This study explores the impact of full windshield head-up display (FHUD) cues on the visual attention allocation of drivers under different scenarios. Forty-eight participants with driving experience were randomly divided into two groups and asked to drive on a preset route in various virtual driving scenarios created in advance. The full windshield highlights situational cues related to the driving task, such as lane lines, safe vehicle distance warnings, navigation guidance and pedestrian cues. Regarding the perception of situational cues, the number of fixations and mean fixation duration with FHUD were lower than those without FHUD. Furthermore, the dwell time percentage of the driver’s forward view with FHUD was larger than that without FHUD, and the dwell time percentage on both sides was smaller than that without FHUD. In conclusion, FHUD may help drivers more effectively perceive cues and improve drivers’ visual attention allocation. Practitioner summary: FHUD may affect drivers’ attention while driving. We examined the effect of FHUD on number of fixations, fixation duration and dwell time percentage in the area of interest under different weather scenarios. Experimental results indicated that FHUD could improve drivers’ visual attention allocation and help drivers more effectively perceive cues. Abbreviations: FHUD: full windshield head-up display; HUD: head-up display; HDD: head-down display; AOI: area of interests; AR: augmented reality

To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.

The selection of breeding geese requires the recording of egg production information to correspond to the identity of the breeding geese. However, due to the special physiological characteristics of breeding geese, manual recording in practice can affect the egg-laying performance of breeding geese and can also lead to problems of missing and confusing individual breeding goose data with the number of eggs laid by the geese. For contactless recording of breeding goose identity and egg production information for high-quality breeding, this paper proposes an Internet of things (IoT)-based breeding egg identification and coding method for the selection of high-quality breeding geese. At the sensing level, we deployed a radiofrequency identification (RFID)-based sensor. Each breeding goose wore a foot ring RFID tag on its leg, and the individual information was read by foot ring RFID readers placed at the bottom of the devices. Individual information was uploaded to the cloud server for database management through structured query language (MySQL). The target detection modules were mounted on top of the devices, and the breeding geese and eggs were detected in the delivery rooms by an improved single-shot multi-box detector (SSD) target detection algorithm. The egg body limit transmission device and contactless coding device were activated only in the case of breeding eggs, and the breeding goose information was printed on the egg bodies in the form of quick response codes (QR codes), which enabled the breeding egg information to correspond with the breeding goose information. An evaluative experiment was performed using a system for the selection of high-quality breeding geese, with web cameras and a cloud monitoring platform. The breeding geese were allowed 14 days to become accustomed to the experimental environment before monitoring began. The evaluative experiment results showed that the pass rate of egg body coding reached 98.25%, the improved SSD algorithm was 8.65% more accurate and 62.6 ms faster than traditional SSD, and the accuracy rate corresponding to the individual information of the breeding geese and the surface information of the goose eggs was 97.8%. The experimental results met the requirements of accurate marking of individual information of breeding geese, which can provide technical support for the selection of high-quality breeding geese.

Fuel tank system ignition source failure threatens the safety of the aircraft. For different possible ignition sources in and around the fuel tank system and their airworthiness requirements, this paper proposes a particular method using both Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to analyze the ignition source fail-safe features, which can minimize the hazards caused by the ignition sources failure and improve the safety of the whole aircraft. At last, an example is given, which proves the efficiency of the method.

1. In a non-cage environment, goose eggs are buried in litter and goose feathers, leading to contamination and discolouration. Such random distribution of goose eggs poses a great challenge to the recognition and location of intelligent picking by robot systems on farm. 2. In order to assist in recognition and location of goose eggs in non-cage environment, a novel method was proposed which used three-channel convolutional neural network (T-CNN), composed of improved AlexNet, combined with ‘you only look once’ (YOLOv5), egg contour curve creation and support vector machine (SVM). 3. Using this method, the original goose egg images were inputted into the YOLOv5 model for target detection and segmentation. In parallel, the median filter and maximum interclass variance method (OTSU) were applied to egg segmentation images to obtain the main pixels for each, and the Kirsch operator was used for edge extraction and contour curves fitting by designing the fitting curve equation to obtain segmentation images with goose egg contour curves. 4. In order to further enrich the differences between goose eggs and background, the goose egg segmentation images were divided into three colour components: R, G and B, which were input into T-CNN for feature extraction. Then the eggs were classified by vector stitching and SVM, by adding goose egg contour curves images. 5. The recognition and location results showed that about 95.65% of the goose egg pixel blocks in the segmented images are recognised correctly. About 3.81% of the pixel blocks in the segmented images were recognised incorrectly, and the centre of mass offset was about 4.45 pixels. 6. This study demonstrated accurate goose egg recognition and location effects using the proposed method in a non-cage environment This highlighted its application prospect in intelligent goose eggs picking.