Selection Of Parameters Research Articles

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

Multi-soliton solutions of coupled Lakshmanan-Porsezian-Daniel equations with variable coefficients under nonzero boundary conditions

Abstract This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan-Porsezian-Daniel equations with variable coefficients under nonzero boundary conditions. These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers. By analyzing the Lax pair and the Riemann-Hilbert problem, we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system. Furthermore, we study the impacts of group velocity dispersion or fourth-order dispersion on soliton behaviors. Through appropriate parameter selection, we observe various nonlinear phenomena, including the disappearance of solitons after interaction and their transformation into breather-like solitons, as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.

Multiobjective Optimization of Hard Turning on OHNS Steel Using Desirability and TOPSIS Approaches

Machining hard materials with 45–48 HRC is difficult in turning operation because of the improvident cutting parameter selections for the operation. The OHNS (AISI/SAE-01–48HRC) steel is mainly preferred for the production of shafts, gears, cams, and press tools. The OHNS material was turned at a dry state using VP-coated carbide inserts. The seventeen experimental trials were designed by central composite design (CCD) with different levels of cutting parameters, like feed rate, cutting speed, and depth of cut. Design Expert-11 software desirability approach and TOPSIS (Technique for Order Preference by Simulating the Ideal Solution) were used to analyse the experimental results to obtain a single optimal solution that defines better results on metal removal rate (MRR) and surface finish (Ra). RSM solution with 81.3% desirability, the cutting speed of 60 m/min, feed rate of 0.08 mm/rev, and depth of cut 1 mm as the optimal cutting parameters; similarly, TOPSIS algorithm calculation identifies the cutting parameter combinations, such as 40 m/min cutting speed, 0.09 mm/rev feed rate, and 1 mm depth cut to enrich the quality of the machined steel; however, the desirability approach cutting parameter setting is better for the surface finish achievement, while TOPSIS solution is better to obtain significant MRR. The confirmation test results validated for the predicted values of both approaches; as such, the experimental results were maintained better convenience than the predicted one. For the optimum cutting parameter combinations, an MRR of 22.032 gm/min and surface roughness of 0.781 μm were obtained at 60 m/min cutting speed, 0.08 mm/rev feed rate, and 1 mm depth of cut.

Phase tracking using a Kalman filter based on probability density distribution in frequency-scanning interferometry

Frequency-scanning interferometry (FSI) utilizing external cavity diode lasers (ECDL) stands out as a potent technique for absolute distance measurement. Nevertheless, the inherent scanning nonlinearity of ECDL and phase noise pose a challenge, as it can compromise the accuracy of phase extraction from interference signals, thereby reducing the measurement accuracy of FSI. In this study, we propose a composite algorithm aimed at mitigating non-orthogonal errors by integrating the least-squares and Heydemann correction technique. Furthermore, we employ Kalman filtering for precise phase tracking. We introduce a parameter selection strategy based on the statistical distribution of instantaneous frequency to achieve the fusion estimation of phase observation values and theoretical models, which starts a new perspective for the application of multi-dimensional data fusion in FSI measurement. Through simulation and experimental validation, the efficacy of this approach is confirmed. The experimental results show promising outcomes: with an average phase error of 0.12%, a standard deviation of less than 1.7 µm in absolute distance measurement, and an average positioning accuracy error of 0.29 µm.

A novel coordinate transformation stability criterion and parameter selection for grid-connected inverter

The negative resistance of grid-connected inverter (GCI) and the increasing number of GCI in power grid pose great challenges to the stability of GCI. This paper proposes a coordinate transformation stability criterion (CTSC) based on the single-input single-output (SISO) impedance model for GCI system. A low complexity fractional polynomial model of GCI is derived by the Pade approximation and the SISO impedance model in the αβ domain. CTSC is proposed by applying the Nyquist stability criterion, which simplifies the graph trajectory criterion to a numerical criterion. Since the effect of parameter on stability is usually limited to the neighborhood, average parameter assessment metric (APAM) is proposed based on CTSC to evaluate the impact of each parameter over a wide range. Additionally, joint parameter assessment metric (JPAM) is proposed to simultaneously adjust multiple parameters on stability. Finally, compared with the three criteria for conservatism analysis, CTSC possesses the lowest conservatism. The theory proposed in this paper is verified by experiment.

Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot.

Feasibility and optimization of19F MRI on a clinical 3T with a large field-of-view torso coil.

The purpose of this work is to: 1) demonstrate fluorine-19 (19F) MRI on a 3T clinical system with a large field-of-view (FOV) multi-channel torso coil 2) demonstrate an example parameter selection optimization for a 19F agent to maximize the SNR-efficiency for SPGR, bSSFP, and phase-cycled bSSFP (bSSFP-C), and 3) validate detection feasibility in ex vivo tissues.&#xD;Methods: Measurements were conducted on a 3.0T Discovery MR750w MRI (GE Healthcare, USA) with an 8-channel 1H/19F torso coil (MRI Tools, Germany). Numerical simulations were conducted for perfluoropolyether (PFPE) to determine the theoretical parameters to maximize SNR-efficiency for the sequences. Theoretical parameters were experimentally verified, and the sensitivity of the sequences was compared with a 10-minute acquisition time with a 3.125x3.125x3mm3 in-plane resolution. Feasibility of a bSSFP-C was also demonstrated in phantom and ex vivo tissues.&#xD;Results: Flip angles (FA) of 12 and 64 degrees maximized the signal for SPGR and bSSFP, and validation of optimal FA and receiver bandwidth showed close agreement with numerical simulations. Sensitivities of 2.47, 5.81, and 4.44 ms^(-0.5) mM^(-1) and empirical detection limits of 20.3, 1.5, and 6.2 mM were achieved for SPGR, bSSFP, and bSSFP-C, respectively. bSSFP and bSSFP-C achieved 1.8-fold greater sensitivity over SPGR (p<0.01).&#xD;Conclusion: bSSFP-C was able to improve sensitivity relative to simple SPGR and reduce both bSSFP banding effects and imaging time. The sequence was used to demonstrate the feasibility of 19F MRI at clinical FOVs and field strengths within ex-vivo tissues.

Study on the distribution of average wind speeds at a mountainous bridge site for structural durability design

Conventional wind speed distribution methods (e.g., Rayleigh distribution and Weibull distribution) may not adequately capture complex characteristics of wind fields in mountainous areas. To address this problem, this study proposes a semi-parametric mix method for modeling the distribution of average wind speeds based on the combination of nonparametric Kernel Density Estimation (KDE) and Generalized Pareto Distribution (GPD). In the proposed method, KDE focuses on capturing the distribution in the main part of average wind speeds, while GPD aims at performing the distribution in terms of those in the extreme part. The segment point (i.e., the threshold) between KDE and GPD distributions is determined based on the combination of conditional mean excesses criterion and empirical rule. Meanwhile, the selection of modeling parameters should ensure that the mix distribution model is continuous and differentiable at the identified threshold point. Then, the commonly-used conditional probability model is further introduced to describe the wind direction distribution. Finally, a case study based on the measured 10-min average wind speeds at a mountainous bridge site is employed to demonstrate the effectiveness of the proposed method. The results indicate that: (1) the distribution of omnidirectional average wind speeds in the mountainous bridge site exhibits an obviously single-peak characteristic, while those considering wind directionality present a certain bimodal characteristic; (2) the proposed method can effectively describe wind speed distributions with different statistical characteristics, and the fitting accuracy outperforms the frequently-employed Weibull distribution model.

Development of a Model to Support the Management of the Resistance Welding Process

The work was carried out as part of a project to improve the corrosion resistance of welded joints. The optimal selection of input parameters reduces the consumption of welded material as well as the negative impact on the environment. The samples were welded by electrical resistance welding, more precisely by cross-wire welding. Electric resistance welding is a process for welding with electricity in which of high electrical resistance is used to generate heat at the contact point of the welded parts. Steel wire S235 with a diameter of 4 and 3 mm was used. The article shows the creation of the model to predict the percentage of setdown and weld strength as a function of the input parameters, welding current and welding time. The first task was to create a Design of experiments in which the process parameters or the range of input parameters are determined. The Design of the experiments was created for both wire diameters. This covers the range of parameters with which a welded joint of wires can be realised. A statistical analysis of the process follows, where it was found that for 4 and 3 mm diameter wires the percentage setdown increases with increasing welding current and time. An increase in strength as a function of welding current and welding time is also observed, but the results overlap in most cases and the range of their values is greater than that of setdown. The appearance of the weld was analysed for each sample. The visual inspection revealed seven categories of welds, which were divided into two groups, i.e. good and poor welds. All results were listed in a table with the percentage of expectation for a particular category of weld appearance. Based on the analysis, a model for determining the welding result parameters depending on the specified input parameters was developed, which can be used in practise to support the management of the electric resistance welding process.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Chaos generation of superconducting quantum bits coupled with LC resonant circuits

The dynamics of chaos have been widely used in nonlinear science, such as neural networks, extreme event statistics, and the biophysics of chaos self-organization. Superconducting qubits are artificial atoms based on the Josephson junction structure of nonlinear superconducting devices, offering high design flexibility and ease of coupling and control. In this paper, the generation of chaos through the coupling between superconducting qubits and LC resonant circuits is investigated. By varying the ratio of Josephson energy to charge energy, the coupling coefficient, and the energy of the external driving field, the generation and control of chaos within the system are numerically analyzed. This study provides theoretical support for parameter selection ensuring the confidentiality and fidelity of quantum information transmission based on the chaotic dynamics of superconducting qubits.

An enhanced hybrid scheme for ship roll prediction using support vector regression and TVF-EMD

In order to represent the complex characteristics of ship roll motion such as nonlinearity, uncertainty, and time-varying dynamics thus improving the ship roll prediction accuracy, an enhanced hybrid prediction scheme is proposed by using improved swarm intelligent optimization, time series decomposition, and machine learning. Firstly, the novel time-varying filtering-based empirical mode decomposition (TVF-EMD) is employed to decompose the ship roll motion data into multiple mode components with dynamic time-varying characteristics, which immunes of the mode mixing and intermittency problems of traditional empirical mode decomposition (EMD). Then, the support vector regression is utilized to train and predict mode components. Finally, the predicted results of individual components are reconstructed to achieve the final ship roll prediction angles. To avoid the unfavorable influence of manual parameter selection for TVF-EMD, an improved black widow optimization algorithm is employed to optimize the parameter configuration. The feasibility and effectiveness of the enhanced hybrid model are validated by ship roll prediction simulation based on the measured data of M.V. YuKun at sea. The experimental results show that the enhanced hybrid scheme can improve the prediction accuracy of ship roll motion and outrank those by using methods of EMD, ensemble empirical mode decomposition, and variational mode decomposition.

Quantitative analysis of social influence and digital piracy contagion with differential equations on networks

Illegal file sharing of copyrighted contents through popular file sharing networks poses an enormous threat to providers of digital contents, such as, games, softwares, music and movies. Though empirical studies of network effects on piracy is a well-studied domain, the dynamics of peer effect in the context of evolving social contagion has not been enough explored using dynamical models. In this research, we methodically study the trends of online piracy with a continuous ODE approach and differential equations on graphs to have a clear comparative view. We first formulate a compartmental model to study bifurcations and thresholds mathematically. We later move on with a network-based analysis to illustrate the proliferation of online piracy dynamics with an epidemiological approach over a social network. We figure out a solution for this online piracy problem by developing awareness among individuals and introducing media campaigns, which could be a valuable factor in eradicating and controlling online piracy. Next, using degree-block approximation, network analysis has been performed to investigate the phenomena from a heterogeneous approach and to derive the threshold condition for the persistence of piracy in the population in a steady state. Considering the dual control of positive peer influence and media-driven awareness, we examine the system through realistic parameter selection to better understand the complexity of the dynamics and suggest policy implications.

Hand Vibration Reduction Using Nonlinear Vibration Absorber for the Vibro-Impact Hammer Model

Abstract This work investigates the effectiveness of a passive vibration control device to minimize the amplitude of vibrations from a hand-held impact machine (HHIM) transmitted to a hand-arm system (HAS). For this purpose, we employ a simplified lumped parameter model of an HHIM and the hand-arm system consisting of lumped masses, springs, and viscous dampers. For a better understanding of the system dynamics, we use a vibro-impact model for a more accurate representation of HHIM–ground interactions. We then examine the effectiveness of a cubic nonlinear vibration absorber in minimizing these undesirable vibrations via a nonlinear bifurcation analysis of the system. A parametric study on the numerical bifurcation analysis of the system reveals the criticality of different design parameters of the proposed nonlinear vibration absorber on the system dynamics. Our findings demonstrate that an appropriate selection of absorber parameters could significantly change the amplitude and the periodicity of vibrations transmitted to the hand. Furthermore, we observe the existence of complex motions such as period-2, period-4, and chaotic attractors in the system for the given values of the absorber and operating parameters.

A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network

Introduction: An array of hazards currently exists in cyberspace, prompting extensive research to tackle these concerns. Intrusion Detection Systems (IDS) are a mechanism used to provide security in Wireless Sensor Networks (WSN). The IDS continue to encounter significant challenges in accurately identifying unknown attacks. Conventional Intrusion Detection Systems (IDS) commonly rely on Deep Learning (DL) algorithms, which utilise binary classifiers to classify attacks. The data dimension attribute is affected inside large-scale high-dimensional data sets. Methods: This research introduces a hybrid GFSO (HGFSO) model combined with Deep Learning Driven Intrusion Detection (HGFSO-DLIDS) to tackle this problem. The HGFSO approach is developed by merging the parameter selection methods of the Felis Margarita Swarm Optimisation (FMSO), the Grampus optimisation algorithm (GOA), and the Deep Convolutional Neural Network (DCNN) with BiLSTM (Bidirectional Long Short-Term Memory) algorithm. Results: The model training utilised real-time traffic statistics, including the KDDCup 99 and WSN-DS datasets. After being trained and validated using the datasets, the model's performance is assessed by multi-class classification, achieving accuracy rates of 99.89% and 99.64% respectively.Conclusion: As a result, this occurrence leads to a decrease in the overall effectiveness of detecting assaults. Deep learning may enhance the creation of an intrusion detection system by eliminating complex features in the raw data, resulting in a more precise classification method.

A Study of the Thermal Management and Discharge Strategies of Lithium-Ion Batteries in a Wide Temperature Range

The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery module during the charge/discharge process. The effects of working temperature, current rate, and convective heat transfer coefficient are investigated by establishing an electrochemical and thermal model. The results are obtained by conducting numerous parameterized scans to analyze the system’s state across various operating conditions, enabling the determination of its temperature and the selection of appropriate cooling measures accordingly. Based on the internal and external conditions of battery operation, parameter selection corresponding to the operating range is divided into several stages, with thermal management strategies provided for each stage. The existing framework facilitates the design of battery packs equipped with efficient thermal management strategies, thereby enhancing the battery systems’ reliability and performance. Furthermore, it aids in establishing optimal operational and safety boundaries for batteries.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively.

Investigation of Machining Parameters for Turning Process

Manufacturing industries mainly concentrates on how to minimize the cost of the products. The process planers have to prepare the process plan of the product based on the availability of the recourses. All the information is available in the plan including inspection and delivery date of the product. Based on the recommendation of the manufacturing and inspection methods, components are produced at affordable cost. Most of the research work concentrated on selection of machining parameters using optimization techniques and also prove that the machining time / cost were minimized for the particular components / operations. In this work, tool nomenclature is to be considered for the investigation for selection of machining parameters. Work presents an experimental investigation of the influence of the three most important machining parameters of depth of cut, feed rate and spindle speed on surface roughness during turning of aluminium alloy. In this study, the design of experiment which is a powerful tool for experimental design is used to optimize the machining parameters for effective machining of the work piece. L9 orthogonal array experimental design method as well as analysis of variance (ANOVA) is used to analyse the influence of machining parameters on MRR &amp; Machining Time. Two different grade of aluminium alloy i.e. AL 6063 &amp; AL 6068 were machined with input parameters of depth of cut and speed. The output parameters are MRR and machining time. Based on the results empirical equations are formed and optimized results are validation .The optimal results are recommended to manufacturing industries.

Rola i wykorzystanie badań wysokoprzepustowych w poszukiwaniu związków chemicznych o działaniu terapeutycznym

With the development of medicine and the aging population, the demand for more effective therapies is escalating. A tool that facilitates the discovery and introduction of new therapeutic drugs is High-Throughput Screening (HTS). These tests, consisting of a wide set of various assays, allow testing hundreds of thousands of compounds in a short period of time. The aim is to accurately identify active compounds that could become potential therapeutic candidates in the pharmaceutical industry. HTS is the first step in the quest for potential drugs, therefore it is one of the crucial tests that determine whether a particular drug candidate will be discovered. In this review, different stages of high-throughput screening studies will be described, as well as methods utilized in these studies. The necessary steps in the optimization of these tests, selection of equipment, automation and key quality control parameters for reliably performed screening will also be presented.

Selection of long-term shear strength parameters for strain softening geosynthetic interfaces (2023 IGS Rowe Lecture)

The behavior of strain-softening geosynthetic interfaces that can lead to progressive failures in lined containment facilities has been a source of confusion in slope stability evaluations for over 30 years. The paper presents fifteen mechanisms that can potentially induce displacements along strain-softening interfaces, along with measures that can be considered to reduce strain-softening displacement. New quantifications of shear strength variability that can be caused by manufacturing, installation, and construction practices are introduced. Guidance and recommendations are given that are applicable to numerical continuum as well as limit-equilibrium approaches to assist in selecting appropriate geosynthetic shear strength parameters for containment facilities that have strain-softening interfaces. While most of the paper focuses on deep-seated critical interfaces for high normal stress bottom liners, low normal stress veneer covers are also addressed.