BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection

Arbitrary shape scene text detection is of great importance in scene understanding tasks. Due to the complexity and diversity of text in natural scenes, existing scene text algorithms have limited accuracy for detecting arbitrary shape text. In this paper, we propose a novel arbitrary shape scene text detector through boundary points dynamic optimization(BPDO). The proposed model is designed with a text aware module (TAM) and a boundary point dynamic optimization module (DOM). Specifically, the model designs a text aware module based on segmentation to obtain boundary points describing the central region of the text by extracting a priori information about the text region. Then, based on the idea of deformable attention, it proposes a dynamic optimization model for boundary points, which gradually optimizes the exact position of the boundary points based on the information of the adjacent region of each boundary point. Experiments on CTW-1500, Total-Text, and MSRA-TD500 datasets show that the model proposed in this paper achieves a performance that is better than or comparable to the state-of-the-art algorithm, proving the effectiveness of the model.Comment: Accepted to ICASSP 202

Text Region Multiple Information Perception Network for Scene Text Detection

Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention. Existing segmentation-based scene text detection algorithms usually only segment the pixels in the center region of the text, while ignoring other information of the text region, such as edge information, distance information, etc., thus limiting the detection accuracy of the algorithm for scene text. This paper proposes a plug-and-play module called the Region Multiple Information Perception Module (RMIPM) to enhance the detection performance of segmentation-based algorithms. Specifically, we design an improved module that can perceive various types of information about scene text regions, such as text foreground classification maps, distance maps, direction maps, etc. Experiments on MSRA-TD500 and TotalText datasets show that our method achieves comparable performance with current state-of-the-art algorithms.Comment: Accepted to ICASSP 202

CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

Scene text recognition, as a cross-modal task involving vision and text, is an important research topic in computer vision. Most existing methods use language models to extract semantic information for optimizing visual recognition. However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text. To tackle this issue, we propose a novel cross-modal fusion network (CMFN) for irregular scene text recognition, which incorporates visual cues into the semantic mining process. Specifically, CMFN consists of a position self-enhanced encoder, a visual recognition branch and an iterative semantic recognition branch. The position self-enhanced encoder provides character sequence position encoding for both the visual recognition branch and the iterative semantic recognition branch. The visual recognition branch carries out visual recognition based on the visual features extracted by CNN and the position encoding information provided by the position self-enhanced encoder. The iterative semantic recognition branch, which consists of a language recognition module and a cross-modal fusion gate, simulates the way that human recognizes scene text and integrates cross-modal visual cues for text recognition. The experiments demonstrate that the proposed CMFN algorithm achieves comparable performance to state-of-the-art algorithms, indicating its effectiveness.Comment: Accepted to ICONIP 202

Climate matching characteristics of proton exchange membrane water electrolyzer driven by different commercial photovoltaic panels

The hydrogen production rate and lifetime of photovoltaic (PV)-driven proton exchange membrane (PEM) water electrolysis systems are significantly related to the fluctuating input characteristics to the PEM, which is caused by the weather conditions and types of PV cells. However, most of the existing studies focus on the theoretical optimization under steady state conditions, and there is a lack of understanding on the matching characteristics of weather conditions and different PV-PEM systems. Therefore, this study selects the commercial PV panel based on monocrystalline silicon (mono-Si) and PV panel based on cadmium telluride (CdTe), and compares the dynamic hydrogen production performance of the two types of PV-PEM systems under different weather conditions, both experimentally and numerically. The experimental results show that under sunny weather conditions, the average daily power generation of the PV system based on CdTe is 5.06% higher than that of the PV system based on mono-Si, and the hydrogen production is increased by 4.79%. While with significant solar irradiation fluctuation, the output power fluctuation of PV panels based on CdTe is 15.74% lower than that of PV panels based on mono-Si. However, limited by the lower nominal efficiency of the PV panels based on CdTe, the solar-to-hydrogen (STH) efficiency of the CdTe PV-PEM system is 7.60–8.65%, which is lower than that of the PV system based on mono-Si (10.89–12.15%). To enhance hydrogen production and extend system lifespan, PV technology based on CdTe demonstrates greater advantages than PV technology based on mono-Si when coupled with PEM electrolyzers, particularly in regions where installation space is not a constraint.</p

Seismic and Power Generation Performance of U-Shaped Steel Connected PV-Shear Wall under Lateral Cyclic Loading

BIPV is now widely used in office and residential buildings, but its seismic performance still remained vague especially when the photovoltaic (PV) modules are installed on high-rise building facades. A new form of reinforced concrete shear wall integrated with photovoltaic module is proposed in this paper, aiming to apply PV module to the facades of high-rise buildings. In this new form, the PV module is integrated with the reinforced concrete wall by U-shaped steel connectors through embedded steel plates. The lateral cyclic loading test is executed to investigate the seismic behavior and the electric and thermal performance with different drift angles. The seismic behavior, including failure pattern, lateral force-top displacement relationship, and deformation capacity, was investigated. The power generation and temperature variation on the back of the PV module and both sides of the shear wall were also tested. Two main results are demonstrated through the experiment: (1) the U-shaped steel connectors provide enough deformation capacity for the compatibility of the PV module to the shear wall during the whole cyclic test; (2) the electricity generation capacity is effective and stable during this seismic simulation test

Modelling infectious disease transmission dynamics in conference environments: An individual-based approach

The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simulate transmission processes within a condensed timeframe (one day), beginning with sporadic cases. Our model intricately captures the activities of individual attendees within the conference venue, encompassing meetings, rest intervals, and meal breaks. While meetings entail proximity seating, rest and lunch periods allow attendees to interact with diverse individuals. Moreover, the restroom environment poses an additional avenue for potential infection transmission. Employing an individual-based model, we meticulously replicated the transmission dynamics of infectious diseases, with a specific emphasis on close-contact interactions between infected and susceptible individuals. Through comprehensive analysis of model simulations, we elucidated the intricacies of disease transmission dynamics within conference settings and assessed the efficacy of control strategies to curb disease dissemination. Ultimately, our study proffers a numerical framework for assessing the risk of infectious disease transmission during short-duration conferences, furnishing conference organizers with valuable insights to inform the implementation of targeted prevention and control measures.Comment: 25 pages; 8 figure