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关键词： Accuracy   Integrated circuit modeling   Artificial intelligence   Feature extraction   YOLO   Deep learning   Voltage   Pipelines   Lighting   Data preprocessing   hand-drawn   image segmentation   netlist generation   node detection   object detection   SPICE   validation   EDA   CAD  

摘要： This paper presents an automated framework for generating SPICE compatible netlists from both printed and hand-drawn circuit diagrams. The system combines advanced image preprocessing, deep learning based object detection, and contour based node analysis to address challenges such as inconsistent drawing styles, illumination variations, and non-standardized symbols. A unified preprocessing module incorporating denoising, contrast enhancement, adaptive thresholding, morphological filtering, and skeletonization ensures robust inputs for downstream tasks. Multiple YOLO (You Only Look Once) architectures were trained and evaluated, demonstrating strong performance across subtasks: YOLOv8L achieved 97.50% for transistor detection, YOLOv11L reached 98.55% for terminal segmentation, YOLOv11X attained 96.13% for voltage segmentation, and YOLOv8L obtained 99.23% for ground detection. These results confirm the framework's reliability in symbol interpretation. Beyond component-level recognition, the system integrates a specialized transistor terminal segmentation model and an advanced contour-based node detection module, enabling the accurate extraction of connectivity, even in dense, multi-component circuits. A novel validation mechanism further enhances robustness by fully automated simulating generated netlists in LTSpice and comparing node voltages with those of reference designs. Experimental evaluation demonstrates superior performance on printed diagrams (93.33% accuracy) and competitive performance on hand-drawn sketches (85.33% accuracy), despite stylistic irregularities. Overall, the proposed pipeline provides a scalable and accurate end-to-end solution, reducing human error and ensuring functional equivalence. Its ability to process complex, large-scale hand-drawn schematics under diverse conditions highlights its contributions to Electronic Design Automation (EDA), industrial applications, and intelligent design assistance. In addition, the framework incorporates a

关键词： 三维凸轮   参数摆动   故障树   故障处理流程图  

摘要： 针对一起主燃油泵调节器三维凸轮行程参数摆动故障,以主燃油泵调节器功能及原理为基础,建立了以“主燃油泵调节器三维凸轮行程参数摆动故障”为顶事件的故障树与故障处理流程图,对故障原因进行分析,最终确定了故障源。根据控制原理分析与检查结果,判定本次故障的具体原因为离心飞重上的配合间隙较大所致,经过验证成功排除故障,并提出了改进措施。

关键词： 智能体域热管理纺织品   光热调控   辐射制冷   辐射保暖   自适应  

摘要： 纺织品作为人体与外界环境之间的重要媒介,在调控热量传递、提升热舒适性以及应对全球气候变暖引发的极端气候挑战中发挥着关键作用。近年来,基于光热调控原理的智能纺织品凭借零能耗、可调控和可持续的体域热管理特性,在个人热管理领域展现出广阔应用前景。本文系统综述了光热调控技术在纺织领域的发展脉络,涵盖理论创新、材料设计、关键技术突破及产业化进展,重点阐述了辐射制冷、辐射保暖与自适应热管理纺织品的研究进展及材料演变,并比较了不同技术路径的性能优势与局限性。此外,本文还深入分析了当前该领域面临的主要挑战,包括性能与舒适度的平衡、规模化制备与成本控制、长期耐久性及环境适应性等问题。在此基础上,结合前沿研究趋势,展望了光热调控智能纺织品未来的发展方向,特别是在多功能一体化设计、可持续绿色制造与产业化应用方面的潜在机遇。

摘要： “十四五”时期,在全球外贸环境日趋复杂、有效需求不足、供应链波动等多重压力下,中国家纺行业牢牢把握“科技、时尚、绿色”核心发展方向,以韧性抵御风险,以创新驱动转型,在产业结构、创新能力、质量标准、绿色发展等方面取得全方位突破,为“十五五”时期迈向高质量发展筑牢根基。韧性筑基,产业迈上新台阶“十四五”期间,行业在压力中展现强劲发展韧性,核心经济指标稳中有进。2021—2024年,规模以上家纺企业营业收入年均增长0.71%,出口额年均增长0.39%,出口额占全球家用纺织品出口总额超40%,持续稳居全球最大生产及出口国地位。产业结构持续优化,年纤维消耗量稳定在1550万吨,占纺织行业总量的27%。

关键词： Computer aided design  

摘要： Modern product development spans a long lifecycle from planning to design, simulation, manufacturing, and customer delivery. Manufacturers place great emphasis on reducing development lead time to strengthen competitiveness. In recent manufacturing industries, most designed components feature standardized and structured geometric configurations, which can be efficiently generated through parametric design methodologies. With the rapid advancement of artificial intelligence (AI) and large language models (LLMs), research that integrates these technologies into the design and 3D simulation stages has gained increasing attention. This paper proposes a practical LLM-assisted framework for automated mechanical design of regularized geometries in a CAD system. Unlike data-driven generative CAD approaches that require large-scale CAD training datasets and may not guarantee strict dimensional correctness, the proposed method executes predefined parametric templates implemented via CAD APIs, while using an LLM to translate natural-language design intents into executable command-line instructions and to support interactive parameter refinement. A case study on ball bearing design demonstrates that the proposed framework reduces per-instance design execution time by approximately 94–95% compared to conventional manual CAD modeling, while generating CAD-native outputs that can be directly extended to downstream CAE and manufacturing workflows. © 2013 IEEE.

关键词： Diabetic retinopathy   Retina   Visualization   Diseases   Lesions   Hemorrhaging   Blood   Biomedical imaging   Reviews   Image color analysis   explainable AI   class activation map   medical imaging  

摘要： Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, and deep learning systems have achieved competitive performance for DR screening. However, the lack of transparent visual explanations remains a barrier to clinical adoption. Class Activation Map (CAM) methods are widely used to visualize model attention on fundus images, yet their behavior, evaluation, and clinical validity in DR remain fragmented across the literature. We conducted a PRISMA-ScR-guided scoping review of CAM-based visual explanation methods applied to DR classification and lesion-level analysis on color fundus photographs. We searched PubMed, IEEE Xplore, Scopus, Web of Science, and arXiv from 2016 to 2024, using combinations of keywords related to "diabetic retinopathy", "class activation map", and "explainable AI". Studies were included if they applied CAM-family techniques to DR grading or lesion detection on retinal fundus images. The results are a total of 28 studies that met our criteria, covering 14 public and private datasets, predominantly EyePACS, Messidor, APTOS, IDRiD, and FGADR. We summarize the use of Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM, LayerCAM, and several DR-specific CAM variants across common backbones (e.g., ResNet, EfficientNet, DenseNet) and categorize evaluation metrics into pixel- and lesion-level localization (IoU, Dice, DAUC/IAUC, ADD, DC/IC, ADCC) and causal faithfulness. We identify recurrent failure modes such as vessel-focused heatmaps, optic disc glare, and poor sensitivity to low-contrast microaneurysms. This review provides an evidence map of CAM-based explanations in DR, highlights gaps between visual plausibility and causal faithfulness, and proposes a compact "what works when" guideline relating DR task, dataset, backbone, and CAM choice. We present a clinical validity lens for lesion-level fidelity and referable DR triage, along with practical recommendations for standardized evaluation and clinically grounded CAM developme

关键词： 可视化教学   CAD   微课   教学资源建设  

摘要： 随着教育数字化的发展,为职业教育构建专业资源已变得至关重要,其有助于拓展优质资源的使用范围、推动教育教学创新与变革、提升职业教育的质量。文章通过构建基于可视化教学理念的计算机辅助设计(Computer Aided Design, CAD)微课资源,实现CAD操作技能的可视化呈现。微课资源的教学案例以“项目式”牵引,学习任务以“地图式”导航,技能点支持“字典式”检索,为个性化学习提供支撑。微课设计整合企业产品工程图,通过开展项目式学习、模拟职业情境、设计个性化学习路径等教学手段,将教学重心由知识传授转向能力培养,进而契合产业升级背景下对技术人才的需求。

关键词： Breast cancer   Convolutional neural networks   Transformers   Accuracy   Mammography   Histopathology   Magnetic resonance imaging   Deep learning   Computer architecture   Visualization   energy-based scoring   grad-CAM   histopathology images   outlier exposure   transformer  

摘要： This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The proposed model combines the local feature extraction strengths of EfficientNet-E0 with the global representation power of the Swin Transformer, enhanced by Outlier Exposure (OE), and energy-based Out of Distribution Detection (OOD) scoring to improve safety and real-world adaptability. The proposed model (SwinCNN+OE) exhibited consistently high performance across all primary classification metrics, achieving an overall accuracy of 97.83% and precision, recall, and F1-score of 97.88%, 97.64%, and 97.68%, respectively. Extensive experiments on a real-world histopathology dataset demonstrated the model's superior diagnostic performance, achieving an overall accuracy of 96.36%, a macro-averaged F1-score of 96.35%, and a mean AUC score of 99.49%. The model also achieved perfect detection (AUC = 1.00) of unknown samples, illustrating its ability to identify out of distribution cases using energy-based scoring. Furthermore, Grad-CAM heatmaps confirmed that the model consistently focused on biologically meaningful regions such as densely packed nuclei and irregular tissue structures. Integrating Grad-CAM for interpretability and energy-based OOD detection contributes significantly to the trustworthiness and safety of Artificial Intelligence (AI) applications in clinical pathology.

关键词： 纺织品   全氟和多氟化合物   检测方法   未来趋势  

摘要： 全氟和多氟烷基化合物(PFAS)凭借其卓越的防水、防油性能,在纺织领域得到了广泛应用。然而,PFAS具有持久性和生物毒性,全球对其管控日益严格。本文对纺织品中PFAS的检测方法展开综述,涵盖气相色谱—质谱法、液相色谱—串联质谱法、氧弹燃烧—离子色谱法以及在线燃烧—离子色谱法。详细剖析了各方法的原理、在纺织行业的应用情况,并对比其优缺点。结果认为,液相色谱—串联质谱法因灵敏度高、稳定性好而成为当前主流检测方法;在线燃烧—离子色谱法则适用于总有机氟的快速筛查。最后提出开发高通量、高灵敏的快速筛查方法势在必行。

关键词： 马蒂斯   民间艺术收藏   纺织品   跨文化   现代主义  

摘要： 亨利·马蒂斯(Henri Matisse,1869—1954)不仅是现代艺术史上的巨匠,更是一位系统性的民间艺术品收藏家。本文以其跨文化的收藏实践为研究对象,探讨其如何将民间艺术资源转化为个人创作语言。通过分析其收藏的纺织品、陶瓷、家具等物品与绘画作品的对应关系,揭示民间艺术对其形式革命与色彩体系构建的深刻影响。研究表明,马蒂斯通过建立“工作室图书馆”,将收藏品作为视觉词汇库,实现了对传统艺术元素的现代性转换,形成了独特的“艺术炼金术”。