123 research outputs found
Real Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller
With the rapid development of big data and edge computing, many researchers
focus on improving the accuracy of bearing fault classification using deep
learning models, and implementing the deep learning classification model on
limited resource platforms such as STM32. To this end, this paper realizes the
identification of bearing fault vibration signal based on convolutional neural
network, the fault identification accuracy of the optimised model can reach
98.9%. In addition, this paper successfully applies the convolutional neural
network model to STM32H743VI microcontroller, the running time of each
diagnosis is 19ms. Finally, a complete real-time communication framework
between the host computer and the STM32 is designed, which can perfectly
complete the data transmission through the serial port and display the
diagnosis results on the TFT-LCD screen.Comment: 6 pages, 9 figure
Observer Based Traction/Braking Control Design for High Speed Trains Considering Adhesion Nonlinearity
Train traction/braking control, one of the key enabling technologies for automatic train operation, literally takes its action through adhesion force. However, adhesion coefficient of high speed train (HST) is uncertain in general because it varies with wheel-rail surface condition and running speed; thus, it is extremely difficult to be measured, which makes traction/braking control design and implementation of HSTs greatly challenging. In this work, force observers are applied to estimate the adhesion force or/and the resistance, based on which simple traction/braking control schemes are established under the consideration of actual wheel-rail adhesion condition. It is shown that the proposed controllers have simple structure and can be easily implemented from real applications. Numerical simulation also validates the effectiveness of the proposed control scheme
Human activities accelerated the degradation of saline seepweed red beaches by amplifying topâdown and bottomâup forces
Salt marshes dominated by saline seepweed (Suaeda heteroptera) provide important ecosystem services such as sequestering carbon (blue carbon), maintaining healthy fisheries, and protecting shorelines. These salt marshes also constitute stunning red beach landscapes, and the resulting tourism significantly contributes to the local economy. However, land use change and degradation have led to a substantial loss of the red beach area. It remains unclear how human activities influence the topâdown and bottomâup forces that regulate the distribution and succession of these salt marshes and lead to the degradation of the red beaches. We examined how bottomâup forces influenced the germination, emergence, and colonization of saline seepweed with field measurements and a laboratory experiment. We also examined whether topâdown forces affected the red beach distribution by conducting a field survey for crab burrows and density, laboratory feeding trials, and waterbird investigations. The higher sediment accretion rate induced by human activities limited the establishment of new red beaches. The construction of tourism facilities and the frequent presence of tourists reduced the density of waterbirds, which in turn increased the density of crabs, intensifying the topâdown forces such as predators and herbivores that drive the degradation of the coastal red beaches. Our results show that sediment accretion and plantâherbivory changes induced by human activities were likely the two primary ecological processes leading to the degradation of the red beaches. Human activities significantly shaped the abundance and distribution of the red beaches by altering both topâdown and bottomâup ecological processes. Our findings can help us better understand the dynamics of salt marshes and have implications for the management and restoration of coastal wetlands
DataRaceBench V1.4.1 and DataRaceBench-ML V0.1: Benchmark Suites for Data Race Detection
Data races pose a significant threat in multi-threaded parallel applications
due to their negative impact on program correctness. DataRaceBench, an
open-source benchmark suite, is specifically crafted to assess these data race
detection tools in a systematic and measurable manner. Machine learning
techniques have recently demonstrated considerable potential in
high-performance computing (HPC) program analysis and optimization. However,
these techniques require specialized data formats for training and refinement.
This paper presents the latest update to DataRaceBench, incorporating new data
race contributions from Wu et al. \cite{wu2023model}, and introduces a derived
dataset named DataRaceBench-ML (DRB-ML) \cite{drbml}. DRB-ML aligns with the
emerging trend of machine learning and large language models. Originating from
DataRaceBench, this dataset includes detailed labels that denote the presence
of a data race and provides comprehensive details of associated variables, such
as variable names, line numbers, and the operation (read/write). Unique to
DRB-ML, we have also integrated a series of tailored prompt-response pairs
specifically designed for LLM fine-tuning
- âŠ