Group Of Optimal Parameters Research Articles

Application of group decomposition and 1.5-dimensional spectra to acoustic composite fault diagnosis of rolling bearings

Abstract Under the background of strong noise and mutual interference coupling of each fault, the acoustic compound fault diagnosis of rolling bearing is very challenging。 For the composite fault features which are difficult to be extracted due to strong noise interference and uneven distribution of fault intensity, put forward the optimization of swarm decomposition combined with 1.5-dimensional(1.5-d) spectrum method of acoustic composite rolling bearing fault feature separation. The method firstly uses the composite index to iteratively search for the optimal group decomposition threshold value, the adaptive group decomposition of composite fault acoustic signals is realized by optimal parameter group disassembly, and then selects the sensitive components for the decomposed components, and then further analyzes the envelope signal of the sensitive components to reduce the redundancy components and the noise interference, and selects the 1.5-d spectrum to further analyze the envelope signal, thus realizes the effective separation of the composite faults of the rolling bearings acoustic faults. Rolling bearing simulations and experimental acoustic signals verify the validity of the proposed method, and this work gives a new tool for composite fault diagnosis of revolving machinery.

Cube-Evo: A Query-Efficient Black-Box Attack on Video Classification System

The current progressive research in the domain of black-box adversarial attack enhances the reliability of deep neural network (DNN)-based video systems. Recent works mainly carry out black-box adversarial attacks on video systems by query-based parameter dimension reduction. However, the additional temporal dimension of video data leads to massive query consumption and low attack success rate. In this article, we embark on our efforts to design an effective adversarial attack on popular video classification systems. We deeply root the observations that the DNN-based systems are sensitive to adversarial perturbations with high frequency and reconstructed shape. Specifically, we propose a systematic attack pipeline Cube-Evo, aiming to reduce the search space dimension and obtain the effective adversarial perturbation via the optimal parameter group updating. We evaluate the proposed attack pipeline on two popular datasets: UCF101 and JESTER. Our attack pipeline reduces query consumption and achieves a high success rate on various DNN-based video classification systems. Compared with the state-of-the-art method Geo-Trap-Att, our pipeline averagely reduces 1.6× query consumption in untargeted attacks and 2.9× in targeted attacks. Besides, Cube-Evo improves 13% attack success rate on average, achieving new state-of-the-art results over diverse video classification systems.

Study on the effects of the processing parameters on the single tracks and the block support structures fabricated by selective laser melting

In order to prevent the deformation of the part, enhance heat conduction, and establish a stable foundation, additional support structures are typically incorporated beneath the parts during the selective laser melting (SLM) process. These structures are subsequently eliminated once the SLM process is completed, thereby facilitating the attainment of near-net forming. Therefore, their SLM processing parameters should be different from those of the solid parts to obtain the desirable functions and removability. However, there is so far very little research focusing on the optimization of the SLM processing parameters of the support structures compared with the solid objects. In this work, the widely used block support was illustrated as an instance, and an optimization strategy of its SLM processing parameters was provided. The effects of laser power, scanning speed, and layer thickness on the morphology and qualities of single track, support sample, and support tensile sample were systematically investigated. The results showed that the SLM processing parameters have a significant impact on the properties of the block support by regulating the qualities of the single tracks. At last, a group of optimal SLM processing parameters (80 W of laser power, 400 mm/s of scanning speed, 60 μm of layer thickness) was determined for the Ti-6Al-4V titanium alloy, and the corresponding ultimate tensile strength of the support structure reached 416 MPa.

Multi‐Objective Optimization Design of Vacuum Interrupter Structure Based on Improved Artificial Jellyfish Search Algorithm

An improved multi‐objective optimization method based on artificial jellyfish search algorithm is proposed to solve the multivariable and multi‐objective optimization problem of vacuum interrupter with mutual coupling of structural parameters and mutual restriction of performance indexes. The finite element model of 40.5 kV/31.5kA vacuum interrupter is established. The structural parameters of seven important parts such as contact diameter and contact sheet thickness in vacuum interrupter are taken as optimization variables. The optimization objective is to maximize the longitudinal magnetic field strength and minimize the conductor resistance at the peak breaking short‐circuit current of vacuum interrupter. The artificial jellyfish search algorithm is improved on the population position initialization strategy and the population mutation cross‐update strategy. The Pareto front solution of the structural parameters of the vacuum interrupter is obtained by the joint simulation of finite element analysis and numerical calculation software. Finally, the multi‐parameter and multi‐objective optimization design of vacuum interrupter structure is realized. The comprehensive performance index is introduced into the optimal parameter group of jellyfish on the Pareto front. Under the constraint of weight variables, the optimal structural parameters of contact system are obtained. The axial magnetic induction intensity of the optimized vacuum interrupter contact is increased by 13.2%, and the conductor resistance is reduced by 25.4%. The comprehensive performance of vacuum interrupter is improved by 17.8%, and the stability and reliability of the power system are further improved. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Investigation on Multiple Reignitions Caused by Vacuum Circuit Breaker Switching Off Shunt Reactor Considering Contact Travel in Offshore Wind Farms

The dielectric recovery strength (DRS) of vacuum circuit breaker (VCB) interrupter is closely related to the travel characteristic of contact, which determines the overvoltage caused by switching off shunt reactor (SR) in offshore wind farm (OWF). Firstly, the travel characteristic of contact is analyzed in detail. A field experiment of 35 kV substation is performed and the relationship between contact travel and DRS in different periods is obtained. Then, a novel VCB model considering the travel characteristic is proposed and used to study the arc behavior of the VCB interrupter. The mechanism of multiple reignitions and the sensitivity analysis for switching overvoltage are discussed. Finally, a hybrid method of grey relational grade (GRG) and response surface method (RSM) is employed to optimize the contact travel in order to suppress arc reignition. A group of optimal travel parameters of VCB contact are obtained for minimizing the overvoltage peak.

Thermodynamic performance evaluation and power/cooling energy trade-off estimation of an isolated system driven by nuclear energy

The wide temperature range requirement of components in an isolated system offers the possibility for power regulation for combined cooling and power system by adjusting the cooling energy supplement amount. To achieve the flexible operation and combined energy output for the independent nuclear power system, this paper proposes an isolated combined cooling and power system driven by nuclear energy to track power load by making an energy trade-off between net power output and cooling capacity by combing the steam Rankine cycle with the ammonia-water mixture cycle. Parametric analysis and single objective optimization are carried out to explore the effect of the key parameters on system performance. Multi-objective optimization is presented to obtain the optimal parameters group. The regulation capacity of power and cooling is analyzed by trade-off strategy for the proposed system based on the design condition. Results show that the optimal thermal efficiency of the proposed system is 31.60 % based on the single objective optimization. The rated thermal efficiency is 32.8 % with 2229.7 kW power energy and 1045.7 kW cooling energy output based on multi-objective optimization. In addition, the power load can be regulated from 78.1 % to 111.8 %, and the corresponding cooling load is adjusted from 176.1 % to 58.7 %. Furthermore, the improvement of the component performance should focus on the steam turbine and evaporator. The trade-off strategy offers an original conception of the regulation method for an isolated system.

Calibration of Ramie Stalk Contact Parameters Based on the Discrete Element Method

To obtain the physical parameters and contact parameters of ramie stalk decorticating simulation, the structural dimensions, density, moisture content, elastic modulus, and contact parameters of the ramie stalk were measured in this study based on the phloem and xylem of the ramie stalk. The physical stacking angles of the phloem and xylem were measured by the cylinder lift method and the extraction of the partition method, respectively. The contact parameters between the xylem and phloem of the ramie stalk were directly calibrated. Additionally, the contact parameters of the phloem–phloem, phloem–Q235A steel, xylem–xylem, and xylem–Q235A steel were used as calibration objects, and the simulated stacking angle was used as the evaluation index. Then, the Plackett–Burman test was designed to screen for the parameters which were significantly affecting the simulated stacking angle. Furthermore, the steepest ascent test determined the optimal range of values for two significant parameters of the phloem and three significant parameters of the xylem. Based on the central composite design, the second-order regression equations between the significant parameters of the phloem and xylem and the stacking angle were established, respectively. The physical stacking angles of 37.93° for phloem and 27.17° for xylem were the target values to obtain the optimal parameter group. The results showed that the restitution, static, and rolling friction coefficients between the xylem and phloem were 0.60, 0.53, and 0.021, respectively. The static and rolling friction coefficients between the phloem and phloem were 0.41 and 0.056, respectively. The rolling friction coefficient between the xylem and Q235A steel was 0.033, and the static and rolling friction coefficients between the xylem and xylem were 0.44 and 0.016, respectively. The verification test showed that the relative error values were less than 2.11%, which further indicated that the modeling method and parameter calibration of the ramie stalk phloem and xylem models were accurate and reliable. They can be used for the subsequent calibration simulation tests of ramie stalk bonding parameters and ramie stalk decorticating simulations.

Comparative Analysis of Reinforcement Learning Algorithm based on Tennis Environment

Reinforcement learning and deep reinforcement learning, as a research hotspot in the field of machine learning, have been widely used in our daily life. In this field, game is playing an extremely important role in the developing of reinforcement learning algorithms. Based on the Tennis environment built by Unity ML Agents, this paper used three algorithms, Proximal Policy Optimization (PPO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG) and Soft Actor-Critic (SAC), combined with PyTorch framework, solved the continuous control problem of this environment. Meanwhile, a group of optimal parameters are obtained through multiple trainings, so that Agents can achieve a perfect effect of solving the continuous control problem in Tennis environment. At the end, this paper compared and analyzed the difference among these three algorithms, summarized the application and properties of each algorithm. For different parameters of the algorithm, this paper also maked a comparison and explained the reasons for some special cases as well which can be used for the future work.

Study on tool wear mechanism under cryogenic CO2-assisted minimum quantity lubrication technology

Cryogenic CO2-assisted minimum quantity lubrication milling technology is a green processing technology with broad application prospects. Aiming at the problem of tool wear in the application of cryogenic CO2-assisted minimum quantity lubrication in difficult-to-machine materials and the influence of relevant parameters on tool wear, this study used coated cemented carbide tools to perform milling experiments under cryogenic CO2-assisted minimum quantity lubrication technology conditions. The micro-morphology of the tool and chip was observed, and the energy spectrum of the tool chip contact area was analyzed. The results show that reducing CO2 temperature and increasing the oil flow of minimum quantity lubrication can improve the tool wear. The tool wear mechanisms under cryogenic CO2-assisted minimum quantity lubrication are mainly abrasive wear, diffusion wear, and oxidation wear. The chip sawtooth degree of the optimal parameter group is more conducive to chip breaking than that of dry-cutting and wet-cutting groups. The temperature of the tool-chip contact area is an important factor affecting tool wear; the higher the temperature, the faster the tool wear. At the same time, it is verified that cryogenic CO2-assisted minimum quantity lubrication technology can replace cutting fluid in hard-to-machine materials under certain conditions.

Design of Electric Vehicle’s Wireless Power Transfer System Based on Deep Learning Combined With Multi-Objective Optimization

During the wireless power transmission of an electric vehicle (EV), due to the influence of the compensation circuit’s manufacturing process, the driver’s operating technique, and other factors, the wireless power transfer (WPT) efficiency features strong uncertainty. The article chooses the coil group’s spatial parameters as well as the compensation circuit’s parameters as input variables, and then builds the deep learning network as the framework of uncertainty quantification, so as to obtain the relevant statistical characteristic parameters of WPT efficiency, through comparing with the calculation results of classical Monte Carlo (MC), it can be verified that the uncertainty quantification based on deep learning has almost the same solution accuracy and higher computational efficiency. At the same time, the article adopts tent chaotic mapping and adaptive inertia weight strategy to improve the traditional Aquila Optimization (AO) algorithm, then uses the improved multiobjective AO algorithm to accomplish the optimization of the WPT system’s structure. Through the comparison of improved AO and other optimization algorithms, it can be concluded that the improved AO can search out the optimal design parameter group with faster speed and higher accuracy, which provides effective guidance for the actual optimized design of the EVs WPT system.

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.

Optimal Design of a Novel Composite Anchorage for Carbon-Fiber-Reinforced Polymer (CFRP) Tendons.

In this study, we proposed a novel composite anchorage that considers the anchoring performance and dimension simultaneously. The design concept of this composite anchorage was first introduced, followed by comparison with the traditional inner-cone bond-type anchorage and traditional composite anchorage through theoretical and experimental methods. Then, a parametric study was conducted to determine the influence of different parameters on the anchoring performance, and the optimal design parameters were recommended according to the finite element (FE) and test results. Finally, the practicability of the optimal design parameters were validated through experiments on the anchorage with multiple CFRP tendons. Results showed that the novel composite anchorage could improve the anchoring performance compared with the traditional inner-cone bond-type anchorage by promoting increased anchorage efficiency by 60.4% and, with an ideal failure mode of tendon rupture. Moreover, the novel composite anchorage had smaller dimensions and avoided the presence of a vulnerable position at the junction of the mechanical and bond parts compared with the traditional composite anchorage. In addition, a group of optimal design parameters of this composite anchorage with a pre-tightening force of 130 kN, an inclinational differential angle of 0.1°, an inclination angle of 2.9°, and an embedded length of 30 d~40 d were proposed. The composite anchorage with five CFRP tendons designed with the proposed parameters failed with the rupture of the tendons and exhibited an anchoring efficiency of 1.05. This result showed that the optimal parameters were suitable for this novel composite anchorage to grip multiple tendons. This study can provide an experimental and theoretical basis for designing large-tonnage anchorage for multiple FRP tendons used as hangers or cables in real bridges.

FDTD Modeling of Au/Ag Nanoparticles Incorporated Au/Ag Photonic Crystal for Seeking the Maximal Localized Electric Field

AbstractPhotonic crystals (PhC) can be elaborately tailored to enhance the electromagnetic properties of materials and are drawing increasing attention in recent years. However, there is no sufficient theoretical framework to optimize photonic crystal designs for electric enhancement. A comprehensive theoretical model is presented to illustrate Au/Ag photonic crystal enhancement on electric fields and obtain a group of optimal photonic crystal parameters after large number of statistics and parameter scans. The second and deeper layers of PhC only contribute to both 5% extinction and absorption in the far‐field, and there is almost no difference in |E|max among different layers with the coefficient variation at ≈1%. Furthermore, increment of both lattice constants and NP diameters can trigger dramatic changes in |E|max, and the maximal |E|max derived from 190 nm AuNP (nanoparticle)‐Au PhC with the lattice constant of 560 nm. This structure is 47.7, 7.39, 4.63, and 2.69 times that of AuNP incorporated silica wafer, silicon wafer, Ag wafer, and Au wafer, respectively. The strong electric field enhancement attributes to not only the plasmonic effect but also the photonic crystal enhancement effect. The simulation procedures open the possibility of precise photonic crystal structure design and pave the way for various electric field enhancement applications.

Review of Wear Model Optimization for Ceramic and Metal Total Hip Arthroplasty with Medical Physics Applications

In a series of previous contributions, two types of bioengineering studies of total hip arthroplasty (THA) modelling optimization were presented. Namely, computational modelling THA in vitro wear modelling parameter optimization for ceramic-on-ceramic (CoC), and metal-on-metal THA (MoM) one. The objective of the research was to obtain the optimal-principal model parameters for in vitro tribological predictions. These optimal are an adimesional K constant parameter, hardness, and hip-biomechanical load over the THA implant. In those contributions, the aim was carry out both dual and multi objective optimization. In ceramic, dual optimization was presented for Alumina (Al3O2), and Zirconium (ZrO2) CoC, and secondly a multi objective one for Alumina, Zirconium, ZTA Biolox, and ZTA Biolox-Delta. In metal, dual optimization was obtained for Titanium and Co-Cr-Mo materials. This article presents a review of those most important results in Numerical, 2D Graphical, and 3D Interior Optimization. Applications obtained are useful/efficacious for in vitro tribology predictions in the area of clinical medical physics and bioengineering—with the new 4D Interior Optimization included. A second innovation related to the reviewed papers group is the GNU-Octave software design/comparison with Matlab for 4D Interior Optimization graphics. Most important of these usages in ceramic and metal are an adimensional K for wear modelling, and a optimal model parameter group for THA erosion/durability tribopredictions. Clinical orthopedics, bioengineering, and medical physics applications emerge from all the results.

Establishing Surrogate Model to Predict the Optimal Thermodynamic and Economic Performance of a Packed Bed Humidifier via Multi-Objective Optimization

In this paper, to optimize the thermodynamic and economic performance of a packed bed humidifier, a multi-objective optimization combined response surface method with a genetic algorithm is employed. The critical parameters, including geometric and thermodynamic parameters, are designated as the impact factors, and the objective functions contain unit humidification capacity of volume and unit humidification capacity of cost in a Box–Behnken design. The results of the analysis of variance demonstrated that the quadratic regression models of objectives are reliable and robust. It is found that the liquid–gas ratio, the interaction of the liquid–gas ratio, and inlet water temperature are simultaneously the strongest influence factors for the thermodynamic and economic indicators among the independent and interactive parameters. In addition, the optimal parameter group is found out through a genetic algorithm, and the actual optimal results are obtained as 0.11 kgs−1m−3 for thermodynamic performance and 15.86 kg$−1 for economic performance. Furthermore, it is shown that the thermodynamic performance improves by 56% and the economic performance increases by 6.55%, compared with optimum experimental design points. During the optimization design process, the computational time to find the optimal values reduces from 69,000 s with previous mathematical models to 10 s with established regression models. Additionally, a series of Pareto-optimal points for possible best thermodynamic and economic performance give the reference for the designers of packed bed humidifiers.

Operation characteristics and methods of the hydraulic power take-off system

The power take-off system plays a vital role in the wave energy generating unit. Here, for studying the operation characteristics and methods of the hydraulic power take-off system, its basic model is built relying on the operating principles of every component. Meanwhile, a proportional–integral–derivative (PID) controller is also designed to regulate the rotational speed of the motor. Then, a test platform for the hydraulic power take-off system is constructed to verify the correctness of the model. Fortunately, based on model analyses, some useful results are found. Firstly, the PID controller has a visible effect on stabilizing the rotational speed. In addition, a group of optimal control parameters are obtained. Secondly, the influences of the displaced volume on the operation characteristics of the hydraulic power take-off system are found. Meanwhile, the optimal displaced volume is also presented by weighing the efficiency and stability. Finally, the operation modes and regions of the hydraulic power take-off system are obtained, and its rationality is also proved by a simulated running of the system. More importantly, these results can provide a reference to the design and operation of the hydraulic power take-off system.

Multiobjective Parameter Optimization of a Four-Plate Capacitive Power Transfer System

For a four-plate capacitive power transfer (CPT) system, the existing coupling and compensation design methods can only provide an approximate local parameter optimization solution, instead of a globally optimized solution. This article proposes an accurate calculation method for cross-coupling capacitance and a multiobjective optimization algorithm combining nondominated sorting and Kent map for an SS-type CPT system. Mathematical models of the cross-coupling capacitances and port capacitances are established based on a two-port capacitive coupling model of the four plates. Besides, the constraints considering the reasonable region of the parameter values are given. Taking the output power, efficiency, and total harmonic distortion (THD) as objective functions, with the power transfer distance, operating frequency, load resistance, and input voltage as decision variables, the Pareto-optimal fronts are obtained by the proposed algorithm. A group of global optimal parameters is chosen from the solution sets, and a simulation model and a prototype of the CPT system are constructed. The efficiency of 87.1% at 252.3-W output power has been achieved. The simulation and experimental results verified the feasibility and effectiveness of the proposed calculating method for the cross-coupling capacitance and optimization method.

Numerical design of a highly efficient microfluidic chip for blood plasma separation

Blood plasma separation may be one of the most frequent operations in daily laboratory analysis so that a highly efficient separation could save time, cost, and labor for laboratory operators. A numerical technique is demonstrated in this work to design a highly efficient microfluidic chip that can separate 64% plasma from blood with 100% purity. Simulations are carried out for the blood flow by a hybrid method of smoothed dissipative particle dynamics and immersed boundary method (SDPD-IBM). SDPD is used to model the motion of blood flow, while IBM is used to handle the interaction between cells and plasma. A single bifurcation, as the elementary component of the microfluidic chip, is first examined to find an optimal parameter group of flow rate and branch angle, which can generate a maximum separation efficiency on the premise of 100% purity. Then, the microfluidic chip is designed based on the optimal parameter group and compared with the existing experimental chip to analyze its performance. It is shown that the designed chip has a separation efficiency about 40% larger than the experimental one. Finally, the performance of the designed chip is analyzed by investigating the parameter dependence, and two critical parameters are studied, the cell hematocrit and inflow rate. The results provide an optimal hematocrit of 10.4% and an optimal inflow rate of 13.3 μl/h in order to obtain a high efficiency and 100% purity, which provides guidance for the level of diluting blood and the speed of injecting blood in experiments.

Optimization of relative parameters in transfer entropy estimation and application to corticomuscular coupling in humans

BackgroundAs a non-modeled information theoretical measure, the transfer entropy (TE) could be applied to quantitatively analyze the linear and nonlinear coupling characteristics between two observations. However, the parameters selection of TE (the parameters used in state space reconstruction and estimating Shannon entropy) has a serious influence on the accuracy of its results. New MethodIn this study, the hybrid particle swarm optimization (HPSO) was applied to improve the accuracy of TE by optimizing its parameters. In HPSO, the TE calculation and significant analysis were integrated into the fitness function, and the optimal parameters group within the parameter space could be automatically found through an iteration process. ResultsThe TE results computed under the parameters optimized by HPSO (HPSO-TE), was assessed with a numerical non-linear model, the neural mass model and the recorded electroencephalogram (EEG) and electromyogram (EMG) signals. Compared with TE, HPSO-TE could reduce the ‘false positive’ in non-linear model, and ‘spurious coupling’, i.e. two nonzero TEs for unidirectionally coupled systems, especially when coupling strength was weak. The robustness against noise and long time-delay was improved. Moreover, the experimental data analysis showed HPSO-TE revealed the dominant direction (EEG → EMG) in corticomuscular coupling, and had higher values than TE which showed the same dominant direction. Comparison with Existing MethodThe implication of HPSO improved the accuracy of TE in estimating the coupling strength and direction. ConclusionsThe efficiency of TE could be improved by HPSO for estimating coupling relationships, especially for weakly coupled, strong noisy and long time-delay series.

Optimization of sizing parameters and multi-objective control of trailing edge flaps on a smart rotor

Abstract In this study, a wind turbine model with a smart rotor is presented to optimize the sizing parameters of the trailing edge flap (TEF) and design the multi-objective TEF controller. The proposed model consists of TEF actuators, and the aerodynamic, applied load, structural, drive chain, and generator models. Additionally, under standard wind turbine control, the present model shows good agreements with FAST at different wind conditions. At the rated steady wind condition, the TEFs yield exactly the same effects as those elicited by FAST. An approach is proposed using two orthogonal experiments to optimize the TEF sizing parameters for maximizing the TEF effects on blade load alleviation and wind turbine output power smoothness. As a result, a group of optimal TEF sizing parameters is obtained. A multi-objective TEF controller adopting multivariable dynamic matrix control (M–DMC) and nonlinear dynamic matrix control (N–DMC) is used to control the flapwise blade root moment and output power. The simulations indicate that the average reduction rates of the flapwise root moment power spectral density (PSD) of blade 1 are within the range of 87–97% at 1 P frequency, and that the average reduction rate of the generator power standard deviation is within the range of 8–42%.