In this paper, we propose a blind synchronization method for signals with sampling rate offset (SRO) and missing data, which occasionally occurs in distributed recording for acoustic scene classification. In our metho...
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ISBN:
(数字)9798350367331
ISBN:
(纸本)9798350367348
In this paper, we propose a blind synchronization method for signals with sampling rate offset (SRO) and missing data, which occasionally occurs in distributed recording for acoustic scene classification. In our method, the correspondence between short-time frames is first estimated using cross-correlation and dynamic programming (DP) matching. Then, two methods for producing synchronized signals are compared. The first method is based on the overlap-add along the DP path, while the second method uses the DP path only to identify missing data positions and compensates for the SRO with a linear phase model. The performance of these methods is evaluated through experiments. The results are promising, and further applications to acoustic scene classification are expected.
This publication presents a digital image correlation (DIC) based technique applied to a shear test on a carbon-reinforced concrete member. DIC methods are based on image sequences where the first image is recorded un...
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image recognition technology is widely used in fields such as intelligent video analysis, but neural network models often suffer from weak generalization capabilities. To address this challenge, this study proposes an...
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ISBN:
(数字)9798350352719
ISBN:
(纸本)9798350352726
image recognition technology is widely used in fields such as intelligent video analysis, but neural network models often suffer from weak generalization capabilities. To address this challenge, this study proposes an adaptive recognition strategy based on deep reinforcement learning. Firstly, a convolutional neural network that combines the features of ResNet and DenseNet is designed to ensure feature representation capability while improving computational efficiency. Secondly, an adaptive data augmentation mechanism is designed based on online feedback to dynamically adjust image transformation methods and intensity, actively adapting to changes in input distribution patterns. Thirdly, an online update framework based on incremental learning is constructed, using a small amount of new data to continue training the model, enabling continuous knowledge absorption and capability improvement. Finally, through image classification experiments, it is demonstrated that this strategy improves recognition accuracy by more than 5% compared to baseline models, especially demonstrating robustness in transfer learning and adaptation to new environments, laying the foundation for building intelligent upgradable visual analysis systems.
Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amou...
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ISBN:
(纸本)9798400709036
Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size.
Time series forecasting is one of the most common and important problems in time series related tasks. With the rise of deep learning models, time series forecasting develops rapidly. Recurrent neural networks (RNNs) ...
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ISBN:
(数字)9798350352719
ISBN:
(纸本)9798350352726
Time series forecasting is one of the most common and important problems in time series related tasks. With the rise of deep learning models, time series forecasting develops rapidly. Recurrent neural networks (RNNs) were first proposed in 1990 to solve the problem of timing series forecasting with deep learning. After that, LSTM, GRU and other models were developed. In recent years, along with the emergence of models based on the Attention mechanism, a series of methods for timing series forecasting have been proposed based on Transformer. Complete and accurate data sampling of time series signals is the most ideal laboratory condition. But the sampling in real signal processing tasks is mostly incomplete. The existing methods are not satisfied for the forecasting results under incomplete sampling conditions. This paper aims to propose a modified RNN method to improve the forecasting effect of time series under incomplete sampling conditions.
Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connec...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connected to the text recognition network. They form a linear pipeline which uses text rectification on all input images, even for images that can be recognized without it. Undoubtedly, the rectification network improves the overall text recognition performance. However, in some cases, the rectification network generates unnecessary distortions on images, resulting in incorrect predictions in images that would have otherwise been correct without it. In order to alleviate the unnecessary distortions, the portmanteauing of features is proposed. The portmanteau feature, inspired by the portmanteau word, is a feature containing information from both the original text image and the rectified image. To generate the portmanteau feature, a non-linear input pipeline with a block matrix initialization is presented. In this work, the transformer is chosen as the recognition network due to its utilization of attention and inherent parallelism, which can effectively handle the portmanteau feature. The proposed method is examined on 6 benchmarks and compared with 13 state-of-the-art methods. The experimental results show that the proposed method outperforms the state-of-the-art methods on various of the benchmarks.
Data parallelprocessing is a key concept to increase the scalability and elasticity in event streaming systems. Often data parallelism is accomplished in a splitter-merger architecture where the splitter divides inco...
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A significant part in computational fluid dynamics (CFD) simulations is the solving of large sparse systems of linear equations resulting from implicit time integration of the Reynolds-averaged Navier-Stokes (RANS) eq...
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ISBN:
(纸本)9783031396977;9783031396984
A significant part in computational fluid dynamics (CFD) simulations is the solving of large sparse systems of linear equations resulting from implicit time integration of the Reynolds-averaged Navier-Stokes (RANS) equations. The sparse linear system solver Spliss aims to provide a linear solver library that, on the one hand, is tailored to these requirements of CFD applications but, on the other hand, independent of the particular CFD solver. Spliss allows leveraging a range of available HPC technologies such as hybrid CPU parallelization and the possibility to offload the computationally intensive linear solver to GPU accelerators, while at the same time hiding this complexity from the CFD solver. This work highlights the steps taken to establish multi-GPU capabilities for the Spliss solver allowing for efficient and scalable usage of large GPU systems. In addition, this work evaluates performance and scalability on CPU and GPU systems using a representative CODA test case as an example. CODA is the CFD software being developed as part of a collaboration between the French Aerospace Lab ONERA, the German Aerospace Center (DLR), Airbus, and their European research partners. CODA is jointly owned by ONERA, DLR and Airbus. The evaluation examines and compares performance and scalability in a strong scaling approach on Nvidia A100 GPUs and the AMD Rome architecture.
With the rapid development of information technology, big data has become an important strategic resource for enterprises. However, with the increase in data volume, data quality issues are becoming increasingly promi...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
With the rapid development of information technology, big data has become an important strategic resource for enterprises. However, with the increase in data volume, data quality issues are becoming increasingly prominent. Low data quality may lead to enterprises being unable to make accurate decisions, and even bring huge economic losses. This article first discusses the definition of data quality and the dimensions of data quality measurement, and then focuses on the measurement methods and architecture of data quality. It mainly summarizes the data quality comparison mode, technical implementation deployment, and proposes a distributed data quality audit architecture. Finally, it provides the steps and common software implementations for data quality management within the enterprise. In summary, this article aims to explore the significance, solutions, and implementation methods of data quality management, providing an effective theoretical basis for improving the data quality for enterprises.
There is an increase in medicine data quantity and image resolution requirements due to the modern medicine development, which leads to the necessity of strong computing resources and huge computer memory amount durin...
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