Adaptive Speed Research Articles

Robustness of the Fuzzy Adaptive Speed Control of a Multi-Phase Asynchronous Machine

Fuzzy controllers are a powerful tool for controlling complex processes. However, its robustness capacity remains moderately limited because it loses its property for large ranges of parametric variations. In this paper, the proposed control method is designed, based on a fuzzy adaptive controller used as a remedy for this problem. For increase the robustness of the vector control and to maintain the performance of the five-phase asynchronous machine despite the presence of disturbances (variation of rotor resistance, rotor inertia variations, sudden variations in the load etc.), by applying the method of behaviour model control (BMC). The results of simulation show that the fuzzy adaptive control provides best performance and has a more robustness as the fuzzy (FLC) and as a conventional (PI) controller.

The turbulent future of expertise and training markets

PurposeTo explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality, cost structure and system dynamics applied to supply.Design/methodology/approachTwo illustrative scenarios are presented. The first is a third-party Best Treatments site, and its effect on the expert advice pharmaceutical representatives provide doctors. The second scenario is an online higher education business course module with embedded AI.FindingsBoth scenarios demonstrate the advantages of online expertise and teaching platforms over the in-person alternative in variable and marginal cost, ease and convenience of use, quality conformance, scalability, knowledge reach and depth and most importantly, speed of evolutionary adaptability. Despite such overwhelming advantages, a number of reasons why the substitution might be slowed are presented, and some strategies firms might adopt are discussed. Opportunities for service scholars to confirm, challenge and extend the conclusions are presented throughout the paper.Originality/valueIncreasing cost structure and adaptability advantages of online technology and AI over in-person delivery of expertise and training services are demonstrated. It is also demonstrated that the innovation-imitation cycle is accelerating because of exogenous innovation in knowledge access and online influence networks and an endogenous effect where imitators accelerate their innovation that drives innovators to accelerate their innovation, which drives imitators to further accelerate their imitation.

Traffic Sign Detection in Complex Scenarios based on YOLOV7

In recent years, thanks to the development of the automobile industry and the advancement of industrial intelligence, driving assistance systems have been developing rapidly under the requirements of national policies and the demands of the consumer market. Traffic sign detection module, as an indispensable part of the driving assistance field, is in urgent need of new target detection algorithms in this field to solve the problems of poor detection accuracy, low computing speed and poor adaptability of the existing algorithms to the vehicle hardware. To solve the application problem, in this paper, we propose a traffic target detection algorithm based on YOLOV7. The model is trained using the CCTSDB dataset and tested in complex scenarios such as different lighting conditions and weather conditions. The mAP of YOLOV7 is stable at over 82.46% and the mAP value of YOLOV7-tiny is stable at over 78.57%, demonstrating the effectiveness of our method that it has the ability to detect and recognize our traffic signs in practical scenarios. In addition, this paper conducts a comparative study on the commonalities and differences of the above two models and draws the corresponding conclusions. In summary, YOLOV7 series can be effectively used in traffic sign detection work.

An Adaptive Conversion Speed Q-Learning Algorithm for Search and Rescue UAV Path Planning in Unknown Environments

An Adaptive Conversion Speed Q-Learning Algorithm for Search and Rescue UAV Path Planning in Unknown Environments

Analysis and Experiment of a Bioinspired Multimode Octopod Robot

Legged robots use isolated footholds to support, which have the merit of good terrain trafficability but lack speed ability. In contrast, wheeled robots have the advantages of high speed and efficiency but only run on flat roads. To improve the moving speed and terrain adaptability of the legged robot, this paper proposes a bioinspired multimode octopod robot with rolling, walking, and obstacle-surmounting modes. First, inspired by the multimode locomotion of the Cebrennus rechenbergi spider, the high-speed mobility of the legged robot is realized in involute kick-rolling mode through the extendable appendages. Then, the foot and appendage trajectories are analyzed by kinematic method and optimized for walking stability. Based on the static and the kinematic analyses, the terrain adaptability is improved by adhesive obstacle-surmounting mode with the assistance of the appendages affiliated to the main feet. The deformable trunk with one DoF is designed to switch between three modes. Finally, a series of dynamic simulations and experiments are carried out to verify the theoretical analyses of the adhesive obstacle-surmounting mode and the mobility of the involute kick-rolling mode. It is shown that the multimode octopod robot can integrate the advantages of high speed and good terrain trafficability from different types of robots and is suitable for performing tasks in unstructured terrains.

Impact of federated deep learning on vehicle-based speed control in mixed traffic flows

This study investigates the application of Federated Learning (FL) in an environment of mixed traffic flow with Connected and Automated Vehicles (CAVs). The focus of this study is set on the CAV cruise control speed adjustment which is enforced by the Intelligent Speed Adaptation (ISA) system. It is modelled as an Actor-Critic-based learning architecture with the goal of computing the vehicle speed based on the data of the nearby vehicles. Some CAVs achieve poor travel time performance in comparison to others. Thus, the proposed FL framework selects periodically best-performing CAVs which are local clients in the context of FL. The updated global model is computed based on the weighted averaging of the best-performing models of clients. The use case environment is modelled as a simulation of a synthetic motorway with an induced capacity drop in the form of a work-zone under moderate and high traffic demand. It is shown that the FL framework can stabilise the individual client learning convergence and reduce their travel time compared to the local learning approach. The overall motorway throughput is also increased by the proposed FL framework. This indicates that the global model of FL can utilise the optimal ratio between generalisation and bias towards the most effective control policy of individual speed control. Results show that those effects are more prominent under moderate traffic demand.

Effects of Search Strategies on Collective Problem-Solving

In today’s dynamic and complex social environments, collaborative human groups play a critical role in addressing a wide range of real-world challenges. Collective problem-solving, the process of finding solutions through the collaboration of individuals, has become imperative in addressing scientific and technical problems. This paper develops an agent-based model to investigate the influence of different search strategies (simple local search, random search, and adaptive search) on the performance of collective problem-solving under various conditions. The research involves simulations on various problem spaces and considers distinct search errors. Results show that random search initially outperforms other strategies when the search errors are relatively small, yet it is surpassed by adaptive search in the long term when the search errors increase. A simple local search consistently performs the worst among the three strategies. Furthermore, the findings regarding adaptive search reveal that the speed of adaptation in adaptive search varies across problem spaces and search error levels, emphasizing the importance of context-specific parameterization in adaptive search strategies. Lastly, the values of Ps=0.9 and Pf=0.2 obtained through human subject experiments in adaptive search appear to be a favorable choice across various scenarios in this simulation work, particularly for complex problems entailing substantial search errors. This research contributes to a deeper understanding of the effectiveness of search strategies in complex environments, providing insights for improving collaborative problem-solving processes in real-world applications.

Relationship-specific and relationship-independent behavioural adaptivity in affiliation and bonding: A multi-adaptive dynamical systems approach

Humans often adapt their behaviour toward each other when they interact. From a neuroscientific perspective, such adaptivity can involve mechanisms based on adaptive connections (synaptic plasticity) and adaptive excitability thresholds (nonsynaptic plasticity) within the mental or neural network concerned. It is, however, often left unaddressed which of the types of adaptation are specific for the relationship and which are more general for multiple relationships. We focus on this differentiation between relationship-specific and relationship-independent adaptation in social interactions. We analysed computationally how an interplay of adaptive relation-specific and relation-independent mechanisms occurs within the causal pathways for social interaction. As part of this, we cover also the context-sensitive control of these types of adaptation (adaptive speeds and strengths of adaptation), which is sometimes termed higher-order adaptation or metaplasticity. The model was evaluated by a number of explored runs where within a group of four agents each agent randomly has episodes of interaction with one of the three other agents. The outcomes of the analysis of the (stochastic) simulation results show a strong dependence of adaptation on the extent of social interaction: more social interaction leads to more adaptation of the interaction behaviour. This holds both for the short-term and long-term first-order adaptation and for the second-order adaptation, which is long-term.

49 Adaptive Functioning in a Mixed Clinical Sample of Older Adults: The Importance of Processing Speed

Objective:The presence of cognitive impairment corresponds with declines in adaptive functioning (Cahn-Weiner, Ready, & Malloy, 2003). Although memory loss is often highlighted as a key deficit in neurodegenerative diseases (Arvanitakis et al., 2018), research indicates that processing speed may be equally important when predicting functional outcomes in atypical cognitive decline (Roye et al., 2022). Additionally, the development of performance-based measures of adaptive functioning offers a quantifiable depiction of functional deficits within a clinical setting. This study investigated the degree to which processing speed explains the relationship between immediate/delayed memory and adaptive functioning in patients diagnosed with mild and major neurocognitive disorders using an objective measure of adaptive functioning.Participants and Methods:Participants (N = 115) were selected from a clinical database of neuropsychological evaluations. Included participants were ages 65+ (M = 74.7, SD = 5.15), completed all relevant study measures, and were diagnosed with Mild Neurocognitive Disorder (NCD; N = 69) or Major NCD (N = 46). They were majority white (87.8%) women (53.0%). The Texas Functional Living Scale was used as a performance-based measure of adaptive functioning. The Coding subtest from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS-CD) was used to measure information processing speed. Composite memory measures for Immediate Recall and Delayed Recall were created from subtests of the RBANS (List Learning, Story Memory, and Figure Recall) and the Wechsler Memory Scale-IV (Logical Memory and Visual Reproduction). Multiple regressions were conducted to evaluate the importance of memory and information processing speed in understanding adaptive functioning. Age and years of education were added as covariates in regression analyses.Results:Significant correlations (p < .001) were found between adaptive functioning and processing speed (PS; r = .52), immediate memory (IM; r = .43), and delayed memory (DM; r = .32). In a regression model with IM and DM predicting daily functioning, only IM significantly explained daily functioning (rsp = .24, p = .009). A multiple regression revealed daily functioning was significantly and uniquely associated with IM (rsp = .28, p < .001) and PS (rsp = .41, p < .001). This was qualified by a significant interaction effect (rsp = -.29, p = .001), revealing that IM was only associated with adaptive functioning at PS scores lower than the RBANS normative 20th percentile.Conclusions:Results suggest that processing speed may be a more sensitive predictor of functional decline than memory among older adults with cognitive disorders. These findings support further investigation into the clinical utility of processing speed tests for predicting functional decline in older adults.

Optimal phenotypic adaptation in fluctuating environments

Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.

Hydrodynamic pursuit by cognitive self-steering microswimmers

The properties of biological microswimmers are mostly determined by fluid-mediated interactions, which govern their propulsion, perception of their surrounding, and the steering of their motion for pursuit. Transferring similar functionalities to synthetic microswimmers poses major challenges, and the design of favorable steering and pursuit strategies is fundamental in such an endeavor. Here, we employ a squirmer model to investigate the pursuit of pursuer-target pairs with an implicit sensing mechanism and limited hydrodynamic steering abilities of the pursuer. Two hydrodynamic steering strategies are studied for the pursuer’s propulsion direction by adaptation of its surface flow field: reorientation toward the target with limited maneuverability, and alignment with the target’s propulsion direction combined with speed adaptation. Depending on the nature of the microswimmer propulsion (puller, pusher) and the velocity-adaptation scheme, stable cooperatively moving states can be achieved, where pursuer and target mutually affect their motion and trajectories.

Frequency control using electric vehicles with adaptive latency compensation and variable speed wind turbines using modified virtual inertia controller

In this paper, to reduce the effect of low inertia consequences, electric vehicle (EV) and variable speed wind turbine (VSWT) are adopted to collaborate in the power system frequency regulation. A virtual inertia controller based on the IEC 61851 standard is proposed to benefit from EV contribution in frequency regulation. In this controller, the rate-of-change-of-frequency (ROCOF) is measured and an appropriate EV current set point is obtained. Furthermore, to decrease the effect of frequency regulation on EVs owners, a stochastic allocation method of the regulation sequence among EVs parking lots is presented. One of the barriers of employing EV in the frequency regulation is the communication latency. To overcome this issue, according to the measured latency, a weighted combination of several compensators which is called adaptive latency compensator (ALC) is utilized. After compensating the delay, the output power of the EVs is calculated and collaborated in frequency regulation. In addition, an algorithm is proposed for collaborating VSWT in frequency control. In the proposed algorithm, according to the wind velocity, the output power of the VSWT increases to the maximum acceptable value and will be fixed. A method is developed to find the maximum acceptable increment power considering VSWT constraints. After a supportive part, a recovery part is started to return VSWT hub speed to a desired value. Two PID controllers are designed in this algorithm for support and recovery parts. During the recovery part, a second frequency dip emerges and a method is introduced which at first identifies the second frequency dip moment and EVs collaboration is applied to alleviate this unfavorable outcome. To evaluate the performance of the proposed algorithms and controllers, different simulation studies are conducted using the modified IEEE 39-bus test power system. Results confirm the effectiveness of the proposed methods in system frequency support.

Real-Time Implementation of Sensorless DTC-SVM Applied to 4WDEV Using the MRAS Estimator

This article presents the DTC-SVM approach for controlling a sensorless speed induction motor. To implement this approach, a practical prototype is built using a microcontroller, an embedded GPS module, and a memory card to collect real-time data during the driving route, such as road geographical data, speed, and time. These data are then utilized in the laboratory to implement the control law (DTC-SVM) on the electric vehicle. The d-q model of the induction motor is first presented to explain the requirements for calculating the rotor speed. Then, an adaptive model reference system speed estimator is developed based on the rotor flux, along with a controller and DTC-SVM strategy, which are implemented using the dSpace 1104 board to achieve the desired performance. The simulation results demonstrate satisfactory speed regulation with the proposed system. In this study too, an electronic differential system is modeled for the four wheels of an electric vehicle equipped with an integrated motor, all controlled by the DTC-SVM strategy. Vehicle speed and electrical vehicle steering angle variations, as well as wheel speeds estimated by code system, are verified using MATLAB/Simulink simulations.

Adaptive Dynamic Path Planning Method for Autonomous Vehicle Under Various Road Friction and Speeds

Path planning is a crucial technology for autonomous vehicle (AV). However, it is difficult to adapt to dynamic driving environment, and AV may lose lateral dynamic stability due to high speed and various friction. This paper presents an adaptive dynamic path planning method (ADPPM) for AV to address the challenges. The ADPPM is comprised of three components: 1) A dynamic state-fused steering decision method based on a hierarchical fuzzy inference system is designed to calculate the steering position of AV in each iteration step, and the method fuses the multi-state of the dynamic environment; 2) dynamic path optimization method is designed to reduce the mean curvature of the path based on the particle swarm optimization method, which improves the lateral dynamics stability of AV; 3) adaptive speed inference method is proposed to provide desired steering speed for AV according to various road friction and reduce AV’s steering burden. The ADPPM provides path planning in the dynamic environment, and it also improves the stability of AV under various road friction and speeds. Finally, the proposed method is verified by CarSim.

Powered Knee and Ankle Prosthesis Control for Adaptive Ambulation at Variable Speeds, Inclines, and Uneven Terrains.

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

Securing the Future Railway System: Technology Forecast, Security Measures, and Research Demands

The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.

Estimating US farmers' speed of climate change adaptation: the case of subsurface tile drainage

PurposeClimate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.Design/methodology/approachUsing farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.FindingsThe authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.Originality/valueUnderstanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

Vector Control Technology of Five‐Phase Induction Motor without Speed Sensor Based on Speed‐Adaptive Full‐Order Observer

The five‐phase induction motor, being the most elementary form of multiphase induction motors, boasts low noise, low torque fluctuations, and high load‐bearing capacity, and is hence suitably employed in domains where high power and reliability are required. In light of the susceptibility of the speed encoder to interference from external environments, this paper proposes a sensorless vector control technique for the five‐phase induction motor based on a speed‐adaptive full‐order observer. The proposed approach first employs the nearest four‐vector space vector pulse width modulation (NFV‐SVPWM) as the modulation technique for the five‐phase inverter, in order to provide a constant switching frequency and low harmonic losses. Subsequently, the expression for the speed‐adaptive full‐order observer is constructed based on the mathematical model of the fundamental space of the five‐phase induction motor, and the expression for the adaptive speed law is deduced using the Lyapunov function. Additionally, the bilinear transformation method is utilized as the discretization technique for the full‐order observer during the process of digital implementation, in order to achieve higher discrete accuracy. Finally, an experimental study involving a 4.5 kW five‐phase induction motor was conducted to validate the accuracy and effectiveness of the proposed method. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A drum-like piezoelectric rotational energy harvester via magnetic beating

In this Letter, an indirectly excited approach of introducing an air-filled separation chamber is proposed to develop a drum-like piezoelectric rotational energy harvester (DL-PREH) via magnetic beating. The harvester delivers the external excitation to the piezoelectric transducer via the intermediate air chamber, and the electric output of transducer is induced by the air pressure inside the chamber. Thus, a high reliability can be guaranteed for the harvester under an unexpected excessive impact due to the air-filled separation zone. Moreover, the harvester can easily implement a resonant frequency tuning by altering the drum height to improve the rotation speed adaptability. Its potential applications as a sustainable power source to charge different capacitors and power commercial light-emitting diodes (LEDs) are demonstrated experimentally. The fabricated DL-PREH device can achieve a maximum output power of 10.63 mW with a drum height of 6 mm at the matching resistance of 24 kΩ. Also, it can light up at least 100 blue commercial LEDs in parallel. The designed harvester exhibits its good power generation capability and resonance frequency tunability.