A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Zhang, Yang; Yan, Jiali; Qiao, Liu; Gao, Hongbin

doi:10.1007/s00521-022-07248-8

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…The most common pooling operation is Max Pooling, where the maximum value within a specific region is taken as the output. [10] Pooling helps reduce image details while retaining important features.…”

Section: Development Trends Of Wind Power Generation In Domestic and ...mentioning

confidence: 99%

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Shu,

Tong,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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This research utilized a series of historical wind speed observations and corresponding wind power generation data as training data. Based on the SSA-CNN-BiLSTM neural network model, the paper first preprocessed the historical data to extract relevant features related to wind power generation. Subsequently, a deep learning model based on the integration of Sparrow Search Algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network was constructed. This model leveraged the global search optimization capability of SSA and the advantages of CNN-BiLSTM in sequence and image processing. Through experimentation, predictive results were obtained and compared with real data to validate the rationality and accuracy of the algorithm. The experimental outcomes demonstrated a significant improvement in wind power generation prediction using the SSA-CNN-BiLSTM model. This confirmed the superiority of the model in addressing wind power generation prediction tasks and provided valuable references and applications for enhancing the reliability and efficiency of the wind power industry.

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Section: Development Trends Of Wind Power Generation In Domestic and ...mentioning

confidence: 99%

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Shu,

Tong,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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“…The convolutional neural network refers to a neural network that uses convolution operation instead of ordinary matrix penalty operation in at least one layer of the network and completes the extraction of local features of the original image through convolution and pooling operations. It is widely used in image classification, object detection, and other machine vision fields [24]. In fault diagnosis, CNN is usually regarded as a feature extractor composed of convolutional layers and pooling layers.…”

Section: The Structure Of Convolutional Neural Network and Residual N...mentioning

confidence: 99%

Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM

Xie

Shi

2022

Machines

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To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep learning algorithm is used to establish a fault diagnosis model to distinguish the types of electric drive faults. First, the powerful fault feature extraction ability of the residual network is used to deeply mine the fault features in the signals. Then, perform time-series learning through a bidirectional long short-term memory network, and further excavate the transient time-series features in the fault features so as to achieve the accurate classification of drive inverter faults. The effectiveness of the method is verified using noise-free fault data, and the robustness of the method is verified using data with varying degrees of noise. The results show that compared with conventional deep learning algorithms, Res-BiLSTM has the fastest and most stable training process, the diagnostic performance is improved, and the accuracy can be maintained over 95% under 25–19 dB. It has certain robustness and can be applied to marine electric propulsion systems drive inverter fault diagnosis, and its results can provide data support for the implementation of self-healing control strategies.

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“…However, the existing technique is a preliminary exploration of pattern matching to optimize programming languages and their lack of prototype implementation. Zhang et al 5–10 presented several concurrency‐related refactoring tools to improve lock patterns and detect code smell patterns. In the software industry, refactoring plugins for pattern matching are integrated into modern IDEs like Visual Studio and IntelliJ IDEA.…”

Section: Introductionmentioning

confidence: 99%

“…prototype implementation. Zhang et al [5][6][7][8][9][10] presented several concurrency-related refactoring tools to improve lock patterns and detect code smell patterns. In the software industry, refactoring plugins for pattern matching are integrated into modern IDEs like Visual Studio and IntelliJ IDEA.…”

mentioning

confidence: 99%

ReInstancer: An automatic refactoring approach for Instanceof pattern matching

Zhang,

Hong

2024

J Software Evolu Process

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The instanceof pattern matching can improve the code quality and readability by removing the redundant typecasting and simplifying the design in different scenarios. However, existing works do not provide sufficient support for refactoring instanceof pattern matching. This paper first identifies several cases that cannot be well handled by existing IDEs. Based on these observations, we propose a novel approach called ReInstancer to refactor instanceof pattern matching automatically. ReInstancer conducts program analysis for multi‐branch statements to obtain pattern variables. After analyzing these patterns, the multi‐branch statements are optimized and finally refactored into switch statements or expressions. ReInstancer is evaluated by 20 real‐world projects with more than 7,700 instanceof pattern matching. The experimental results demonstrate that a total of 3,558 instanceof expressions and 228 multi‐branch statements are refactored within 10.8 s on average for each project. ReInstancer improves the code quality by reducing redundant typecasting, demonstrating its effectiveness.

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A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Cited by 7 publications

References 29 publications

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM

ReInstancer: An automatic refactoring approach for Instanceof pattern matching

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