Research on Technical Points of Installation and Construction of Mechanical and Electrical Engineering

With the rapid development of China's overall economy, the construction engineering industry is gradually improving. Architectural engineering is an important environment for people to live, and its quality and safety are directly related to people's life and property. The most important part of the construction project is the mechanical and electrical installation, to realize the cost control of the project is the key point of the project. This article mainly explains the mechanical and electrical installation engineering in building engineering, and introduces the installation and construction technology in mechanical and electrical installation engineering in detail, and analyzes the control difficulties. It provides an important reference for the management of mechanical and electrical installation technology in the future

RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization

Human pose estimation, as the basis of advanced computer vision, has a wide application perspective. In existing studies, the high-capacity model based on the heatmap method can achieve accurate recognition results, but it encounters many difficulties when used in real-world scenarios. To solve this problem, we propose a lightweight pose regression algorithm (RepNet) that introduces a multi-parameter network structure, fuses multi-level features, and combines the idea of residual likelihood estimation. A well-designed convolutional architecture is used for training. By reconstructing the parameters of each level, the network model is simplified, and the computation time and efficiency of the detection task are optimized. The prediction performance is also improved by the output of the maximum likelihood model and the reversible transformation of the underlying distribution learned by the flow generation model. RepNet achieves a recognition accuracy of 66.1 AP on the COCO dataset, at a computational speed of 15 ms on GPU and 40 ms on CPU. This resolves the contradiction between prediction accuracy and computational complexity and contributes to research in lightweight pose estimation

Belowground Bud Bank Distribution and Aboveground Community Characteristics along Different Moisture Gradients of Alpine Meadow in the Zoige Plateau, China

The belowground bud bank plays an important role in plant communities succession and maintenance. In order to understand the response of the bud bank to the sod layer moisture, we investigated the bud bank distribution, size, and composition of six different water gradient alpine meadows through excavating in the Zoige Plateau. The results showed: (1) The alpine meadow plant belowground buds were mainly distributed in the 0–10 cm sod layer, accounting for 74.2%–100% of the total. The total bud density of the swamp wetland and degraded meadow was the highest (16567.9 bud/m3) and the lowest (4839.5 bud/m3). (2) A decrease of the moisture plant diversity showed a trend of increasing first and then decreasing. Among six alpine meadows the swamp meadow plant diversity was the highest, and species richness, Simpson, Shannon–Wiener, and Pielou were 10.333, 0.871, 0.944, and 0.931, respectively. (3) The moisture was significantly positively correlated with the total belowground buds and short rhizome bud density. There were significant positive correlations with sod layer moisture and tiller bulb bud density. This study indicates that the moisture affected bud bank distribution and composition in the plant community, and the results provide important information for predicting plant community succession in the alpine meadow with future changes in precipitation patterns

Design of Efficient Human Head Statistics System in the Large-Angle Overlooking Scene

Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose a human head statistics system that consists of head detection, head tracking and head counting, where the proposed You-Only-Look-Once-Head (YOLOv5-H) network, improved from YOLOv5, is taken as the head detection benchmark, the DeepSORT algorithm with the Fusion-Hash algorithm for feature extraction (DeepSORT-FH) is proposed to track heads, and heads are counted by the proposed cross-boundary counting algorithm based on scene segmentation. Specifically, Complete-Intersection-over-Union (CIoU) is taken as the loss function of YOLOv5-H to make the predicted boxes more in line with the real boxes. The results demonstrate that the recall rate and [email protected] of the proposed YOLOv5-H can reach up to 94.3% and 93.1%, respectively, on the SCUT_HEAD dataset. The statistics system has an extremely low error rate of 3.5% on the TownCentreXVID dataset while maintaining a frame rate of 18FPS, which can meet the needs of human head statistics in monitoring scenarios and has a good application prospect

Design of Efficient Floating-Point Convolution Module for Embedded System

The convolutional neural network (CNN) has made great success in many fields, and is gradually being applied in edge-computing systems. Taking the limited budget of the resources in the systems into consideration, the implementation of CNNs on embedded devices is preferred. However, accompanying the increasingly complex CNNs is the huge cost of memory, which constrains its implementation on embedded devices. In this paper, we propose an efficient, pipelined convolution module based on a Brain Floating-Point (BF16) to solve this problem, which is composed of a quantization unit, a serial-to-matrix conversion unit, and a convolution operation unit. The mean error of the convolution module based on BF16 is only 0.1538%, which hardly affects the CNN inference. Additionally, when synthesized at 400 MHz, the area of the BF16 convolution module is 21.23% and 18.54% smaller than that of the INT16 and FP16 convolution modules, respectively. Furthermore, our module using the TSMC 90 nm library can run at 1 GHz by optimizing the critical path. Finally, our module was implemented on the Xilinx PYNQ-Z2 board to evaluate the performance. The experimental results show that at the frequency of 100 MHz, our module is, separately, 783.94 times and 579.35 times faster than the Cortex-M4 with FPU and Hummingbird E203, while maintaining an extremely low error rate