Search Results (832)

This study aimed to examine how downward load applied during instrumentation affects the stress generation and shaping properties in thermally treated and non-treated NiTi rotary instruments. ProTaper Universal (PTU; non-thermally treated) and ProTaper Gold (PTG; thermally treated) were used to prepare J-shaped canals in resin blocks. Load-controlled automated instrumentation and torque/force sensing devices were employed with preset downward loads of 1, 2, or 3 N (n = 10 each). The torque/force, instrumentation time, and canal-centering ratio were measured and analyzed using two-way or one-way analysis of variance with Tukey’s test (α = 0.05). In the PTU-1N group, instrumentation was not completed because a ledge was formed in all canals. The PTU-3N group showed significantly greater upward force (screw-in force) and clockwise torque, along with a significantly smaller canal-centering ratio (less deviation) at the apical 0 mm level, than the PTU-2N group (p < 0.05). The downward load did not influence the instrumentation time (p > 0.05). In the PTG groups, these effects of downward load on the force generation and canal-centering ratio were not significant (p > 0.05). In the non-thermally treated PTU instruments, greater downward loads enhanced screw-in force while decreasing apical canal deviation; however, these effects were abolished in the thermally treated PTG instruments. This study highlights the importance of adapting the instrumentation technique with instrument characteristics: thermally treated flexible instruments facilitate smoother use, while stiffer, non-thermally treated ones may require precise control of downward loads. Full article

The newly developed automorphism ensemble decoder (AED) leverages the rich automorphisms of Reed–Muller (RM) codes to achieve near maximum likelihood (ML) performance at short code lengths. However, the performance gain of AED comes at the cost of high complexity, as the ensemble size required for near ML decoding grows exponentially with the code length. In this work, we address this complexity issue by focusing on the factor graph permutation group (FGPG), a subgroup of the full automorphism group of RM codes, to generate permutations for AED. We propose a uniform partitioning of FGPG based on the affine bijection permutation matrices of automorphisms, where each subgroup of FGPG exhibits permutation invariance (PI) in a Plotkin construction-based information set partitioning for RM codes. Furthermore, from the perspective of polar codes, we exploit the PI property to prove a subcode estimate convergence (SEC) phenomenon in the AED that utilizes successive cancellation (SC) or SC list (SCL) constituent decoders. Observing that strong SEC correlates with low noise levels, where the full decoding capacity of AED is often unnecessary, we perform path pruning to reduce the decoding complexity without compromising the performance. Our proposed SEC-aided path pruning allows only a subset of constituent decoders to continue decoding when the intensity of SEC exceeds a preset threshold during decoding. Numerical results demonstrate that, for the FGPG-based AED of various short RM codes, the proposed SEC-aided path pruning technique incurs negligible performance degradation, while achieving a complexity reduction of up to 67.6%. Full article

The large-scale utilization of hydrogen energy is currently hindered by challenges in low-cost production, storage, and transportation. This study focused on investigating the impact of the diaphragm cavity profile on the movement behavior and stress distribution of a dual-stage diaphragm compressor. Firstly, an experimental platform was established to test the gas mass flowrate and fluid pressures under various preset conditions. Secondly, a simulation path integrating the finite element method simulation, theoretical stress model, and movement model was developed and experimentally validated to analyze the diaphragm stress distribution and deformation characteristics. Finally, comparative optimization analyses were conducted on different types of diaphragm cavity profiles. The results indicated that the driving pressure differences at the top dead center position reached 85.58 kPa for the first-stage diaphragm and 75.49 kPa for the second-stage diaphragm. Under experimental conditions of 1.6 MPa suction pressure, 8 MPa second-stage discharge pressure, and 200 rpm rotational speed, the first-stage and second-stage diaphragms reached the maximum center deflections of 4.14 mm and 2.53 mm, respectively, at the bottom dead center position. Moreover, the cavity profile optimization analysis indicated that the double-arc profile (DAP) achieved better cavity volume and diaphragm stress characteristics. The first-stage diaphragm within the optimized DAP-type cavity exhibited 173.95 MPa maximum principal stress with a swept volume of 0.001129 m³, whereas the second-stage optimized configuration reached 172.57 MPa stress with a swept volume of 0.0003835 m³. This research offers valuable insights for enhancing the reliability and performance of diaphragm compressors. Full article

Island microgrids are essential for the exploitation and utilization of offshore renewable energy resources. However, voltage regulation and accurate reactive power sharing remain significant technical challenges that need to be addressed. To tackle these issues, this paper proposes an algorithm that integrates a dynamic leader election (DLE) mechanism and model-free reinforcement learning (RL). The algorithm aims to address the issue of fixed leaders restricting reactive power flow between buses during heavy load variations in island microgrids, while also overcoming the challenge of obtaining model parameters such as resistance and inductance in practical microgrids. First, we establish a voltage containment control and reactive power error model for island alternating current (AC) microgrids and construct a corresponding value function based on this error model. Second, a dynamic leader election algorithm is designed to address the issue of fixed leaders restricting reactive power flow between buses due to preset voltage limits under unknown or heavy load conditions. The algorithm adaptively selects leaders based on bus load, allowing the voltage limits to adjust accordingly and regulating reactive power flow. Then, to address the difficulty of accurately acquiring parameters such as resistance and inductance in microgrid lines, a model-free reinforcement learning method is introduced. This method relies on real-time measurements of voltage and reactive power data, without requiring specific model parameters. Ultimately, simulation experiments on offshore island microgrids are conducted to validate the effectiveness of the proposed algorithm. Full article

Aiming at the problem of a full-width horizontal-axis roadheader being prone to diverge from the preset trajectory of the tunnel, a deviation correction control method based on particle swarm optimization–backpropagation (PSO-BP) neural network proportional–integral–derivative (PID) control is proposed. The track error model of the walking system and the transfer function model of the deviation correction control are established. The PSO-BP PID controller is designed; the beginning weights of BP are enhanced by the PSO, and the BP receives the optimal weights to instinctively adapt the PID parameters. An experiment on deviation correction control of the roadheader was carried out. The experimental results indicate that the maximum steady-state error of PSO-BP PID for deflection angle and angular velocity is reduced by 41.03% and 44.93%, respectively, compared with BP PID, and the average rise time for deflection angle and angular velocity is reduced by 75.76%. Full article

Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The relationship between response values and influencing factors is mainly established using regression analysis techniques. These equations are then used to generate contour and surface response plots to provide researchers with further insights. The impact of regression techniques on response surface methodology (RSM) model building has not been studied in detail. This study uses complete regression techniques to analyze sixteen datasets from the literature on semiconductor manufacturing, steel materials, and nanomaterials. Whether each variable significantly affected the response value was assessed using backward elimination and a t-test. The complete regression techniques used in this study included considering the significant influencing variables of the model, testing for normality and constant variance, using predictive performance criteria, and examining influential data points. The results of this study revealed some problems with model building in RSM studies in the literature from three engineering fields, including the direct use of complete equations without statistical testing, deletion of variables with p-values above a preset value without further examination, existence of non-normality and non-constant variance conditions of the dataset without testing, and presence of some influential data points without examination. Researchers should strengthen training in regression techniques to enhance the RSM model-building process. Full article

In a multi-point source system, increasing the jamming power can expand the distribution area of the equivalent radiation center, but significantly increases the system exposure risk. Therefore, in order to achieve an optimal balance between the two, this paper proposes a joint optimization method for jamming power and an array of multi-point source systems. First, based on determining the spatial geometric relationship between the triplet antenna and the target, the distribution law of the equivalent radiation center of the triplet antenna under the condition of the target echo is derived. Second, by introducing the angle factor, the jamming power and equivalent radiation center distribution area are combined to construct the joint optimization model of jamming power and array in omnidirectional and non-omnidirectional situations. Third, based on the non-dominated sorting whale optimization algorithm (NSWOA), an adaptive inertia weight based on the cosine function and logistic chaotic map is introduced to obtain the optimal arrangement. The experimental results show that in the omnidirectional case, when the average jamming-to-signal ratio is 13.83 dB, the equilateral triangle array can achieve the goal of protecting the target while avoiding the exposure of the triplet antenna position. In the non-omnidirectional case, when the average jamming-to-signal ratio is 13.90 dB, the equilateral triangle array can achieve the optimal balance between the jamming power and the area of the distribution area of the equivalent radiation center, and control the distribution of the equivalent radiation center to strictly meet the preset angular domain constraints. Furthermore, the optimal JSR value was reduced by an average of 1.14 dB compared with that of the conventional selection scheme. Full article

Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement. Full article

Pipe-insulating joints are common cathodic protection devices in long-distance oil and gas pipeline infrastructures. To ensure safety, they are often designed too conservatively, resulting in large dimensions, high self-weight, and substantial costs. This study analyzed an insulating joint under the most unfavorable conditions to identify the component of the maximum stress in the insulating joint, which is the right flange. Then, using parameterized finite element calculations, five independent dimensions of the right flange were combined and arranged to obtain a dataset of the right flange dimensions and their maximum stress. Subsequently, four different fitting algorithms were trained with this dataset, and the ridge regression algorithm, which showed the best predictive performance, was used to establish a surrogate model for calculating the maximum stress of the right flange. Finally, the surrogate model was combined with a genetic algorithm to determine the optimal design dimensions of the right flange. This study also provides examples verifying the accuracy and reliability of the surrogate model and genetic algorithm. In these examples, the maximum stress under the design dimensions given by the optimization algorithm has a maximum error of 8.98% and an average error of 4.63% compared to the preset maximum stress target, while the stress predicted by the surrogate model has a maximum error of 9.65% and an average error of 5.33% compared to the actual stress. This improves the computational efficiency of the optimization algorithm by establishing a surrogate model, which can be used to optimize the dimensions of insulation joints. Full article

The nonlocal macro–meso-scale damage (NMMD) model, implemented in the framework of the finite element method, has been demonstrated to be a promising numerical approach in simulating crack initiation and propagation with reliable efficacy and high accuracy. In this study, the NMMD model was further enhanced by employing an identical degradation mechanism for both the tensile and shear components of shear stiffness, thereby overcoming the limitation of equal degradation in shear and tensile stiffness inherent in the original model. Additionally, a more refined and physically sound seepage evolution function was introduced to characterize the variation in permeability in porous media with geometric damage, leading to the development of an improved NMMD model suitable for simulating coupled seepage–stress problems. The reliability of the enhanced NMMD model was verified by the semi-analytical solutions of the classical KGD problem. Finally, based on the modified NMMD model, the effects of preset fracture spacing and natural voids on hydraulic fracture propagation were investigated. Full article

Experimental design is important for researchers and those in other fields to find factors affecting an experimental response. The response surface methodology (RSM) is a special experimental design used to evaluate the significant factors influencing a process and confirm the optimum conditions for different factors. RSM models represent the relationship between the response and the influencing factors established with the regression analysis. Then these equations are used to produce the contour and response surface plots for observers to determine the optimization. The influence of regression techniques on model building has not been thoroughly studied. This study collected twenty-five datasets from the literature. The backward elimination procedure and t-test value of each variable were adopted to evaluate the significant effect on the response. Modern regression techniques were used. The results of this study present some problems of RSM studies in the previous literature, including using the complete equation without checking the statistical test, using the at-once variable deletion method to delete the variables whose p-values are higher than the preset value, the inconsistency between the proposed RSM equations and the contour and response surface plots, the misuse of the ANOVA table of the sequential model to keep all variables in the linear or square term without testing for each variable, the non-normal and non-constant variance conditions of datasets, and the finding of some influential data points. The suggestions for applying RSM for researchers are training in the modern regression technique, using the backward elimination technique for sequential variable selection, and increasing the sample numbers with three replicates for each run. Full article

With the evolution of magnetic devices toward structural innovation and high reliability, the traditional design methods, such as size optimization and shape optimization, are limited by preset structural forms, making it challenging to generate novel structures and topologies. Topology-optimized design methods can achieve an optimal distribution of constituent materials of magnetic devices by optimizing the objective performance subject to certain constraints, and can provide greater freedom for designers. Based on the above background, this paper firstly investigates the principles of deterministic topology optimization methods, and introduces the latest specific applications in magnetic devices. It also demonstrates the advantages of topology optimization technology in enhancing operating performance, fostering structural innovation, and improving material utilization in magnetic devices. To manage uncertainties in design and manufacturing processes of magnetic devices, this paper analyzes uncertainty topology optimization methods, respectively, reliability and robustness-based topology optimization algorithms. To facilitate manufacturing, this paper summarizes the filter strategy for the new structure obtained by topology optimization. Finally, the problems faced by the topology optimization method in the field of magnetic devices are discussed, and a future development direction is projected. Full article

Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified. Full article

In complex electromagnetic environments, spatial coupling uncertainties—position errors and timing jitter—increase false target information entropy, reducing strategy effectiveness and posing challenges for robust UAV swarm track deception. This paper proposes an error-constrained entropy-minimizing compensation framework to model radar/UAV errors and their spatial coupling. The framework establishes closed-form gate association conditions based on the principle of entropy minimization, ensuring mutual consistency of false target measurements across multiple radars. Two strategies are proposed to reduce false target information entropy: 1. Zonal track compensation forms dense “information entropy bands” around each preset false target by inserting auxiliary deception echoes, enhancing mutual information concentration in the measurement space; 2. Formation jamming compensation adaptively reshapes the UAV swarm into regular polygons, leveraging geometric symmetry to suppress spatial diffusion of position errors. Simulation results show that compared with traditional methods, the proposed approach reduces the spatial inconsistency entropy by 50%, improving false target consistency and radar deception reliability. Full article

Deep-sea hydraulic systems, powering a wide range of numerous deep-sea operating equipment, employ many poppet valves to adjust the pressure and flow rate, thereby realizing precise movements of the actuators. With greater depths and ambient pressures, the hydraulic oil viscosity increases exponentially, leading to a significant difference in the performance of the poppet valve compared to on-land usage and across varying depths. Based on the shear stress transport (SST) k-$\omega$ turbulence model and the dynamic mesh method, a computational fluid dynamics (CFD) model of the poppet valve was established. With the viscosity–pressure characteristics considered, the performance of the poppet valve was analyzed under different depths, different inlet flow rates, and different cracking pressures. The results indicate significant performance deterioration in poppet valves at increased depths, characterized by increased pressure loss and extended response rise time. At 11 km underwater, the pressure loss can be 7 MPa larger than the preset cracking pressure of 10 MPa, and the rise time is doubled compared with the land condition. It is recommended to use hydraulic oils with a lower initial viscosity and a slower increase in viscosity with pressure in deep sea conditions. Full article