Scratches on the surface of optical components have serious impacts on optical system such as imaging quality of lens and/or mirrors in optical imaging systems, light-collecting abilities of laser fusion and solar concentrator systems. The size of the scratches is a key issue for analyzing and assessing the impacts quantitatively. Most of the available testing methods for scratches depend on human visual inspection (HVI) with naked eyes by workers, which leads to low efficiency and accuracy. This paper presents an automatic detecting method for the scratches on optical surface with machine vision inspection (MVI) method. The microscopic dark-field scattering imaging system is used as the front end of the detection system. A dedicated algorithm is designed for non-closing scratch detection. The core merits of this algorithm lies in three folds: 1) automatic processing capabilities, which includes positioning, clustering, and precise estimation of the length of the scratches; 2) high efficiency, which is characterized by a short time interval, i.e., about 0.138 second per binary image with 2724 × 2724 pixels in our experiments; 3) high accuracy, where the error rate of the total length of the scratches detected is less than 5% when compared with the nominal visual measurement result obtained via HVI method. The proposed scratch detecting algorithm can be used for non-destructive testing (NDT) of the glass-like surfaces.

The Hurst exponent is a significant indicator for characterizing the time sequence (TS) with the long-term memory property. It has wide applications in physics, technologies, engineering, mathematics, statistics, economics, psychology and so on. Currently, the available methods for estimating the Hurst exponent of time sequence with long-term memory can be divided into different categories: time-domain methods and spectrum-domain methods based on the representation of time sequence, linear regression methods and Bayesian methods based on parameter estimation methods. Although various methods are discussed in literature, there are still some deficiencies: the descriptions of the estimation methods are just mathematics-oriented and the algorithmic pseudo-codes are missing; the effectiveness and accuracy of the estimation algorithms are not clear; the classification of estimation methods is not considered and the lack of guidance for selecting the estimation methods decreases the efficiency of data analysis. The contributions of this article lie in four folds: the optimal sequence partition method is proposed for designing the estimation algorithms for Hurst exponent; the mathematical computational methods for estimating the Hurst exponent are converted to algorithmic pseudo-codes in the sense of computer science, which improves the understandability and usability of the estimation methods and also reduces the difficulty of implementation these methods with concrete computer programming languages; the performance assessment is carried for the typical estimation algorithms via the ideal time sequences with given Hurst exponent and the practical data of TS; the guidance for selecting the algorithms for estimating the Hurst exponent is presented. It is expected that the assessment of the various estimation methods could help the researchers to select, implement and apply the estimation algorithms of interest in practical situations in an easy way.

Complex networks have encouraged scholars to develop an effective method for abstracting and optimizing aviation networks. However, researchers often overlook the aviation network’s temporal attribute and treat it as a static network. Aviation networks have strong temporal characteristics and the dynamic connection cannot be realistically described by a static network. It is necessary to more accurately and realistically represent these connections during the operation of an aviation network. This study explored temporal structures of the Chinese aviation temporal network (CATN) based on flight schedules and actual operational time data. Temporal networks based on time windows were represented to analyze the temporal topology features and robustness of the CATN. The results demonstrated the following: (1) based on the spatial-temporal aviation network, there is a morning departure peak (7:00–8:00) and an evening arrival peak at the airline hub (20:00–21:00); (2) examining the centrality of each airport in the CATN at various time intervals exposed fluctuations in their rankings, which could not be identified by a static network, and (3) the robustness of the CATN was found to be unaffected by time windows, but it displayed poor resilience against deliberate attacks, particularly when subjected to betweenness and closeness attacks, which target the network’s shortest paths. For obtaining a greater understanding of the operating situation of civil aviation, displaying the topological features and robustness of the temporal network is of great importance.

The intrinsic factors (IF) influencing visual attention performance (VAP) might cause potential human errors, such as “error/mistake”, “forgetting” and “omission”. It is a key issue to develop a systematic assessment of IF in order to distinguish the levels of VAP. Motivated by the Stimulus-Response (S-R) model, we take an interactive cancellation test—Neuron Type Test (NTT)—to explore the IF and present the corresponding systematic assessment. The main contributions of this work include three elements: a) modeling the IF on account of attention span, attention stability, distribution-shift of attention with measurable parameters by combining the psychological and statistical concepts; b) proposing quantitative analysis methods for assessing the IF via its computational representation—intrinsic qualities (IQ)—in the sense of computational model; and c) clustering the IQ of air traffic control (ATC) students in the feature space of interest. The response sequences of participants collected with the NTT system are characterized by three parameters: Hurst exponent, normalized number of decisions (NNoD) and error rate of decisions (ERD). The K-means clustering is applied to partition the feature space constructed from practical data of VAP. For the distinguishable clusters, the statistical inference is utilized to refine the assessment of IF. Our comprehensive analysis shows that the IQ can be classified into four levels, i.e., excellent, good, moderate and unqualified, which has a potential application in selecting air traffic controllers subject to reducing the risk of the inadequacy of attention performances in aviation safety management.