An Improved Generative Adversarial Network-Based and U-Shaped Transformer Method for Glass Curtain Crack Deblurring Using UAVs.

Drones have emerged as a critical tool for the detection of high-altitude glass curtain cracks. However, their utility is often compromised by vibrations and other environmental factors that can induce motion blur, compromising image quality and the accuracy of crack detection. This paper presents a novel GAN-based and enhanced U-shaped Transformer network, named GlassCurtainCrackDeblurNet, designed specifically for the deblurring of drone-captured images of glass curtain cracks.

Associative asymmetry of the recognition without cued-recall effect in thematic relations.

In the present study, two experiments were conducted to examine whether thematic relation can produce recognition without cued-recall effect and whether the direction of the association has an influence on this effect. The participants provided higher familiarity ratings for studied items than for unstudied items during target retrieval failure. Additionally, the thematic relation-elicited recognition without cued-recall effect was larger in the forward association than in the backward association.

Pressure vessel-oriented visual inspection method based on deep learning.

The detection of surface parameters of pressure vessel welds guarantees safe operation. To address the problems of low efficiency and poor accuracy of traditional manual inspection methods, a method for welding morphological parameters combined with vision and structured light is proposed in this study. First, a feature point extraction algorithm for weld parameters based on deep convolution was proposed.

Prediction and associated factors of hypothyroidism in systemic lupus erythematosus: a cross-sectional study based on multiple machine learning algorithms.

Objectives: The prevalence of hypothyroidism in systemic lupus erythematosus (SLE) is significantly higher than that in the common public. While SLE itself can affect multiple organs, abnormal thyroid function may exacerbate organ damage in patients with SLE. We aimed to predict abnormal thyroid function and to examine the associated factors with multiple machine learning approaches.

Vertical tank capacity measurement based on Monte Carlo method.

Vertical tanks are commonly used appliances for liquids, and its capacity is very important for quantitative liquid ratio and liquid trade. In order to measure the capacity of vertical tanks more conveniently, this paper proposes a vertical tank capacity measurement method based on Monte Carlo Method. The method arranges a plurality of sensor points on the inner surface of the tank, and then performs Monte Carlo tests by generating a large number of random sample points, and finally calculates the capacity by counting the sample points that meet the criterion.

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on the method of online semantic segmentation under complex backgrounds. First, the fewer-parameters optimization of the atrous convolution architecture is studied.

Unitization facilitates familiarity-based cross-language associative recognition.

The unitization effect means a phenomenon in which familiarity can contribute to associative recognition judgments when pairs of items are treated as a single entity rather than two separate items. Cumulative evidences suggested that the unitization effect was not influenced by the type of language, and this effect could be generalized to bilinguals when they performed an associative recognition in their second language. In the present study, the influence of familiarity on cross-language associative retrieval under unitization and the underlying neurophysiological mechanism behind this effect were investigated.

Improved Bound Fit Algorithm for Fine Delay Scheduling in a Multi-Group Scan of Ultrasonic Phased Arrays.

Multi-group scanning of ultrasonic phased arrays (UPAs) is a research field in distributed sensor technology. Interpolation filters intended for fine delay modules can provide high-accuracy time delays during the multi-group scanning of large-number-array elements in UPA instruments. However, increasing focus precision requires a large increase in the number of fine delay modules.

Improved scheduling algorithm for signal processing in asynchronous distributed ultrasonic total-focusing-method system.

Compared to the conventional ultrasonic phased-array system, a large-element phased-array system employing the total focusing method (TFM) can yield improved image resolution and accuracy, providing more flexible scanning methods and image merging functionality. In order to meet various forms of ultrasonic multi-group scanning, an architecture for multi-group scan integration called the "asynchronous distributed ultrasonic TFM system" is proposed, and a novel scheduling algorithm called "the sum of start time and processing time adjacent (SSPA) algorithm" is presented. The architecture adds a focus and group scheduler (FGS) and signal processing scheduler (SPS) to the traditional ultrasonic phased array system and constructs a signal processing arbitration (SPA) with several signal processing modules (SPMs).

Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in fish fillet using physarum network and genetic algorithm (PN-GA) method.

A multispectral model for the detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in grass carp and salmon fillet was developed using physarum network and genetic algorithm (PN-GA) method for the first time. Partial least-squares regression (PLSR), multiple linear regressions (MLR), and principal component regression (PCR) algorithms were used to predict the DHA and EPA using optimal wavelengths selected by PN-GA. The MLR models showed the best DHA prediction results for both grass carp and salmon fillets, and also showed good prediction for EPA in grass carp fillet but poor prediction in salmon fillet.

An Improved Scheduling Algorithm for Data Transmission in Ultrasonic Phased Arrays with Multi-Group Ultrasonic Sensors.

High data transmission efficiency is a key requirement for an ultrasonic phased array with multi-group ultrasonic sensors. Here, a novel FIFOs scheduling algorithm was proposed and the data transmission efficiency with hardware technology was improved. This algorithm includes FIFOs as caches for the ultrasonic scanning data obtained from the sensors with the output data in a bandwidth-sharing way, on the basis of which an optimal length ratio of all the FIFOs is achieved, allowing the reading operations to be switched among all the FIFOs without time slot waiting.

Adaptive multidirectional frequency domain filter for noise removal in wrapped phase patterns.

In order to avoid the detrimental effects of excessive noise in the phase fringe patterns of a laser digital interferometer over the accuracy of phase unwrapping and the successful detection of mechanical fatigue defects, an effective method of adaptive multidirectional frequency domain filtering is introduced based on the characteristics of the energy spectrum of localized wrapped phase patterns. Not only can this method automatically set the cutoff frequency, but it can also effectively filter out noise while preserving the image edge information. Compared with the sine and cosine transform filtering and the multidirectional frequency domain filtering, the experimental results demonstrate that the image filtered by our method has the fewest number of residues and is the closest to the noise-free image, compared to the two aforementioned methods, demonstrating the effectiveness of this adaptive multidirectional frequency domain filter.