System Response Time Research Articles

Fostering idealogical and polical education via knowledge graph and KNN model: an emphasis on positive psychology

As the primary domain of ideological and political education in higher education institutions, ideological and political courses must align with principles rooted in human psychology and education. Integrating educational psychology into ideological and political teaching in universities enhances the scientific, targeted, and forward-thinking nature of such education. The burgeoning exploration of knowledge graph applications has extended to machine translation, semantic search, and intelligent question answering. Diverging from traditional text matching, the knowledge spectrum graph transforms information acquisition in search engines. This paper pioneers a predictive system for delineating the relationship between educational psychology and ideological and political education in universities. Initially, it extracts diverse psychological mapping relationships of students, constructing a knowledge graph. By employing the KNN algorithm, the system analyzes psychological characteristics to effectively forecast the relationship between educational psychology and ideological and political education in universities. The system's functionality is meticulously detailed in this paper, and its performance is rigorously tested. The results demonstrate high accuracy, recall rates, and F1 values. The F1 score can reach 0.95enabling precise sample classification. The apex of the average curve for system response time peaks at approximately 2.5 s, maintaining an average response time of less than 3 s. This aligns seamlessly with the demands of practical online teaching requirements. The system adeptly forecasts the relationship between educational psychology and ideological and political education in universities, meeting response time requirements and thereby fostering the scientific and predictive nature of ideological and political teaching in higher education institutions.

A flexible mixed-optimization with H∞ control for coupled twin rotor MIMO system based on the method of inequality (MOI)- An experimental study.

This article introduces a cutting-edge H∞ model-based control method for uncertain Multi Input Multi Output (MIMO) systems, specifically focusing on UAVs, through a flexible mixed-optimization framework using the Method of Inequality (MOI). The proposed approach adaptively addresses crucial challenges such as unmodeled dynamics, noise interference, and parameter variations. Central to the design is a two-step controller development process. The first step involves Nonlinear Dynamic Inversion (NDI) and system decoupling for simplification, while the second step integrates H∞ control with MOI for optimal response tuning. This strategy is distinguished by its adaptability and focus on balancing robust stability and performance, effectively managing the intricate cross-coupling dynamics in UAV systems. The effectiveness of the proposed approach is validated through simulations conducted in MATLAB/Simulink environment. Results demonstrated the efficiency of the proposed robust control approach as evidenced by reduced steady-state error, diminished overshoot, and faster system response times, thus significantly outperforming traditional control methods.

Trajectory Tracking Control of Remotely Operated Vehicles via a Fast-Sliding Mode Controller with a Fixed-Time Disturbance Observer

Time-varying nonlinear external disturbances, as well as uncertainties in model and hydrodynamic parameters, make remotely operated vehicles (ROVs) trajectory tracking control complex and difficult. To solve this problem, this paper proposes a fast sliding mode controller with a fixed-time disturbance observer (FSMC-FDO), which consists of a sliding mode controller based on a fast reaching law and a novel fixed-time disturbance observer. The FSMC can solve the contradiction between system response time and chatter amplitude in sliding mode control. The FDO can compensate for time-varying external disturbances. The Lyapunov theory is used to prove the stability of the entire control scheme. Simulation results show that FSMC-FDO exhibits a good trajectory tracking performance with a better robustness than the conventional sliding mode control (CSMC) on the basis of exponential reaching law (ERL), while significantly reducing chatter.

Development of AVR controller performance using exponential distribution and transit search optimization techniques

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

A family of experiments about how developers perceive delayed system response time

Collecting and analyzing data about developers working on their development tasks can help improve development practices, finally increasing the productivity of teams. Indeed, monitoring and analysis tools have already been used to collect data from productivity tools. Monitoring inevitably consumes resources and, depending on their extensiveness, may significantly slow down software systems, interfering with developers’ activity. There is thus a challenging trade-off between monitoring and validating applications in their operational environment and preventing the degradation of the user experience. The lack of studies about when developers perceive an overhead introduced in an application makes it extremely difficult to fine-tune techniques working in the field. In this paper, we address this challenge by presenting an empirical study that quantifies how developers perceive overhead. The study consists of three replications of an experiment that involved 99 computer science students in total, followed by a small-scale experimental assessment of the key findings with 12 professional developers. Results show that non-negligible overhead can be introduced for a short period into applications without developers perceiving it and that the sequence in which complex operations are executed influences the perception of the system response time. This information can be exploited to design better monitoring techniques.

Development of Distributed Search Algorithm for Improving Efficiency in Web Search Engines

This research article presents the development of a distributed search algorithm aimed at enhancing the efficiency of web search engines. Through a qualitative methodology involving literature review and library research, the study seeks to address the growing demand for improved search engine performance in handling large-scale data while maintaining fast response times. The literature review explores existing search algorithms and their limitations in processing vast amounts of information distributed across various servers. By analyzing prior research, the article identifies the need for a distributed approach to optimize search engine functionality and reduce latency. The developed distributed search algorithm employs parallel processing techniques to distribute search queries across multiple nodes, thereby improving overall system efficiency and response time. By leveraging distributed computing resources, the algorithm enhances the scalability and reliability of web search engines, enabling them to handle increased user traffic and data volume. The findings underscore the effectiveness of the proposed algorithm in significantly reducing search latency and improving search engine performance, as evidenced by experimental results. Furthermore, the article discusses potential applications of the distributed search algorithm in various domains, including e-commerce, information retrieval, and data analytics.

Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection

The condition monitoring system for an in-situ pipeline is an innovative concept that uses MQ-5 sensors to detect fuel leaks in pipelines and relay concentration data to a receiver station. The essence of the monitoring process is to ensure the safety and security of engineering properties and lives. The project addresses the crucial requirement for rapid detection of fuel leaks to avoid environmental problems, economic losses, and safety risks connected with pipeline circulation. The goal is to create a leak detection monitoring system. The project required the design and execution of four transmitter stations, each responsible for detecting fuel leaks at various places along a small-scale pipeline and transmitting concentration data to a central receiver station. The system's response time, a critical performance parameter for this project, was measured by timing how long it took for an alert to arrive at the receiving station after the transmitter detected a leak. This time was measured at three distances between the transmitter and receiver stations: 1m, 2m, and 3m. Multiple measurements were taken at each distance, and the average response time was computed. The results showed that as the distance between the stations increased, so did the reaction time. The average response time at 1m was 3.37 seconds, whereas, at 2m and 3m, it was 3.856 seconds and 4.198 seconds, respectively. A t-test inference was performed to check that there was a distinct difference between the response times for each distance, and a significant difference was detected. The built system effectively demonstrated its ability to detect fuel leaks, and the observed response times offered useful information about the system's performance at various distances. This technology demonstrates the potential to improve pipeline transportation safety and efficiency by enabling early identification of fuel leaks.

The first global multi-timescale daily SPEI dataset from 1982 to 2021

Global warming accelerates water cycle, causing more droughts globally that challenge monitoring and forecasting. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess drought characteristics and response time of natural and economic systems at various timescales. However, existing SPEI datasets have coarse spatial or temporal resolution or limited spatial extent, restricting their ability to accurately identify the start or end dates or the extent of drought at the global scale. To narrow these gaps, we developed a global daily SPEI dataset (SPEI-GD), with a 0.25° spatial resolution from 1982 to 2021 at multiple timescales (5, 30, 90, 180 and 360 days), based on the precipitation from European Center for Medium Weather Forecasting Reanalysis V5 (ERA5) dataset and the potential evapotranspiration from Singer’s dataset. Compared to widely used SPEIbase dataset, the SPEI-GD can improve the spatial-temporal resolution and the accuracy of SPEI in areas where meteorological sites are lacking. The SPEI-GD significantly correlates with site-based SPEI and soil moisture. Our dataset solidly supports sub-seasonal and daily-scale global and regional drought research.

McDonald's food target recognition and calorie display based on YOLOv5 algorithm

This research paper delves into the development and assessment of a novel food recognition and evaluation system tailored for McDonald's menu items, leveraging the capabilities of the YOLOv5 algorithm. The study demonstrates that the system can successfully identify McDonald's food items from images and seamlessly query calorie and nutritional information from a backend database. The data is then presented to the user, aiding in more informed dietary choices and promoting public health awareness. The system has particular utility for McDonald's customers, facilitating real-time decisions that align with individual health goals and nutritional requirements. Our experimental findings show a high degree of accuracy and efficiency, although the system's scope is currently limited to five key menu items. Future directions for this work include expanding the range of recognizable food categories and implementing user feedback mechanisms to refine recognition accuracy. Moreover, the paper discusses potential optimizations for reducing system response time and further enhancing the practical utility of the technology. This research serves as a significant step towards utilizing computer vision technologies for public health interventions, aiming to combat the rise of obesity and related diseases.

Sliding mode control for gasifier reactor temperature control

This paper presents a sliding mode control (SMC) design for gasifier reactor temperature control. Temperature control is a critical aspect of biomass gasification for quality syngas production. However, accurate modeling and effective control method are the major challenges of gasifier reactor temperature control. The gasification system processes are not yet well understood to model from the first principle due to the complex and nonlinear nature of the process. Also, conventional control methods such as proportional, integral, and derivative control have not been effective in controlling gasification systems. Hence, this work has used experimental data from a 500 kVA updraft gasifier reactor to develop a data-driven mathematical model. The system identification result predicts 86.36% goodness of fit on the model data and 88.26% on the validation data. Discrete time sliding mode control has been designed from the model and implemented in Simulink to investigate the performance of the control method on the gasifier. The result of the system time response shows that the controller effectively drives the temperature monotonically from 25°C to 700 °C in finite time (11.37 minutes). It also establishes a quasi-sliding motion with an ultimate band of ±1℃ for the remainder of the simulation time. Hence, the control technique guarantees optimal temperature control for any feedstock type which will result in higher conversion efficiency, more syngas yield, and improve syngas quality and durability of the gasifier reactor among others.

Simulation of screw size effect on performance of electro-mechanical braking system

Electro-mechanical brakes (EMB) are promising alternatives to conventional braking systems, as they are eco-friendly and have lower maintenance requirements. Currently, researchers and automotive manufacturers are interested in developing the performance of EMB systems for implementation in commercial vehicles. Current studies are either conducted experimentally and/ or modeled using mathematical based simulation models using different vehicles kinematic and dynamic models. These models are time-consuming, and expensive in case of experimental implementation. This paper presents a simple and easy-to-construct simulation model using MATLAB Simulink Graphical user interface (GUI) without using conventional mathematical modelling techniques. The presented simulation model studies the effect of the lead screw size on performance of EMB in terms of the generated clamping force and system response time. The accuracy and reliability of the proposed model were statistically verified using ANOVA. In addition, its performance was verified by comparing its results with the same input data used in previously validated experimental and mathematical-based simulation models from the literature review. The proposed model can be used to improve the performance of EMB systems and facilitate comparisons among different EMB designs.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

Triple-range adjustable plane-concave multipass gas cell for high-precision trace gas detection based on TDLAS

A triple-range adjustable plane-concave multipass gas cell (PC-MPGC) was developed for high-precision trace gas detection based on tunable diode laser absorption spectroscopy. Effective optical path lengths (EOPLs) of 64.5, 42.5, and 20.5 m could be easily achieved by adjusting the incident beam angle. The trace gas sensor system employed a 1.653 µm distributed feedback diode laser to target a strong methane (CH4) absorption line, and direct absorption spectroscopy (DAS) was used to evaluate the performance of the system with a 64.5 m EOPL. A Savitzky–Golay filtering algorithm was employed to denoise the measured signal, and a third harmonic signal was used for laser frequency locking. A signal-to-noise ratio (SNR) of 191 was achieved, which corresponds to a detection sensitivity of 0.78 ppmv. The system response time was obtained through measurement of CH4 at concentration of 0 ppmv and 20 ppmv at 298 K and 1 atm, and it was found to be 11 s. Kalman filtering was applied to improve the detection sensitivity of the gas sensor. The stability of the system was analyzed using Allan deviation, and a minimum detectable limit of 21 ppbv was obtained for an integration time of 580 s. In situ continuous monitoring of atmospheric CH4 characteristics was performed for 72-h. The development of a new type of PC-MPGC paves the way for the detection of multicomponent gases at different absorption path lengths.

Reduction of response time of fire detection and containment systems in compartments

Bench tests were performed on promising sensors used in fire safety systems in compartments. Activation delay times of fire sensors and systems were analyzed during the thermal decomposition and suppression of burning construction and interior design materials typical of compartments: wood, particle board, fiberboard, linoleum, cardboard, different types of wallpaper, paper and plastic. The thermal decomposition and ignition characteristics were identified using commercially available fire detectors of different types and designs (thermal, smoke and flame detectors), contact and non-contact temperature measurement instruments, a gas detection system and video recording equipment. Devices with the highest efficiency in terms of response time and detection reliability were determined that are necessary and sufficient to identify the thermal decomposition of construction and interior design materials at an early stage of fire. When generalizing the experimental data, mathematical equations were derived to predict the integral characteristics of the processes under study. Promising methods of reducing the response time of fire sensors and systems in general were determined. Guidelines were proposed on upgrading the existing systems of fire prevention in buildings and developing new automatic ones to significantly accelerate the detection of fires in buildings and optimize their containment and suppression.

Graph Learning based Performance Analysis for Queueing Networks

Queueing networks serve as a popular performance model in the analysis of business processes and computer systems [4]. Solving queueing network models helps the decision making of system designers. System response time and throughput are two key performance measures in queueing networks. The most widely used algorithms for solving these measures are mean value analysis (MVA) and its approximate extensions [3, §8-9]. However, conventional analytic methods are inaccurate at solving non-product form queueing networks that are frequently encountered in modeling most real systems. Approximation formulas typically rely on assumptions that may lead, on particular regions of the parameters, to inaccurate and misleading results. Simulation modeling is an accurate way, but it requires to be designed for each specific problem, and usually takes longer time to converge.

Grid Secondary Frequency Control by Fuzzy PI–PD Controller

This paper develops a new approach for controlling the frequency in a two-area power system using a fuzzy PI PD controller. Frequency stabilization is one of the important factors for any power system to operate efficiently and reliably. The fluctuating characteristics of RES, like solar PV and wind energy, due to changing atmospheric conditions, require additional reserves to compensate for fluctuations in generated power. With growing application of EVs and their charging requirement, charting huge loads may add to the power grid and lead to stability problems if not properly regulated. This paper coordinates EV charging into the secondary frequency regulation, aided by an enhanced fuzzy controller designed for dealing with fluctuating imbalances between electricity demand and generation. In this context, a control technique is suggested that incorporates the flexibility of fuzzy logic with the accurate PI and PD methods for control. The controller continuously monitors changes in frequency at each node in the power system network. The fuzzy logic works on uncertain input data in terms of prescribed linguistic variables and rules that allow it to achieve a reasoned output. The difference between the desired and actual frequencies alters the control variable through the PI component of the controller, while the PD component considers the rate of change of this error to make the system's response time better. The effectiveness of the fuzzy PI PD controller is proven through detailed simulation experiments under different operational conditions and disturbances. It can be rundown that the controller efficiently reduces these frequency deviations, which guarantees smooth and reliable operation of the two-area power system network. In addition, it can be seen that the controller has robust performance but is rather reactive to dynamical changes in system parameters. Case studies are included in the research to test the performance of the proposed control approach in reducing frequency deviations; simulation findings always prove its efficacy. This paper therefore presents a complex method of frequency regulation in power systems using fuzzy logic and combined PI PD control to deal with problems caused by RES integration and EV charging. The proposed technique enhances the frequency stability in order to enhance both the reliability and efficiency of power grid operations due to the ever-changing energy demands and conditions.

Design of Multi-Competitors Winner-Take-All Neural Networks Based on DNA Strand Displacement for Molecular Pattern Recognition

DNA strand displacement technology (DSDT) provides flexible and powerful technical support for DNA molecular computing. DNA-based neural networks with Winner-Take-All (WTA) strategy has a great potential for nonlinear calculation. However, so far it has been limited to achieving the simultaneous competition of two competitors. Optimizing the calculation model and reducing system response time to recognize complex and functional molecular patterns remains a huge challenge. Here a novel neural network with WTA strategy based on DSDT was constructed, which allowed three competitors to participate in the competition at the same time. Firstly, the feasibility of the three-competitor WTA neural network was proved by 9-bit pattern recognition. Then the three-competitors WTA neural network was further extended to larger scale pattern recognition, which successfully recognized 64-bit letters A, B, and C and 100-bit handwritten digits 0, 2, and 4, respectively. Simulations showed that when recognizing the same target patterns with same number bits, compared with two-competitors WTA neural network, the three-competitors WTA network only used down to two-thirds DNA strands, and the system response time was reduced by more than ten times. This paper demonstrated the efficient recognition ability of the three-competitor WTA neural network, which is expected to be used to identify more complex information.

Design of a Decentralized PID Controller Using the Relative Gain Array Technique for a Coupled Flotation Process

This study focuses on the design of the decentralized Proportional-Integral-Derivative (PID) controller for a coupled flotation system. It emphasizes the difficulties encountered when dealing with interconnected Multiple-Input Multiple-Output (MIMO) systems that cannot be effectively regulated using traditional linear feedback controllers. The significance of this article lies in its examination of how the Relative Gain Array (RGA) can be utilized to mitigate the impact of process connections. With the use of a thoughtful selection of single-loop pairings, the decentralized controller is created following the RGA technique. The decentralized controller that has been developed serves the purpose of reducing the influence caused by process interfaces. A set-point tracking technique, based on Internal Model Controllers (IMCs) is adopted in the design. The system time response and overshoot were also analyzed. The paper proposed a decentralized control mechanism and provided a comprehensive process for applying it to both the system under investigation and different MIMO industrial processes. The performance of the suggested closed-loop system was simulated and confirmed using Matlab/Simulink.

Transient Response Analysis of Reset PID Control Systems

Reset controllers have demonstrated their efficacy in enhancing transient responses, such as the overshoot and response time in motion control systems. Designing these systems to meet specific transient requirements requires a method for analyzing transient responses. However, the inherent nonlinearity of reset control systems presents challenges in this regard, limiting their widespread application. This study introduces a novel method for analyzing the step responses of closed-loop reset control systems. By decomposing the step response of the reset system into piece-wise functions, with each piece-wise function computed based on linear systems, this analysis method offers new insights into understanding reset systems. Experimental validation conducted on eleven reset Proportional-Integral-Derivative (PID) control systems implemented on a precision motion stage confirms the effectiveness of the proposed method. The experimental results also underscore the applicability of the method as a tool for selecting optimized parameters and reset control structures to achieve enhanced transient responses.