With the continuous advancement of the avionics system, crew members are correspondingly reduced, and Single Pilot Operations (SPO) has attracted widespread attention from scholars. To meet the flight requirements in SPO mode, it is necessary to further strengthen air-ground coordination system integration, but at the same time, there will be some safety issues caused by resource integration, function fusion, and task synthesis. Aimed at the safety problems caused by task synthesis, an efficient differential bicluster mining algorithm--DFCluster algorithm is proposed in this paper to discover potential hazardous elements or propagation mechanisms through mining the resource-function matrixes. To mine efficiently, several pruning techniques are designed for generating maximal biclusters without candidate maintenance. The experimental results show that the DFCluster algorithm is more efficient than the existing differential biclustering algorithms under different scales of artificial datasets and public datasets. Then, a typical flight scenario is designed based on SPO air-ground collaborative system architecture, and combined with our proposed DFCluster algorithm for task synthesis safety analysis. Based on the mining results, the SPO air-ground collaborative system architecture is modified, which ultimately improves the safety of the SPO system.

Based on the relevant literature, this paper study the impact of online shopping return policy on consumer purchase behavior from the dimension of return policy, consumer perception, consumer purchase behavior and so on. In the online shopping environment, the return problem between retailers and consumers is more obvious. Based on the researching achievements of predecessors, return policy can be divided into three dimensions: return cost, return time limit and efforts. Consumer psychological perception of return policy is based on the traditional consumer perception, which can be summarized into three aspects: perceived risk, perceived quality and perceived fairness. In the online shopping environment, the consumer purchase behavior is still a specific research and in-depth discussion. The return policy, as the key information between the two decision points, is of great importance to consumers' purchase and return behaviors.

Occlusions and complex backgrounds are common factors that hinder many computer vision applications. In a street scene, the challenge of accurately predicting pedestrian trajectories comes from the complexity of human behavior and the diversity of the external environment. It is difficult, if not impossible, to extract relevant information to accurately predict pedestrian trajectories in dynamic scenes. Synthetic aperture imaging (SAI) uses an array of cameras to mimic a camera with a large virtual convex lens by projecting images of a scene from different views onto a virtual focal plane. It is commonly used to reconstruct occluded objects, and in a street scene, can provide observation of pedestrians occluded by other objects and pedestrians. In this paper, we propose a joint prediction method based on autofocusing of SAI to predict pedestrian trajectories in dynamic scenes. The main contributions of this paper include: 1) The task of pedestrian trajectory prediction in dynamic scenarios is redefined as pedestrian trajectory prediction and SAI autofocusing from a practical but more challenging perspective. 2) The proposed method is based on an existing SAI-based method to extract information in heavily occluded views, which can obtain more accurate results but with less computational cost and without using other sensors such as LiDAR or depth cameras. 3) A new pedestrian trajectory prediction model, an attention-based trajectory prediction variational autoencoder (ATP-VAE), is proposed to extract complex human behavior and social interactions in dynamic scenes through a new Intention Attention Unit. The experimental results on multiple public datasets show that the proposed method achieves state-of-the-art results in the first-person perspective and in aerial view.

In the context of functional integration of avionics, the complex operational process interaction has increased the complexity of the cascading failure analysis, which is significant to evaluate the overall safety status and design rationality. In order to achieve a dynamic and dimensional evaluation of cascading failures, this paper proposes a safety analysis method based on operational process state, with an operational process-orientated hierarchical system functional framework established by means of the state machine. Then the cascading failure causation dynamic search algorithms and the cascading failure causation tree structure are designed respectively to describe the failure propagation in combination. Consequently, cascading failure propagation paths and minimum cut sets are generated automatically based on the search result. By using the aircraft integrated surveillance system as a research object, the effect and cause of cascading failure assessed in various failure scenarios have validated the effectiveness of the proposed method, and the comparison against the existing model based safety analysis methods demonstrates the higher flexibility and efficiency of the proposed method. The proposed method enables the dynamic and overall assessment of system safety status concentrating on the cascading failure utilizing the operational process state, and further enhances the systemization and automation level of the safety evaluation process in the early development phase.

The high costs of pilot training and remuneration have placed a heavy financial burden on airlines, prompting people to actively study Single Pilot Operations (SPO). Achieving SPO undoubtedly requires the development of the new conceptual framework, and how to reallocate system functions among new agents to obtain optimal system design has become the primary problem in the early stages of the system lifecycle. To solve this problem, this paper applied the Human-centered Design (HCD) approach for the first time to the development and evaluation of SPO in the typical approach and landing scenario. Firstly, the combination of Hierarchical Task Analysis (HTA) and Abstraction Hierarchy (AH) was used to identify five function requirements and six function assumptions for the transition from the current Two-crew Operations (TCO) to the future SPO to develop the SPO model. Subsequently, the TCO and SPO models were transformed into two network models to evaluate the result of system function reallocation from the network level and node level using Social Network Analysis (SNA). The network parameters of both levels show that the future SPO developed in this paper has the advantages of better stability, less pilot workload and higher safety than the current TCO.