Generate Sample Points Research Articles

Methodology for Reliability Analysis of Reinforced Concrete Structures over time Considering Combined Corrosion

One of the main causes of deterioration in reinforced concrete structures (RC) is the corrosion of steel bars. Many factors, including materials, environments, and loading conditions, which often have uncertain characteristics, affect the performance of RC structures. Some uncertainties may depend on time and location, and associated reliability issues become more difficult to address. This study presents a comprehensive method for assessing the temporal reliability of reinforced concrete structures, taking into account combined effects of carbonation (general corrosion) and chloride penetration (pitting corrosion). Probabilistic models are used to capture uncertainties related to the beginning, propagation, and combined effects of corrosion on structural performance over time. The time-dependent reliability of bridge structures is evaluated using sophisticated numerical simulations in conjunction with the FORM method for reliability analysis. Both time and space variables are treated as uniformly distributed random variables within specific intervals when generating sample points. Engineers and decision-makers can assess the reliability of reinforced concrete bridges using a systematic framework that considers the complex interactions of corrosion mechanisms. This aids an informed maintenance planning and effective resource allocation to ensure long-term structural safety and durability. Finally, the suggested method is tested on a reinforced concrete bridge with corroded steel reinforcement bars. The original work is validated, and the results are compared with the crude Monte Carlo simulation (MCS) method.

Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022.

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.

Research on the application of a dynamic programming method based on geodesic distance for path planning in Robot-Assisted laser scanning measurement of Free-Form surfaces

The use of robots equipped with 3D laser line scanners is currently one of the mainstream geometric measurement methods for free-form surfaces. The key to this technology is to plan an effective measurement path based on the geometric features of the surface. In order to improve the measurement efficiency, a new path planning method for free-form surface measurement is proposed. First, an algorithm is developed to adaptively generate sample points of the surface to be measured based on the geodesic distance. Then, the geometric length of the scanning measurement path and the amplitude variation of the Euler angle are used as the objective function, and the optimal robot scanning measurement path is generated by minimising the objective function based on a dynamic programming algorithm. Finally, experiments show that the scanning measurement path generated based on the proposed method can effectively improve the measurement efficiency compared with existing research methods.

Kriging surrogate model for optimizing outlet temperature distribution in low-emission combustors without dilution holes

Designing advanced combustors that operate at high temperatures and produce little pollution, especially in the absence of primary and dilution holes, is a difficult task that may bring significant challenges. In this regard, this paper introduces a Kriging surrogate model approach to optimize the outlet temperature distribution of the combustor to achieve such advanced low-pollution combustors. Building upon previous research, this study explores the effects of the swirler blade installation angle on the outlet temperature distribution of the combustor without primary or dilution holes. Traditional methods, such as the control variable method using computational fluid dynamics (CFD) for numerical simulation, are limited in application due to the complex coupling of flow, heat transfer, mass transfer, and combustion processes. In contrast, surrogate models, especially the Kriging model, offer a rapid and efficient alternative to extensive CFD simulations that provide accurate predictions and error estimates for the solution of the problem. In summary, this paper details the process of generating sample points through three-dimensional numerical simulations, develops a Kriging surrogate model through Latin hypercube sampling, and optimizes the model to identify the most uniform outlet temperature distribution achievable by adjusting the installation angle of the swirl blade. The optimal design parameters, which are quickly obtained through the Kriging model, showed a significant reduction in the overall temperature distribution function and the radial temperature distribution function by 21% and 27.14%, respectively.

A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold.

The efficiency of the rapidly exploring random tree (RRT) falls short when efficiently guiding targets through constricted-passage environments, presenting issues such as sluggish convergence speed and elevated path costs. To overcome these algorithmic limitations, we propose a narrow-channel path-finding algorithm (named NCB-RRT) based on Bi-RRT with the addition of our proposed research failure rate threshold (RFRT) concept. Firstly, a three-stage search strategy is employed to generate sampling points guided by real-time sampling failure rates. By means of the balance strategy, two randomly growing trees are established to perform searching, which improves the success rate of the algorithm in narrow channel environments, accelerating the convergence speed and reducing the number of iterations required. Secondly, the parent node re-selection and path pruning strategy are integrated. This shortens the path length and greatly reduces the number of redundant nodes and inflection points. Finally, the path is optimized by utilizing segmented quadratic Bezier curves to achieve a smooth trajectory. This research shows that the NCB-RRT algorithm is better able to adapt to the complex narrow channel environment, and the performance is also greatly improved in terms of the path length and the number of inflection points. Compared with the RRT, RRT* and Bi-RRT algorithms, the success rate is increased by 2400%, 1900% and 11.11%, respectively.

Data-driven non-intrusive shape-topology optimization framework for curved shells

Aiming at obtaining an optimal configuration of curved shells in the compact design space, it is an effective way to perform the shape and topology optimization simultaneously. Considering the complexity in solving the coupled shape and topology optimization problem, this paper presents an easy-to-implement data-driven non-intrusive shape-topology optimization framework for curved shells, including two steps. In the off-line step, the shape equation-driven mesh deformation method is firstly proposed, contributing to using a small number of design variables for fast shape change and optimization of the skin of curved shell without re-modeling and re-meshing. Latin hypercube sampling is used to generate sample points as the input data in the design space, and the corresponding skin shape is obtained. In each design domain determined by different skin shapes, the topology optimizations are performed in parallel or by multiple optimization solvers, after that above multi-source optimization results are collected into the output dataset. The mapping relationship is trained between the input data and output data via surrogate modeling technique, and this non-intrusive concept contributes to integrating the independent shape and topology optimization into a single-loop data-driven optimization. The cross validation-Voronoi adaptive model updating method is used to improve the prediction accuracy. In the on-line step, the single-loop data-driven optimization is carried out using covariance matrix adaptive evolution strategy and adaptive optimization updating method, which can obtain optimized results more efficiently than direct topology optimization or shape optimization method. In order to verify the effectiveness of the proposed data-driven non-intrusive shape-topology optimization framework, three optimization examples are carried out. Example results show that compared with the results by the single topology optimization, the strain energy results by the proposed framework can be reduced by 20.55%, 15% and 51.6%, respectively, highlighting the outstanding optimization ability of the proposed framework.

Efficient ship weather routing using probabilistic roadmaps

This paper introduces the probabilistic roadmap (PRM) concept in maritime transportation for solving the ship weather routing (SWR) problem. SWR is a path finding problem that seeks for the optimum route (path) for a ship moving from an initial port to a final desired port considering the weather conditions (winds, waves, sea currents). PRM is an old effective algorithm for robot motion planning based on a systematic use of a graph-based map which represents the robot's workspace. Using this map PRM explores feasible paths around the obstacles until finding a collision-free path between the starting and goal configurations of the robot. Considering the digital map of the ship's navigation area as well as forecasting data (updated every 3 h) concerning the weather conditions, a modified PRM algorithm is presented for tackling SWR. The proposed algorithm starts by taking random samples across the ship's navigation area which follow a particular distribution. Then, it uses a local planner to construct a large undirected graph (a roadmap) with nodes all the generated sample points connected with feasible (collision-free) edges (paths). Each path is related with a weight estimated by considering the weather conditions in the corresponding sea's region. Finally, given the departure and desired arrival ports, a modified Dijkstra's algorithm is applied to the resulting graph to search for the optimal path in terms of both fuel consumption and travel time that connects the two ports in the roadmap. The proposed algorithm operates in real-time determining the best solution within less than 1 min. Simulation simulations over the Aegean archipelagos as well as the Mediterranean Sea demonstrate the efficiency of the developed algorithm.

Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach

Driven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios.

Inverse Gaussian Process Modeling for Evolutionary Dynamic Multiobjective Optimization.

For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs.

Temporal-spatial reliability analysis of RC bridges with corroded steel reinforcement bars

Corrosion of steel bars is one of the most significant causes of deterioration of reinforced concrete (RC) structures. The performance of RC structures is affected by many factors such as materials, environments, and load conditions, which usually possess uncertain features. Some of uncertainties may be time- and space-dependent, and the corresponding reliability problems become more challenging. To address these issues, this paper studies the temporal-spatial reliability analysis of RC bridges with corroded steel reinforcement bars. At first, a compound corrosion model is developed in which the combined effects of carbonation (general corrosion) and chloride penetration (pitting corrosion) are considered. In the reliability analysis, both the temporal and spatial variability are accounted through a stochastic process and a random field, respectively. An extended instantaneous response surface (t-IRS) method is then proposed to evaluate the temporal-spatial reliability. Unlike the original t-IRS method, in addition to the original variables, the random variables transformed from the random fields and the space variable are also used as the input variables of the extended surrogate model. When generating sample points, both the time variable and the space variable are treated as uniformly distributed random variables within given intervals. Finally, the proposed method is applied to a RC bridge example with corroded steel reinforcement bars and the results are compared with that of Monte Carlo simulation (MCS) method, demonstrating the effectiveness of the extended t-IRS method.

Media use in gynecological and obstetric care and women’s perceived level of education received of lifestyle-related risks: A cross-sectional study

Objective:The application of media on lifestyle-related risk factors (LRRFs) by healthcare providers to educate women may improve women’s adherence, health literacy, and awareness of LRRFs, as well as offspring’s health outcomes. This study investigated whether exposure to media-based education in gynecological and obstetric care is associated with LRRFs perceived levels of education received during pregnancy and lactation.Methods:We conducted a cross-sectional, observational study across 14 randomly generated sample points in the 12 most populated cities in Baden-Württemberg, southwest Germany. Women were recruited from gynecological and obstetric institutions. Participants were 219 women who met our inclusion criteria and completed the quantitative questionnaire. We applied ordinal logistic regression analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) of women’s perceived level of education received related to healthcare providers’ exposure to media-based education.Results:Media-based education on LRRFs during pregnancy through gynecologists and/or midwives were significantly associated with women’s perceived level of education received (gynecologists: OR = 4.26 (95% CI: 2.04, 8.90; p < .001); midwives: OR = 3.86 (95% CI: 1.66, 8.98; p = .002)). Similar results were found for media-based education through gynecologists and/or midwives on LRRFs during lactation and its association with women’s self-assessed level of perceived level of education received (gynecologists: OR = 4.76 (95% CI: 2.15, 10.56; p < .001); midwives: OR = 7.61 (95% CI: 3.13, 18.53; p < .001)).Conclusions:This study suggests that the exposure to media-based education in gynecological and obstetric care increases women’s perceived level of education received of LRRFs during pregnancy and lactation. Therefore, it is recommendable to apply media in gynecological and obstetric care settings.

Research on expansion and classification of imbalanced data based on SMOTE algorithm

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments.

HYPERSPECTRAL IMAGE BAND SELECTION BASED ON SUBSPACE CLUSTERING

Aiming at the problems in hyperspectral image classification, such as high dimension, small sample and large computation time, this paper proposes a band selection method based on subspace clustering, and applies it to hyperspectral image land cover classification. This method considers each band image as a feature vector, clustering band images using subspace clustering method. After that, a representative band is selected from each cluster. Finally feature vector is formed on behalf of the representative bands, which completes the dimension reduction of hyperspectral data. SVM classifier is used to classify the new generated sample points. Experimental data show that compared with other methods, the new method effectively improves the accuracy of land cover recognition.

Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method

The product quality, productivity, and cost are mainly considered to make the manufacturing plan in plastic injection molding (PIM). The process parameters in PIM play a crucial role in determining the product quality, productivity, and cost. There are actually contradictions between above three properties. Therefore, it is difficult to quickly and accurately obtain the process parameters setting that meet the product quality requirement under the premise of acceptable productivity and cost. In this paper, a differential sensitivity fusion method (DSFM) is proposed to perform the multi-objective optimization of process parameters in PIM for the product quality and productivity improvement and the cost-saving, which integrates sampling strategy, numerical simulation, metamodeling method, and multi-objective optimization algorithm. The sampling strategy is utilized to generate sampling points from the design space at different parameter levels. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, the metamodeling method is applied to construct the response predictors to calculate the objective responses for any sampling point in the global design space. The multi-objective optimization algorithm is executed to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. The automobile front bumper is taken as the case study to verify the proposed method. The numerical results demonstrate that the proposed metamodeling method has better prediction accuracy and performance compared to some classical methods (e.g., response surface model, Kriging) and the multiple objectives cannot reach the optimal simultaneously. Moreover, the trade-off analysis identifies the better solution for decision-making, which helps to quickly and effectively select the optimal process parameters setting.

Lattice Kalman Filters

In this paper, a new filter in the nonlinear Kalman filtering framework is proposed. The new filter is referred to as the lattice Kalman filter (LKF) and is based on a class of quasi-Monte Carlo (QMC) methods known as lattice rules. The proposed LKF method uses the Korobov type lattice rule to deterministically generate sample points that are randomly shifted based on the Cranley-Patterson shift method in order to approximate multi-dimensional integrals in the Gaussian filtering context. The mathematical formulation of the proposed LKF method as well as its error bound propagation are discussed. To evaluate the efficiency of the LKF, it is applied on a nonlinear aerospace system and compared with four other well-known methods presented in the literature. Simulation results demonstrate LKF uses significantly fewer sampling points yielding a significantly lower computational burden than another variant of QMC filter while maintaining the estimation accuracy. Furthermore, it provides asymptotically similar results to the unscented Kalman filter (UKF) but with less computational complexity, which is an important consideration in real applications.

Development of an Adaptive-Feedrate Planning and Iterative Interpolator for Parametric Toolpath with Normal Jerk Constraint

Compared to conventional linear and circular segments, the parametric curve has advantages in approximating and machining, guaranteeing higher machining precision and better machining quality. Therefore, in Computer Numerical Control (CNC) systems, parametric toolpath has become more and more popular for its high-performance in practical machining and the performance of the parametric interpolator has become a standard to illustrate the advanced CNC systems. In this paper, adaptive-feedrate planning and the iterative interpolator have been developed to generate sampling points with respect to error and higher order kinematic constraints. Firstly, a jerk-limited feedrate profile and look-ahead algorithm are utilized to provide reachable feedrate at critical points. Afterwards, normal jerk (i.e. the jerk in a normal direction) are limited to improve machining precision and quality. In theory, it is proved that the normal jerk limitations have relationship with curvature derivative and a novel iterative interpolator is proposed to ensure the normal jerk limitation by considering the corresponding constraints at curvature derivative extreme points. Finally, simulation and experiments are conducted to demonstrate the efficiency and contour performance of the proposed method compared to conventional method and time-optimal method.

Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model

Jet pump efficiency heavily relies on the geometrical parameters of the pump design and parameter global optimization in the full variable space is still a big challenge. This paper proposed a global optimization method for annular jet pump design combining computational fluid dynamics (CFD) simulation, the Kriging approximate model and experimental data. The suction angle, the flow ratio, the diffusion angle, and the area ratio are selected as the design variables for optimization. The optimal space filling design (OSF) method is used to generate sampling points from the design space of the four design variables. The optimization method solves the constrained optimization problem with a given head ratio by building the functional relationship established by the Kriging model between efficiency and design parameters, which makes the method more applicable. The design result shows that the annular jet pump efficiency is predicted well by the Kriging model; m is a key variable affecting the annular jet pump efficiency. As the area ratio m decreases, the mixing effect at the suction chamber outlet can be improved, but the frictional resistance increases.

Compact Interchannel Sampling Difference Descriptor for Color Texture Classification

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods.

Multi-objectives nonlinear structure optimization for actuator in trajectory correction fuze subject to high impact loadings

This paper presents an actuator used for the trajectory correction fuze, which is subject to high impact loadings during launch. A simulation method is carried out to obtain the peak-peak stress value of each component, from which the ball bearings are possible failures according to the results. Subsequently, three schemes against impact loadings, full-element deep groove ball bearing and integrated raceway, needle roller thrust bearing assembly, and gaskets are utilized for redesigning the actuator to effectively reduce the bearings’ stress. However, multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield stress. Four gasket’s structure parameters and three bearings’ peak-peak stress are served as the four optimization variables and three objectives, respectively. Optimized Latin hypercube design is used for generating sample points, and Kriging model selected according to estimation result can establish the relationship between the variables and objectives, representing the simulation which is time-consuming. Accordingly, two optimization algorithms work out the Pareto solutions, from which the best solutions are selected, and verified by the simulation to determine the gaskets optimized structure parameters. It can be concluded that the simulation and optimization method based on these components is effective and efficient.

A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving

Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.