Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models

Diffusion models have become a popular approach for image generation and reconstruction due to their numerous advantages. However, most diffusion-based inverse problem-solving methods only deal with 2D images, and even recently published 3D methods do not fully exploit the 3D distribution prior. To address this, we propose a novel approach using two perpendicular pre-trained 2D diffusion models to solve the 3D inverse problem. By modeling the 3D data distribution as a product of 2D distributions sliced in different directions, our method effectively addresses the curse of dimensionality. Our experimental results demonstrate that our method is highly effective for 3D medical image reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing MRI, and sparse-view CT. Our method can generate high-quality voxel volumes suitable for medical applications.Comment: ICCV23 poster. 15 pages, 9 figure

An Optical and Infrared Photometric Study of the Young Open Cluster IC 1805 in the Giant H II Region W4

We present deep wide-field optical CCD photometry and mid-infrared Spitzer/IRAC and MIPS 24micron data for about 100,000 stars in the young open cluster IC 1805. The members of IC 1805 were selected from their location in the various color-color and color-magnitude diagrams, and the presence of Halpha emission, mid-infrared excess emission, and X-ray emission. The reddening law toward IC 1805 is nearly normal (R_V = 3.05+/-0.06). However, the distance modulus of the cluster is estimated to be 11.9+/-0.2 mag (d = 2.4+/-0.2 kpc) from the reddening-free color-magnitude diagrams, which is larger than the distance to the nearby massive star-forming region W3(OH) measured from the radio VLBA astrometry. We also determined the age of IC 1805 (tau_MSTO = 3.5 Myr). In addition, we critically compared the age and mass scale from two pre-main-sequence evolution models. The initial mass function with a Salpeter-type slope of Gamma = -1.3+/-0.2 was obtained and the total mass of IC 1805 was estimated to be about 2700+/-200 M_sun. Finally, we found our distance determination to be statistically consistent with the Tycho-Gaia Astrometric Solution Data Release 1, within the errors. The proper motion of the B-type stars shows an elongated distribution along the Galactic plane, which could be explained by some of the B-type stars being formed in small clouds dispersed by previous episodes of star formation or supernova explosions.Comment: 45 pages, 32 figures, 9 tables, accepted for publication in ApJ

Area-efficient and high-speed binary divider architecture for bit-serial interfaces

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Reinforcement Learning With Model Based Assistance for Shape Control in Sendzimir Rolling Mills

As one of the most popular tandem cold rolling mills, the Sendzimir rolling mill (ZRM) aims to obtain a flat steel strip shape by properly allocating the rolling pressure. To improve the performance of the ZRM, it is meaningful to adopt recently emerging deep reinforcement learning (DRL) that is powerful for difficult-to-solve and challenging problems. However, the direct application of DRL techniques may be impractical because of a serious singularity, partial observability, and even safety issues inherent in mill systems. In this brief, we propose an effective hybridization approach that integrates a model-based assistant into model-free DRL to resolve such practical issues. For the model-based assistant, a model-based optimization problem is first constructed and solved for the static part of the mill model. Then, the obtained static model-based coarse assistant, or controller, is improved by the proposed reinforcement learning, considering the remaining dynamic part of the mill model. The serious singularity can be resolved using the model-based approach, and the issue of partial observability is addressed by the long short-term memory (LSTM) state estimator in the proposed method. In simulation results, the proposed method successfully learns a highly performing policy for the ZRM, achieving a higher reward than pure model-free DRL. It is also observed that the proposed method can safely improve the shape controller of the mill system. The demonstration results strongly confirm the high applicability of DRL to other cold multiroll mills, such as four-high, six-high, and cluster mills. IEEE11Nsciescopu

Real-World Failure Prevention Framework for Manufacturing Facilities Using Text Data

In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor