Selection Of Optimal Parameters Research Articles

Selection of optimal irradiation parameters for a medical laser for nonablative tumor removal in the periorbital area

Selection of optimal irradiation parameters for a medical laser for nonablative tumor removal in the periorbital area

Analysis of forces on nodules during Coandă-effect-based hydraulic collection

The nodule pickup device is a crucial component of a deep-sea mining system. It is widely perceived that leveraging water flow for the separation and retrieval of nodules is a promising approach. A series of experiments and numerical simulations were conducted to examine the impact of non-dimensional parameters on the force characteristics and flow field of the particle in Coandă-effect-based hydraulic collection. The results showed that the lift coefficient of the particle initially increased before subsequently diminishing from the jet nozzle to the rear. Notably, the lift coefficient α reached its maximum at the convex curved surface between x/d = −0.17 and 0.33. Furthermore, a distinct linear correlation was established between the maximum lift coefficient αmax and Froude number Fr across varying h/d, and an empirical formula for predicting the lift coefficient was developed through data fitting. Upon experimental validation, the prediction exhibited a maximum error of less than 20%. Additionally, numerical simulations revealed that the particle significantly influenced the flow dynamics within the collection area, and the flow field characteristics and the particle forces can be corroborated with each other. The findings not only provided a reliable quantitative tool for assessing the particle force but also facilitated precise predictions of collection performance, aiding in the selection of optimal operational parameters in deep-sea hydraulic collection.

Computational Fluid Dynamic Optimization of Micropatterned Surfaces: Towards Biofunctionalization of Artificial Organs

Modifying surface topography to prevent surface-induced thrombosis in cardiovascular implants allows endothelialization, which is the natural thrombo-resistance of blood-contacting surfaces, and is deemed to be the only long-term solution for hemocompatible materials. We adapted a simulation framework to predict platelet deposition on a modified surface and developed an optimization strategy to promote endothelial retention and limit platelet deposition. Under supraphysiological bulk shear stress, a maximum of 79% linear coverage was achieved. This study concludes that the addition of microtrenches promotes endothelial retention and can be improved through the optimal selection of geometric parameters.

Human Vision as A Multi-Circuit Mathematical Model of the Automated Control System

Abstract The paper contains a proposal an original, extended mathematical model of an automatic system of human vision reaction to a forcing light pulse. A comprehensive mathematical model of the vision process was proposed in the form of an equation described in the frequency (dynamics) domain. Mathematical modelling of human senses is very important. It enables better integration of automation systems with a human cooperating with them, also as an automation system. This provides the basis for reasoning based on a mathematical model instead of intuitive reasoning about human reactions to visual stimuli. A block diagram of the proposed system with five human reaction paths is given. The following can be distinguished in the scheme: the main track consisting of: the transport delay of the eye reaction, the transport delay of the afferent nerves, the inertia of the brain with a preemptive action, the transport delay of the centrifugal nerves and the inertial and transport delay of the neuromotor system. In addition, the scheme of the system includes four tracks of negative feedback of motor and force reactions: upper eyelid, lower eyelid, pupil and lens. In the proposed model, the components of each path along with their partial mathematical models are given and discussed. For each reaction path, their overall mathematical models are also given. Taking into account the comprehensive models of all five reaction paths, a complete mathematical model of the automatic system of human reaction to a forcing light impulse is proposed. The proposed mathematical model opens up many possibilities for synchronizing it with mathematical models of many mechatronics and automation systems and their research. Optimizing the parameters of this model and its synchronization with specific models of automation systems is difficult and requires many numerical experiments. This approach enables the design of automation systems that are better synchronized with human reactions to existing stimuli and the selection of optimal parameters of their operation already in the design phase. The proposed model allows, for example, accurate determination of difficulty levels in computer games. Another example of the use of the proposed model is the study of human reactions to various situations generated virtually, for example in flight simulators and other similar ones.

Enhancing EDM Productivity for Plastic Injection Mold Manufacturing: An Experimental Optimization Study.

Electrical erosion molding (EDM) is an unconventional machining technology widely used in the manufacture of injection molds for plastics injection molding for the creation of complex cavities and geometries. However, EDM productivity can be challenging, directly influencing mold manufacturing time and cost. This work aims to improve EDM productivity in the context of mold manufacturing for plastics injection molding. The research focuses on the optimization of processing parameters and strategies to reduce manufacturing time and increase process efficiency. Through a rigorous experimental approach, this work demonstrates that the optimization of EDM parameters and strategies can lead to significant productivity gains in the manufacture of plastic injection molds without compromising part quality and accuracy. This research involved a series of controlled experiments on a Mitsubishi EA28V Advance die-sinking EDM machine. Different combinations of pre-cutting parameters and processing strategies were investigated using copper electrodes on a heat-treated steel plate. Productivity was evaluated by measuring the volume of material removed, and geometrical accuracy was checked on a coordinate measuring machine. The experimental results showed a significant increase in productivity (up to 61%) by using the "processing speed priority" function of the EDM machine, with minimal impact on geometric accuracy. Furthermore, the optimized parameters led to an average reduction of 12% in dimensional deviations, indicating improved geometric accuracy of the machined parts. This paper also provides practical recommendations on the selection of optimal EDM processing parameters and strategies, depending on the specific requirements of plastic injection mold manufacturing.

Technological Aspects of using 3D Printing Software in Construction

The relevance of introducing additive technologies of 3D printing in construction is to decrease the technological cycle of construction, and minimize expenditure and material costs, while reducing the duration of the development planning process and execution, eliminating inaccuracies and defects, as well as optimizing and automate the process. The technological aspect of 3D printing lies in the optimal selection of parameters based on the created 3D model using effective software. The purpose of the study is to determine the quality indicators of bricks obtained by 3D printing from special concrete. The approach to investigating this matter is rooted in ascertaining the compressive strength of concrete through non-invasive testing techniques, gauging the density of concrete using measurement methods, and validating the efficacy of integrating 3D printing technologies in construction by the method of comparative analysis. The effectiveness of introducing additive technologies of 3D printing was proven drawing upon quality indicators of concrete obtained on a 3D printer which meets all the requirements necessary for its implementation in the construction field. The selected characteristics of concrete compressive strength are fundamental to the basics of selecting a brand of concrete to be utilized in construction. The selected software demonstrated the capacity to generate flawless 3D designs. The result of the study shows a significant reduction in the number of workers involved in the construction process by 38–45%, depending on the chosen method of using 3D printing; a decrease of construction time by 33–42% when printing individual elements, blocks used in production, and by 61–72% when using a printer directly on the construction site; reduction of raw materials costs by 14–28%; as well as reduction of construction downtime by 25%. The results obtained indicate the need to develop and enhance progressive additive technologies for 3D printing in construction.

A comprehensive validation of GRA with ant colony optimization for robust optimal design: A case study on the forming process

Sheet metal forming design involves numerous factors and controlling all these parameters to prevent cracks is a major challenge to maintain the quality. Therefore, the careful selection of process parameters is critical for ensuring product longevity. Several researchers employed different techniques for optimal selection of process parameters but consistency among the techniques and validation of results in real time scenarios will play a pivotal role in determining the suitable parameters. Therefore, the current work presents a validation method of Grey Relational Analysis (GRA) results with Ant Colony Optimization (ACO) for the robust optimum design with an illustrative example of forming process. Al 6061 – T6 alloy was chosen as a material for deep drawing process and L27 Orthogonal Array was planned to conduct experiments with four input parameters such as blank thickness, Die and Blank Temperature, Die Speed, Lubrication at three levels each. Punch force and Thickness variation were considered as the output parameters. GRA Technique was implemented to find the best optimal combination and regression equation was developed to predict the nature of objective function. These results were given as an input to ACO, a nature inspired algorithm to search for the optimal solution with in the design space. Sensitivity analysis has also been performed to highlight the effectiveness of the proposed solution in terms of solution quality and computational efficiency. The ACO method, GRA technique and the experimental results are almost similar with a margin of acceptable deviation. The results indicated the robustness of the projected approach in achieving the quality of solution.

An optimal parameterized Newton-type structure-preserving doubling algorithm for impact angle guidance-based 3D pursuer/target interception engagement

The proposed strategy, finite-time state-dependent Riccati equation (FT-SDRE)-based impact angle guidance, is generally employed to solve the 3D pursuer/target interception model with fixed lateral accelerations. This article expands its application to a general scenario where the lateral acceleration of a target may change. To achieve this, we approximate the accelerations of the azimuth and elevation angles of the target in the inertial frame via second-order finite difference schemes and develop a high-performance FT-SDRE algorithm with structure-preserving doubling algorithms (SDAs). As a result, the update frequency of the controller can be increased, and better guidance of the pursuer can be obtained to address the high maneuverability of the target during the entire interception procedure. At every state of the FT-SDRE, a modified Newton–Lyapunov method is employed to solve the continuous algebraic Riccati equation (CARE), and a new simplified SDA with adaptive optimal parameter selection is proposed for solving the associated Lyapunov equation. Our numerical results demonstrate that the FT-SDRE algorithm accelerated by our proposed methods is approximately three times faster than the FT-SDRE algorithm, in which the MATLAB functions icare and lyap are used to solve the CARE and the Lyapunov equation, respectively, throughout the entire interception procedure. In other words, the control frequency can be increased threefold. In our benchmark cases where the target maneuvers with nonlinear lateral acceleration, the target can be intercepted earlier via the proposed FT-SDRE algorithm.

Optimal Selection of Active Jet Parameters for a Ducted Tail Wing Aimed at Improving Aerodynamic Performance

The foldable tail of the box-type launch vehicle poses a risk of mechanical jamming during the launch process, which is not conducive to the smooth completion of the flight mission. The integrated nonfolding ducted tail proposed in this article can solve the problem of storing the tail in the launch box. Moreover, traditional mechanical control surfaces have been eliminated, and active jet control has been adopted to control the pitch direction of the flight attitude, which can improve the structural reliability of the tail wing. By studying the effects of parameters such as momentum coefficient, jet hole position, jet hole height, and jet angle on improving the aerodynamic performance of ducted tail wing, relatively good jet parameters are selected. Research has found that compared with jet hole height and jet angle, momentum coefficient and jet hole position are more effective in improving the aerodynamic performance of ducted tail wings. Under a trailing edge jet, a relatively good jet condition occurs when the jet hole height is equal to0.25% of the aerodynamic chord length, and the jet angle is equal to 0°. At this time, with the increase of the jet momentum coefficient, the effect of increasing the lift of the ducted tail wing is the best. Finally, a comparative analysis is conducted on the lift and drag characteristics between the ducted tail wing and traditional tail wing, and it is found that the ducted tail wing can generate lift at a 0° attack angle and will not stall in the high attack angle range of 12°~22°, with broad application prospects.

Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection.

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models-long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)-were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method's efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management.

A study on breast cancer image classification based on particle swarm algorithm and transfer learning

Breast cancer is a major disease that poses a serious threat to the lives and health of women. A new framework was proposed to address the common challenges of high dimensional and data imbalances in image classification. This framework integrates particle swarm optimization (PSO) and transfer learning into a convolutional neural network model based on the ResNet34 architecture. The respective strengths complement each other to enhance the performance and efficiency of the classification model. Through parameter optimization and functional selection of PSO, the global search of the model has been improved. Transfer learning lets the model use large pre-trained datasets to learn more quickly on small sample datasets, which is especially helpful in areas where there are a lot of images that don’t have labels. Experimental findings reveal that our framework attains a 97.83% accuracy rate on the dataset and notably shortens the training cycle, demonstrating its effectiveness in improving breast cancer diagnosis performance with small sample sizes.

A HYBRID MEREC-TOPSIS APPROACH FOR THE IMPROVEMENT OF PHYSICAL VAPOUR DEPOSITED TITANIUM CARBO-NITRIDE COATING

Titanium carbo-nitride (TiCN) comes under the metal nitride group which can provide a combined property of titanium carbide (TiC) and titanium nitride (TiN) coating. TiCN Coating possess an excellent mechanical and tribological, properties which leads its application in many industries particularly in the Automotive and aerospace industries. TiCN films can be very hard and strong based on the composition and synthesis process. Physical Vapor Deposition (PVD) technique is widely used for the preparation of thin film coating because of its ability to precisely control the composition and thickness of the coatings. The properties of the developed coating significantly affect by the PVD process parameters and their levels. Multi-Criteria Decision Making (MCDM) methods are widely used in many sectors for the selection of process parameters based upon some specific criteria. In the present work TiCN coatings were synthesized by using one of the most widely used PVD method called magnetron sputtering. L16 orthogonal array was used as a design of experiment for the experimental work by varying the sputtering process parameters like bias voltage, N2 flow rate and substrate to target distance. After the synthesis, the developed films were tested using atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Nanoindentation. From the characterization three response parameters such as hardness (H), Modulus of elasticity (E) and surface roughness (Ra) of the coating has been considered. A hybrid method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (MEREC-TOPSIS) approach has been considered for the for the process parameters selection. The results have also been compared with other weight calculation techniques. In all the cases it is observed that experiment no 16 and 15 have got the 1st and second rank respectively. However, experiment no 1 is the last rank in all the cases. From the correlation analysis it is found a strong positive correlation between MEREC-TOPSIS and other methods. The method provides a significant advancement in the selection of optimal parameters, ensuring enhanced coating properties crucial for industrial applications.

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

Enhancement of material properties of SS 316 L base metal by TIG cladding process

The Tungsten Inert Gas (TIG) welding process was utilized to apply stainless steel (SS) 304 filler cladding onto the stainless steel (SS) 316 L base metal. This cladding resulted in an enhancement of the base metal's properties. An optical study and scanning electron microscope (SEM) analysis conducted to investigate the metallurgical properties revealed refined grain structures, indicating improved deposition of the SS 304 filler. The optical survey classified the different zones as base metal, weld metal, and transition line, with the transition line distinguishing the deposition of the filler metal through the cladding process. The refined grain structures, optimal process parameter selection (current, gas flow rate, and travel speed at a minimal level), and double-pass cladding have significantly influenced microhardness and tensile test values. The cladding of SS 304 fillers on SS 316 L alloys demonstrated improvement in the microhardness and tensile properties by 42.5 % and 13.2 %, respectively. Further investigation into the nozzle gun's heat input, current, and travel speed showed that maintaining a consistent travel speed with a steady heat source had a substantial impact on the material properties of the base metal.

Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Path Optimization of Two-Posture Manipulator of Apple Packing Robots

Automated packing is urgently needed in apple production. This paper proposes an improved genetic algorithm fused with an optimal parameter selection algorithm to optimize the two-posture manipulator working path of packing robots. First, the structure and working principle of the packing robot were designed. Second, the kinematics and packing paths of the two-posture manipulator were analyzed. Finally, the path optimization method for the two-posture manipulator was introduced. The method was based on the improved genetic algorithm by using a two-level coding and region crossover operator. The parameter values can be automatically determined by the optimal parameter selection algorithm. Ten repeated comparative tests show that the total packing time is 23.86 s under the working conditions of four grasping points and fourteen placing points. The optimal performance of the proposed algorithm is better than that of the traditional genetic algorithm, and the average optimization amplitudes are 14.63%, 15.42%, 16.24%, and 13.82% for 9-groove, 12-groove, 14-groove, and 16-groove trays, respectively. The proposed algorithm can effectively prevent the premature convergence problem of the traditional genetic algorithm and the optimization process instability problem, improve the range of optimization, and reduce the manipulator working time during packing.

ECONOMIC EFFICIENCY OF APPLICATION OF OPTIMIZED MILL BED

This paper presents the selection of optimization parameters for the bed geometry of a long metal cutting machine (MCM), the design problem and boundary conditions were given. Five different geometries were designed for further mathematical analysis to meet the requirements of the problem. Static analysis of the mathematical model of the bed shells was performed by finite element method (FEM) using BETA CAE System tool, ABAQUS, which showed that by using stiffeners in the form of two variable pitch spirals and three hollow tubes, as well as changing the design at the connection point of the beam support to the bollards of the long bed of the metal cutting machine, the stresses that occurred in these places at an angle of 90° were reduced. The geometries were chosen in such a way that it was possible to reduce the stresses occurring at the point of cutting, to reduce the deflection, to increase the rigidity of the structure. The economic calculation aimed at determining the cost of a long metal-cutting machine tool with the proposed geometry has been carried out. The analysis of the unit cost of the product (long bed of the metal-cutting machine tool made of polymer concrete with optimized geometry) has been carried out, the calculation of the annual economic effect from the use of the proposed method of manufacturing has been carried out. Keywords: MCM, FEM, statistical analysis, product cost, economic calculation.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Coupled mode theory-based analytical model of a ring resonator refractive index sensor incorporating bending loss and dispersion

This paper presents an analytical model of a silicon nitride-based 2D ring resonator refractive index (RI) sensor using coupled mode theory (CMT). The proposed model decomposes the ring resonator into two coupling regions and employs coupled-mode equations to describe input and output amplitudes via scattering matrix analysis. The proposed sensor, operating with varying refractive indices in the background cladding, demonstrates a sensitivity of 218 nm/RIU and a total quality factor of 1198. A comprehensive analysis of the bending loss in the proposed sensor is conducted, elucidating its impact on sensitivity, coupling quality factor, and intrinsic quality factor. This analysis aids in the selection of optimal ring resonator parameters, including radius, width, and gap, to achieve superior sensing performance. Furthermore, the paper examines the effect of dispersion on sensitivity and quality and compares the results with those obtained from CMT-based silicon core ring resonator and disk resonator RI sensors. This study provides valuable insights for the design and optimization of high-performance silicon nitride-based RI sensors for various applications.

Investigation of urban low-altitude long-range atmospheric links for FSO coherent communication

Due to the complexity of low-altitude atmospheric channel conditions, the commercial application of FSO communication systems is less prevalent than satellite applications. This is mainly because there is a trade-off between achieving system stability through active compensation technology and maintaining low costs. Different application scenarios and atmospheric channel states correspond to different optimal structural parameter selections, which is crucial for low-cost FSO systems without complex compensation mechanisms. Therefore, measuring actual channel conditions is essential. One existing issue is that current research does not sufficiently discuss the connection between theoretical analysis and practical experiments in FSO communication, thus, establishing models and analysis methods is necessary that can accurately predict the performance of actual FSO atmospheric communication systems. We have developed a comprehensive FSO model to evaluate the effects of channel and structural factors on system performance. This model is vital for cost-effective parameter selection in system design and for analyzing actual system performance. Additionally, we established 6.6 km and 12.7 km FSO transmission links in an urban low-altitude environment. Subsequently, we conducted tests on atmospheric attenuation, channel statistical characteristics, and DPSK/QPSK coherent communication performance. Based on the analysis that combines experiments with theory, we have deduced the atmospheric attenuation of FSO links. The channel statistical characteristics provided the statistical properties of the two links under weak, moderate, and strong turbulence, along with fitting curves. Long-term channel statistical experiments indicate that the channel is most stable during the early morning hours of the day, and the 12.7 km communication experiment without turbulence correction technology is only feasible at this time. Furthermore, we presented a method for using theory to evaluate the performance of actual FSO systems in random atmospheric channels.