Modeling in SolidWorks and analysis of temperature and thermal stress during construction of intake tower

AbstractWith a focus on the intake tower of the Yanshan Reservoir, this paper discusses the method of modeling in the 3D CAD software SolidWorks and the interface processing between SolidWorks and the ANSYS code, which decreases the difficulty in modeling complicated models in ANSYS. In view of the function of the birth-death element and secondary development with APDL (ANSYS parametric design language), a simulation analysis of the temperature field and thermal stress during the construction period of the intake tower was conveniently conducted. The results show that the temperature rise is about 29.934 °C over 3 or 4 days. The temperature differences between any two points are less than 24 °C. The thermal stress increases with the temperature difference and reaches its maximum of 1.68 MPa at the interface between two concrete layers

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

Medical image segmentation methods often rely on fully supervised approaches to achieve excellent performance, which is contingent upon having an extensive set of labeled images for training. However, annotating medical images is both expensive and time-consuming. Semi-supervised learning offers a solution by leveraging numerous unlabeled images alongside a limited set of annotated ones. In this paper, we introduce a semi-supervised medical image segmentation method based on the mean-teacher model, referred to as Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency regularization, pseudo-labels, and data augmentation to enhance the efficacy of semi-supervised segmentation. Firstly, the proposed model comprises both student and teacher models with a shared encoder and two distinct decoders employing different up-sampling strategies. Minimizing the output discrepancy between decoders enforces the generation of consistent representations, serving as regularization during student model training. Secondly, we introduce mixup operations to blend unlabeled data with labeled data, creating mixed data and thereby achieving data augmentation. Lastly, pseudo-labels are generated by the teacher model and utilized as labels for mixed data to compute unsupervised loss. We compare the segmentation results of the DCPA model with six state-of-the-art semi-supervised methods on three publicly available medical datasets. Beyond classical 10\% and 20\% semi-supervised settings, we investigate performance with less supervision (5\% labeled data). Experimental outcomes demonstrate that our approach consistently outperforms existing semi-supervised medical image segmentation methods across the three semi-supervised settings

On the design of reliable hybrid wired-wireless network-on-chip architectures

With the ever increase in transistor density over technology scaling, energy and performance aware hybrid wire- less Network-on-Chip (WiNoC) has emerged as an alternative solution to the slow conventional wireline NoC design for future System-on-Chip (SoC). However, combining wireless and wireline channels drastically reduces the total reliability of the commu- nication fabric. Besides being lossy, existing feasible wireless solution for WiNoCs, which is in the form of millimeter wave (mm-Wave), relies on free space signal radiation which has high power dissipation with high degradation rate in the signal strength per transmission distance. Alternatively, low power wireless communication fabric in the form of surface wave has been proposed for on-chip communication. With the right design considerations, the reliability and performance benefits of the surface wave channel could be extended. In this paper, we propose a surface wave communication fabric for emerging WiNoCs that is able to match the channel reliability of traditional wireline NoCs. Here, a carefully designed transducer and commercially available thin metal conductor coated with a low cost dielectric material are employed to general surface wave signal to improve the wireless signal transmission gain. Our experimental results demonstrate that, the proposed communication fabric can achieve a 5dB operational bandwidth of about 60GHz around the center frequency (60GHz). By improving the transmission reliability of wireless layer, the proposed communication fabric can improve maximum sustainable load of NoCs by an average of 20.9% and 133.3% compared to existing WiNoCs and wireline NoCs, respectively