35 research outputs found
Study on the Comprehensive Properties and Microstructures of A3-3 Matrix Graphite Related to the High Temperature Purification Treatment
At the beginning, a comparative analysis was made on the oxidation corrosion rate and ash content of A3-3 matrix graphite (MG) pebbles lathed before and after high temperature purification (HTP) treatment. Their oxidation corrosion rate and ash contents were almost identical, which indicated that the HTP process was to purify the entire MG pebbles and not limited on the surfaces. Furthermore, the multiple mechanical and thermal properties of MG treated without and with the treatment of HTP at ~1900°C were compared and their microstructure features were characterized as well. As the crush strength, oxidation corrosion rate, and erosion rate of MG without HTP treatment did not satisfy the specifications, the comprehensive properties and purity of MG with HTP were improved in various degrees through the HTP process so that all performances met the requirements of the A3-3 MG. The improvement of crush strength and erosion rate of MG in the HTP process could be mainly attributed to the upgradation of ordered microstructure and corresponding increase of density. However, the enhancement of oxidation corrosion rate was due to the synergistic effects of microstructural optimization and reduction of impurity elements, especially the transition metal elements of MG in the HTP process
Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism
Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses
Efficient federated learning on resource-constrained edge devices based on model pruning
Abstract Federated learning is an effective solution for edge training, but the limited bandwidth and insufficient computing resources of edge devices restrict its deployment. Different from existing methods that only consider communication efficiency such as quantization and sparsification, this paper proposes an efficient federated training framework based on model pruning to simultaneously address the problem of insufficient computing and communication resources. First, the framework dynamically selects neurons or convolution kernels before the global model release, pruning a current optimal subnet and then issues the compressed model to each client for training. Then, we develop a new parameter aggregation update scheme, which provides training opportunities for global model parameters and maintains the complete model structure through model reconstruction and parameter reuse, reducing the error caused by pruning. Finally, extensive experiments show that our proposed framework achieves superior performance on both IID and non-IID datasets, which reduces upstream and downstream communication while maintaining the accuracy of the global model and reducing client computing costs. For example, with accuracy exceeding the baseline, computation is reduced by 72.27% and memory usage is reduced by 72.17% for MNIST/FC; and computation is reduced by 63.39% and memory usage is reduced by 59.78% for CIFAR10/VGG16
Cluster-Based Structural Redundancy Identification for Neural Network Compression
The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically compress models based on importance, removing unimportant filters. This paper reconsiders model pruning from the perspective of structural redundancy, claiming that identifying functionally similar filters plays a more important role, and proposes a model pruning framework for clustering-based redundancy identification. First, we perform cluster analysis on the filters of each layer to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we propose a pruning scheme that automatically determines the pruning rate of each layer. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework
NT-ARS-RRT: A novel non-threshold adaptive region sampling RRT algorithm for path planning
Rapidly-Exploring Random Tree (RRT) algorithm is a widely used path planning method. However, it suffers from low solution efficiency, no search guidance, poor quality of the obtained path and the problem of obvious reduction of search efficiency in the narrow exit environment, which greatly reduces its performance. To overcome these issues, this paper proposes a novel non-threshold adaptive region sampling RRT (NT-ARS-RRT) algorithm for path planning. First, a map preprocessing method is proposed to reduce the explore space of the random tree and improves the path search efficiency especially at narrow exits. Second, a branch-and-leaf backtracking method is proposed to optimize the selection of the nearest node. Third, a distance threshold connection constraints optimization method between the newest node and the target point is proposed to reduce the generation of unnecessary nodes and thus further improves the algorithm’s speed. Fourth, the initial path is optimized by using a rewiring method based on triangular inequality to improve the path quality. Experimental results show the effectiveness of the proposed algorithm