Displays最新文献_第2页

SGLPER: A safe end-to-end autonomous driving decision framework combining deep reinforcement learning and expert demonstrations via prioritized experience replay and the Gipps model SGLPER：一种安全的端到端自动驾驶决策框架，通过优先体验回放和Gipps模型，将深度强化学习和专家演示相结合

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-07 DOI: 10.1016/j.displa.2025.103041

Jianping Cui , Liang Yuan , Wendong Xiao , Teng Ran , Li He , Jianbo Zhang

{"title":"SGLPER: A safe end-to-end autonomous driving decision framework combining deep reinforcement learning and expert demonstrations via prioritized experience replay and the Gipps model","authors":"Jianping Cui , Liang Yuan , Wendong Xiao , Teng Ran , Li He , Jianbo Zhang","doi":"10.1016/j.displa.2025.103041","DOIUrl":"10.1016/j.displa.2025.103041","url":null,"abstract":"<div><div>Despite significant advancements in deep reinforcement learning (DRL), existing methods for autonomous driving often need to overcome the cold-start problem, requiring extensive training to converge and fail to fully address safety concerns in dynamic driving environments. To address these limitations, we propose an efficient DRL framework, SGLPER, which integrates Prioritized Experience Replay (PER), expert demonstrations, and a safe speed calculation model to improve learning efficiency and decision-making safety. Specifically, PER mitigates the cold-start problem by prioritizing high-value experiences and accelerating training convergence. The Long Short-Term Memory (LSTM) method also captures spatiotemporal information from observed states, enabling the agent to make informed decisions based on past experiences in complex, dynamic traffic scenarios. The safety strategy incorporates the Gipps model, introducing relatively safe speed limits into the reinforcement learning (RL) process to enhance driving safety. Moreover, Kullback–Leibler (KL) divergence combines RL with expert demonstrations, enabling the agent to learn human-like driving behaviors effectively. Experimental results in two simulated driving scenarios validate the robustness and effectiveness of the proposed framework. Compared to traditional DRL methods, SGLPER demonstrates safer strategies, higher success rates, and faster convergence. This study presents a promising approach for developing safer, more efficient autonomous driving systems.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103041"},"PeriodicalIF":3.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual examiner consistency learning with dynamic receptive fields and class-balance refinement for Barely-supervised brain tumor segmentation 基于动态接受野的双主考官一致性学习和类平衡改进在几乎没有监督的脑肿瘤分割中的应用

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-04 DOI: 10.1016/j.displa.2025.103054

Xiaofei Ma , Manman Tian , Jianming Ye , Yuehui Liao , Yu Chen , Changxiong Xie , Ruipeng Li , Panfei Li , Jianqing Wang , Xiaomei Xu , Xiaobo Lai

{"title":"Dual examiner consistency learning with dynamic receptive fields and class-balance refinement for Barely-supervised brain tumor segmentation","authors":"Xiaofei Ma , Manman Tian , Jianming Ye , Yuehui Liao , Yu Chen , Changxiong Xie , Ruipeng Li , Panfei Li , Jianqing Wang , Xiaomei Xu , Xiaobo Lai","doi":"10.1016/j.displa.2025.103054","DOIUrl":"10.1016/j.displa.2025.103054","url":null,"abstract":"<div><div>Brain tumor segmentation from magnetic resonance imaging data is a critical task in medical image analysis, yet it remains challenging due to the complex and heterogeneous nature of tumors, as well as the scarcity of labeled data. In this study, we present a novel barely-supervised learning (BSL) framework for accurate brain tumor segmentation, specifically designed to address the limitations imposed by limited labeled data. Our approach introduces two key components: the dual examiner strategy (DES) and the dynamic receptive convolutional network (DRCN). The DES combines consistency learning with adversarial training to make efficient use of both labeled and unlabeled data. This strategy encourages the model to learn robust and generalized features from unlabeled data while simultaneously ensuring high accuracy through labeled data. To further enhance segmentation performance, the DRCN module adaptively adjusts the receptive fields during feature extraction, enabling the model to better capture tumor boundaries, which are often irregular and spatially varied. Additionally, we propose a novel class-balancing refinement (CBR) loss to address the problem of class imbalance commonly encountered in tumor segmentation tasks. This loss function dynamically reweights the classes during training, allowing the model to focus on underrepresented regions, thereby improving segmentation accuracy for smaller tumor areas. We validate our approach on the BraTS 2019, 2020, and 2021 datasets, achieving significant improvements in segmentation performance with minimal labeled data. Our results demonstrate that the proposed method outperforms existing techniques in terms of both accuracy and robustness, offering a promising solution for brain tumor segmentation in data-scarce scenarios.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103054"},"PeriodicalIF":3.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autism screening for children based on appearance features across multiple paradigms 基于多范式外貌特征的儿童自闭症筛查

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-03 DOI: 10.1016/j.displa.2025.103049

Xiaofeng Lu , Siyao Yue , Chaozhen Li , Xia Yang , Yulin Wang , Zhi Liu

引用次数: 0

MWPRFN: Multilevel Wavelet Pyramid Recurrent Fusion Network for underwater image enhancement 多层小波金字塔递归融合网络用于水下图像增强

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-02 DOI: 10.1016/j.displa.2025.103050

Jinzhang Li, Jue Wang, Bo Li, Hangfan Gu

{"title":"MWPRFN: Multilevel Wavelet Pyramid Recurrent Fusion Network for underwater image enhancement","authors":"Jinzhang Li, Jue Wang, Bo Li, Hangfan Gu","doi":"10.1016/j.displa.2025.103050","DOIUrl":"10.1016/j.displa.2025.103050","url":null,"abstract":"<div><div>Underwater images often suffer from color distortion, blurry details, and low contrast due to light scattering and water-type changes. Existing methods mainly focus on spatial information and ignore frequency-difference processing, which hinders the solution to the mixing degradation problem. To overcome these challenges, we propose a multi-scale wavelet pyramid recurrent fusion network (MWPRFN). This network retains low-frequency features at all levels, integrates them into a low-frequency enhancement branch, and fuses image features using a multi-scale dynamic cross-layer mechanism (DCLM) to capture the correlation between high and low frequencies. Each stage of the multi-level framework consists of a multi-frequency information interaction pyramid network (MFIPN) and an atmospheric light compensation estimation network (ALCEN). The low-frequency branch of the MFIPN enhances global details through an efficient context refinement module (ECRM). In contrast, the high-frequency branch extracts texture and edge features through a multi-scale difference expansion module (MSDC). After the inverse wavelet transform, ALCEN uses atmospheric light estimation and frequency domain compensation to compensate for color distortion. Experimental results show that MWPRFN significantly improves the quality of underwater images on five benchmark datasets. Compared with state-of-the-art methods, objective image quality metrics including PSNR, SSIM, and NIQE are improved by an average of 3.45%, 1.32%, and 4.50% respectively. Specifically, PSNR increased from 24.03 decibels to 24.86 decibels, SSIM increased from 0.9002 to 0.9121, and NIQE decreased from 3.261 to 3.115.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103050"},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Layer Cross-Modal Prompt Fusion for No-Reference Image Quality Assessment 多层跨模态提示融合无参考图像质量评估

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-02 DOI: 10.1016/j.displa.2025.103045

Yang Lu , Zilu Zhou , Zifan Yang , Shuangyao Han , Xiaoheng Jiang , Mingliang Xu

{"title":"Multi-Layer Cross-Modal Prompt Fusion for No-Reference Image Quality Assessment","authors":"Yang Lu , Zilu Zhou , Zifan Yang , Shuangyao Han , Xiaoheng Jiang , Mingliang Xu","doi":"10.1016/j.displa.2025.103045","DOIUrl":"10.1016/j.displa.2025.103045","url":null,"abstract":"<div><div>No-Reference Image Quality Assessment (NR-IQA) predicts image quality without reference images and exhibits high consistency with human visual perception. Multi-modal approaches based on vision-language (VL) models, like CLIP, have demonstrated remarkable generalization capabilities in NR-IQA tasks. While prompt learning has improved CLIP’s adaptation to downstream tasks, existing methods often lack synergy between textual and visual prompts, limiting their ability to capture complex cross-modal semantics. In response to this limitation, this paper proposes an innovative framework named MCPF-IQA with multi-layer cross-modal prompt fusion to further enhance the performance of CLIP model on NR-IQA tasks. Specifically, we introduce multi-layer prompt learning in both the text and visual branches of CLIP to improve the model’s comprehension of visual features and image quality. Additionally, we design a novel cross-modal prompt fusion module that deeply integrates text and visual prompts to enhance the accuracy of image quality assessment. We also develop five auxiliary quality-related category labels to describe image quality more precisely. Experimental results demonstrate MCPF-IQA model delivers exceptional performance on natural image datasets, with SRCC of 0.988 on the LIVE dataset (1.8% higher than the second-best method) and 0.913 on the LIVEC dataset (1.0% superior to the second-best method). Furthermore, it also exhibits strong performance on AI-generated image datasets. Ablation study results demonstrate the effectiveness and advantages of our method.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103045"},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Misleading Supervision Removal Mechanism for self-supervised monocular depth estimation 自监督单目深度估计的误导性监督去除机制

IF 3.7 2区工程技术

Displays Pub Date : 2025-04-01 DOI: 10.1016/j.displa.2025.103043

Xinzhou Fan, Jinze Xu, Feng Ye, Yizong Lai

{"title":"Misleading Supervision Removal Mechanism for self-supervised monocular depth estimation","authors":"Xinzhou Fan, Jinze Xu, Feng Ye, Yizong Lai","doi":"10.1016/j.displa.2025.103043","DOIUrl":"10.1016/j.displa.2025.103043","url":null,"abstract":"<div><div>Self-supervised monocular depth estimation leverages the photometric consistency assumption and exploits geometric relations between image frames to convert depth errors into reprojection photometric errors. This allows the model train effectively without explicit depth labels. However, due to factors such as the incomplete validity of the photometric consistency assumption, inaccurate geometric relationships between image frames, and sensor noise, there are limitations to photometric error loss, which can easily introduce inaccurate supervision information and mislead the model into local optimal solutions. To address this issue, this paper introduces a Misleading Supervision Removal Mechanism(MSRM), aimed at enhancing the accuracy of supervisory information by eliminating misleading cues. MSRM employs a composite masking strategy that incorporates both pixel-level and image-level masks, where pixel-level masks include sky masks, edge masks, and edge consistency techniques. MSRM largely eliminate misleading supervision information introduced by sky regions, edge regions, and images with low viewpoint changes. Without altering network architecture, MSRM ensures no increase in inference time, making it a plug-and-play solution. Implemented across various self-supervised monocular depth estimation algorithms, experiments on KITTI, Cityscapes, and Make3D datasets demonstrate that MSRM significantly improves the prediction accuracy and generalization performance of the original algorithms.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103043"},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perspective distortion correction in a compact, full-color holographic stereogram printer 小型全彩色全息立体图打印机中的透视畸变校正

IF 3.7 2区工程技术

Displays Pub Date : 2025-03-31 DOI: 10.1016/j.displa.2025.103051

Hosung Jeon , Youngmin Kim , Joonku Hahn

{"title":"Perspective distortion correction in a compact, full-color holographic stereogram printer","authors":"Hosung Jeon , Youngmin Kim , Joonku Hahn","doi":"10.1016/j.displa.2025.103051","DOIUrl":"10.1016/j.displa.2025.103051","url":null,"abstract":"<div><div>Holography technology has advanced so much that it is now possible to record the object wavefront information on thin holographic recording media. Holographic stereogram printing techniques capture numerous ’hogels’—the smallest unit in holographic printing—storing an extensive range of optical information that surpasses the capabilities of other holographic applications. In this paper, we design a compact holographic stereogram printer that utilizes optical fibers to achieve significant system miniaturization. Specifically, the integration of polarization maintaining/single mode (PM/SM) fibers allows for the customization of the printer’s optical path. However, due to the wide field of view of our holographic stereograms, perspective distortion is hard to be avoided especially when the wavelengths or positions of the light source are not the same as designed values. The flat transverse plane is bent if the light source deviates from the optical axis. This distortion is easily understood by using a k-vector diagram, which illustrates how the direction of the outgoing light’s k-vector changes when it is diffracted by the grating vector of the volume hologram due to the incident light with undesirable direction. In this paper, the feasibility of our perspective-distortion correction algorithm is experimentally demonstrated.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103051"},"PeriodicalIF":3.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online dynamic object removal for LiDAR-inertial SLAM via region-wise pseudo occupancy and two-stage scan-to-map optimization 基于区域伪占用和两阶段扫描到地图优化的激光雷达惯性SLAM在线动态目标去除

IF 3.7 2区工程技术

Displays Pub Date : 2025-03-30 DOI: 10.1016/j.displa.2025.103030

Huilin Yin , Mina Sun , Linchuan Zhang , Gerhard Rigoll

{"title":"Online dynamic object removal for LiDAR-inertial SLAM via region-wise pseudo occupancy and two-stage scan-to-map optimization","authors":"Huilin Yin , Mina Sun , Linchuan Zhang , Gerhard Rigoll","doi":"10.1016/j.displa.2025.103030","DOIUrl":"10.1016/j.displa.2025.103030","url":null,"abstract":"<div><div>SLAM technology has become the core solution for mobile robots to achieve autonomous navigation. It provides the foundational information required for path planning. However, dynamic objects in the real world, such as moving vehicles, pedestrians, and temporarily constructed walls, affect the accuracy and stability of localization and mapping. Existing dynamic methods still face challenges, such as poor localization accuracy caused by reliance on IMU to provide initial poses before removing dynamic objects in highly dynamic environments, and decreased execution efficiency after incorporating complex additional processing modules. To improve positioning accuracy and efficiency in complex environments, this paper introduces dynamic object removal in front-end registration. Firstly, a two-stage scan-to-map optimization strategy is implemented to ensure the accuracy of poses before and after the removal of dynamic objects, where initial scan-to-map optimization is performed for precise pose estimation, followed by the identification and removal of dynamic objects, and a subsequent scan-to-map optimization to fine-tune the pose. Secondly, during the identification and filtering of dynamic objects, the method encodes the query frame and local map data that have already defined volume of interest (VOI) to generate a region-wise pseudo occupancy descriptor (R-POD), respectively. Subsequently, a scan ratio test (SRT) is conducted between query frame R-POD and the local map R-POD, identifying and filtering out dynamic objects region by region. This approach removes dynamic objects online and has demonstrated good mapping results and accuracy across multiple sequences in both the MulRan and UrbanLoco datasets, enhancing the performance of SLAM systems when dealing with dynamic environments.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103030"},"PeriodicalIF":3.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

U-TransCNN: A U-shape transformer-CNN fusion model for underwater image enhancement U-TransCNN：一种用于水下图像增强的u形变换- cnn融合模型

IF 3.7 2区工程技术

Displays Pub Date : 2025-03-27 DOI: 10.1016/j.displa.2025.103047

Yao Haiyang , Guo Ruige , Zhao Zhongda , Zang Yuzhang , Zhao Xiaobo , Lei Tao , Wang Haiyan

{"title":"U-TransCNN: A U-shape transformer-CNN fusion model for underwater image enhancement","authors":"Yao Haiyang , Guo Ruige , Zhao Zhongda , Zang Yuzhang , Zhao Xiaobo , Lei Tao , Wang Haiyan","doi":"10.1016/j.displa.2025.103047","DOIUrl":"10.1016/j.displa.2025.103047","url":null,"abstract":"<div><div>Underwater imaging faces significant challenges due to nonuniform optical absorption and scattering, resulting in visual quality issues like color distortion, contrast reduction, and image blurring. These factors hinder the accurate capture and clear depiction of underwater imagery. To address these complexities, we propose U-TransCNN, a U-shape Transformer- Convolutional Neural Networks (CNN) model, designed to enhance underwater images by integrating the strengths of CNNs and Transformers. The core of U-TransCNN is the Global-Detail Feature Synchronization Fusion Module. This innovative component enhances global color and contrast while meticulously preserving the intricate texture details, ensuring that both macroscopic and microscopic aspects of the image are enhanced in unison. Then we design the Multiscale Detail Fusion Block to aggregate a richer spectrum of feature information using a variety of convolution kernels. Furthermore, our optimization strategy is augmented with a joint loss function, adynamic approach allowing the model to assign varying weights to the loss associated with different pixel points, depending on their loss magnitude. Six experiments (including reference and non-reference) on three public underwater datasets confirm that U-TransCNN comprehensively surpasses other contemporary state-of-the-art deep learning algorithms, demonstrating marked improvement in visualization quality and quantization parameters of underwater images. Our code is available at <span><span>https://github.com/GuoRuige/UTransCNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103047"},"PeriodicalIF":3.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous detail enhancement framework for low-light image enhancement 微光图像增强的连续细节增强框架

IF 3.7 2区工程技术

Displays Pub Date : 2025-03-27 DOI: 10.1016/j.displa.2025.103040

Kang Liu, Zhihao Xv, Zhe Yang, Lian Liu, Xinyu Li, Xiaopeng Hu

{"title":"Continuous detail enhancement framework for low-light image enhancement","authors":"Kang Liu, Zhihao Xv, Zhe Yang, Lian Liu, Xinyu Li, Xiaopeng Hu","doi":"10.1016/j.displa.2025.103040","DOIUrl":"10.1016/j.displa.2025.103040","url":null,"abstract":"<div><div>Low-light image enhancement is a crucial task for improving image quality in scenarios such as nighttime surveillance, autonomous driving at twilight, and low-light photography. Existing enhancement methods often focus on directly increasing brightness and contrast but neglect the importance of structural information, leading to information loss. In this paper, we propose a Continuous Detail Enhancement Framework for low-light image enhancement, termed as C-DEF. More specifically, we design an enhanced U-Net network that leverages dense connections to promote feature propagation to maintain consistency within the feature space and better preserve image details. Then, multi-perspective fusion enhancement module (MPFEM) is proposed to capture image features from multiple perspectives and further address the problem of feature space discontinuity. Moreover, an elaborate loss function drives the network to preserve critical information to achieve excess performance improvement. Extensive experiments on various benchmarks demonstrate the superiority of our method over state-of-the-art alternatives in both qualitative and quantitative evaluations. In addition, promising outcomes have been obtained by directly applying the trained model to the coal-rock dataset, indicating the model’s excellent generalization capability. The code is publicly available at <span><span>https://github.com/xv994/C-DEF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"88 ","pages":"Article 103040"},"PeriodicalIF":3.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0