An IDEF0 Design For PDM-based Die Integrated Intelligent Design System Functional Model

AbstractThis paper establishes a PDM–based integrated design system IDEF0 functional model. The system consist four charts: A-0 chart, A0 chart, A1chart and A2chart. A-0chart defines the range of the system; A0 chart includes 4 modules (task granting, outline design, detail design, evaluation); A1chart and A2 chart decompose and illuminate detail design module and machining processing design module. The system can be easily adapt and applied to many domains of product design and engineering design

CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting

Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision tasks and has achieved promising performance. However, it remains underexplored whether CLIP can be generalized to 3D hand pose estimation, as bridging text prompts with pose-aware features presents significant challenges due to the discrete nature of joint positions in 3D space. In this paper, we make one of the first attempts to propose a novel 3D hand pose estimator from monocular images, dubbed as CLIP-Hand3D, which successfully bridges the gap between text prompts and irregular detailed pose distribution. In particular, the distribution order of hand joints in various 3D space directions is derived from pose labels, forming corresponding text prompts that are subsequently encoded into text representations. Simultaneously, 21 hand joints in the 3D space are retrieved, and their spatial distribution (in x, y, and z axes) is encoded to form pose-aware features. Subsequently, we maximize semantic consistency for a pair of pose-text features following a CLIP-based contrastive learning paradigm. Furthermore, a coarse-to-fine mesh regressor is designed, which is capable of effectively querying joint-aware cues from the feature pyramid. Extensive experiments on several public hand benchmarks show that the proposed model attains a significantly faster inference speed while achieving state-of-the-art performance compared to methods utilizing the similar scale backbone.Comment: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23

Planar Metasurfaces Enable High‐Efficiency Colored Perovskite Solar Cells

The achievement of perfect light absorption in ultrathin semiconductor materials is not only a long‐standing goal, but also a critical challenge for solar energy applications, and thus requires a redesigned strategy. Here, a general strategy is demonstrated both theoretically and experimentally to create a planar metasurface absorber comprising a 1D ultrathin planar semiconductor film (replacing the 2D array of subwavelength elements in classical metasurfaces), a transparent spacer, and a metallic back reflector. Guided by derived formulisms, a new type of macroscopic planar metasurface absorber is experimentally demonstrated with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film. To demonstrate the power and simplicity of this strategy, a prototype of a planar metasurface solar cell is experimentally demonstrated. Furthermore, the device model predicts that a colored planar metasurface perovskite solar cell can maintain 75% of the efficiency of its black counterpart despite the use of a perovskite film that is one order of magnitude thinner. The displayed cell colors have high purities comparable to those of state‐of‐the‐art color filters, and are insensitive to viewing angles up to 60°. The general theoretical framework in conjunction with experimental demonstrations lays the foundation for designing miniaturized, planar, and multifunctional solar cells and optoelectronic devices.A type of macroscopic planar metasurface absorber with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film is theoretically and experimentally demonstrated via a general strategy. Guided by this strategy, colored perovskite solar cells are further designed to meet all the desired characteristics including high power conversion efficiency, high‐purity, tunability, and angle‐insensitive colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/1/advs793.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/2/advs793-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/3/advs793_am.pd

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data

Effects of five different oil production processes on the variety of flavor substances in peanut oil

In order to explore the effects of different oil production processes on the variety of flavor substances in peanut oil. In this study, headspace solid phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS) were used to detect the volatile flavor substances in peanut oil prepared by the ancient, cold-pressed, hot-pressed methods and the self-made unbaked and baked aqueous enzymatic method respectively. The results showed that there were 41, 39, 41, 56 and 56 volatile substances in peanut oil measured by ancient method, cold press, hot press, unbaked and baked water enzymatic method respectively. In addition, according to the analysis of the contents of various substances, it was found that aldehydes had a higher variety in the five kinds of peanut oil, and had a greater influence on the flavor. Alcohols mainly exist in peanut oil prepared by cold press and unbaked water enzymatic method. Pyrazines and phenyl-containing substances mainly existed in peanut oil prepared by ancient method, hot pressing and baking water enzymatic method, and contributed greatly to their flavor. Ester substances mainly affect the flavor of peanut oil by ancient pressing and enzymatic method without baking. The proportion of acid in peanut oil by heat pressing and baking water enzymatic method was higher, which had a greater influence on its flavor. Ketones, ethers, alkanes, alkenes and alkynes account for a small proportion and have a high taste threshold, so their contribution to the five peanut oils is relatively low