Random Sample Points Research Articles

GPU-based discrete element model of realistic non-convex aggregates: Mesoscopic insights into ITZ volume fraction and diffusivity of concrete

Aggregates with realistic non-convex morphologies and their surrounding interfacial transition zones (ITZs) significantly affect the diffusivity of concrete. Herein, we initially propose a mathematical-controllable parameterized method to devise three-dimensional (3D) non-convex aggregates. Then, we develop a GPU-based discrete element modelling (DEM) to generate random close packing of polydisperse non-convex aggregates. Next, we establish three-phase concrete mesostructures containing non-convex aggregates and ITZs and obtain the ITZ volume fraction by a Monte Carlo random point sampling method. Finally, we develop a dual-probability-Brownian motion (DP-BM) scheme to obtain the effective diffusivity of concrete. Moreover, the effects of volume fraction, size polydispersity and shape of aggregates as well as ITZ thickness on the ITZ volume fraction and concrete diffusivity are researched to probe rigorous component-structure-property relations for concrete. This work provides a comprehensive numerical framework for mesostructure characterization and diffusivity evaluation for concrete, which can be a valuable tool for durability assessment of concrete.

Efficient Path Planning for a Microrobot Passing through Environments with Narrow Passages.

This paper presents an efficient path-planning algorithm for microrobots attempting to pass through environments with narrow passages. Because of the extremely small size of a microrobot, it is suitable for work in this kind of environment. The rapidly exploring random tree (RRT) algorithm, which uses random sampling points, can quickly explore an entire environment and generate a sub-optimal path for a robot to pass through it; however, the RRT algorithm, when used to plan a path for a microrobot passing through an environment with narrow passages, has the problem of being easily limited to local solutions when it confronts with a narrow passage and is unable to find the final path through it. In light of this, the objectives of the considered path planning problem involve detecting the narrow passages, leading the path toward an approaching narrow passage, passing through a narrow passage, and extending the path search more efficiently. A methodology was proposed based on the bidirectional RRT in which image processing is used to mark narrow passages and their entrances and exits so that the bidirectional RRT can be quickly guided to them and combined with the deterministic algorithm to find paths through them. We designed the methodology such that RRT generates the sampling points for path growth. The multiple importance sampling technique is incorporated with bidirectional RRT, named MIS-BiRRT, to make the path grow faster toward the target point and narrow passages while avoiding obstacles. The proposed algorithm also considers multiple candidate paths simultaneously to expand the search range and then retain the best one as a part of the planning path. After validation from simulation, the proposed algorithm was found to generate efficient path planning results for microrobots to pass through narrow passages.

UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree.

Determinantal consensus clustering

Random restart of a given algorithm produces many partitions that can be aggregated to yield a consensus clustering. Ensemble methods have been recognized as more robust approaches for data clustering than single clustering algorithms. We propose the use of determinantal point processes or DPPs for the random restart of clustering algorithms based on initial sets of center points, such as k-medoids or k-means. The relation between DPPs and kernel-based methods makes DPPs suitable to describe and quantify similarity between objects. DPPs favor diversity of the center points in initial sets, so that sets with similar points have less chance of being generated than sets with very distinct points. Most current inital sets are generated with center points sampled uniformly at random. We show through extensive simulations that, contrary to DPPs, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets. The latter are two key properties that make DPPs achieve good performance. Simulations with artificial datasets and applications to real datasets show that determinantal consensus clustering outperforms consensus clusterings which are based on uniform random sampling of center points.

Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint.

Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters.

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction.

The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

A data-adaptive method for estimating density level sets under shape conditions

Given a random sample of points from some unknown density, we propose a method for estimating density level sets, for a positive threshold t, under the r-convexity assumption. This shape condition generalizes the convexity property and allows to consider level sets with more than one connected component. The main problem in practice is that r is an unknown geometric characteristic of the set related to its curvature, which may depend on t. A stochastic algorithm is proposed for selecting its value from data. The resulting reconstruction of the level set is able to achieve minimax rates for Hausdorff metric and distance in measure uniformly on the level t.

Study on Trajectory Optimization of Hypersonic Vehicle Based on Neural Network

For the horizontal take-off hypersonic cruise aircraft, research on the combined design method of multi-section was carried out, the main design parameters of different sections were analyzed, the parametric design model of the flight path was established, and the characteristics of the typical flight path were studied. On this basis, the calculation of sample points was carried out, and a prediction model of aircraft range and flight time based on the design parameters of the four main flight sections was established based on the neural network method. The genetic algorithm is used to optimize the flight path of the prediction model with the range as the objective function. The research results show that the neural network prediction model based on the parametric design of the trajectory can predict random sample points better than the trajectory model For the prediction of random sample points, compared with the calculation results of the trajectory model, the maximum errors of the flight range and flight time are within 0.82% and 0.45%. The prediction model is optimized with the flight range as the objective function, and the relative error between the optimal range and the trajectory model under the corresponding section parameters is less than 0.2%, which shows that the model established in this paper can better predict the range and flight time according to the section design parameters. Parametric modeling and neural network optimization are feasible methods for aircraft trajectory design and section parameter optimization.

Discrete element modeling of 3D irregular concave particles: Transport properties of particle-reinforced composites considering particles and soft interphase effects

Particles of complex morphologies and their surrounding soft interphase are main constituents that play a significant role in the transport properties of particle-reinforced composites (PRCs). It has been a crucial but unresolved issue how to derive the volume fraction of soft interphase around three-dimensional (3D) irregular concave particles, as well as it remains to be virgin to explore the effective transport properties of three-phase PRCs composed of homogeneous matrix, 3D irregular concave particles of a relatively high packing density and their surrounding soft interphase. To this end, this work initially develops a discrete element modeling (DEM) of 3D irregular concave particles with monodispersity in size to generate random dense packing structures, where an innovative mathematical-controllable parameterized method is proposed to devise diverse irregular concave particle shapes. The proposed particle design method is not only universal and controllable to generate a given particle shape, but also can be directly linked with an experimental measurement using digital scanning. In addition, an efficient two-step numerical strategy is presented to check the inter-particle contact detection and collision. Then, the volume fraction of soft interphase around monodisperse concave particles is numerically derived for the first time by using the Monte Carlo random point sampling simulation. The Monte Carlo simulation has good accuracy to obtain the soft interphase volume fraction, which is confirmed by comparing with the theoretical formulae for spherical particle systems. Next, a dual-probability-Brownian motion (DP-BM) scheme along with the level set algorithm is extended to investigate the effective transport properties like thermal conductivity of three-phase PRCs. Comparison against the finite element method (FEM), the proposed DP-BM scheme is more user-friendly and efficient to estimate the effective transport properties of PRCs within an accuracy level. The proposed DP-BM scheme can overcome the two intrinsic limitations of the classical effective medium approximations that one limitation is the ellipsoidal inclusion shape and another is a relative dilute level of inclusion volume fraction. Moreover, the effects of the concave particle shape and packing density, and the interphase dimension on the volume fraction of soft interphase and the effective normalized transport properties of PRCs are evaluated. The results elucidate rigorous component-structure–property relations, which can provide sound guidance for durability evaluation and material design of PRCs.

Fine Crop Classification Based on UAV Hyperspectral Images and Random Forest

The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra and a large amount of calculation, three feature transform methods for dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), and principal component analysis (PCA), were studied. Then, RF was used to finely classify a variety of crops in hyperspectral images. The results showed: (1) The MNF–RF combination was the best ideal classification combination in this study. The best classification accuracies of the MNF–RF random sample set in the Longkou and Honghu areas were 97.18% and 80.43%, respectively; compared with the original image, the RF classification accuracy was improved by 6.43% and 8.81%, respectively. (2) For this study, the overall classification accuracy of RF in the two regions was positively correlated with the number of random sample points. (3) The image after feature transform was less affected by the number of sample points than the original image. The MNF transform curve of the overall RF classification accuracy in the two regions varied with the number of random sample points but was the smoothest and least affected by the number of sample points, followed by the PCA transform and ICA transform curves. The overall classification accuracies of MNF–RF in the Longkou and Honghu areas did not exceed 0.50% and 3.25%, respectively, with the fluctuation of the number of sample points. This research can provide reference for the fine classification of crops based on UAV-borne hyperspectral images.

Desert Locust Cropland Damage Differentiated from Drought, with Multi-Source Remote Sensing in Ethiopia

In 2020, Ethiopia had the worst desert locust outbreak in 25 years, leading to food insecurity. Locust research has typically focused on predicting the paths and breeding grounds based on ground surveys and remote sensing of outbreak factors. In this study, we hypothesized that it is possible to detect desert locust cropland damage through the analysis of fine-scale (5–10 m) resolution satellite remote sensing datasets. We performed our analysis on 121 swarm point locations on croplands derived from the Food and Agriculture Organization (FAO) of the United Nations, and 94 ‘non-affected’ random cropland sample points generated for this study that are distributed within 20–25 km from the ‘center’ of swarm affected sample locations. Integrated Drought Condition Indices (IDCIs) and Vegetation Health Indices (VHIs) calculated for the affected sample locations for 2000–2020 were strongly correlated (R2 > 0.90) with that of the corresponding non-affected group of sample sites. Drought indices were strongly correlated with the evaluation Standardized Precipitation Evapotranspiration Indices (SPEIs), and showed that 2020 was the wettest year since 2000. In 2020, the NDVI and backscatter coefficient of cropland phenologies from the affected versus non-affected cropland sample sites showed a slightly wider, but significant gap in March (short growing season) and August-October (long growing season). Thus, slightly wider gaps in cropland phenologies between the affected and non-affected sites were likely induced from the locust damage, not drought, with fine scale data representing a larger gap.

Comparing Fire Extent and Severity Mapping between Sentinel 2 and Landsat 8 Satellite Sensors

Mapping of fire extent and severity across broad landscapes and timeframes using remote sensing approaches is valuable to inform ecological research, biodiversity conservation and fire management. Compiling imagery from various satellite sensors can assist in long-term fire history mapping; however, inherent sensor differences need to be considered. The New South Wales Fire Extent and Severity Mapping (FESM) program uses imagery from Sentinel and Landsat satellites, along with supervised classification algorithms, to produce state-wide fire maps over recent decades. In this study, we compared FESM outputs from Sentinel 2 and Landsat 8 sensors, which have different spatial and spectral resolutions. We undertook independent accuracy assessments of both Sentinel 2 and Landsat 8 sensor algorithms using high-resolution aerial imagery from eight training fires. We also compared the FESM outputs from both sensors across 27 case study fires. We compared the mapped areas of fire severity classes between outputs and assessed the classification agreement at random sampling points. Our independent accuracy assessment demonstrated very similar levels of accuracy for both sensor algorithms. We also found that there was substantial agreement between the outputs from the two sensors. Agreement on the extent of burnt versus unburnt areas was very high, and the severity classification of burnt areas was typically either in agreement between the sensors or in disagreement by only one severity class (e.g., low and moderate severity or high and extreme severity). Differences between outputs are likely partly due to differences in sensor resolution (10 m and 30 m pixel sizes for Sentinel 2 and Landsat 8, respectively) and may be influenced by landscape complexity, such as terrain roughness and foliage cover. Overall, this study supports the combined use of both sensors in remote sensing applications for fire extent and severity mapping.

SARS-CoV-2 Viral Incidence, Antibody Point Prevalence, Associated Population Characteristics, and Vaccine Attitudes, South Carolina, February 2021.

The SARS-CoV-2 outbreak from October 2020 through February 2021 was the largest outbreak as of February 2021, and timely information on current representative prevalence, vaccination, and loss of prior antibody protection was unknown. In February 2021, the South Carolina Department of Health and Environmental Control conducted a random sampling point prevalence investigation consisting of viral and antibody testing and an associated health survey, after selecting participants aged ≥5 years using a population proportionate to size of South Carolina residents. A total of 1917 residents completed a viral test, 1803 completed an antibody test, and 1463 completed ≥1 test and a matched health survey. We found an incidence of 2.16 per 100 residents and seroprevalence of 16.4% among South Carolina residents aged ≥5 years. Undetectable immunoglobulin G and immunoglobulin M antibodies were noted in 28% of people with a previous positive test result, highlighting the need for targeted education among people who may be susceptible to reinfection. We also found a low rate of vaccine hesitancy in the state (13%). The results of this randomly selected surveillance and associated health survey have important implications for prospective COVID-19 public health response efforts. Most notably, this article provides a feasible framework for prompt rollout of a statewide evidence-based surveillance initiative.

CORAL POINT COUNT WITH EXCEL EXTENSIONS (CPCE) SOFTWARE: CORAL REEF CONDITION AT SMALL ISLANDS IN INDONESIA

Coral reefs are marine ecosystem that provide homes at less 25% of all marine species. In the last decade, the use of Underwater Photo Transect (UPT) methods for coral reef monitoring has gained increased popularity. This study aimed to monitoring of coral reef condition at Dua Island, Tikus Island, Belanda Island, and Dapur Island. This study used Coral Point Count with Excel extensions (CPCe) software, to increase the efficiency of coral reef monitoring efforts. Photo transects were conducted by scuba diving. Coral reef community image was capture for every 1m across 50m transect line. A total of 30 random point samples was selected for each photo frame. Multiple photo frame were combine and for each point coded according to the code of each category, biota, and substrate at the random point. The results showed that the condition of the coral reefs in Bengkulu was in the fair to good category, while the condition of the coral reefs in the Seribu Islands was in the poor to fair category. The most common coral genera found in Bengkulu are Porites and Pocillopora, while in the Seribu Islands are Porites and Acropora.

CORAL POINT COUNT WITH EXCEL EXTENSIONS (CPCE) SOFTWARE: CORAL REEF CONDITION AT SMALL ISLANDS IN INDONESIA

Coral reefs are marine ecosystem that provide homes at less 25% of all marine species. In the last decade, the use of Underwater Photo Transect (UPT) methods for coral reef monitoring has gained increased popularity. This study aimed to monitoring of coral reef condition at Dua Island, Tikus Island, Belanda Island, and Dapur Island. This study used Coral Point Count with Excel extensions (CPCe) software, to increase the efficiency of coral reef monitoring efforts. Photo transects were conducted by scuba diving. Coral reef community image was capture for every 1m across 50m transect line. A total of 30 random point samples was selected for each photo frame. Multiple photo frame were combine and for each point coded according to the code of each category, biota, and substrate at the random point. The results showed that the condition of the coral reefs in Bengkulu was in the fair to good category, while the condition of the coral reefs in the Seribu Islands was in the poor to fair category. The most common coral genera found in Bengkulu are Porites and Pocillopora, while in the Seribu Islands are Porites and Acropora.

Effects of Thinning Intensity on Forest Floor and Soil Biochemical Properties in an Aleppo Pine Plantation after 13 Years: Quantity but Also Quality Matters

In order to quantify the impacts of silvicultural treatments in semiarid forests, it is necessary to know how they affect key aboveground processes and also properties characterizing the forest floor and mineral soil compartments. The general objective of this work is to study the mid-term effects of thinning intensity on forest floor and soil properties after 13 years following the intervention. The experimental design consisted of a randomized block design with four thinning treatments (3 thinning intensity plots plus a control or unmanaged plot) and three blocks or replicates. Several determinations, such as total organic carbon, dissolved organic carbon, or basal respiration, were performed for characterizing forest floor and mineral soil by considering three random sampling points per experimental plot. Thirteen years after thinning, total organic content, the different organic carbon fractions studied, and basal respiration were higher in the forest floor of the unmanaged plot. These results, however, were contrasted to those obtained for the mineral soil, where significant differences between the treatments were only observed in basal respiration and C/N ratio, while the different organic carbon fractions were not affected by thinning intensity. Our results suggest better soil quality where biological activity is enhanced as a consequence of improved environmental conditions and also litterfall input. The latter is especially important in forests with tree leaves of low biodegradability, where new understorey species promoted by thinning can provide higher nutrient availability for the remaining trees and, therefore, better forest resilience.

Safety lifetime analysis method for multi-mode time-dependent structural system

It is important to determine the safety lifetime of Multi-mode Time-Dependent Structural System (MTDSS). However, there is still a lack of corresponding analysis methods. Therefore, this paper establishes MTDSS safety lifetime model firstly, and then proposes a Kriging surrogate model based method to estimate safety lifetime. The first step of proposed method is to construct the Kriging model of MTDSS performance function by using extremum learning function. By identifying possible extremum mode of MTDSS, the performance function of MTDSS can be equivalently transformed into the one of Single-mode Time-Dependent Structure (STDS). The second step is to use the Advanced First Failure Instant Learning Function (AFFILF) to train the Kriging model constructed in the first step, so that the convergent Kriging model can identify the possible First Failure Instant (FFI) of STDS. Then safety lifetime can be searched quickly by dichotomy search. By using AFFILF, the minimum instant that the state is not accurately identified by the current Kriging model is selected as the training point, which avoids the unnecessary calculation which may be introduced into the existing First Failure Instant Learning Function (FFILF). In addition, the Candidate Sample Pool (CSP) reduction strategy is also adopted. By adaptively deleting the random candidate sample points whose FFI have been accurately identified by the current Kriging model, the training efficiency is further improved. Three cases show that the proposed method is accurate and efficient.

Path planning of a manipulator based on an improved P_RRT* algorithm

Aiming to build upon the slow convergence speed and low search efficiency of the potential function-based rapidly exploring random tree star (RRT*) algorithm (P_RRT*), this paper proposes a path planning method for manipulators with an improved P_RRT* algorithm (defined as improved P_RRT*), which is used to solve the path planning problem for manipulators in three-dimensional space. This method first adopts a random sampling method based on a potential function. Second, based on a probability value, the nearest neighbour node is selected by the nearest Euclidean distance to the random sampling point and the minimum cost function, and in the expansion of new nodes, twice expansion methods are used to accelerate the search efficiency of the algorithm. The first expansion adopts the goal-biased expansion strategy, and the second expansion adopts the strategy of random sampling in a rectangular area. Then, the parent node of the new node is reselected, and the path is rerouted to obtain a clear path from the initial point to the target point. Redundant node deletion and the maximum curvature constraint are used to remove redundant nodes and minimize the curvature on the generated path to reduce the tortuosity of the path. The Bezier curve is used to fit the processed path and obtain the trajectory planning curve for the manipulator. Finally, the improved P_RRT* algorithm is verified experimentally in Python and the Robot Operating System (ROS) and compared with other algorithms. The experimental results verify the effectiveness and superiority of the improved algorithm.

ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

Abstract. Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

Advanced single-loop Kriging surrogate model method by combining the adaptive reduction of candidate sample pool for safety lifetime analysis

It is necessary to execute the safety lifetime analysis to ensure the safety service of the structure. At present, the existing safety lifetime analysis methods used different learning functions to construct a Kriging model in the time interval of interest. The constructed Kriging model can estimate the time-dependent failure probability (TDFP) in any time subinterval accurately. Then the safety lifetime can be estimated by the constructed Kriging model. However, when the time-dependent performance function is highly nonlinear in the time interval of interest, the existing methods need a lot of training samples to construct the Kriging model. Therefore, this paper proposes an advanced single-loop Kriging surrogate model method (ASLK) by combining the adaptive reduction of candidate sample pool (CSP) for safety lifetime analysis. In every possible safety lifetime obtained by dichotomy search, the ASLK is employed to construct a new Kriging model to estimate the corresponding TDFP. To make full use of the known training point information, all existing training points are recorded in the process of searching safety lifetime. When constructing the Kriging model in the time interval of the visited possible safety lifetime by the dichotomy search, the recorded training points falling into the current safety lifetime interval are used as the initial training set. Since it is much easier to build a Kriging model that can accurately estimate the TDFP in a given time interval than the one that can accurately estimate the TDFP in any subinterval, the ASLK is more efficient than the existing methods. At the same time, the ASLK adopts the adaptive CSP reduction strategy, in which the random input sample points with the states accurately identified by the current Kriging model will be deleted from CSP to improve the efficiency further. The example results fully verify the accuracy and efficiency of the proposed method for solving the safety lifetime.