A Text Recognition Algorithm Based on a Dual-Attention Mechanism in Complex Driving Environment

In response to many problems such as complex background of text recognition environment, perspective distortion, shallow handwriting, and mixed Chinese and English characters, we have designed an OCR algorithm framework with features such as landmark extraction and correction, image enhancement, text detection, and text recognition. We have designed a DBNet based on dual attention mechanism and content-aware upsampling. We have also designed a text recognition module incorporating the central loss CRNN + CTC to improve content awareness. Experimental results show that the improved text detection network in this paper has increased accuracy by 5.09%, recall by 2.12%, and F-score by 3.46% on the ICDAR2015 dataset. The text recognition network has improved the accuracy of recognizing Chinese and English characters by 1.2%

Release Profile of Nitrogen during Thermal Treatment of Waste Wooden Packaging Materials

In this paper, the fast pyrolysis experiment of particle board was carried out on a fixed bed reactor and a Py-GC/MS equipment. The effects of temperature and gas phase residence time on the product yields and its components distribution were investigated. The effect of components of particle board on product yields and its components distribution was also investigated. The results showed that the temperature has a great influence on the yields of fast pyrolysis products, and the yield of pyrolysis oil reached the highest at 550°C. The urea-formaldehyde resin would prevent the pyrolysis of particle board. Compared with the bio-oil from fast pyrolysis of wood, the major components of the bio-oil from fast pyrolysis of particle board did not change much

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

Pricing Mining Concessions Based on Combined Multinomial Pricing Model

A combined multinomial pricing model is proposed for pricing mining concession in which the annualized volatility of the price of mineral products follows a multinomial distribution. First, a combined multinomial pricing model is proposed which consists of binomial pricing models calculated according to different volatility values. Second, a method is provided to calculate the annualized volatility and the distribution. Third, the value of convenience yields is calculated based on the relationship between the futures price and the spot price. The notion of convenience yields is used to adjust our model as well. Based on an empirical study of a Chinese copper mine concession, we verify that our model is easy to use and better than the model with constant volatility when considering the changing annualized volatility of the price of the mineral product

A Text Recognition Algorithm Based on a Dual-Attention Mechanism in Complex Driving Environment

In response to many problems such as complex background of text recognition environment, perspective distortion, shallow handwriting, and mixed Chinese and English characters, we have designed an OCR algorithm framework with features such as landmark extraction and correction, image enhancement, text detection, and text recognition. We have designed a DBNet based on dual attention mechanism and content-aware upsampling. We have also designed a text recognition module incorporating the central loss CRNN + CTC to improve content awareness. Experimental results show that the improved text detection network in this paper has increased accuracy by 5.09%, recall by 2.12%, and F-score by 3.46% on the ICDAR2015 dataset. The text recognition network has improved the accuracy of recognizing Chinese and English characters by 1.2%

Preparation of MOF-Based Core-Shell Gel Particles with Catalytic Activity and Their Plugging Performance

Drilling fluid systems for deep and ultra-deep wells are hampered by both high-temperature downhole environments and lengthy cycle periods. Suppose that the gel particle-plugging agent, the primary treatment agent in the system, fails to offer durable and stable plugging performance. In such a scenario, the borehole wall is susceptible to instability and landslide after prolonged immersion, leading to downhole accidents. In this study, novel core-shell gel particles (modified ZIF) with ZIF particles employed as the core material and organosilicon-modified polyethylene polyamine (PEPA) as the polymer shell were fabricated using PEPA, in-house synthesized (3-aminopropyl) triethoxysilane (APTS), and the ZIF-8 metal-organic framework (MOF) as the raw materials to enhance the long-term plugging performance of gel plugging agents. The modified ZIF particles are nanoscale polygonal crystals and differ from conventional core-shell gel particles in that they feature high molecular sieve catalytic activity due to the presence of numerous interior micropores and mesopores. As a result, modified ZIF exhibits the performance characteristics of both rigid and flexible plugging agents and has an excellent catalytic cross-linking effect on the sulfonated phenolic resin (SMP-3) and sulfonated lignite resin (SPNH) in drilling fluids. Consequently, a cross-linking reaction occurs when SMP-3 and SPNH flow through the spacings in the plugging layer formed by the modified ZIF particles. This increases the viscosity of the liquid phase and simultaneously generates an insoluble gel, forming a particle-gel composite plugging structure with the modified ZIF and significantly enhancing the long-term plugging performance of the drilling fluid

Generating 3D architectural models based on hand motion and gesture

This paper presents a novel method for rapidly generating 3D architectural models based on hand motion and design gestures captured by a motion capture system. A set of sign language-based gestures, architectural hand signs (AHS), has been developed. AHS is performed on the left hand to define various “components of architecture”, while “location, size and shape” information is defined by the motion of Marker-Pen on the right hand. The hand gestures and motions are recognized by the system and then transferred into 3D curves and surfaces correspondingly. This paper demonstrates the hand gesture-aided architectural modeling method with some case studies

Improving Graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for Graph Anomaly Detection

The era of Industry 4.0 is gradually transforming our society into a data-driven one, which can help us uncover valuable information from accumulated data, thereby improving the level of social governance. The detection of anomalies, is crucial for maintaining societal trust and fairness, yet it poses significant challenges due to the ubiquity of anomalies and the difficulty in identifying them accurately. This paper aims to enhance the performance of the current Graph Convolutional Network (GCN)-based Graph Anomaly Detection (GAD) algorithm on datasets with extremely low proportions of anomalous labels. This goal is achieved through modifying the GCN network structure and conducting feature extraction, thus fully utilizing three types of information in the graph: node label information, node feature information, and edge information. Firstly, we theoretically demonstrate the relationship between label propagation and feature convolution, indicating that the Label Propagation Algorithm (LPA) can serve as a regularization penalty term for GCN, aiding in training and enabling learnable edge weights, providing a basis for incorporating node label information into GCN networks. Secondly, we introduce a method to aggregate node and edge features, thereby incorporating edge information into GCN networks. Finally, we design different GCN trainable weights for node features and co-embedding features. This design allows different features to be projected into different spaces, greatly enhancing model expressiveness. Experimental results on the DGraph dataset demonstrate superior AUC performance compared to baseline models, highlighting the feasibility and efficacy of the proposed approach in addressing GAD tasks in the scene with extremely low proportions of anomalous data

Super-resolution enhancement of UAV images based on fractional calculus and POCS

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images