Time Error Research Articles

Localization for surface microseismic monitoring based on arrival time correction and VFOM

Abstract Unconventional resources have emerged as the primary source to meet the escalating demand for energy consumption, with hydraulic fracturing standing out as an effective means of boosting production. The utilization of microseismic monitoring is crucial for acquiring real-time or semi-real-time extension information of the fracture network to guide the fracturing process. The precise positioning of microseismic events is a fundamental aspect of microseismic monitoring. Traditional methods relying on (relative) arrival time significantly impact positioning accuracy due to picking errors. While waveform-based methods offer high accuracy, they require precise velocity models and are time-consuming. To overcome challenges associated with arrival time pickup and velocity accuracy, we introduce a virtual field optimization method (VFOM) based on arrival time correction. Initially, an equivalent velocity model is established, and the arrival time difference resulting from the model transformation of the master event is calculated to correct the observed arrival time of the target event. Subsequently, we match detector pairs, establish hyperboloids based on the corrected arrival time difference, and employ the intersection point of all hyperboloids as the positioning result. After that, we use the location results of the master event to enhance the accuracy of the target event. Finally, we apply the proposed method to both synthetic test and field datasets, demonstrating a significant improvement in the positioning accuracy and stability provided by the novel method. The robustness against arrival time error renders it a suitable choice for surface monitoring applications where signal quality is compromised. Furthermore, the simplified velocity model significantly diminishes the computational requirements in the positioning process, enhancing its efficiency, and consequently holds vast potential for application in real-time monitoring.

An innovative approach for establishing the unconstrained melting correlation of phase change materials encapsulated within hot water tanks

This study introduces a novel methodology that utilizes stepwise regression-elimination to establish an unconstrained melting correlation for phase change materials (PCMs) encapsulated within hot water tanks. This approach progressively eliminates the impact of influencing parameters, enabling the development of correlations without presupposing its specific form. Moreover, it remains robust even when the total melting time exhibits a nonlinear relationship with influencing parameters under logarithmic conditions. To validate the methodology, a detailed exploration was conducted for PCMs encapsulated in cylindrical latent heat storage units (LHSUs) within hot water tanks. Initially, a comprehensive analysis was performed to understand the relationship between various influencing parameters and the total melting time. Subsequently, correlations were established for the liquid fraction and total melting time, respectively. The findings revealed that the total melting time does not always exhibit a linear correlation with influencing factors under logarithmic conditions. The established correlations successfully captured the relationships between influencing parameters and the liquid fraction or total melting time. When comparing the numerical results of the correlation, the errors for the liquid fraction ranged from −15 % to +15 %. Furthermore, the absolute error for the total melting time was below 4.83 %, demonstrating the high accuracy of the proposed method.

Ensemble Random Forest-based Gradient Optimization based Energy Efficient Video Processing System for Smart Traffic Surveillance System

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.

QCASBC: An algorithm for hardware-in-the-loop simulation of 3-link RRR robotic manipulator

In this paper, a quick convergence adaptive structure is proposed for trajectory tracking of an articulated type robotic manipulator using the Hardware in the Loop (HIL) simulation technique. A novel Nonlinear Quick Convergence Adaptive Sliding Backstepping Control (QCASBC) algorithm is implemented on a C2000 real-time controller board. The performance of the proposed control algorithm is inspected concerning the sliding mode PID (SM-PID) control technique to estimate its correlation with the proposed algorithm. The experimental part of the HIL simulation tests has been carried out on a simulated model of a three-link serial robot manipulator. A dynamic model of the robotic manipulator has been developed using Matlab Simulink software and its performance is analyzed using the HIL technique via a C2000 real-time controller for tracking the desired trajectory. Results show that the speed of convergence while tracking the desired trajectory of the manipulator is better in the proposed algorithm. It is also estimated that the positional error of the QCASBC algorithm is superior to the SM-PID control algorithm. The observed average angle error is improved by 14% and the average response time error is improved by 11% by using the proposed QCASBC algorithm compared to the SM-PID approach.

Time delay calibration techniques for receiving arrays in SNSPD-based optical communication systems

Superconducting nanowire single photon detectors (SNSPDs) have attracted considerable attention in the field of free-space optical communications, especially for ultra-long-distance communications. Calibrating the inherent time delay in the optoelectronic circuit of SNSPD arrays is critical to enabling photon detection with high time accuracy. In this paper, we propose two methods for estimating the inherent timing error: Gaussian fitting calibration (GFC) and maximum-likelihood calibration (MLC). In particular, MLC estimates the timing error by taking into account the deadtime and jitter characteristics of SNSPD. Experimental results demonstrate the effectiveness of the proposed methods.

Design of an artificial neural network and proportional‐integral‐derivative controller using particle swarm optimization for Boeing 747‐400 aircraft pitch control

AbstractThis paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747‐400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747‐400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747‐400 with small disturbance theory linearization methods and ANN based non‐linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747‐400 commercial aircraft was presented. The ANN controller outperformed the PID‐PSO and PID‐GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID‐GUI, PID‐PSO, and ANN controllers for pitch control of the Boeing 747‐400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID‐PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory.

The Symmetric Dual Inductor Hybrid Converter for Direct 48V-to-PoL Conversion

This work introduces the symmetric dual inductor hybrid (SDIH) dc-dc converter topology, which is suitable for large conversion ratios where regulation is required, such as direct 48 V to point-of-load (PoL) applications. A dickson-type switched capacitor network is used to effectively produce two interleaved PWM outputs with a greatly reduced voltage amplitude relative to the input voltage, allowing the subsequent magnetic volume to be reduced while retaining modest switching frequencies. Distinct from related variations, part count is significantly reduced while both even and odd order switched capacitor networks can be used with straightforward split-phase control; allowing either network type to achieve complete soft-charging of all flying capacitors. Additionally, charge flow is uniformly distributed through all elements, with equal capacitor and inductor values being preferred. Subsequently this topology is expected to simplify component selection, improve electrical and thermal performance, and reduce cost. Furthermore, analysis is presented that calculates precise phase durations without making small ripple assumptions, revealing up to a 75% timing error in cases where either inductor or capacitor ripple is ignored. Finally, a discrete prototype validates this analysis and demonstrates very high measured power densities of 1,029 W/in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> , 754 W/in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> , and 663 W/in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> for 48 V input and regulated output voltages of 3 V, 2 V, and 1 V, respectively, while switching at a frequency of 750 kHz.

Disturbance rejection based consensus controller for swarm system

This article introduces a robust controller designed to facilitate consensus among leader-follower agents in a swarm system. The method employs algebraic graph theory to establish a consensus framework and incorporates a sliding surface-based uncertainty and disturbance estimator (SUDE) to mitigate the effects of nonlinearities, external disturbances, and parametric uncertainty inherent in swarm systems. The stability of the SUDE controller is examined using Lyapunov theory. Importantly, this approach operates without prior knowledge of disturbance bounds, relying instead on the dynamics of disturbance error. Simulation studies demonstrate the effectiveness of the proposed control methodology, showing that the SUDE controller expedites consensus achievement while ensuring smooth and non-chattering control behavior. The proposed SUDE controller demonstrates a notable improvement in achieving position and velocity consensus in both the X and Y directions, with a 75% and 66% faster convergence rate, respectively, when compared to the conventional sliding mode controller (SMC). Additionally, the performance of the proposed controller is assessed under various external disturbances and parametric uncertainty conditions. Comparative analyses using root mean square error (RMSE) and integral absolute time error (IATE), both based on consensus error, confirm the effectiveness of the proposed SUDE consensus controller.

Controlling a Single Tank Liquid Level System with Classical Control Methods and Reinforcement Learning Methods

In this study, the control of the single tank liquid level system used in control systems has been carried out. The control of the single tank liquid level system has been performed with the classic PI, modified PI, state feedback with integrator action, and Q learning algorithm and SARSA algorithms, one of the artificial intelligence methods. The tank system to be modelled was carried out using classical physics, namely Newton's laws. Then, the mathematical model obtained of the system that are continuous model in time is acquired. The originality of the study; the non-linear liquid tank system is controlled by classical controllers and reinforcement methods. For this purpose, the system was firstly designed to model the system, then the system has been linearized at a specific point in order to design classic PI, modified PI, and state feedback with integral. After that, agents of the Q Learning algorithm and SARSA algorithms were trained for the system. Then the agents have controlled the single-level tank system. The results of the classic controllers and supervised controllers are contrasted with regard to performance criteria such as rising time, settling time, overshoot and integral square error. Consequently, Q learning method has produced 0.0804-sec rising time, 0.943 sec settling time and 0.574 integral square errors. So, Q learning algorithm has produced and exhibited more thriving and successful results for controlling single liquid tank system than PI, Modified PI, state feedback controllers and SARSA.

Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic

Reduced precision number formats have become increasingly popular in various fields of computational science, as they offer the potential to enhance energy efficiency, reduce silicon area, and improve processing speed. However, this is often at the expense of introducing arithmetic errors that can impact the accuracy of a system. The optimal balance must be struck, judiciously choosing a number format using as few bits as possible, while minimising accuracy loss.In this study, we examine one such format, posit arithmetic as a replacement for floating-point when conducting spiking neuron simulations, specifically using the Izhikevich neuron model. This model is capable of simulating complex neural firing behaviours, 20 of which were originally identified by Izhikevich and are used in this study. We compare the accuracy, spike count, and spike timing of the two arithmetic systems at different bit-depths against a 64-bit floating-point gold-standard. Additionally, we test a rescaled set of Izhikevich equations to mitigate against arithmetic errors by taking advantage of posit arithmetic’s tapered accuracy.Our findings indicate that there is no difference in performance between 32-bit posit, 32-bit floating-point, and our 64-bit reference for all but one of the tested firing types. However, at 16-bit, both arithmetic systems diverge from the 64-bit reference, albeit in different ways. For example, 16-bit posit demonstrates an 18× improvement in accumulated spike timing error over a 1000ms simulation compared to 16-bit floating-point when simulating regular (tonic) spiking. This finding holds particular importance given the prevalence of this particular firing type in specific regions of the brain. Furthermore, when we rescale the neuron equations, this error is eliminated altogether. Although current Posit Arithmetic Units are no smaller than Floating Point Units of the same bit-width, our results demonstrate that 64-bit floating-point can be replaced with 16-bit posit which could enable significant area savings in future systems.

MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.

3D Refraction Travel Time Accuracy Study in High Contrasted Media

Among all the existing methods to solve the eikonal equation, three methods are chosen to verify accuracy, symmetry, reciprocity and error propagation along large offsets of refracted waves in seismic near surface exploration context. Performance is extremely highlighted nowadays and accuracy is being neglected, then an eikonal solver poorly explored in geoscience is used. A classical solver, the Fast Iterative and the modified Fast Sweeping Method are applied in three modeling schemes: a simple two layers model, a large four layers and a complex benchmark model. The three methods compute the first arrival of refracted waves in high contrast media and the results are compared to the analytical solution. A circular geometry is considered in all experiments to explore the method applicability using full azimuth angles. On the first scheme, the errors in travel time are computed among the three methods using different model sample spacing and we discuss accuracy, symmetry and reciprocity of first arrivals. On the second scheme, three circular receivers are placed in different offsets to check errors along refracted wave propagation. Finally, the third scheme, four shots are strategically positioned over the SEG/EAGE Overthrust model in order to compare the full acoustic wavefield with the eikonal solvers and then check the similarities. Although the focus is on methods accuracy, the algorithm run time is also considered and the comparison shows that the modified Fast Sweeping Method is the most accurate. The most computational efficient eikonal solver is the Fast Iterative Method, but its geoscience applicability needs to be cautious, because of its inaccurate results.

Алгоритм совместной оценки и компенсации ошибок временной и частотной синхронизации сигналов с возможностью регулирования точности оценки

An analysis of the influence of synchronization errors on signal processing is presented, a review of existing approaches for estimating and compensating for time and frequency synchronization errors is given, a new approach is proposed that allows joint evaluation of time and frequency offsets, providing a more accurate estimate compared to existing analogues and, moreover, regulating accuracy of fractional error estimation of both time and frequency synchronization.

Алгоритм совместной оценки и компенсации ошибок временной и частотной синхронизации сигналов с возможностью регулирования точности оценки

Представлен анализ влияния ошибок синхронизации на обработку сигнала, дан обзор существующих подходов оценки и компенсации ошибок временной и частотной синхронизации, предложен новый подход, позволяющий совместно оценить смещения по времени и частоте, обеспечить более точную оценку по сравнению с существующими аналогами и, более того, регулировать точность оценки дробной ошибки и временной, и частотной синхронизации.

Automated assessment of hydrostatic pressure resistance in woven fabrics using the enhanced YOLOv8 network

Hydrostatic pressure resistance serves as a crucial metric in the assessment of water resistance in woven fabrics. The expeditious, precise, and efficient conduct of hydrostatic pressure tests holds paramount importance in advancing the progress and production of high-performance textiles. Addressing the challenges posed by intricate printed patterns on woven fabrics, and the presence of small, widely scattered water droplets, the study leverages the enhanced YOLOv8 model to develop a machine vision-based automated detection technique for assessing water resistance in woven fabric. The proposed method incorporates convolutional block attention module attention mechanisms into the backbone and neck network, replaces the path aggregation network structure of YOLOv8 with the bidirectional feature pyramid network structure, and introduces a dedicated detection head for small targets. These enhancements facilitate accurate identification of water outlet points on the woven fabric and precise recording of frame positions, enabling the precise measurement of hydrostatic pressure. Validation of the proposed model is conducted through a series of comparative experiments utilizing a self-collected dataset. The experimental results underscore the exemplary performance of the proposed model, evidenced by an AP0.5 score of 92.18%, showcasing superior overall efficacy in comparison with alternative models. Notably, the target localization time error is found to be less than 2 s when contrasted with manual detection. This method substantially enhances the accuracy of water droplet detection and localization in hydrostatic pressure resistance testing of woven fabric, characterized by complex surface patterns, thereby contributing to refinement of hydrostatic pressure testing methodologies in woven fabric analysis.

Prediction of Next Word in Balochi Language Using N-gram Model

Balochi Language is among the oldest languages, spoken by approximately 10 million people worldwide. The Balochi language has been spoken for a very long period. In comparison to other languages like English, Urdu, French etc. it has a research gap in Natural language processing (NLP). The next word prediction system is one of the techniques of NLP for suggesting standardization and corpus collection. This research aims to provide a next-word prediction system and a corpus with no ambiguity for the Balochi language. N-gram model for the next word prediction has been utilized, i.e. Unigram, Bigram, Trigram, Quad-gram, and so on. A trained model has been embedded in an application after being evaluated extrinsically and intrinsically. It plays a crucial role in typing through a keyboard and helps users to type faster. Additionally, it helps native users to have fewer typing errors in less time. The results of the research show that Five-gram model has the highest performance of 93% while Quad-gram model has 80% and Trigram model has 76% respectively.

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle’s design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Most two-wheeler electric motors are mounted on the rear side of the wheel, which has a limited speed range. Therefore, an analysis has been performed to increase the vehicle’s speed range using a dual motor, from 45 km hr−1 to 62 km hr−1, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. The proposed method has several advantages, including ease of implementation, a consistent convergence characteristic, and high computational efficiency. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.

Fully discrete Galerkin scheme for a semilinear subdiffusion equation with nonsmooth data and time-dependent coefficient

We couple the L1 discretization of the Caputo fractional derivative in time with the Galerkin scheme to devise a linear numerical method for the semilinear subdiffusion equation. Two important points that we make are nonsmooth initial data and time-dependent diffusion coefficient. We prove the stability and convergence of the method under weak assumptions concerning regularity of the diffusivity. We find optimal pointwise in space and global in time errors, which are verified with several numerical experiments.

Digital PID Controller based Speed Control of DC Motor

PID controllers are commonly utilised in industry because of their ease of use and adaptability. To adjust these controllers for desirable responses, a number of tuning rules have been developed. This work aims to regulate a robotic vehicle's speed by utilising a PID controller to adjust the motor speed in reaction to impediments. We offer a technique that uses an Arduino microcontroller to automatically tune the PID controller to control the speed of a DC motor. An Arduino Uno device interfaces the DC motor with Simulink and functions as an affordable data collecting device. In order to maintain the motor speed within setpoint limitations, the PID controller modifies it in response to object distance data obtained from sensors. The study suggests utilising a PID controller to regulate the DC motor's angular speed and a microcontroller to implement it in hardware. The L298 motor controller is utilised, the encoder sensor determines angular speed, while the Arduino Uno is utilised for data processing. The proportional controller affects rising time, overestimation, and steady-state errors in the hardware implementation. Overshoot and undershoot are impacted by the integral controller, although overshoot is very slightly impacted by the derivative controller. Top of Form Keywords— Arduino Controller, LCD Display, PID Controller, DC Motor, Motor Driver etc.