课程文献中心 更多>>

关键词： CCS Concepts   center dot Computing methodologies -> Shape modeling  

摘要： In professional design workflows, designers often begin by creating sketch drawings before converting them into CAD programs. However, prior work on automatically interpreting these sketches has been limited to simplified inputs and fails to account for construction lines that are ubiquitous in real-world drawings. We present CADrawer, a system that translates 3D sketches into CAD programs using an autoregressive approach, leveraging construction lines as a rich source of information for recovering intermediate CAD operations. At each step, CADrawer predicts the next modeling operation and its parameters based on a graph-based representation of the sketch, which explicitly encodes spatial and temporal relationships between strokes. To improve generation quality, the system maintains multiple candidate programs in parallel, and a learned value function evaluates these partial programs to guide the search toward the most promising candidates. CADrawer is designed as a complement to 3D sketching interfaces, building on existing methods that creates 3D sketches. We evaluate our method across several datasets, including those containing dense construction lines and cases without ground-truth B-rep shapes. (see https://***/publications/class-2012)

摘要： While deep learning has been successfully applied to the data-driven classification of anomalous diffusion mechanisms, how the algorithm achieves the feat remains a mystery. Using a well-known technique aimed at achieving explainable AI, namely the Gradient-weighted Class Activation Map (Grad-CAM), we investigate how deep learning (implemented by ResNets) recognizes the distinctive features of a particular anomalous diffusion model from the raw trajectory data. Our results show that Grad-CAM reveals the portions of the trajectory that hold crucial information about the underlying mechanism of anomalous diffusion, which can be utilized to enhance the robustness of the trained classifier against the measurement noise. Moreover, we observe that deep learning distills unique statistical characteristics of different diffusion mechanisms at various spatiotemporal scales, with larger-scale (smaller-scale) features identified at higher (lower) layers.

关键词： Steel surface defects   Defect detection and classification   DCBS-YOLO model   YOLO fusion   Explainable saliency maps  

摘要： Steel surface defect detection is essential for maintaining industrial production quality. However, traditional single-stage detectors often face a trade-off between localization and classification, limiting their ability to distinguish visually similar or irregular defects. This study proposes a novel explainable hybrid AI CAD framework that separates these tasks into two stages. The detection stage utilizes Fusion YOLO, which integrates an adopted DCBS-YOLO with YOLOv9c and YOLOv8s to perform class-agnostic binary detection, thereby optimizing defect localization. The classification stage employs a hybrid model combining ensemble CNNs and Vision Transformer (ViT) to capture both local textures and global dependencies. The entire pipeline is optimized via an MLOps-based auto hyperparameter tuning, with Grad-CAM providing explainability. Experiments on the NEU-DET dataset show that Fusion YOLO achieves an AP of 83.8%, and the classification stage reaches a 99.7% F1-score. Furthermore, the framework's generalization capability was validated on the GC10-DET dataset, achieving a 71.5% mAP (detection) and a 94.8% F1-score (classification), confirming its robustness for reliable industrial inspection.

关键词： Creativity   Sementic diversity   Large language models   Diversity metrics   Creative AI evaluation   Embedding-based methods  

摘要： Large Language Models (LLMs) are increasingly recognized for their potential in creative applications, yet quantitatively evaluating the diversity of their generated responses remains an underexplored and challenging problem. Beyond simple lexical variation, creative diversity in LLMs refers to the capacity to generate a wide spectrum of conceptually distinct and contextually relevant ideas in response to open-ended tasks. However, existing automated metrics often operate in a 'context-free' manner, disregarding the relationship with the prompt, or, like the recent 'LLM-as-a-Judge' approach, suffer from high costs and low reproducibility. To address these problems, this article proposes a new metric, the Context-Aware Diversity (CAD) score. CAD generates context-conditioned embeddings by explicitly combining each answer with the query, and then quantifies the semantic volume of the generated answer set by applying the eigenvalue entropy calculation from the Vendi score. We validated the CAD score on seven LLMs, spanning the Llama-3, Qwen-2.5, and Gemma-2 families, using a benchmark of seven creative tasks based on the Torrance Tests of Creative Thinking (TTCT). Validation results show that the CAD score demonstrates a consistently higher Spearman correlation with GPT-4-judged flexibility scores than all baselines, including surface-level and other context-free semantic metrics. Notably, in a partial correlation analysis controlling for the confounding variable of answer length, existing baselines failed to retain statistical significance, whereas CAD maintained a robust correlation. Moreover, an ablation version that intentionally removed context information also lost this statistical robustness, demonstrating that context-conditioning is essential for meaningful evaluation. Our further analyses also revealed that model scale does not necessarily correlate with diversity, that performance rankings vary depending on task type, and that models exhibit distinct generat

关键词： Strain gauges   Denture base deformation   Maxillary denture   Zirconia framework   Cobalt chrome framework   CAD/CAM milling  

摘要： Background: A lot of studies have demonstrated the increased hardness of the reinforced maxillary dentures. However, the amount of stress induced by different reinforcement materials under the reinforcement dentures and their effect on the underlying supporting tissues and on the deformation of the denture bases were questionable. This study was conducted to compare the stresses induced within the supporting oral tissues from maxillary denture bases reinforced with computer-aided designing/computer-aided manufacturing CAD/CAM zirconia framework or cobalt chrome (Co-Cr) framework, and to detect the deformation that occurred to these bases. Materials and methods: Twenty-six maxillary complete dentures were fabricated and divided into two groups (n = 13). Group A (test group): zirconia framework reinforcement. Group B (control group): Co-Cr framework reinforcement. All denture bases were subjected to unilateral static loading, and then the strain was monitored using a strain gauge analysis device. Then the reinforced denture bases of both groups were subjected to dynamic cyclic loading, then deformation was detected using Geomagic software. Results: Group A;showed significantly higher strain measured at all measuring areas compared to Group B. The maximum principal strain (MPS) at the 1st right molar area was consistently the highest among all measuring areas, regardless of the denture reinforcement framework type. Deformation results suggested that Group A;showed statistically significant reduced deformation compared to Group B after dynamic cyclic load application. Conclusion: Zirconia framework-reinforced maxillary dentures were associated with higher strains and less framework deformation than cobalt chrome framework-reinforced maxillary dentures when static and dynamic loading were applied.

关键词： Laser Powder Bed Fusion   Anomaly Detection   Histogram Equalization   Region Segmentation   Convolutional Neural Network  

摘要： Laser powder bed fusion (L-PBF) is highly sensitive to process instabilities, making reliable in-situ anomaly detection essential for quality assurance. In this study, we propose a synergistic framework that integrates image preprocessing, CAD-guided region segmentation, and lightweight convolutional neural networks (CNNs) for layer-wise anomaly detection using CCD images acquired during L-PBF. Process-specific image preprocessing techniques, including histogram equalization and contrast enhancement, are employed to improve defect visibility under low-contrast conditions. Furthermore, CAD-guided segmentation is used to explicitly separate part and powder regions, enabling region-specific classification of anomalies such as super-elevation and recoater streaking. Lightweight CNN models are then trained on segmented regions to achieve robust performance with reduced computational cost. Experimental results obtained from twelve datasets demonstrate that the proposed framework significantly improves detection accuracy compared to raw-image-based learning, with accuracy increasing from below 23% to over 80% in representative cases, while maintaining stable performance across different anomaly types. The results confirm that combining process-aware preprocessing with region-specific learning effectively enhances anomaly discrimination and supports practical, low-compute deployment for real-time monitoring in industrial L-PBF systems.

关键词： computer-aided detection (CAD)   pulmonary nodules   ground-glass opacity (GGO)   false negative   CT value   quantitative analysis  

摘要： Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign-faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma-remains a critical, unquantified gap. This study aimed to perform a rigorous failure analysis to define the specific conditions under which commercial AI/CAD systems fail in a low-dose CT (LDCT) screening setting. Methods: In this retrospective diagnostic accuracy study, a primary cohort of 100 patients and an external validation cohort of 50 patients with moderate/low-risk nodules on LDCT were included. An expert reference standard was established by a consensus panel of three thoracic radiologists. Two independent, commercially deployed AI/CAD systems from different vendors (Vendor A & Vendor B) processed all cases. Nodules confirmed by experts but missed by AI were analyzed. Their morphology was categorized, and their mean CT attenuation (HU) was measured via manual region-of-interest placement. Results: The AI systems demonstrated significant and comparable false negative rates in the combined cohort: 12.7% for Vendor A and 14.7% for Vendor B. The vast majority of missed nodules were GGOs (92.3% and 78.6%, respectively, in the primary cohort). Crucially, quantitative analysis revealed a consistent density threshold for AI failure: the mean CT value of missed GGOs was -737 +/- 51.50 HU for Vendor A and -727 +/- 70.07 HU for Vendor B. This algorithmic blind spot was fully corroborated by the external validation cohort (-741 +/- 48.2 HU and -733 +/- 62.5 HU, respectively). Anatomical complexity (juxta-pleural/endobronchial location) was a secondary failure factor. Conclusions: This study identifies a quantifiable "-730 HU blind spot" as a common limitation of current commercial AI/CAD systems in diagnosing early lung adenocarcinoma. This finding represents a pivotal advancement in un

关键词： 同轴静电纺丝   防护纺织品   智能响应   核壳结构   功能集成  

摘要： 随着防护需求从传统“被动防护”向“主动响应”升级,传统防护材料在复杂环境中的动态适应能力不足问题日益突出。同轴静电纺丝技术依托核壳结构设计,为智能响应型防护纺织品的构建提供了新的技术路径。围绕其成纤机理与结构形成机制,系统梳理核壳结构对力学增强、阻隔性能提升及功能组分稳定负载的调控规律;进一步总结不同材料体系在同轴静电纺丝技术中的研究进展;重点阐述其在防护纺织品中的应用。同时,展望了同轴静电纺丝技术在智能防护纺织品功能定制化方面的潜力,指出其在未来个性化防护与多功能集成领域的发展方向。研究可为同轴静电纺丝智能响应型防护纺织品的后续研究及产业化应用提供有益参考。

关键词： Dispatch communication systems   Emergency medical dispatch   Emergency medical dispatcher   Emergency medical services   Pediatric emergency medicine   Pediatrics   Telemedicine   Video streaming  

摘要： BACKGROUND:Assessment of pediatric emergencies by telephone is challenging and may be associated with uncertainty and overtriage. Video streaming has been introduced in emergency medical dispatch, but evidence regarding its impact in pediatric emergency calls remains limited. We evaluated associations between video-assisted dispatch assessment and urgency allocation, EMS resource use, and patient safety in pediatric calls. METHODS:This predefined substudy of the cluster-randomized CAM-VISION trial included emergency calls concerning children ≤ 15 years handled at the Emergency Medical Dispatch Center in the Central Denmark Region between January 1 and April 30, 2023. Dispatchers were randomized to video-assisted or telephone-only communication. Prespecified pediatric outcomes were the proportion of children assigned the lowest urgency level (response E), hospital admission within 24 h after response E, and the distribution of all urgency levels (A-E). Additional analyses included EMS resource allocation, urgency changes between dispatch and scene, non-conveyance, time intervals, ICU admission, and 30-day mortality. Analyses followed an intention-to-treat approach using clustered regression models. RESULTS:Among 1,303 pediatric emergency calls, 586 were allocated to video-assisted dispatch and 717 to telephone-only communication. Video was successfully established in 74.7% of calls in the video group. Among predefined outcomes, the lowest urgency level (response E) was assigned more frequently in the video-assisted group (34.8% vs 28.0%; absolute difference 6.8 percentage points, 95% CI 0.1 to 13.4). No hospital admissions within 24 h occurred among children dispatched at response E in either group. The highest urgency level (response A) was less frequent in the video-assisted group (37.9% vs 45.0%; absolute difference - 7.2 percentage points, 95% CI - 14.0 to - 0.3). Additional analyses showed that physician-staffed vehicles arrived less often in the video-assisted

关键词： Cam-linkage mechanism   Disc-type cam   Error model   Least squares method with equality constraints   Sensitivity coefficient  

摘要： Over time, the synchronous cam mechanism experiences progressive wear and tear during operation, ultimately impairing its core performance *** original design data are unavailable, disk cams are mainly maintained using curve fitting design methods. The choice of measurement points is crucial in this context. Currently, sample data is primarily collected by measuring the inner contour of disk cams for curve fitting to produce replacement cams. However, the synchronization precision at the execution end remains inadequate. To address this issue, this study introduces a method of measuring the center points of rollers to enhance accuracy. This paper develops cam error models for both surveying approaches. By integrating these with the linkage mechanism’s error model—constructed using the Modified Denavit-Hartenberg (MDH) method—an error analysis and comparison are conducted, with output-end sensitivity as the evaluation metric. This reveals the sources of errors and the pattern of error accumulation. Additionally, the synchronous segments of the cam are divided, and techniques such as constrained least squares and orthogonal basis reconstruction are employed to generate optimized cam curves. Experimental validation confirms the effectiveness of these methods. The findings indicate that synchronization errors in both schemes increase proportionally with rotational speed. Under the maximum rotational speed condition, the synchronous error at the execution end of the cam obtained using the new surveying scheme is reduced to 57.2% of that associated with the original scheme. This improvement can be mainly attributed to the fact that the new scheme mitigates the error accumulation effect arising from inner-contour-based radial data transmission and reduces the uncertainty introduced by complex nonlinear geometric mapping. This study constructs a comparative framework for independent error propagation models under two distinct surveying approaches, demonstratin