摘要：
This paper uses a Monte Carlo method to study the thermal conductivity of graphene nanoplatelet (GNP) composites. Firstly, a large number of GNPs are randomly set in a representative volume element. Then, based on a temperature satisfying the Laplace equation in a matrix, a coated surface (CS) is set up on each GNP surface, and the temperature of the CS and GNP can be obtained by the walk-on-spheres (WoS) method. Finally, the WoS method continues to be applied to calculate the heat flux density of the composite materials, further obtaining the thermal conductivity of the composites. We add the influence of interlayers in random walks. We incorporate the influence of interlayers in the WoS process, and the points that walk onto the interlayer surface have a very low probability of reaching the GNP due to the extremely low thermal conductivity of the interlayer. The calculated results are consistent with the experimental data. The model also studies the effects of the size, orientation, and aggregation of GNPs on the thermal conductivity of composite materials.
摘要：
When partial discharges occur in air-insulated equipment, the air decomposes to produce a variety of contamination products, resulting in a reduction in the insulation performance of the insulated equipment. By monitoring the concentration of typical decomposition products (CO, NO, and NO(2)) within the insulated equipment, potential insulation faults can be diagnosed. MoS(2) has shown promising applications as a gas-sensitive semiconductor material, and doping metal oxides can improve the gas-sensitive properties of the material. Therefore, in this work, MoS(2) has been doped using the popular metal oxides (ZnO, TiO(2)) of the day, and its gas-sensitive properties to the typical decomposition products of air have been analyzed and compared using density functional theory (DFT) calculations. The stability of the doped system was investigated using molecular dynamics methods. The related adsorption mechanism was analyzed by adsorption configuration, energy band structure, density of states (DOS) analysis, total electron density (TED) analysis, and differential charge density (DCD) analysis. Finally, the practical application of related sensing performance is evaluated. The results show that the doping of metal oxide nanoparticles greatly improves the conductivity, gas sensitivity, and adsorption selectivity of MoS(2) monolayer to air decomposition products. The sensing response of ZnO-MoS(2) for CO at room temperature (25 °C) reaches 161.86 with a good recovery time (0.046 s). TiO(2)-MoS(2) sensing response to NO(2) reaches 3.5 × 10(6) at 25 °C with a good recovery time (0.108 s). This study theoretically solves the industrial challenge of recycling sensing materials and provides theoretical value for the application of resistive chemical sensors in air-insulated equipment.
摘要：
Insulation performance testing of gas-insulated switchgear (GIS) and treatment of SF6 decomposition products under partial discharge are two crucial studies. In this paper, the adsorption behavior and sensor properties of five characteristic decomposition gases of SF6 (HF, H2S, SO2, SOF2, SO2F2) on intrinsic and CoO-doped SnSe monolayers are investigated in detail based on density-functional theory. The results showed that the adsorption behaviors of HF, H2S, SOF2 and SO2F2 on SnSe monolayers were all physisorption among which the best adsorption was SO2 with an adsorption energy of -0.618 eV, which is a weak chemisorption. After doping with CoO, the adsorption capacity of SnSe monolayer for five gases was significantly enhanced, in which the adsorption energies for SO2 and SOF2 reached -1.356 eV and -1.175 eV, respectively. In addition, the conductivity of the system is greatly improved, with the bandgap changing from 1.129 eV to 0 eV. The microscopic mechanism of the interaction of gas molecules with CoO-SnSe monolayers has been revealed by energy band structure (Eg), density of states (DOS), Milligan charge analysis (Delta Q) and electrostatic potential. Finally, the sensitivity (S) and desorption time (tau) of the five adsorption systems were calculated to illustrate the macroscopic gas-sensitive properties of the system. This work will help to explore the application of CoO-SnSe monolayers in SF6 decomposition gas sensing detection and adsorption treatment.
摘要：
We investigate both bipartite and multipartite nonlocality in the Heisenberg model. Bipartite nonlocality is measured by the Clauser–Horne–Shimony–Holt inequality, while multipartite nonlocality is explored through Bell-type inequalities. Our findings reveal that neither ground-state nor full thermal-state nonlocality reliably characterizes quantum phase transitions (QPTs). However, we uncover that the mixed-state nonlocality of the ground and first excited states exhibits distinctive characteristics applicable to both bipartite and multipartite scenarios. We also demonstrate how mixed-state quantum correlation behaviors depend on varying temperature regimes. In the bipartite case, we observe a phenomenon known as 'correlation reversal' with increasing temperature, a previously unreported occurrence in other models. For the multipartite case, the ability to signify phase transitions is significantly enhanced as the temperature rises. Furthermore, we discover a linear scaling effect that provides valuable insights for extrapolating QPTs in the thermodynamic limit as . Additionally, we identify the critical temperature at which mixed-state nonlocality becomes a reliable indicator of phase transitions.
通讯机构：
[Fang, C ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Accuracy;Target recognition;Oceans;Scattering;Lighting;Object detection;Feature extraction;Environmental monitoring;Kernel;Convergence;Deep learning;underwater object detection;DWR;LSKA;IoU loss function
摘要：
Underwater object detection technology is widely used in fields such as ocean exploration. However, due to the complex underwater environment, issues like light attenuation and scattering lead to low detection accuracy, which fails to meet the requirements. To address these issues, we propose an improved YOLOv8n-based model called YOLOv8-UC. This model incorporates a modified Dilation-wise Residual (DWR) C2f module to enhance the ability to extract features from the network’s high-level expandable receptive fields. It also integrates the Large Separable Kernel Attention (LSKA) module with the SPPF of YOLOv8 to enhance multi-scale feature extraction capabilities, reducing the loss of details. To solve the problem of redundant parameters and computational load in the detection head, the original detection head is replaced with a shared parameter structure, and RepConv is introduced. Additionally, the Inner-SIoU loss function is improved by using auxiliary boundaries at different scales to accelerate bounding box regression and improve detection accuracy. Experimental results show that the designed YOLOv8-UC achieves an mAP@0.5 of 79.3%, with a 6.9% increase in detection accuracy (P) and a 5.9% increase in precision (mAP@0.5) compared to YOLOv8n, demonstrating the effectiveness and application prospects of this method.
摘要：
With the acceleration of urbanization and the increase of traffic load, the quality and safety of concrete pavement are crucial to the reliability of transportation infrastructure. Traditional pavement monitoring methods are limited by time and space, making it difficult to achieve comprehensive and real-time monitoring of the pavement. This study introduces wireless sensor technology to build a real-time monitoring system, which selects various sensors such as temperature and humidity, pressure, strain, etc. through wireless sensor networks to transmit data in real time to the monitoring center. By analyzing sensor data, it can timely capture temperature and humidity changes and strain of the concrete pavement, so as to detect potential safety hazards in the pavement in time. The research results show that the proposed wireless sensor technology has good real-time performance and accuracy in concrete pavement monitoring. Compared with traditional methods, this technology can more comprehensively evaluate the quality and safety of the pavement, and is expected to provide scientific basis for urban traffic management, reduce maintenance costs, and improve road users' sense of security.
期刊：
Colloid and Interface Science Communications,2024年60:100784 ISSN：2215-0382
通讯作者：
Chen, DC
作者机构：
[Miao, Qing; Chen, Dachang; Li, Jie; Zhao, Renchu; Liu, Ke] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.;[Xiao, Beibei] Jiangsu Univ Sci & Technol, Sch Energy & Power Engn, Zhenjiang 212003, Peoples R China.
通讯机构：
[Chen, DC ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Density functional theory;Toxic gases;ZnFe 2 O 4 (111) surface;Gas sensors;Sensing mechanism;Selective adsorption
摘要：
ZnFe 2 O 4 possesses an excellent gas-sensing performance, but its sensing mechanism towards different toxic gas molecules requires further exploration. In this study, the competitive adsorption and sensing properties of several toxic gases (NO 2 , NO, SO 2 , CO, H 2 S, and NH 3 ) on the ZnFe 2 O 4 (111) surface were investigated using density functional theory (DFT) calculations. The adsorption energy, charge transfer (Q T ), occupation function, adsorption free energy, charge density difference (CDD), and density of states (DOS) were compared. The results reveal that the ZnFe 2 O 4 (111) surface exhibits obvious adsorption for NH 3 , H 2 S, NO 2 , and H 2 O, besides the selectivity of NH 3 molecule is highest. Strong chemical interactions exist between these harmful gas molecules and the ZnFe 2 O 4 (111) surface. This study offers valuable theoretical insights into the selective adsorption and sensing mechanism, contributing to the development of high-performance gas sensors to detect toxic gases.
ZnFe 2 O 4 possesses an excellent gas-sensing performance, but its sensing mechanism towards different toxic gas molecules requires further exploration. In this study, the competitive adsorption and sensing properties of several toxic gases (NO 2 , NO, SO 2 , CO, H 2 S, and NH 3 ) on the ZnFe 2 O 4 (111) surface were investigated using density functional theory (DFT) calculations. The adsorption energy, charge transfer (Q T ), occupation function, adsorption free energy, charge density difference (CDD), and density of states (DOS) were compared. The results reveal that the ZnFe 2 O 4 (111) surface exhibits obvious adsorption for NH 3 , H 2 S, NO 2 , and H 2 O, besides the selectivity of NH 3 molecule is highest. Strong chemical interactions exist between these harmful gas molecules and the ZnFe 2 O 4 (111) surface. This study offers valuable theoretical insights into the selective adsorption and sensing mechanism, contributing to the development of high-performance gas sensors to detect toxic gases.
期刊：
2024 3rd International Symposium on Aerospace Engineering and Systems (ISAES),2024年:148-154
作者机构：
[Xin Gao; Xiaogang Zheng] School of Electrical and Electronic Engineering, Wuhan Polytechnic University,Wuhan,China;[Yongnan Rao] Chinese Academy of Sciences,Key Laboratory of Time Reference and Applications,Xi'[Yongnan Rao] an,China
会议名称：
2024 3rd International Symposium on Aerospace Engineering and Systems (ISAES)
会议时间：
22 March 2024
会议地点：
Nanjing, China
会议论文集名称：
2024 3rd International Symposium on Aerospace Engineering and Systems (ISAES)
关键词：
component;QMBOC Signal;Distortions;Bei Dou Satellite Navigation System;Signal Process
摘要：
This study examines the distortion characteristics of the QMBOC (Quadrature Multiplexed Binary Offset Carrier) modulation signal. The QMBOC ideal signal creation formula extends the digital and analog distortion model suited for QMBOC by mathematical modeling, based on the 2-OS distortion model and BOC distortion model. The legend is then generated using a simulation platform. The inconsistency in the weight proportion of the distortion of the in-phase and orthogonal components of the QMBOC signal is attributed to the γ power factor of the signal. The simulation results indicate that the primary signal distortion is primarily associated with the Q branch in the investigation and examination of the duty ratio and distortion value. Additionally, the simulation analysis of distortion also supports the corresponding experimental findings. This offers a theoretical backing for examining the distortion properties of QMBOC.
通讯机构：
[Cao, L ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Image color analysis;Feature extraction;Imaging;Image restoration;Image quality;Image enhancement;Histograms;Convolutional neural networks;Channel estimation;Colored noise;Underwater navigation;Underwater image enhancement;multi-branch color enhancement;multi-scale pyramid;parallel dual attention;multi-color space stretch
摘要：
Due to the absorption and scattering of light by suspended particles, underwater images may suffer from color casts, low contrast, and blurred texture details. Traditional statistics-based and physical model-based methods have improved image quality to some extent, yet they fall short in effectively addressing the complex underwater environment and light conditions. Despite significant improvements in handling complex underwater scenes, existing deep learning-based methods still have limitations in restoring texture details and improving image contrast. To address these issues, a novel composite network is proposed based on parallel dual attention. Firstly, a pair of complementary modules, which consists of a multi-branch color enhancement module and a multi-scale pyramid module, is designed to better extract image features from multiple color channels and multiple scales, respectively. Subsequently, a parallel dual attention module is proposed by combining channel and pixel attention mechanisms to further obtain more useful texture details. Finally, a multi-color space stretch module is used to adaptively increase the contrast of images by adjusting histogram distribution in multiple color spaces. Numerous experiments on public datasets have verified the effectiveness and superiority of our composite network in enhancing different underwater images. Compared with state-of-the-art methods, our method achieves excellent performance on paired datasets in terms of full-reference image quality assessment metrics, and has competitive performance on unpaired datasets as well in terms of reference-free image quality assessment metrics, with minimal computational complexity.
通讯机构：
[Zhang, C ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430048, Hubei, Peoples R China.
关键词：
Plant protection;Multi-stage co-supervision;Agricultural Pest Management;Image Classification
摘要：
Pest infestation poses a major challenge in the field of global plant protection, seriously threatening crop safety. To enhance crop protection and optimize control strategies, this study is dedicated to the precise identification of various pests that harm crops, thereby ensuring the efficient use of agricultural pesticides and achieving optimal plant protection. Currently, pest identification technologies lack accuracy, especially in recognizing pests across different growth stages. To address this issue, we constructed a large pest dataset that includes 102 pest species and 369 pest stages, totaling 51,670 images. This dataset focuses on the identification of pest growth stages, aimed at improving the efficiency of pest management and the effectiveness of plant protection. Moreover, we have introduced two innovative technologies to tackle the significant differences between pest growth stages: a Multi-stage Co-supervision mechanism and a Spatial Attention module. These technologies significantly enhance the model’s ability to extract key features, thus boosting recognition accuracy. Compared to the industry-leading Vision Transformer-based methods, our model shows a significant improvement, increasing accuracy by 3.67% and the F1 score by 2.49%, without a significant increase in the number of parameters. Extensive experimental validation has demonstrated our model’s significant advantages in enhancing pest identification accuracy, which holds substantial practical significance for the precise application of pesticides and crop protection.
通讯机构：
[Chen, DC ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Density functional theory;dissolved gas analysis (DGA);gas sensor;TM-MoTe2
摘要：
Equipment faults in power transformers can be detected by analyzing the dissolved gas in their internal insulation oil. In this study, the adsorption behavior and sensing properties of several characteristic dissolved gases (H2, CO, C2H2, and C2H4) on pristine and late transition metal (TM) (Fe, Co, Ni, and Cu)-doped MoTe2 were investigated using density functional theory (DFT) calculations. The adsorption energy, charge transfer ( ${Q}_{T}{)}$ , charge density difference (CDD), band structure, density of states (DOS), work function, and occupation function were compared to understand the gas adsorption behavior and electronic properties. The results demonstrate that TM-MoTe2 monolayers exhibit excellent sensing properties for H2, CO, C2H2, and C2H4 compared to pristine MoTe2. Among the four TM atoms, Co-doped MoTe2 shows the highest increase in adsorption energy for H2, CO, C2H2, and C2H4. The DOS analysis reveals clear hybridization between the molecular orbitals of the adsorbed gas molecules and the TM atomic orbitals for CO, C2H2, and C2H4 adsorption systems, indicating strong chemical interactions between these three gases and TM-MoTe2 surfaces. Moreover, the Co-MoTe2 surface exhibits superior sensitivity toward the adsorption of these four gas molecules. The introduction of TM doping shows a significant enhancement in adsorption selectivity, and TM-MoTe2 demonstrates the most dominant selective adsorption of CO molecules. This study provides valuable theoretical insights for the design of a new gas sensor aimed at detecting dissolved gases.
会议论文集名称：
2024 43rd Chinese Control Conference (CCC)
关键词：
Artificial potential field algorithm;COLREGS;Path planning;USV
摘要：
Path planning is a core issue of autonomous navigation for unmanned surface vehicles (USVs). To guarantee navigation safety, this paper proposes an Improved Artificial Potential Field (IAPF) algorithm for path planning of USVs. On the one hand, to solve the local optimal problem of artificial potential field (APF) algorithm, an adjustment factor is introduced in the repulsive potential field function. On the other hand, to ensure the collision avoidance safety in complex encounter scenarios, the International Regulations for Preventing Collisions at Sea (COLREGS) is utilized in the IAPF algorithm. The simulation experiments verify that the path planned via the IAPF algorithm is safer and more efficient than APF algorithm.
作者机构：
[Changyou (57206694833)] Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang 641100, China;[Yiyi (56527525100); Jiefeng (36548896000)] School of Electrical Engineering, Guangxi University, Nanning 530004, China;Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Guangxi University, Nanning 530004, China;[Dachang (57191954967)] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China;[Pengfei (55227620100)] Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang 641100, China<&wdkj&>School of Electrical Engineering, Guangxi University, Nanning 530004, China
通讯机构：
[Mingxiang Wang] S;School of Electrical Engineering, Guangxi University, Nanning 530004, China<&wdkj&>Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Guangxi University, Nanning 530004, China
摘要：
In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C(3)N(4) interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NO(x)) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C(3)N(4) was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C(3)N(4) is most suitable as a potential gas-sensitizing material for NO(2) gas sensors, followed by CO. Interestingly, Pd-modified g-C(3)N(4) is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C(3)N(4) remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C(3)N(4) as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases.
摘要：
Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fusion-based models have garnered considerable attention, exhibiting superior segmentation performance when compared with traditional single-modal techniques. Nonetheless, the majority of these multi-modal models, which rely on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for feature fusion, face limitations in terms of remote modeling capabilities or computational complexity. This paper presents a novel Mamba-based multi-modal fusion network called MFMamba for semantic segmentation of remote sensing images. Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). To capitalize on the distinct attributes of the multi-modal remote sensing data from both branches, a feature fusion block (FFB) is designed to synergistically enhance and integrate the features extracted from the dual-branch structure at each stage. Extensive experiments on the Vaihingen and the Potsdam datasets have verified the effectiveness and superiority of MFMamba in semantic segmentation of remote sensing images. Compared with state-of-the-art methods, MFMamba achieves higher overall accuracy (OA) and a higher mean F1 score (mF1) and mean intersection over union (mIoU), while maintaining low computational complexity.
通讯机构：
[Wang, H ] W;Wuhan Polytech Univ, Sch Math & Comp, Wuhan 430048, Peoples R China.
关键词：
Encoding;Feature extraction;Transformers;Data mining;Long short term memory;Decoding;Data models;Text summarization;Text-summarization;transformer;lstm;vit;cnn
摘要：
In the field of abstract text summarization, architectures based on encoder-decoder frameworks are widely applied to sequence-to-sequence generation tasks and can effectively handle sequences of unlimited length. Subsequently, the transformer model use a global attention mechanism, allowing encodings at different distances to mutually interact, greatly enhancing the model's contextual awareness. However, this context-awareness is global, requiring the model to additionally learn to extract different levels of information to increase understanding. We improve the structure of the model to introduce prior knowledge so that it can learn from the global and local information and enhance the model's understanding ability. This paper proposes global information-aware encoding and local information-aware encoding, which enhance the understanding of documents from coarse-grained and fine-grained perspectives respectively. Global encoding adds an extra feature to the encoder stage and performs attention with the document, generating a global summary encoding of the entire document to guide the generation of the summary content. Local encoding is to perform local convolution on the features extracted by the encoder, use prior knowledge to extract local features of the document and enable the model to quickly extract local detail information. Experiments show that the improved model proposed in this paper has higher rouge scores than the baseline model on the LCSTS and CSL datasets, and also has advantages over some mainstream models. The generated summaries are more accurate and informative. The code is available on github. url: https://github.com/keptupp/A-text-summarization-approach-to-enhance-global-and-local-information-awareness-of-transformer.
