Optimal Settings Research Articles

A novel hybrid multi-criteria optimization of 3D printing process using grey relational analysis (GRA) coupled with principal component analysis (PCA)

Fused deposition modeling (FDM) is renowned as a prominent approach in the realm of 3D printing, where objects are built layer by layer using a heated nozzle to extrude melted materials. This research was conducted to identify the most effective FDM process variables to enhance tensile strength while simultaneously reducing surface roughness. Polylactic Acid (PLA) was chosen to fabricate test samples, showcasing the applications of 3D printing. In the course of this research, we conducted a series of 27 experiments to investigate the fundamental relationship between the parameters and the corresponding responses. The central aim of this study lies in optimizing the input variables viz. printing speed, layer thickness, and carbon deposition (C-deposition) for the technological manufacturing process of embossing parts in the context of Industry 4.0. To enhance both tensile strength and surface roughness simultaneously, a new hybrid method has been suggested. This approach integrates grey relational analysis (GRA) with principal component analysis (PCA) to determine the optimal combination of process parameters in the 3D printing process. Notably, the experiment trial exhibited the highest grey relational grade (GRG), indicating optimal process parameter settings at a printing speed of 100 mm s−1, layer thickness of 0.1 mm, and C-deposition of 15 mg respectively. Additionally, mathematical models are created through response surface methodology to explore the impact of FDM parameters on the grey relational grade. The findings from this study can be utilized in various industries and applications where FDM 3D printing is employed.

Enhanced Irradiance Levels using Synergistically Engineered Monochromatic Wavelength Ultraviolet-C Arrays Configuration

Contamination in healthcare environment poses risk of microbial transmission to healthy people. Recently, conventional ultraviolet (UV) technologies, particularly low-pressure mercury lamps have caught researcher’s attention for disinfection. Nonetheless, drawbacks such as hazardous components, limited lifespan, warm-up time, maintenance and fragility have driven researchers to delve into light emitting diode (LED) technologies as an appealing alternative. However, due to the diminishing intensity exhibited by LEDs over extended distances, prior studies primarily focused on short-distance investigations. Consequently, the phenomenon remained insufficient owing to the limited exploration in this field. Furthermore, frequently investigated LEDs in ambient settings, potentially created a gap in completely understanding the capability for irradiance attainability. Hence, this study aimed to overcome this limitation by evaluating the effectiveness of UV-C LEDs with synergistic arrangements within an enclosed chamber, offering optimum settings for examining irradiance performance. The analyzed recorded data aimed to identify trends in emission performance over different distances (5 to 60 cm), providing insights into how the arrangement of LEDs could influence irradiance output. The study unveiled that the synergistic effects of 6, 8, and 10-LED, when strategically engineered, significantly contributed to enhanced intensity. Notably, 10-SEMW LEDs exhibited a remarkable ability to maintain significant irradiance levels across extended distances. This was evident in the range between 0.037 and 0.016 mW.cm⁻² observed at 5 and 60 cm, respectively. In conclusion, the findings stress the importance of strategically arranging LEDs in a synergistic configuration to achieve optimal irradiance levels across a diverse range of spatial parameters for effective long-range disinfection.

Model of Artifi cial Intelligence for Forming the Federal Reserve as a Driver for Digitalization of the Economy

The article discusses theoretical issues regarding the personnel reserve, the formation of the "Random Forest" DL model for the selection of applicants for the Russian federal reserve in modern conditions. The relevance of the study lies in the fact that in modern conditions artifi cial intelligence systems based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems are increasingly being used. The scientific novelty lies in the fact that in the study conducted, a deep learning model DL-model "Random Forest" was formed, which allows the selection of applicants when forming the federal reserve. The practical significance of the study is that the results obtained can be recommended for implementation in practice to provide support for management decisions in the formation of the federal reserve. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. A questionnaire was developed to identify the qualitative characteristics of applicants, which made it possible to create a "Random Forest" DLmodel dataset. The criterion for the success of the predictive properties of the DL model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree, which has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change; the input parameters to various trees were selected randomly by the algorithm.

Suppression of Railway Catenary Galloping Based on Structural Parameters’ Optimization

Railway catenary galloping, induced by aerodynamic instability, poses a significant threat by disrupting the electric current connection through sliding contact with the contact wire. This disruption leads to prolonged rail service interruptions and damage to the catenary’s suspension components. This paper delves into the exploration of optimizing the catenary system’s structure to alleviate galloping responses, addressing crucial parameters such as span length, stagger dropper distribution, and tension levels. Employing a finite element model, the study conducts simulations to analyze the dynamic response of catenary galloping, manipulating structural parameters within specified ranges. To ensure accurate and comprehensive exploration, the Sobol sequence is utilized to generate low-discrepancy, quasi-random, and super-uniform distribution sequences for the high-dimensional parameter inputs. Subsequent to the simulation phase, a genetic algorithm based on neural networks is employed to identify optimal parameter settings for suppressing catenary galloping, taking into account various constraints. The results gleaned from this investigation affirm that adjusting structural parameters can effectively diminish the galloping amplitude of the railway catenary. The most impactful strategy involves augmenting tension and reducing span length. Moreover, even when tension and span length are fixed, adjusting other parameters demonstrates efficacy in reducing galloping amplitudes. The adjustment of messenger-wire tension, dropper distribution, and stagger can achieve a 22.69% reduction in the maximum vertical galloping amplitude. Notably, maintaining a moderate stagger value and a short steady arm–dropper distance is recommended to achieve the minimum galloping amplitude. This research contributes valuable insights into the optimization of railway catenary systems, offering practical solutions to mitigate galloping-related challenges and enhance overall system reliability.

Performance evaluation of quantitative material decomposition in slow kVp switching dual-energy CT.

Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet. This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings. To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance. Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging. Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings.

Optimization of flip angle and radiofrequency pulse phase to maximize steady-state magnetization in three-dimensional missing pulse steady-state free precession.

Missing pulse (MP) steady-state free precession (SSFP) is a magnetic resonance imaging (MRI) pulse sequence that is highly tolerant to the magnetic field inhomogeneity. In this study, optimal flip angle and radiofrequency (RF) phase scheduling in three-dimensional (3D) MP-SSFP is introduced to maximize the steady-state magnetization while keeping broadband excitation to cover widely distributed frequencies generated by inhomogeneous magnetic fields. Numerical optimization based on extended phase graph (EPG) simulation was performed to maximize the MP-SSFP steady-state magnetization. To limit the specific absorption rate (SAR) associated with the broadband excitation in 3D MP-SSFP, SAR constraint was introduced in the numerical optimization. Optimized flip angle and RF phase settings were experimentally tested by introducing a linear inhomogeneous magnetic field in a range of 10-20 mT/m and using a phantom with known T1/T2 relaxation and diffusion parameters at 3 T. The experimental results were validated through comparisons with EPG simulation. Image contrasts and molecular diffusion effects were investigated in in vivo human brain imaging with 3D MP-SSFP with the optimal flip angle and RF phase settings. In the phantom measurements, the optimal flip angle and RF phase settings improved the MP-SSFP steady-state magnetization/signal-to-noise ratio by up to 41% under the fixed SAR conditions, which matched well with EPG simulation results. In vivo brain imaging with the optimal RF pulse settings provided T2-like image contrasts. Diffusion effects were relatively minor with the linear inhomogeneous field of 10-20 mT/m for white and gray matter, but cerebrospinal fluid showed conspicuous signal intensity attenuation as the linear inhomogeneous field increased. Numerical optimization achieved significant improvement in the steady-state magnetization in MP-SSFP compared with the RF pulse settings used in previous studies. The proposed flip angle and RF phase optimization is promising to improve 3D MP-SSFP image quality for MRI in inhomogeneous magnetic fields.

Theta burst stimulation: what role does it play in stroke rehabilitation? A systematic review of the existing evidence

Various post-stroke dysfunctions often result in poor long-term outcomes for stroke survivors, but the effect of conventional treatments is limited. In recent years, lots of studies have confirmed the effect of repetitive transcranial magnetic stimulation (rTMS) in stroke rehabilitation. As a new pattern of rTMS, theta burst stimulation (TBS) was proved recently to yield more pronounced and long-lasting after-effects than the conventional pattern at a shorter stimulation duration. To explore the role of TBS in stroke rehabilitation, this review summarizes the existing evidence from all the randomized controlled trials (RCTs) so far on the efficacy of TBS applied to different post-stroke dysfunctions, including cognitive impairment, visuospatial neglect, aphasia, dysphagia, spasticity, and motor dysfunction. Overall, TBS promotes the progress of stroke rehabilitation and may serve as a preferable alternative to traditional rTMS. However, it’s hard to recommend a specific paradigm of TBS due to the limited number of current studies and their heterogeneity. Further high-quality clinical RCTs are needed to determine the optimal technical settings and intervention time in stroke survivors.

Influence of generator parameters on cutting width during WEDM process

The primary objective of this paper is to rigorously analyse the influence of selected parameters on the cut width in the context of Wire Electric Discharge Machining (WEDM). The parameters under investigation, each varied at different levels, include pulse width, servo reference mean voltage, time interval between two pulses, discharge frequency, and wire feed speed. As the workpiece material, sintered carbide K10 was used. Data for the response variable, cut width, was meticulously collected using two high-precision instruments: the Zoller Genius 3s and the Alicona InfiniteFocusSL. Among the parameters studied, the mean reference voltage emerged as the most influential variable. It holds a commanding lead with a percentage contribution of as much as 82.4 %, making it nearly 15 times more impactful than the second most influential parameter, which is pulse width. At its lowest setting, or first level, the mean reference voltage results in a record low cut width. Conversely, at its highest setting, or third level, it leads to a record high cut width. This comprehensive analysis not only quantifies the relative importance of each parameter but also provides valuable insights into the optimal settings for achieving desired cut widths. The findings have significant implications for improving the efficiency and precision of WEDM processes.

Enhanced decentralised fractional-order control using tree seed optimisation and singular value analysis

Higher-order multi-input multi-output systems pose challenges in terms of stability and control performance due to increased complexity and interactions between multiple subsystems. Hence, this research work proposes a novel comprehensive approach to design a decentralised fractional order control strategy for higher-order systems using tree seed algorithm. The design focuses on enhancing control performance by imposing constraints on performance indices – the integral of absolute error and closed-loop amplitude ratio. Further, this work incorporates stability analysis through the singular values of the sensitivity function. The proposed decentralised fractional-order proportional-integral-derivative (FOPID) controller parameters are optimised using the tree seed algorithm. Besides, the stability is examined using the singular values of the sensitivity function. The tree seed algorithm explores the parameter space and converges to optimal controller settings that satisfy the imposed constraints by ensuring system stability. Simulation studies are conducted on various higher-order systems to analyse the proposed approach. The findings demonstrate that the decentralised control with tree seed algorithm-based FOPID controller achieves improved control performance by reducing the loop interactions among subsystems with enhanced stability. Furthermore, robustness is ensured by considering output uncertainty and parameter variations.

Optimal filters for ERP research I: A general approach for selecting filter settings.

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

A data-driven active learning approach to reusing ML solutions in scientific applications

Artificial intelligence can revolutionize scientific projects, but scientists face challenges in reusing, integrating, and deploying cost-effective and high-quality machine learning solutions. Determining suitable algorithms and parameters is difficult, especially for non-programmer scientists. Some algorithms, like deep learning-based methods, offer flexibility but require extensive training on annotated data. This poses a hurdle in labor-intensive tasks like biological image segmentation that relies on expert annotations.In this paper, we present a data-driven framework designed to assist scientists in selecting, reusing, and training machine learning solutions for microscopy image segmentation. The framework is based on establishing a mapping between object morphology features and the optimal segmentation algorithms and settings for individual objects. This mapping is iteratively refined through a combination of unsupervised learning and active learning iterations. To expedite convergence, objects are initially clustered based on their morphology. In each active learning iteration, the most informative and uncertain samples are selected and queried within a specific cluster. Through a biological case study, we demonstrate that our method enables the selection and training of segmentation algorithms specific to object types. Additionally, the selective requests for user input significantly reduce the number of user interactions required for this task.

An innovative pulsed current arc welding technology for armor steel: Processes, microstructure, and mechanical properties

This study presents a novel pulsed current arc welding technology, specifically pulsed current gas tungsten arc welding (PC-GTAW), single and double pulse gas metal arc welding (SP/DP-GMAW), and dual process welding (D-process). This study aims to investigate the effectiveness of these welding techniques for armor steel weldments. This research focused on investigating the welding processes, resulting microstructures, and mechanical properties of welded joints. Based on the macrography analysis, it was determined that the weldments exhibit no signs of porosity, inclusions, or inadequate penetration. This finding substantiates the optimum settings for the weldment production process. The weldment optical microstructure, fusion, and heat-affected zone were examined in the weld interface. The microstructural properties of the FZ include δ-ferrite, austenite, bainite, and acicular martensite. The HAZ has a bainite-untempered martensite microstructure. The chemical composition and variations across the weldment (base, weld and HAZ) were identified. The grain borders contained greater quantities of Cr, Ni, Mn, Si, and Mo than those of the grain cores. EBSD revealed average weld zone grain sizes of 17.7 µm, 32.6 µm, 30.8 µm, and 24.6 µm for PC-GTAW, SP-GMAW, DP-GMAW, and D-Process, respectively.Furthermore, the hardness measurements showed a progressive transition across different regions. Additionally, the D-Process-fabricated material exhibited a maximum ultimate tensile strength of 872 MPa and an elongation of 4%. The mechanical properties exhibited by this new process variant exceed those of other weldments, indicating its potential for fabricating armor steel. The impact toughness demonstrated superior performance in all the weldments. Within this context, the new use of pulsed current advanced welding methods has shown the ability to generate welded parts of exceptional quality.

Alleviation of radiation combined skin injury in rat model by topical application of ascorbate formulation

Purpose This research endeavor was undertaken to elucidate the impact of an innovative ascorbate formulation on the regeneration process of full-thickness excision wounds in a rat model exposed to whole-body gamma irradiation, replicating conditions akin to combat or radiation emergency scenarios. Materials and Methods We established a comprehensive rat model by optimizing whole body γ-radiation doses (5–9 Gy) and full-thickness excision wound sizes (1–3 cm2) to mimic radiation combined injury (RCI). The developed RCI model was used to explore the healing potential of ascorbate formulation. The study includes various treatment groups (i.e., sham control, radiation alone, wound alone, radiation + wound, and radiation + wound + formulation). The ascorbate formulation was applied twice daily, with a 12-hour gap between each application, starting 1 hour after the initiation of the wound. The healing potential of the formulation in the RCI context was evaluated over 14 days through hematological, molecular, and histological parameters. Results The combination of a 5 Gy radiation dose and a 1 cm2 wound was identified as the optimal setting to develop the RCI model for subsequent studies. The formulation was used topically immediately following RCI, and then twice daily until complete healing. Treatment with the ascorbate formulation yielded noteworthy outcomes and led to a substantial reduction (p < .05) in the wound area, accelerated epithelialization periods, and an increased wound contraction rate. The formulation’s localized healing response improved organ weights, normalized blood parameters, and enhanced hematopoietic and immune systems. A gene expression study revealed the treatment up-regulated TGF-β and FGF, and down-regulated PDGF-α, TNF-α, IL-1β, IL-6, MIP-1α, and MCP-1 (p < .05). Histopathological assessments supported the formulation’s effectiveness in restoring cellular architecture and promoting tissue regeneration. Conclusion Topical application of the ascorbate formulation in RCI resulted in a significant improvement in delayed wound healing, leading to accelerated wound closure by mitigating the expression of inflammatory responses.

A systematic review of hyperparameter tuning techniques for software quality prediction models

BACKGROUND: Software quality prediction models play a crucial role in identifying vulnerable software components during early stages of development, and thereby optimizing the resource allocation and enhancing the overall software quality. While various classification algorithms have been employed for developing these prediction models, most studies have relied on default hyperparameter settings, leading to significant variability in model performance. Tuning the hyperparameters of classification algorithms can enhance the predictive capability of quality models by identifying optimal settings for improved accuracy and effectiveness. METHOD: This systematic review examines studies that have utilized hyperparameter tuning techniques to develop prediction models in software quality domain. The review focused on diverse areas such as defect prediction, maintenance estimation, change impact prediction, reliability prediction, and effort estimation, as these domains demonstrate the wide applicability of common learning algorithms. RESULTS: This review identified 31 primary studies on hyperparameter tuning for software quality prediction models. The results demonstrate that tuning the parameters of classification algorithms enhances the performance of prediction models. Additionally, the study found that certain classification algorithms exhibit high sensitivity to their parameter settings, achieving optimal performance when tuned appropriately. Conversely, certain classification algorithms exhibit low sensitivity to their parameter settings, making tuning unnecessary in such instances. CONCLUSION: Based on the findings of this review, the study conclude that the predictive capability of software quality prediction models can be significantly improved by tuning their hyperparameters. To facilitate effective hyperparameter tuning, we provide practical guidelines derived from the insights obtained through this study.

Numerical Optimization on Aircraft Wake Vortex Decay Enhancement

Blowing air at the end of the airport runway can accelerate the decay of the near-ground aircraft wake vortex, thereby reducing the negative impact of the vortex on the following aircraft. However, the benefits of accelerating wake dissipation vary for different blowing parameters, so it is necessary to set appropriate parameters in order to obtain better acceleration results. Because of the high cost of traditional optimization methods, this research uses a Kriging surrogate model to optimize the blowing parameters. The results show that the current optimization algorithm can deal with the global optimization problem well. After obtaining 205 sample points, the response surface model of the blowing parameters and blowing yield was accurately established. A relatively optimal parameter setting range was given, and numerical simulation shows that the current parameter setting can obtain improved benefits from accelerated vortex dissipation. In addition, since the optimization process is partially dimensionless, the above optimization results can be used to achieve multi-objective design, that is, the same set of blowing devices can efficiently accelerate the dissipative process of the tail vortices of different aircraft types, thus improving the engineering feasibility of the current blowing method.

Effect of accelerated high-fluence riboflavin and rose bengal-mediated corneal cross-linking on resistance to enzymatic digestion

PurposeThis study evaluated the effect of high-fluence accelerated corneal cross-linking on the resistance to enzymatic digestion, assessing two chromophore/light combinations: riboflavin/UV-A light (RF/UV-A) and rose bengal/green light (RB/green).MethodsFreshly prepared ex-vivo porcine corneas (n = 189) were divided into 8 groups groups. Group A corneas were unirradiated controls without chromophore soaking (A0), or soaked with riboflavin (A1) or rose bengal (A2). Group B corneas underwent accelerated epi-off RF/UV-A CXL at fluences of 5.4 J/cm² (B1), 10 J/cm² (B2), or 15 J/cm² (B3). Group C corneas underwent accelerated epi-off RB/green CXL at fluences of either 10 J/cm² (C1) or 15 J/cm² (C2). Following CXL, all corneas were digested in 0.3% collagenase-A solution, and the time until complete dissolution was measured.ResultsNon-irradiated controls exposed to RF and RB enhanced corneal resistance to collagenase digestion, with RB having a stronger effect than RF. RF/UV-A-treated corneas showed significantly increased digestion resistance with increasing fluence levels. RB/green-treated corneas displayed enhanced digestion resistance with each increase in fluence up to 10 J/cm²; a 15 J/cm² fluence yielded similar digestion resistance times to a 10 J/cm² fluence, suggesting a plateau effect in accelerated RB/green CXL protocols.ConclusionsWhen compared to standard-fluence treatments, high-fluence accelerated epi-off CXL using both riboflavin and rose bengal significantly increases resistance to enzymatic digestion. The optimal settings for clinical protocols might be 15 J/cm² (30 mW/cm² for 8 min 20 s) for RF/UV-A and 10 J/cm² (15 mW/cm² for 11 min 7 s) for RB/Green Light.

Kinetic, Thermodynamic, and Parametric Studies on Batch Synthesis of 1,1‐Diethoxybutane Catalyzed by Amberlyst‐15 Wet

Abstract1,1‐Diethoxybutane is a high‐potential biofuel additive that enhances various fuel properties, which include cetane number, lubricity, biodegradability, and flash point. This work presents a kinetic and thermodynamic study on the acetalization reaction between butanal and ethanol in the presence of Amberlyst‐15 wet in a batch reactor. The experiments were conducted in the temperature range 313–333 K at atmospheric pressure with three different initial molar ratios of reactants and catalyst loadings. An optimal parameter setting to achieve maximum equilibrium conversion of butanal was derived from a parametric study with these three factors. The equilibrium constant for the reaction was determined. Standard thermodynamic properties of the reaction, such as enthalpy, entropy, and Gibbs free energy, were experimentally evaluated. The activity‐based two‐parameter Langmuir‐Hinshelwood‐Hougen‐Watson rate expression was used for reaction kinetics. The activity coefficients were calculated by the UNIFAC method. The kinetic parameters and the activation energy were evaluated from the experimental data. The experimental data and data predicted by the model were found to be in good agreement.

Cloud-SMPC: two-round multilinear maps secure multiparty computation based on LWE assumption

Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide a higher level of data protection while maintaining the benefits of cloud computing. In this case, data can be stored in the cloud and computed through SMPC protocols, thus protecting the privacy and security of the data. However, multiple rounds of information interaction are often required, increasing the communication overhead, and the security strength is limited by the hardness assumption. In this paper, we work to achieve an optimal setting of the number of rounds in secure multi-party computation on the cloud to achieve a sublinear communication overhead and improve the security concept. A 2-round SMPC protocol is constructed in the framework of Universally Composable (UC). A 2-round SMPC protocol is constructed that uses multilinear maps based on the Learning from Errors (LWE) assumption. The participant encodes the input and sends it via broadcast to reduce the interaction, homomorphic computational encoding information for secure access to computational data and secure the SMPC protocol through UC security. This paper extends the participants to multiple parties, reduces the communication rounds to 2, the protocol achieves sublinear communication overhead in poly polynomial time, smaller setup size to poly(k), and static security is achieved.

Unmanned Aerial Vehicle–Light Detection and Ranging-Based Individual Tree Segmentation in Eucalyptus spp. Forests: Performance and Sensitivity

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation.

Optimization of the Control Algorithm for an Active Bearing Support

This article presents a spindle system with an active bearing support for which the author proposed a control system based on a PID controller. This type of solution improves the mechanical properties of this spindle by changing the stiffness, which results in a wider range of permissible rotational speeds, lower power losses and an increased resistance to vibrations of the entire system. This work describes the mechanism of changing the bearing preload. During tests on a real test stand, the correct functioning of the preload control system was checked for various dynamic conditions. In this work, the author describes the controller used to work in a feedback system, explains how the settings for this controller were selected and their optimization. The assessment of the correct operation of the controller is based on minimizing the amplitude of forced vibrations for the front spindle end by actively controlling the preload of the bearings. This publication is intended to present one of the ways to solve the important problem of limiting the maximum values of the vibration amplitude in the spindle system for its wide range of operating conditions. It also shows how important its optimal settings are in control systems.