Adaptive Speed Research Articles

The Effect of Institutional Context, Distance, and Routine Complexity on the Transfer of Routines Across Borders

Transferring routines and practices within Multinational Enterprises (MNEs) is a prevalent and, at times, tedious process. New institutional context imposes forces for local adaptation, which disrupts the stability of the routine - stability due to the interplay between ostensive (the codified and/or abstract version of the routine) and performative (the practiced version) aspects of the routine. Change in routines is then needed to ensure local adaptation and the routine reaches a new state of stability. Our study focuses on the micro-processes of the transfer process and the pertinent states of stability and change in the focal routines and practices. We use simulation experiments and examine the effect of intuitional pressures for local adaptation. The local adaptation speed of ostensive routine is shown to matter. Slow enactment of the revised version of the ostensive routine (i.e. implementation of the locally adapted routine) at the subsidiary level can disrupt the stability of the routine.

Enhanced Machine Learning Framework for Autonomous Depression Detection Using Modwave Cepstral Fusion and Stochastic Embedding

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To address these limitations, this study proposes a novel ModWave Cepstral Fusion and Stochastic Embedding Framework for depression prediction. Then, the Gain Modulated Wavelet Technique removes background noise and normalises audio signals. Difficulties with generalisation, which results in a lack of interpretability, hinder extracting relevant characteristics from speech. To address these issues, an Auto Cepstral Fusion extracts relevant features from speech, capturing temporal and spectral characteristics caused by background voice. Feature selection becomes imperative when choosing relevant features for classification. Selecting irrelevant features can result in overfitting, the curse of dimensionality, and less robustness to noise. Hence, the Principal Stochastic Embedding technique handles high-dimensional data, minimising noise influence and dimensionality. Furthermore, the XGBoost classifier differentiates between depressed and non-depressed individuals. As a result, the proposed method uses the DAIC-WOZ dataset from USC for detecting depressions, achieving an accuracy of 97.02%, precision of 97.02%, recall of 97.02%, F1-score of 97.02%, RMSE of 2.00, and MAE of 0.9, making it a promising tool for autonomous depression detection.

Development and safety evaluation of an adaptive personalized speed guidance system for on-ramp merging in highway service areas

As autonomous driving and Vehicle-to-Everything (V2X) technologies evolve, the efficiency and safety of ramp merging in the highway service areas have become increasing critical. This study introduces a closed-loop feedback speed guidance system that accommodates individual driving styles, aiming to optimize merging behaviour, reduce traffic accidents, and enhance total traffic efficiency. The system dynamically adjusts the merging vehicle speeds by continuously monitoring their speed and location with variable steps to promote smoother merging. Moreover, this research also involves collecting naturalistic driving data from ramp merging scenarios, using the K-means clustering and point estimation method to recognize and analyse driving style characteristics, and integrating these styles into the developed closed-loop feedback speed guidance system. This approach results in personalized speed guidance curves tailored to different driving styles, facilitating more efficient mering. Additionally, the study conducts a Safety of the Intended Functionality (SOTIF) evaluation of this system using the System-Theoretic Process Analysis (STPA) method, which helps identify potential security risks and develop appropriate mitigation strategies to ensure the system’s safe and stable operation. The simulation results confirm that this innovative dynamic speed guidance system substantially improves traffic safety and efficiency in ramp merging areas.

Just in Time Port Call Optimisation: Preliminary Regulatory Compliance Evaluation and Environmental Performance Assessment

Abstract Just in Time (JIT) arrival, as an operational measure towards shipping decarbonisation, promises increased energy savings and reduced emissions. Integrated digital systems targeting the optimisation of the port call process allow vessels to adapt their speed to avoid the “hurry-up-and-wait” syndrome, and thus, reduce their fuel consumption at a certain extend. However, there are a lot of concerns and challenges ahead for a wide adoption of such a solution, that are either related to the regulatory and standardisation frameworks, or to the realisation of the actual benefits of a JIT optimisation system. Thus, this paper attempts to shed light on some of these challenges, by focusing on the maritime regulatory framework around JIT operations and on emission reduction potential from speed adaptation. Results provide a baseline for regulatory compliance evaluation for such a system, and a preliminary environmental performance assessment from JIT arrival applied on an actual ship case. These insights and early results will be further explored within the EU-funded MISSION project towards a full appreciation of the JIT concept benefits and regulatory compliance, which will lead to its wide implementation.

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

The Determinants of Maximal Speed Adaptation During Preseason in Subelite Female Australian Rules Footballers.

Maximal speed is an important physical-fitness attribute for female Australian footballers. The effects of sprint training, maximal strength, and technical training have been reported in laboratory studies. However, no study has determined the combined effect and relative contribution of these training modalities on maximal speed adaptation in situ. Therefore, the aim of this study was to determine the training factors affecting maximal speed adaptation during a preseason in subelite female Australian Rules footballers. Maximal speed during field training, predicted 1-repetition maximum (1RM) for box squat and hip thrust, and sprint biomechanics were assessed during early and late preseason (∼9wk apart) in 15 female subelite Australian Rules Football players (age 20 [3]y). On-field training volume and intensity (total distance, high-speed running, very-high-speed running, and maximal speed) were determined using a Global Positioning System. A multivariate regression model was used to determine the factors associated with changes in maximal speed across the preseason. The preseason training program had a small effect on maximal speed and large to very large effects on strength and sprint biomechanics. The multivariate regression with the greatest fit (P < .001, R2 = .939) included change in estimated 1RM box squat (β = -0.63), total distance per week (β = -0.55), and change in hip projection (β = 0.16) as factors. Multivariate regression of biomechanical factors (P = .044, R2 = .717) and maximal strength factors (P = .003, R2 = .676) were also significant. The development of maximal speed across a preseason is dependent on (1)total distance per week, (2)maximal strength adaptation, and (3)sprint technique adaptation in female subelite Australian rules football players.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Observer-based robust servomechanism control of a three-phase induction motor drive

Induction motors (IMs) are widely used in industrial applications, but harmonic distortion in IM drives can lead to inefficiencies and reduced lifespan. Traditional control strategies and filtering methods have limitations in mitigating lower-order harmonics, particularly the 5th harmonic, which can generate reverse torque and increase overheating risk. Existing solutions often struggle to fully address these issues, especially in high-power applications with dynamic loading conditions. This paper proposes a control strategy to reduce voltage and current harmonics in a Z-source inverter (ZSI)-fed IM drive. The system incorporates an observer-based output feedback robust servomechanism controller, designed using Linear Quadratic Regulator (LQR) and Linear Matrix Inequality (LMI) techniques. It ensures closed-loop stability, precise tracking regulation, and effective disturbance rejection, while also featuring a speed adaptation mechanism for sensorless operation. Simulation results demonstrate significant harmonic mitigation, with the controller achieving 1.01% lower voltage THD and 1.03% lower current THD compared to conventional approaches. This improvement aligns with industry standards and outperforms several existing techniques. The proposed method contributes a comprehensive solution for harmonic mitigation in high-power IM drives, applicable to various industrial applications requiring high-quality power and precise speed control.

Robust Adaptive Speed Control for the DC Motor Based on a Modified MRAC

Robust Adaptive Speed Control for the DC Motor Based on a Modified MRAC

Attentional mediators of the effect of interest on comprehension

BackgroundWhile text reading is still a preferred form of learning, topic interest can be considered an important predictor of reading comprehension. However, the nature of the relationship between interest and comprehension is not yet fully understood. AimsThe present study investigates whether topic interest is related to comprehension when measured in advance and whether two indicators of attention (i.e., mind wandering and cognitive coupling) mediate this relation. SampleParticipants were 238 university students. MethodsParticipants rated their interest in three different topics. One week later, they read one text on each of these topics. The texts’ levels of difficulty were manipulated to change during reading. Measured variables during reading were the individual adaptation of the reading speed to the text difficulty (cognitive coupling) and the frequency of mind wandering. In addition, comprehension was measured after reading each text. ResultsPreliminary assessed topic interest was found to be indirectly but not directly associated with reading comprehension. Specifically, topic interest negatively predicted mind wandering during reading, while mind wandering was, in turn, negatively associated with reading comprehension. In addition, partial evidence was found for the mediating role of cognitive coupling. ConclusionsThe present study adds to the research by showing that topic interest can predict future mind wandering and one type of cognitive coupling. Because mind wandering was, in turn, negatively related to reading comprehension, the study's findings further suggest that topic interest should be encouraged to minimize harmful mind wandering, promote adaption to text properties, and thus improve comprehension.

Design of adaptive speed controllers for matrix‐converter‐based interior permanent magnet synchronous motor drive systems

AbstractInterior permanent magnet synchronous motors (IPMSMs) which are driven by matrix converters have many advantages, including one‐stage power conversion, high efficiency, bidirectional power transfer, near unity input power factor, and low input current harmonics. However, the virtual DC‐link voltages of matrix‐converters vary from 0.866 to and the current commutations of matrix‐converters create serious three‐phase stator harmonic currents as well. Moreover, matrix‐converter‐based IPMSM drive systems, in general, have very large external loads, which require advanced control methods. To solve these problems, two adaptive controllers, including a model reference adaptive controller and a self‐tuning controller, are investigated in this article for matrix‐converter PMSM drive systems. From the experimental results, the proposed two adaptive speed controllers provide excellent dynamic responses, which are superior to PI speed controllers, including faster transient responses, better tracking abilities, and greater robustness. As a result, this proposed matrix‐converter‐based IPMSM drive system is very suitable in industrial applications.

Paradoxes in the coevolution of contagions and institutions.

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions.

Learning Speed Adaptation for Flight in Clutter

Learning Speed Adaptation for Flight in Clutter

A speed coordination control method based on D-S evidence synthesis theory

An adaptive speed coordination control method based on Dempster–Shafer (D-S) evidence synthesis theory is proposed to achieve the speed coordination of the slave manipulator under the condition of a large transmission delay in space teleoperation. First, the D-S evidence synthesis theory is applied to transform the speed coordination rule method. The model for predicting the manipulator’s future state is given to gain confidence in each state. Subsequently, performance comparison experiments of D-S evidence synthesis control theory, cascade control, fuzzy control, and adaptive fuzzy control are completed on the 3-degree-of-freedom (3-DOF) manipulator simulation platform. Finally, according to the experimental results, the accuracy of D-S evidence synthesis theory is 7.49% better than cascade control, 16.84% better than fuzzy control, and 28.45% better than adaptive fuzzy control. The adaptability of D-S evidence synthesis theory is generally superior to cascade control, slightly inferior to fuzzy control and inferior to adaptive fuzzy control.

Adaptative machine vision with microsecond-level accurate perception beyond human retina

Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as feedforward inhibition in bionic two-dimensional (2D) transistor is proposed for fast and high-frequency visual adaptation behavior with microsecond-level accurate perception, the adaptation speed is over 104 times faster than that of human retina and reported bionic sensors. As light intensity changes, the bionic transistor spontaneously switches between avalanche and photoconductive effect, varying responsivity in both magnitude and sign (from 7.6 × 104 to −1 × 103 A/W), thereby achieving ultra-fast scotopic and photopic adaptation process of 108 and 268 μs, respectively. By further combining convolutional neural networks with avalanche-tuned bionic transistor, an adaptative machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both dim and bright conditions.

Design and development of a feedback system for automatic treadmill speed adaptation

Treadmills with automatic speed adjustment offer a unique advantage: they can mimic outdoor conditions for specific training or testing protocols. This versatility makes them highly applicable in both sports and rehabilitation settings. This study presents a novel framework based on a feedback control loop system, conceived to control a treadmill belt speed using a non-invasive and low-cost sensor (Microsoft Kinect) to detect the users’ position and avoid object obstruction issues. The speed of the treadmill belt is regulated according to the user’s speed, by means of a proportional-integrative-derivative (PID) controller and a parabolic gain function. By tuning the gain function parameters, the user can customize the response of the treadmill. Position data collected during exercise using the Microsoft Kinect sensor was compared with that collected with a stereophotogrammetric motion capture system, showing promising results in terms of accuracy in position assessment. The comparison highlighted a 0.9 degree of correlation between the two systems during the running and cross-country indoor skiing tests performed. In addition, upon considering the relationship between the differences and averages of the two measures, no systematic bias was identified. The system proved to be functional for running and cross-country skiing, and it can therefore re-create similar typical characteristics of outdoor environments (e.g., speed and slope) thanks to the non-invasive user’s position detection and the customizable gain function.

Sensorless control method of induction motors with new feedback gain matrix and speed adaptive law for low speed range

Abstract The conventional adaptive full-order observer-based speed sensorless control method for induction motor is prone to instability at low-speed region as improper feedback gain matrix and imprecise speed adaptive law are often used. To address these problems, a new feedback gain matrix design approach as well as a new speed adaptive law design technique are proposed in this paper. Firstly, considering that the d-axis current estimation error in the traditional feedback gain matrix design method has a great influence on the stability of the speed estimation algorithm, especially at low speeds, a new feedback gain matrix design method is proposed to minimize the d-axis current estimation error. Secondly, a new speed adaptive law design technique is studied based on the conventional method, which only requires the d-axis and q-axis current estimation error with a weight coefficient to be designed, simplifying the conventional speed adaptive law. Thirdly, the transfer function from the speed observation error to the proposed adaptive error is analyzed by Routh stability criterion theory, and the weight coefficient suitable for full range stable operation is determined by MATLAB software. Fourthly, the stability of the proposed method in this paper is analyzed using the poles distribution maps. Finally, the proposed method is experimentally verified based on a 2.2 kW induction motor experimental platform. The experimental results show that the proposed method can make the induction motor operate steadily at low-speed and zero-speed region with rated load. In addition, the proposed method has better anti-disturbance performance than the existing method.

Continuous Gait Phase Estimation by Muscle Deformations With Speed and Ramp Adaptability

Continuous Gait Phase Estimation by Muscle Deformations With Speed and Ramp Adaptability

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.