Control Response Time Research Articles

Long-distance hand tractor controlled by android system in Wetland Field

Modernization of agricultural practices is essential to overcome challenges such as decreasing agricultural labor and decreasing agricultural land in Indonesia. This research examines the development and performance of a hand tractor that is controlled remotely via an Android-based system using the NodeMCU ESP8266 module and a WiFi network. This study focuses on the technical and operational performance of hand tractors in wetlands. Modifications were made to the tractor, by installing three DC motors to control the clutch and gas levers. Tests were carried out on the control system accuracy and response time to assess the system's suitability for field operations. The research results show that the Android system has 100% accuracy in executing all commands given, but the response time decreases as the distance between the Android device and the WiFi module increases, with an average delay reaching 6,8 seconds for a maximum 100 m distance. Field efficiency testing shows that a tractor with Android control has a work efficiency of 70.48% in the plowing process and 75.33% in the harrowing process, lower than with manual control which reaches an efficiency of more than 95%. Despite response delays and reduced efficiency, the system is an important step toward agricultural mechanization, especially in labor-intensive wetlands.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.

Integrated Microfluidic-Transistor Sensing System for Multiplexed Detection of Traumatic Brain Injury Biomarkers.

Traumatic brain injury (TBI) is widely recognized as a global public health crisis, affecting millions of people each year, leading to permanent neurologic, emotional, and occupational disability, and highlighting the urgent need for rapid, sensitive, and early assessment. Here, we design a novel and simple lithography-free method for preparing dual-channel graphene-based field-effect transistors (G-FETs) and integrating them with microfluidic channels for simultaneously multiplexed detection of key blood TBI biomarkers: neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP). The G-FET utilizes an ingenious dual-channel electrode array design, where the source is shared between channels and the drains are independent of each other, which is the key to achieving simultaneous output of dual detection signals. At the same time, the microfluidic chip realizes microscale fluidic control and fast sample response time. This integrated detection system shows excellent sensitivity in biological fluids for the TBI biomarkers with detection limits as low as 55.63 fg/mL for NFL and 144.45 fg/mL for GFAP in phosphate-buffered saline (PBS) buffer, respectively. Finally, the clinical sample analysis shows promising performance for TBI detection, with an area under the curve (AUC) of 0.98 for the two biomarkers. And the combined dual-protein assay is also a good predictor of intracranial injury findings on computed tomography (CT) scans (AUC = 0.907). The integrated microfluidic G-FET device with a dual-signal output strategy has important potential for application in clinical practice, providing more comprehensive information for brain injury assessment.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

SPACECRAFT RELATIVE ON-OFF CONTROL VIA REINFORCEMEN T LEARNING

The article investigates the task of spacecraft relative control using reactive actuators, the output of which has two states, “on” or “off”. For cases where the resolution of the thrusters does not provide an accurate approximation of linear control laws using a pulse-width thrust modulator, the possibility of applying reinforcement learning methods for direct finding of control laws that map the state vector and the on-off thruster commands has been investigated. To implement such an approach, a model of controlled relative motion of two satellites in the form of a Markov decision process was obtained. The intelligent agent is presented in the form of “actor” and “critic” neural networks, and the architecture of these modules is defined. It is proposed to use a cost function with variable weights of control actions, which allows for optimizing the number of thruster firings explicitly. To improve the control performance, it is proposed to use an extended input vector for the “actor” and “critic” neural networks of the intelligent agent, which, in addition to the state vector, also includes information about the control action on the previous control step and the control step number. To reduce the training time, the agent was pre-trained on the data obtained using conventional control algorithms. Numerical results demonstrate that the reinforcement learning methodology allows the agent to outperform the results provided by the linear controller with the pulse-width modulator in terms of control accuracy, response time, and number of thruster firings.

Dynamic behaviour and performance evaluation of a biomass-fired organic Rankine cycle combined heat and power (ORC-CHP) system under different control strategies

The dynamic behaviour and performance evaluation of a biomass-fired organic Rankine cycle combined heat and power (ORC-CHP) system under two different control strategies are investigated in this study. The dynamic model is established, while the dynamic characteristics of key operating parameters (superheat degree, evaporating pressure and mass flow rate) with the step change of heat source temperature are examined. The effects of controlled superheat degree and controlled expander shaft work on dynamic behaviours of thermodynamic, economic and environmental performance are discussed and compared under different control methods (feedforward active control, PID passive control and combined control). Results indicate that the superheat degree is significantly influenced by the heat source temperature under the without control strategy, with the variation reaching 8.4 K. The combined control method exhibits the best control performance with overshoot and control response times of 1.2 K and 12.3 s, respectively. Compared to without control, the expander shaft work and ORC thermal efficiency of the control strategy of superheat degree increase by 3.2 kW and 0.8 %, while that of the control strategy of expander shaft work increase by 1.0 kW and 0.3 %, respectively. The control strategy of superheat degree presents a significant improvement in the economic and environmental performance of the biomass-fired ORC-CHP system. This study is expected to provide better guidance for the practical operation and control of biomass-fired ORC-CHP systems.

DDoS Protection System for SDN Network Based on Multi Controller and Load Balancer

DDoS attacks on SDN networks can create a single point of failure that has the potential to disrupt the overall network performance. In a single controller scheme, there is a potential risk of experiencing buffer overload, leading to traffic congestion as switches must wait for responses from the controller before forwarding network packets. To address this challenge, this research implements security measures using a multi-controller and load balancer approach, aiming to enhance SDN network resilience against DDoS attacks. The system operates by distributing the workload from the main controller to a backup controller through a load balancer when indications of a DDoS attack are detected. These attack indications are determined based on the miss rate value of unique forwarding requests exceeding a specific threshold. The results of this approach have proven effective in improving the reliability, responsiveness, and quality of SDN network traffic during DDoS attacks. The testing parameters involved in this research include controller response time and network traffic quality, comprising latency, bandwidth, throughput, and jitter. Based on the test results, the multi-controller and load balancer-based approach successfully enhanced network quality and controller responsiveness by 66.51% compared to the longer single controller scenario, specifically 202.49% during DDoS attacks. In terms of controller responsiveness, there is a very slight increase of around 0.01% in latency between the two. While Multi Controller demonstrated a remarkable 43.21% increase in throughput compared to Single Controller, this improvement in throughput is accompanied by a significant 204% increase in jitter.

Research and Simulation of Fuzzy Expert Anti-surge Control System for Compressor

In order to solve the problem of compressor surge caused by flow fluctuations that cannot be effectively solved by conventional PID control, a fuzzy expert PID controller was designed based on the analysis of the working principles of fuzzy controllers and expert controllers. This controller has the advantages of good stability and high control accuracy of fuzzy control, as well as the advantage of fast response time of expert controllers. The MATLAB simulation results show that the fuzzy expert PID control algorithm has the characteristics of high stability, fast response speed, and small overshoot compared to conventional PID.

Experimental study on shock interaction control of double wedge in high-enthalpy hypersonic flow subject to plasma synthetic jet

The hypersonic shock-shock interaction flow field at double-wedge geometries controlled by plasma synthetic jet actuator is experimentally studied in a Ma = 8 high-enthalpy shock tunnel with the purpose of exploring a novel technique for reducing surface heat flux in a real flight environment. The results demonstrate that increasing the discharge energy is advantageous in eliminating the shock wave, shifting the shock wave interaction point, and shortening the control response time. The oblique shock wave can be completely removed when the actuator's discharge energy grows from 0.4 J to 11.5 J, and the displacement of the shock wave interaction point increases by 124.56%, while the controlled response time is shortened by 30 μs. Besides, the reduction in diameter of the jet exit is firstly proved to have a negative impact on energy deposition in a working environment with incoming flow, which reduces the discharge energy and hence decreases the control effect. The shock wave control response time lengthens when the jet exits away from the second wedge. Along with comparing the change in wall heat flux at the second wedge over time, the control effect of plasma synthetic jet actuator with and without inflation is also analyzed. When plasma synthetic jet works in inflatable mode, both the ability to eliminate shock waves and the shifting effect of the shock wave interaction point are increased significantly, and the wall heat flux is also reduced.

Assembly-free fabrication of the deformable system composed by continuous fiber embedded 4D printing actuators

The 4D printed deformable actuator with embedded continuous fibers exhibits the advantages of convenient direct drive without environment stimulate and flexible control response position or response time. Rapid manufacturing of complex systems consisting of multiple actuators spatial arrangements relies on multi-axis printing strategy. However, existing multi-axis printing methods cannot match the continuous fibers 4D printing predefined design feature, such as the layer distribution and fiber arrangement. Therefore, a novel assembly-free printing method is proposed to print complex 4D printed deformable system with embedded continuous fibers. Based on design features of multi-actuators deformable system, a toolpath planning algorithm is provided to analyze the printing steps of the complex structure and generate toolpaths. Herein, the effect of different nozzle tilt angles on the deformation performance during the printing process is investigated, and the corresponding compensatory strategy is proposed during toolpath generation. After discussing the advantages of assembly free printing, a deformable ratchet and an intelligent five-finger gripper are printed to demonstrate the practicality of the proposed method. The presented method exhibited unique capability in fabricating deformable system with complex 3D geometry or complex drive features for various promising applications on smart robot and metamaterial.

Kinect-based gesture interaction design method for smart terminal APP interface

Abstract In this paper, a static gesture classification model based on a convolutional neural network is proposed to achieve the goal of efficient interaction control and fast response time for gesture recognition, and hand feature extraction is achieved by using alternating convolution and pooling. In the convolutional neural network, image data is obtained by preprocessing operations such as adaptive thresholding and functions such as Gaussian filtering to provide data for the convolutional neural network structure to build the recognition model, which is used to quantify the edge characteristics of the image and construct the image edges with gradient amplitude to build the static gesture recognition algorithm for Kinect. In the accuracy rate for gesture interaction, the average recognition rate for slow movement is 96.5%, while when the interactive gesture is recognized at an accelerated speed, its recognition rate slips to 93.1%, and the total average recognition rate reaches 95.2%.

Prolonged cognitive effort impairs inhibitory control and causes significant mental fatigue after an endurance session with an auditive distractor in professional soccer players

BackgroundThroughout official soccer matches, the presence of cheer by the crowd could be considered a critical auditive distraction that could further impair the cognitive interference control system, multiple object tracking (MOT) skill, heart rate variability (HRV), and increase mental fatigue. As the resource is not immediately replenished, the impairment of the cognitive interference control system may be delayed following a soccer game. Then, evaluating the recovery time course of the cognitive interference control system, MOT skill, HRV, and mental fatigue after prolonged tasks combining physical, endurance, and cognitive effort are essential. PurposeWe aimed to analyze the acute effect of cognitive effort and auditive distractor with 24-h follow-up throughout a prolonged endurance session on inhibitory control, subjective mental fatigue, MOT skill, and HRV in professional soccer players. MethodsTwenty professional male soccer players were recruited (23.56 ± 3.8 years, 78.1 ± 6.9 kg, 1.77 ± 0.06 m, and 12.5 ± 5.3% body fat). The sessions were performed in a randomized and counterbalanced crossover design, divided into four experimental conditions: endurance, endurance + MOT, endurance + MOT + AD, and endurance + AD. The soccer players completed the incongruent Stroop task utilizing an eye-tracker to assess cognitive effort. MOT task, subjective mental fatigue, and HRV were evaluated before the endurance training (60%Δ of maximal aerobic velocity during 40-min) and after 30-min and 24-h of recovery. These sessions were designed to investigate the acute effect of prolonged cognitive effort (repeated MOT throughout the endurance task) and AD (constant crowd noise and coach's voice each 15–40 s, totalizing = 80 voices) on inhibitory control, MOT skills, HRV, and subjective mental fatigue after a fixed endurance training session. ResultsThere was no condition × time interaction for accuracy of inhibitory control (p > 0.05, ηp2 = 0.001). There was a significant condition × time interaction for inhibitory control response time (p < 0.05, ηp2 = 0.16). A higher response time of inhibitory control was found for the endurance + MOT + AD and endurance + MOT experimental sessions (p < 0.05). There was a significant condition × time interaction for subjective mental fatigue (p < 0.05, ηp2 = 0.46). A higher subjective mental fatigue was found for the endurance + MOT + AD and endurance + MOT experimental sessions (p < 0.05). There was no condition × time interaction for HRV (p > 0.05, ηp2 = 0.02). ConclusionWe concluded that cognitive effort throughout a prolonged endurance session impaired inhibitory control and increased mental fatigue without promoting greater MOT skill and HRV changes in professional soccer players.

PBCLR: Prediction-based control-plane load reduction in a software-defined IoT network

The exponential growth of devices and applications in Internet of Things (IoT) networks has caused control-plane traffic to escalate. Experts have suggested Software-Defined Networking (SDN) as a solution for complicated IoT network management. Nevertheless, SDN encounters obstacles in managing the substantial control-plane traffic many IoT devices produce. Prior findings have identified the dynamic switch migration technique as a viable solution for control plane load oscillation. However, their tendency to migrate switches during instances of controller overload restricts the efficiency of traditional migration strategies. As a result, these approaches lead to inefficiencies in the switch migration process, resulting in elevated latency. The present study introduces a prediction-based novel approach for mitigating the control-plane workload by leveraging the dynamic switch migration technique. The proposed method proactively and predictively migrates the switches anticipated to generate excessive control-plane traffic to an alternative controller. We use the learning technique along with time-series analysis to predict the future workload based on the historical control-plane traffic data. The proposed methodology is evaluated through simulation and the results demonstrate that the proposed method outperforms conventional techniques in enhancing load balancing efficacy on a distributed control plane and decreasing migration cost and controller response time by 30.6% on average.

Optimization of Landing Attitude Stability for Quadrotor Drones

During the regular landing process of drones, they are often susceptible to the impact of ground effect and wind disturbances, which results in an inability to maintain parallelism with the ground. Drones are subjected to various types of wind disturbances during landing. Therefore, this study aims to analyze and model the varying characteristics of wind speed under natural conditions and introduce it as environmental noise into the system to evaluate the performance of the control algorithm. The goal of this research is to mitigate the impact of wind disturbances, such as oscillations during the landing process. By maintaining the pitch angle within 20%, we can ensure the drone lands quickly and accurately. In terms of algorithm, this research improves upon the existing PID (Proportional-Integral-Derivative) algorithm control for landing, and adopts a modified fuzzy PID algorithm, which can effectively enhance the control performance and response time of the drone.

An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics.

BLDC motor torque ripple factor lowering and FOPID based motion control using DGOA algorithm

The Brushless Direct Current (BLDC) motor has many benefits due to characteristics like its small size and precise speed regulation. Uncertainty problems are presented by the system’s slow Proportional-Integral (PI) controller response time as well as some BLDC motor operating circumstances. The proposed Fractional-Order Proportional-Integral-Derivative (FOPID) controller and Difference of Orientation Offset Gaussian (DOOG) Grasshopper Optimization Algorithm (GOA) (DGOA) function are used to mitigate torque ripples in BLDC motors and overcome these drawbacks. An adaptive parameter has been introduced to enhance the performance of the conventional GOA method, and the performance of the suggested DGOA is compared to the benchmark functions. The simulation of the driver with the suggested DGOA-FOPID results in an improvement in Torque Ripple Reduction and speed control is obtained that better torque ripple reduction and speed regulation have been achieved.

Methodological Considerations for Assessing Automatic Brightness Control in Endoscopy: Experimental Study.

Endoscopy is a critical application that requires adaptable illumination to adjust to varying imaging conditions. Automatic brightness control (ABC) algorithms ensure optimal brightness throughout the image with rapid but smooth response and render the true colours of the biological tissue under examination. To achieve good image quality, high-quality ABC algorithms are necessary. In this study, we propose a three-assessment method approach for objectively evaluating ABC algorithms based on (1) image brightness and its homogeneity, (2) controller response and response time, and (3) colour rendition. We conducted an experimental study to assess the effectiveness of ABC algorithms in one commercial and two developmental endoscopy systems using the proposed methods. The results showed that the commercial system achieved good, homogeneous brightness within 0.4 s, and its damping ratio was 0.597, indicating a stable system, but its colour rendition was suboptimal. The developmental systems had control parameter values that resulted in either a sluggish response (over 1 s) or a fast (about 0.3 ms) but unstable response with damping ratios above 1, causing flickers. Our findings indicate that the interdependency among the proposed methods can establish tradeoffs in the overall ABC performance better than single-parameter approaches. The study establishes that comprehensive assessments using the proposed methods can contribute to designing new ABC algorithms and optimising already implemented ones for efficient performance in endoscopy systems.

An Analysis of Internet of Things Computer Network Security and Remote Control Technology

In order to solve the problem of the specific application of Internet of Things computer network security monitoring technology in infrastructure construction, a method of remote network monitoring system design is proposed, taking tunnel security construction as an example. According to the characteristics of construction tunnel safety monitoring, this method provides theoretical guidance from several aspects, such as computer remote monitoring, data decision analysis, safety control, personnel positioning, and data management. Through experiments, it is found that in the safety detection experiment, when the data is extracted within 2 minutes for detection, the detection accuracy is higher than 80%, and if it is 4 minutes, the detection accuracy is higher than 90%; in the response time test, the local control response time is generally less than 1 s. In the remote control response time test, the response time can generally be within 3 s, and it may be greater than 5 s. The designed construction tunnel monitoring system transmits the collected data to the monitoring terminal remotely. Then, the sensor data is analyzed for timing decision-making. This method can effectively calculate the safety status of each monitoring unit of the current tunnel according to a certain algorithm and formulate corresponding emergency measures according to the safety status.

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces.

Adsorption of surface-active molecules to fluid-fluid interfaces is ubiquitous in nature. Characterizing these interfaces requires measuring surfactant adsorption rates, evaluating equilibrium surface tensions as a function of bulk surfactant concentration, and relating how surface tension changes with changes in the interfacial area following equilibration. Simultaneous visualization of the interface using fluorescence imaging with a high-speed confocal microscope allows the direct evaluation of structure-function relationships. In the capillary pressure microtensiometer (CPM), a hemispherical air bubble is pinned at the end of the capillary in a 1 mL volume liquid reservoir. The capillary pressure across the bubble interface is controlled via a commercial microfluidic flow controller that allows for model-based pressure, bubble curvature, or bubble area control based on the Laplace equation. Compared to previous techniques such as the Langmuir trough and pendant drop, the measurement and control precision and response time are greatly enhanced; capillary pressure variations can be applied and controlled in milliseconds. The dynamic response of the bubble interface is visualized via a second optical lens as the bubble expands and contracts. The bubble contour is fit to a circular profile to determine the bubble curvature radius, R, as well as any deviations from circularity that would invalidate the results. The Laplace equation is used to determine the dynamic surface tension of the interface. Following equilibration, small pressure oscillations can be imposed by the computer-controlled microfluidic pump to oscillate the bubble radius (frequencies of 0.001-100 cycles/min) to determine the dilatational modulus The overall dimensions of the system are sufficiently small that the microtensiometer fits under the lens of a high-speed confocal microscope allowing fluorescently tagged chemical species to be quantitatively tracked with submicron lateral resolution.

Artificial intelligence in a communication system for air traffic controllers' emergency training

In the last few years, there has been a lot of research into the use of machine learning for speech recognition applications. However, applications to develop and evaluate air traffic controllers' communication skills in emergency situations have not been addressed so far. In this study, we proposed a new automatic speech recognition system using two architectures: The first architecture uses convolutional neural networks and gave satisfactory results: 96% accuracy and 3% error rate on the training dataset. The second architecture uses recurrent neural networks and gave very good results in terms of sequence prediction: 99% accuracy and 𝑒 −7% error rate on the training dataset. Our intelligent communication system (ICS) is used to evaluate aeronautical phraseology and to calculate the response time of air traffic controllers during their emergency management. The study was conducted at International Civil Aviation Academy, with third-year air traffic control engineering students. The results of the trainees' performance prove the effectiveness of the system. The instructors also appreciated the instantaneous and objective feedback.