how if intellectual property management is treated as a core function of a complex technological organization – rather than simply as a vehicle for protecting new inventions and products after the fact or simply as a constituent part of the organization's legal function – it may contribute proactively to the organization's technological innovation performance. We also address the current gap in the academic literature for a clear understanding of the processes by which different function-specific dynamic capabilities may coevolve in a complex organization operating as part of a dynamic and complex adaptive system.

Nonlinear disturbance observer-based finite-time prescribed performance control for MSVs with input saturation

ABSTRACT A finite-time prescribed performance control scheme based on an improved nonlinear disturbance observer is proposed for tracking the trajectory of marine surface vessels (MSVs) under the disturbance of the marine environment. The objective of this paper is to design a nonlinear disturbance observer based on state estimation error feedback that accurately estimates and compensates for the unknown disturbance problem. An adaptive law is applied to estimate the upper bound of its error, removing the requirement of a priori knowledge of the unknown lumped disturbance upper bound. This paper also introduces a non-logarithmic performance function in order to efficiently solve the problem of considering both transient and steady state MSVs performance simultaneously. In addition, the adaptive dynamic system is introduced to overcome the adverse effects caused by nonlinear saturation factors for the input-constrained problem. With this control strategy, the system output error converges to a pre-defined region within a finite amount of time and meets the prescribed performance requirements. Finally, simulation and comparison verify the control scheme's effectiveness and superiority.

Crafting An AI-Powered Adaptive E-Learning Framework: Based on Kolb's Learning Style

In the contemporary educational realm, the convergence of pedagogical advancements with machine learning presents a profound opportunity for transformation. This in-depth exploration delves into the complexities of constructing an adaptive e-learning platform, seamlessly integrating Kolb's learning preferences with state-of-the-art machine learning techniques. At the core of this initiative lies the certification module, meticulously scrutinized to bolster its effectiveness in acknowledging student accomplishments. With a focus on personalized education driven by machine learning for error analysis, learner profiling, and content adaptation, this amalgamation reshapes the educational landscape, offering bespoke and dynamic learning journeys. By proposing an intelligent and dynamic adaptive learning system, this study addresses the constraints of passive and one-size-fits-all platforms, aiming to discern and furnish personalized learning environments tailored to the unique needs of individual learners within the hybrid teaching paradigm.

Dynamic Object Recognition in Video Streams

Dynamic Object Recognition in Video Streams, essen- tial for advancements in video surveillance, autonomous systems, and robotics. Focusing on challenging scenarios like crowded- ness and rapid scene changes, the study introduces novel ob- ject detection and tracking techniques. Object segmentation in crowded scenes and trajectory analysis for simultaneous tracking in dense environments are explored, considering both immediate interactions and long-term movements. Adaptive object recognition algorithms, integrating event detection for rapid scene transitions, are investigated for their responsiveness. Additionally, the study incorporates advanced deep learning frameworks like Temporal Convolutional Networks (TCNs) and one-shot learning methods, enhancing the system’s adaptability to dynamic environments. Real- time processing efficiency is addressed through hardware accelera- tion and online learning strategies, contributing to the development of more efficient and adaptive dynamic object recognition systems for crowded and dynamic scenes. Index Terms—Computer Vision, Object recognition, Video, TCN, Robotics

Enhancing public sector enterprise risk management through interactive information processing.

Federal agencies are increasingly expected to adopt enterprise risk management (ERM). However, public sector adoption of ERM has typically focused on the economic efficiency of tax-financed activities based on control-based practices. This reflects an emphasis on quantifiable concerns that invariably directs attention to risk, that (by definition) relates to identifiable and measurable events, thereby downplaying uncertain and unknown aspects of public exposures. This is a potentially serious shortcoming as government entities often act as society's risk managers of last resort. When extreme events happen what were previously considered private matters can quickly turn into public obligations. Hence, there is a need for proactive assessments of the evolving public risk landscape to discern unpredictable-even unknowable-developments. The article reviews recent empirical studies on public risk management practices, effects of digitalization in public sector institutions, current strategic management research, and insights uncovered from a recent study of risk management practices in federal agencies. On this basis, the article explains how the ability to generate value from ERM can be enhanced when it intertwines with local responsive initiatives and central strategic risk analyses. It can form a dynamic adaptive risk management process where insights from dispersed actors inform updated risk analyses based on local autonomy and open exchange of information. This approach builds on specific structural features embedded in culture-driven aspirations to generate collaborative solutions. Its functional mode is an interactive control system with open discussions across levels and functions in contrast to conventional diagnostic controls that monitor predetermined key performance indicators (KPIs) and key risk indicators (KRIs). Backed by theoretical rationales and empirical research evidence, it is found that applications of ERM frameworks can produce positive results but is unable to deal with a public risk landscape characterized by uncertain unpredictable conditions with potentially extreme outcome effects. It is shown how interactive exchange of fast local insights and slow integrated strategic risk analyses supported by digitized data processing can form a dynamic adaptive system that enable public sector institutions to deal with emergent high-scale exposures. It is explained how the requirement for conducive organizational structures and supportive values require a new strategic risk leadership approach, which is contrasted to observed practices in federal agencies that are constrained by prevailing public governance requirements. The need to deal with uncertainty and unknown conditions demands a cognitive shift in current thinking from a primary focus on risk to also appraise complexity and prepare for the unexpected where data-driven methods can uncover emergent exposures through dynamic information processing. This requires strategic risk leaders that recognize the significance of complex public exposures with many unknowns and a willingness to facilitate digitalized information processing rooted in a collaborative organizational climate. If handled properly, adoption of ERM in public risk management can consider emergent dimensions in complex public exposures applying interactive information processing as a dynamic adaptive risk management approach incorporating digitized methods to solicit collective intelligence for strategic risk updating.

Higher-order adaptive dynamical system modeling of the role of epigenetics in anxiety disorders

In this paper, a fifth-order adaptive self-modelling network model is introduced to describe epigenetic involvement in the development of anxiety disorders and its regulation by a possible epigenetics-based therapeutic method. Multiple orders of adaptivity are used in the model to depict the development process, where a higher pathway of any order of adaptivity adapts characteristics of pathways in lower orders and acts as a form of control. These orders of adaptivity and their interlevel interaction were modelled as a higher-order adaptive dynamical system according to the self-modelling network modelling principle. The model was inspired by the structure of the relevant human biological and neurological processes. In addition to modelling the development of an anxiety disorder, also the possibility of an epigenetics-based therapy is suggested and computationally analyzed in this paper.

Perspectives on adaptive dynamical systems.

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding: A Second-Order Multi-Adaptive Neural Agent Model.

When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behavior of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication-enabling conditions. Moreover, in this paper, mathematical analysis is also addressed for adaptive network models and their positioning within the landscape of adaptive dynamical systems. The first type of analysis addressed shows that any smooth adaptive dynamical system has a canonical representation by a self-modeling network. This implies theoretically that the self-modeling network format is widely applicable, which also has been found in many practical applications using this approach. Furthermore, stationary point and equilibrium analysis was addressed and applied to the introduced self-modeling network model. It was used to obtain verification of the model providing evidence that the implemented model is correct with respect to its design specifications.

Adaptive self-triggered control-based cooperative output regulation of heterogeneous multi-agent systems under sensor and actuator attack

The cooperative output regulation problem for heterogeneous multi-agent systems under sensor and actuator attacks with an adaptive self-triggering strategy is considered in this article. The attack considered is the false data injection attack. To resolve the system, a dynamic internal model is introduced for each agent, and a distributed adaptive self-triggering control scheme is proposed. The results show that intermittent communication is possible for all agents using the proposed control mechanism and no agent shows Zeno behavior. The adaptive controller is used to guarantee that the regulated output for the heterogeneous multi-agent systemss is achieved with cooperative uniform ultimate boundedness under both actuator and sensor attacks. Given the whole state of information is unavailable, the adaptive dynamic system is designed based on receivable compromised or uncompromised output information. The effectiveness of the given control scheme is illustrated by two examples.

Image denoising using adaptive bi-dimensional stochastic resonance system

Using stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.

Adaptive Dynamic Cutting Force Control Analysis for Peripheral Milling

The implementation of condition monitoring tools can improve plant availability and lower downtime costs in general. A reliable adaptive control system can prevent machine downtime or undesired situations such as chatter vibration and excessive tool wear, permitting the best utilization of a tool's life. This study used dynamic force analysis to create an adaptive dynamic control system for Computer Numerical Control (CNC) milling to adjust a controlled system for signals from offline measurements that will be processed and supplied back to the machine tool controller to correct cutting parameters. This paper describes a better adaptive control system for peripheral milling with helical end mills based on a dynamic cutting force model. This theoretical model is based on the oblique cutting principle and takes into account the effects of the size of undeformed chip thickness and the effective rake angle. Simulation results showed that the enhanced dynamic cutting-force model accurately predicted cutting forces in peripheral milling.

Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

Nowadays, teaching involves more and more the usage of digital tools to accompany face-to-face learning. Such a system is being referred to as hybrid teaching. These digital tools include mainly distance learning platforms based on LMS (Learning management systems). LMS help to create on-line digital spaces to store and organize teaching material while providing a pedagogical learning process to connect educational communities. Despite the many forms of content that can be implemented on these platforms, from traditional file-based to interactive and animated content, these systems remain both passive and generic. They lack the ability to adapt to the learner in terms of learning skills, preferences, languages, intellectual abilities, learning patterns and rhythms. This paper proposes a design and modeling of an intelligent and dynamic adaptive learning system based on artificial intelligence with the main objective of identifying and providing personalized learning environments adapted to the learner needs.

A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment

The use of the Internet of Things (IoT) in healthcare is increasing significantly, bringing high-quality health services, but it still generates massive data with massive energy consumption. Due to the limited resources of fog servers and their impact on limiting the time needed for health data analysis tasks, the need to handle this problem in a fast way has become a necessity. To address this issue, many optimization and IoT-based approaches have been proposed. In this paper, a dynamic and adaptive healthcare service deployment controller using hybrid bio-inspired multi-agents is proposed. This method offers optimal energy costs and maintains the highest possible performance for fog cloud computing. At first, IGWO (Improved Grey Wolf Optimization) is used to initialize the deployment process using the nearest available fog servers. Then, an efficient energy-saving task deployment was achieved through Particle Swarm Optimization (PSO) to reduce energy consumption, increase rewards across multiple fog servers, and improve task deployment. Finally, to ensure continuous control of underloaded and overloaded servers, the neighborhood multi-agent coordination model is developed to manage healthcare services between the fog servers. The developed approach is implemented in the iFogSim simulator and various evaluation metrics are used to evaluate the effectiveness of the suggested approach. The simulation outcome proved that the suggested technique provides has better performance than other existing approaches.

Energy financial risk early warning model based on Bayesian network

Oil is a global, non-renewable energy source, which plays a pivotal role in the development of the global economy and the strategic reserve system. With the expansion of crude oil futures trading scale, crude oil is no longer a pure energy commodity, but is increasingly controlled by the financial market. Based on Bayesian network, this paper studies and analyzes the financial risks of non renewable energy such as oil. Finally, the prediction models of BP neural network and Bayesian network are given. Neural network is a mathematical model based on neurons, a nonlinear adaptive dynamic system formed by the connection between neurons, and based on these two models, discusses the financial risk early warning method of petroleum in non-renewable energy. The test results of this paper show that the Bayesian network and BP neural network are used for early warning analysis based on the comprehensive oil financial risk data from 1987 to 2015. The prediction results of neural network show that there is 65% correlation between the two. From the fitting results of the prediction results and real data, the fitting effect of Bayesian network is better than that of BP neural network, and the fitting success rate is as high as 80%. The Bayesian network prediction model is analyzed by using the autoregressive movement of the model itself and the time series measurement results of the data itself. The results show that the prediction model is highly reliable. Therefore, it is very necessary to adopt Bayesian network prediction model in the risk early warning of petroleum finance.

Adaptive dynamical systems modelling of transformational organizational change with focus on organizational culture and organizational learning

Transformative Organizational Change becomes more and more significant both practically and academically, especially in the context of organizational culture and learning. However computational modeling and a formalization of organizational change and learning processes are still largely unexplored. This paper aims to provide an adaptive network model of transformative organizational change and translate a selection of organizational learning and change processes into computationally modelled processes. Additionally, it sets out to connect the dynamic systems view of organizations to self-modelling network models. The creation of the model and the implemented mechanisms of organizational processes are based on extrapolations of an extensive literature study and grounded in related work in this field, and then applied to a specified hospital-related case scenario in the context of safety culture. The model was evaluated by running several simulations and variations thereof. The results of these were investigated by qualitative analysis and comparison to expected emergent behaviour based on related available academic literature. The simulations performed confirmed the occurrence of an organizational transformational change towards a constant learning culture by offering repeated and effective learning and changes to organizational processes. Observations about various interplays and effects of the mechanism have been made, and they exposed that acceptance of mistakes as a part of learning culture facilitates transformational change and may foster sustainable change in the long run. Further, the model confirmed that the self-modelling network model approach applies to a dynamic systems view of organizations and a systems perspective of organizational change. The created model offers the basis for the further creation of self-modelling network models within the field of transformative organizational change and the translated mechanisms of this model can further be extracted and reused in a forthcoming academic exploration of this field.

An adaptive memristive dynamical system to nonsmooth optimization problems

An adaptive memristive dynamical system to nonsmooth optimization problems