蘑菇中毒是食品安全领域的重要挑战之一。传统识别方法依赖专家经验或复杂化学分析,效率和准确性受限。本研究引入TabNet模型,利用其自适应特征选择和端到端训练优势,基于加利福尼亚大学欧文分校(University of California,Irvine ,UCI...蘑菇中毒是食品安全领域的重要挑战之一。传统识别方法依赖专家经验或复杂化学分析,效率和准确性受限。本研究引入TabNet模型,利用其自适应特征选择和端到端训练优势,基于加利福尼亚大学欧文分校(University of California,Irvine ,UCI)公开的蘑菇数据集,构建并训练了TabNet模型,通过对比分析不同模型的准确率,发现TabNet模型的分类准确率最优秀,优于支持向量机和逻辑回归模型。同时,特征分析进一步揭示气味是较高区分度的参考值,结合孢子印花颜色等特征共同提升了毒性判别的准确性。此外,本文开发了一个基于TabNet的在线毒性检测系统,使得用户输入特征即可实现毒性判别的无缝衔接,实现了实时毒性判别。系统利用模型实际验证了多种蘑菇,准确率均超98%,展现了模型对不同地区蘑菇毒性的广泛适用性。该系统能够快速、准确地分析并报告蘑菇的毒性状况,进一步提升了模型的实际应用价值。展开更多
为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预...为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预报存在的小量级风预报偏大、大量级风预报偏小的预报特征,利用近5年地面观测实况以及ECWMF模式过去3 h阵风资料,构建基于Tabnet的日极大风分级订正预报模型。其中,模型的输入设计包含了前期实况、站点的地理信息、ECWMF模式的预报场及其前期预报误差项。该模型在1年半独立检验样本的估测结果中,其预报模型的平均绝对误差相对ECWMF模式插值降低了45.2%,相应的均方根误差也减少了25.7%。进一步地,在1~5级和8~9级以上风力等级的预报上,该预报模型的预报准确率较利用ECWMF模式预报场插值得到的预报方法均有明显提高,表明该预报方法的可行性。展开更多
双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损...双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损耗增加,系统效率下降,还会影响变换器的可靠性和使用寿命。针对上述问题,提出了一种基于TabNet-LN-LSTM协同预测与粒子群优化的电流应力优化方法。该方法通过利用TabNet和层归一化长短期记忆神经网络(Long-short term memory neural network with layer normalization,LN-LSTM)协同构建电感电流时序预测模型,并结合粒子群优化算法对双有源桥变换器在不同运行工况下的电流应力进行优化。通过算法试验和硬件试验证明,所提方法不仅能够精确预测电感电流波形,其预测波形与硬件实测波形相比,其平均绝对误差仅为0.3525,决定系数高达97.17%;同时,能够有效降低双有源桥变换器的电流应力,进一步提升系统的整体效能和可靠性。展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
文摘蘑菇中毒是食品安全领域的重要挑战之一。传统识别方法依赖专家经验或复杂化学分析,效率和准确性受限。本研究引入TabNet模型,利用其自适应特征选择和端到端训练优势,基于加利福尼亚大学欧文分校(University of California,Irvine ,UCI)公开的蘑菇数据集,构建并训练了TabNet模型,通过对比分析不同模型的准确率,发现TabNet模型的分类准确率最优秀,优于支持向量机和逻辑回归模型。同时,特征分析进一步揭示气味是较高区分度的参考值,结合孢子印花颜色等特征共同提升了毒性判别的准确性。此外,本文开发了一个基于TabNet的在线毒性检测系统,使得用户输入特征即可实现毒性判别的无缝衔接,实现了实时毒性判别。系统利用模型实际验证了多种蘑菇,准确率均超98%,展现了模型对不同地区蘑菇毒性的广泛适用性。该系统能够快速、准确地分析并报告蘑菇的毒性状况,进一步提升了模型的实际应用价值。
文摘为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预报存在的小量级风预报偏大、大量级风预报偏小的预报特征,利用近5年地面观测实况以及ECWMF模式过去3 h阵风资料,构建基于Tabnet的日极大风分级订正预报模型。其中,模型的输入设计包含了前期实况、站点的地理信息、ECWMF模式的预报场及其前期预报误差项。该模型在1年半独立检验样本的估测结果中,其预报模型的平均绝对误差相对ECWMF模式插值降低了45.2%,相应的均方根误差也减少了25.7%。进一步地,在1~5级和8~9级以上风力等级的预报上,该预报模型的预报准确率较利用ECWMF模式预报场插值得到的预报方法均有明显提高,表明该预报方法的可行性。
文摘双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损耗增加,系统效率下降,还会影响变换器的可靠性和使用寿命。针对上述问题,提出了一种基于TabNet-LN-LSTM协同预测与粒子群优化的电流应力优化方法。该方法通过利用TabNet和层归一化长短期记忆神经网络(Long-short term memory neural network with layer normalization,LN-LSTM)协同构建电感电流时序预测模型,并结合粒子群优化算法对双有源桥变换器在不同运行工况下的电流应力进行优化。通过算法试验和硬件试验证明,所提方法不仅能够精确预测电感电流波形,其预测波形与硬件实测波形相比,其平均绝对误差仅为0.3525,决定系数高达97.17%;同时,能够有效降低双有源桥变换器的电流应力,进一步提升系统的整体效能和可靠性。
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
