A probabilistic analysis for the variability of pilot performance was performed to model the variation in pilot performance during flight training from the viewpoint of reliability theory. We ...summarized flags among all applicants tallied in flight training to a histogram. Various probability distributions were fitted to the histogram using two bootstrap goodness-of-fit tests. We found that a limit of the marginal distribution of Ryu's bivariate exponential distribution gave the best approximation of the histogram. Defining a random variable for the conditional hazard function as the training step was the key interpreting the physical background of the fitted distribution in terms of the growth process during training. Its hazard function showed keeping the number of flags per flight within a few was important. Also, calculating the ratio to the expectation for each training step and visualizing the transition of the cumulative number of flags revealed a concave growth model as the basic process lying in the background. Moreover, fundamental assumptions of software reliability growth model (SRGM) were interpreted in terms of pilot training, and the existence of a stochastic process was discussed. Visualizing personal processes appearing in reality, we found that their shapes were similar to those of SRGM. Therefore, applying SRGM to pilot training data is expected in the future.
As a matter of course, the unprecedented ascending penetration of distributed energy resources (DERs), mainly harvesting renewable energies (REs), is concomitant with environmentally friendly ...concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system, consequently, necessitates probabilistic analyses of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognized as an appropriate probabilistic method in small scale or even medium scale problems. This paper develops a new methodology for probabilistic optimal power flow (P-OPF) studies for such problems by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables but the proposed method has been equipped with this ability. In order to justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the Mathpower 30-bus test systems are examined using the proposed method, then, the obtained results are compared against the Monte Carlo simulation (MCS) results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.
In this paper, we present a novel probabilistic compact representation of the on-road environment, i.e., the dynamic probabilistic drivability map (DPDM), and demonstrate its utility for predictive ...lane change and merge (LCM) driver assistance during highway and urban driving. The DPDM is a flexible representation and readily accepts data from a variety of sensor modalities to represent the on-road environment as a spatially coded data structure, encapsulating spatial, dynamic, and legal information. Using the DPDM, we develop a general predictive system for LCMs. We formulate the LCM assistance system to solve for the minimum-cost solution to merge or change lanes, which is solved efficiently using dynamic programming over the DPDM. Based on the DPDM, the LCM system recommends the required acceleration and timing to safely merge or change lanes with minimum cost. System performance has been extensively validated using real-world on-road data, including urban driving, on-ramp merges, and both dense and free-flow highway conditions.
Replicated network data are increasingly available in many research fields. For example, in connectomic applications, interconnections among brain regions are collected for each patient under study, ...motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model that reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance-compared to state-of-the-art models-in simulations and application to human brain networks. Supplementary materials for this article are available online.
The capability of object search is a prerequisite for mobile robot to perform everyday tasks in the home environment. Due to the dynamic nature of object and the particularity of home environment, it ...still remains a challenging problem for mobile robot to find the dynamic target object with the low total cost. To address this problem, a priori knowledge-based approach for dynamic object search is presented in this paper. Inspired by human search process, the common sense knowledge about typical spatial location and the relationship between objects is modeled as a priori knowledge. On this basis, we propose a novel search strategy to improve the search efficiency of mobile robot at the scale of the entire home environment. Unlike the existing methods, a trade-off between the inferred spatial location knowledge and the distance of robot position and candidate room is considered, which permits robot to prioritize the search effort for the candidate room in which the target object is most likely to be found. Also, the search space inside the room is further reduced by using a priori knowledge-guided search heuristics. Meanwhile, the update of knowledge is also investigated to maintain its reliability. The proposed approach is implemented in both simulation and real environments, and evaluated through extensive experiments. The experimental results are provided to demonstrate the feasibility and efficiency of our proposal.
•Risk analysis in systems with high penetration of renewable energy.•Economic analysis of coupled Transmission and Distribution System.•Applying stochastic coordination, sharing stochastic variables ...to consider uncertainty of risk and renewable energy.•Polynomial Chaos Expansion for stochastic coordination.•Contingency analysis of transmission and distribution systems and its effect on cost of electricity (Risk-based cost analysis).
High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk based stochastic dispatching challenges due to contingencies and the integration of renewable sources in the TS and DS. A framework of risk constrained probabilistic problem (RCPC) is presented, which is divided into Probabilistic coordination (PC) and risk-based stochastic security-constrained unit commitment (RSSCUC). The PC is a stochastic linear problem and RSSCUC is a stochastic mixed integer non-linear problem. Therefore, the probabilistic analytical target cascading along with polynomial chaos expansion (PCE) has been utilized for solving the PC problem for coupled TS and DS. Here, PCE has provided the proposed algorithm with the needed capability to present stochastic shared variables as coefficients for PC. Further, the benders decomposition algorithm has been utilized for solving RSSCUC, by dividing it into master problem and sub problem to generate feasibility cuts. The proposed technique reduces the computational burden on the system, as it requires single stochastic coordination problem for all scenarios as an alternative of each coordination problem for each scenario and polynomial coefficients instead of multiple scenarios for each shared variable between TS and DS. Different case studies have been performed utilizing the 6-bus system and IEEE 118-bus system as TSs, 7-bus, 9-bus, 85-bus and 69-bus systems as DSs. Results depict the efficacy of the proposed method.
•Field blast tests and axial compression tests reveal a significant decrease in the axial bearing capacity of the shallow-buried RC tunnel subjected to blast loads.•Numerical simulation algorithms ...and material models are verified by field blast test and post-blast axial compression test results.•The shallow-buried RC tunnel exhibits different damage modes under different blast scenarios.•The damage degree of a shallow-buried tunnel is sensitive to the explosive weight and detonation point.•The blast vulnerability of a shallow-buried tunnel under blast load is investigated.
The purpose of this investigation is to analyze the failure probability of a blast in a shallow RC tunnel. The field blast tests are carried out, and a refined finite element model (FEM) is established based on LS-DYNA. To account for blast uncertainty, the Monte Carlo method is used to determine 150 blast conditions. A failure evaluation method based on a mechanical performance index is proposed. The probabilistic blast demand model (PBDM) and the probabilistic blast capacity model (PBCM) are established. Finally, a probability assessment framework based on the structural performance index and the vulnerability curve for shallow tunnels are established. According to the findings, when the mass of TNT in the tunnel exceeds 300 kg, the tunnel is in danger of collapsing. At this moment, it is suggested that people enter the tunnel cautiously. The vulnerability curve predicts the failure probability of a tunnel under various blast loads. At the same time, it could assess its structural performance after the disaster.
•A new statistical, data-driven damage detection algorithm is proposed based on the probabilistic distance of TFs.•The variability of raw TF without postprocessing are modelled by complex-valued ...ratio probabilistic distribution.•The probabilistic distance measure can deal with the deviations in TFs not following Gaussian distribution.•A statistical threshold selection scheme based on fast Bayesian inference strategy is proposed to indicate damage.•Numerical, experimental, and field test studies are conducted to validate the potential of TFs in anomaly detection.
As a mathematical representation of the output-to-output relationship, transmissibility function (TF) has been extensively applied in structural damage detection due to its robustness to influences of the input variations. As in most engineering fields, dealing with the problem of uncertainty in TF-based feature detection is an issue of fundamental importance. In this study, a new statistical, data-driven damage detection algorithm is proposed by rigorously modelling the variability of TF without postprocessing with circularly-symmetric complex Gaussian ratio distribution. The probabilistic distance of Symmetric Kullback-Leibler (SKL) divergence between TFs under baseline condition and potential damage scenarios which can measure the dissimilarity of probability distributions for the TFs under different states are computed as a damage index (DI) to detect structural anomaly. Compared against Mahalanobis distance which has the implicit assumption that the normal condition set is governed by Gaussian statistics, the probabilistic distance measure proposed in this study can deal with the deviations in TFs not following Gaussian distribution. A statistically rigorous threshold selection scheme integrating Bayesian inference strategy and Monte Carlo discordancy test is proposed to detect the the presence of damage by accommodating the uncertainties of measurements and the probabilistic model of TF. Numerical, experimental, and field test studies are conducted to validate the potential of probabilistic distance measure of TFs in anomaly detection under ambient vibration instead of forced vibration testing. Results demonstrate satisfactory performance of the proposed approach for detecting the existence and quantify the relative damage severity from a global perspective.
Creep-fatigue experiments have been conducted in nickel-based superalloy GH720Li at an elevated temperature of 650°C with a stress ratio of 0.1, based on which, different dwell times at the maximum ...loading were applied to investigate the effect of dwell time on the creep-fatigue behaviors. The tested specimens were cut from the rim region of an actual turbine disc in the hoop direction. The grain size and precipitates of the GH720Li superalloy were examined through scanning electronic microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses. Experimental data shows creep-fatigue lifetime decreases as the dwell time prolongs. Further, different scattering was observed in the creep-fatigue lifetime at different dwell times. Then a probabilistic model based on the applied mechanical work density (AMWD), with a linear heteroscedastic function that evaluates the non-constant deviation in the creep-fatigue lifetime, was formulated to describe the dependence of creep-fatigue lifetime on the dwell time. Finally, the possible microscopic mechanism of the creep-fatigue behavior has been discussed by SEM with EDS on the fracture surfaces.
We experimentally demonstrate 16-GBaud probabilistic shaped 256-ary quadrature amplitude modulation (PS-256QAM) signal transmission over 104-m wireless distance at 339 GHz in a photonics-aided ...terahertz (THz)-wave communication system. Thanks to the pair of poly tetra fluoroethylene (PTFE) lenses and PS technique, we successfully achieve a record single line rate of 124.8 Gbit/s and net spectral efficiency (SE) of 6.2 bit/s/Hz. To the best of our current knowledge, it is the first time to achieve >100 m and >100 Gbit/s single-carrier 256QAM THz-wave signal wireless delivery.
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