No abstract

Semantic segmentation is increasingly being applied on mobile devices due to advancements in mobile chipsets, particularly in low-power consumption scenarios. However, the lightweight design of mobile devices poses limitations on the receptive field, which is crucial for dense prediction problems. Existing approaches have attempted to balance lightweight designs and high accuracy by downsampling features in the backbone. However, this downsampling may result in the loss of local details at each network stage. To address this challenge, this paper presents a novel solution in the form of a compact and efficient convolutional neural network (CNN) for real-time applications: our proposed model, local spatial perception convolution (LSPConv). Furthermore, the effectiveness of our architecture is demonstrated on the Cityscapes dataset. The results show that our model achieves an impressive balance between accuracy and inference speed. Specifically, our LightSeg, which does not rely on ImageNet pretraining, achieves an mIoU of 76.1 at a speed of 61 FPS on the Cityscapes validation set, utilizing an RTX 2080Ti GPU with mixed precision. Additionally, it achieves a speed of 115.7 FPS on the Jetson NX with int8 precision.

In recent years, as AI technology has advanced, online monitoring of dams has garnered increasing interest. In addition, surrogate model technology is a crucial component of online monitoring. As a result, developing a high-quality surrogate model has become one of the pillars of dam online monitoring. This work proposes a local radial basis function based on sensitivity modification to address the deficiencies of the current radial basis function. In addition, a benchmark function is utilized to validate the method’s viability. Comparisons with BP neural network and RBF demonstrate the usefulness of the proposed strategy. The analysis demonstrates that the proposed strategy for constructing a surrogate model of the dam’s structural behavior is possible and accurate. This paper aims to establish a high-quality surrogate model to provide technical support for dam online monitoring.

Dam monitoring model is a contentious and complicated topic in dam safety monitoring research. The single measuring point model and the multidimensional and multi-measuring point models are the most common dam

Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.