Based on the theory of language transfer in second language acquisition, this study explored the dynamic acquisition of English double object construction by Chinese EFL learners through comparative analysis. Altogeth...Based on the theory of language transfer in second language acquisition, this study explored the dynamic acquisition of English double object construction by Chinese EFL learners through comparative analysis. Altogether 120 subjects participated in this experiment and were required to take the proofreading exercise in limited time. The experimental results showed that Chinese EFL learners at different levels of proficiency acquired the core subclass of double object construction better than peripheral ones;meanwhile, learners at higher levels of proficiency outperformed those at lower levels, especially in the peripheral types. Relevant theoretical interpretations were given thereafter to the above research findings, with the hope to shed some light on the learning of double object construction by Chinese EFL learners.展开更多
利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代...利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代理模型优化方法。首先,依据物理结构之间的几何相似性构建易于精确解析化的相似电机;随后,建立相似电机设计变量-优化目标的解析映射模型并开展灵敏度分析;进而,对设计变量分层,将变量空间划分为高-低灵敏度子空间,以提高相似电机迁移结果与原型优化结果的一致性。少变量的高灵敏度参数空间通过原电机有限元分析(finite element analysis,FEA)数据建立常规代理模型进行优化,而多变量的低灵敏度参数空间则基于充足的相似电机解析数据并结合少量原型电机FEA数据,利用迁移学习训练多重保真代理模型完成最终优化。所提方法突破了精确解析模型拓扑限制,降低了结构复杂电机解析建模难度,并通过分层优化策略结合多重保真迁移显著提升高维优化效率,在保证精度前提下大幅减少计算量。该方法已用于内置式交替极永磁游标电机多目标优化,样机试验验证了有效性。展开更多
The effects of rod falling and moving, external flow field, boiling film and radiation were investigated on fluid flow and heat transfer of AISI 4140 steel horizontal rod during direct quenching by mathematical modeli...The effects of rod falling and moving, external flow field, boiling film and radiation were investigated on fluid flow and heat transfer of AISI 4140 steel horizontal rod during direct quenching by mathematical modeling. The flow field and heat transfer in quenching tank were simulated by computational fluid dynamics （CFD） method considering falling and moving of rods during process. Therefore, modeling of flow field was done by a fixed-mesh method for general moving objects equations, and then, energy equation was solved with a numerical approach so that effeet of boiling film heat flux was considered as a source term in energy equation for solid-liquid boundary. Simulated results were verified by comparing with published and experimental data and there was a good agreement between them. Also, the effects of external forced flow and film boiling were investigated on heat flux output, temperature distribution and heat transfer coefficient of rod. Also simulated results determined optimum quenching time for this process.展开更多
Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively a...Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively applied for underwater environment observation. Different from the conventional methods, video technology explores the underwater ecosystem continuously and non-invasively. However, due to the scattering and attenuation of light transport in the water, complex noise distribution and lowlight condition cause challenges for underwater video applications including object detection and recognition. In this paper, we propose a new deep encoding-decoding convolutional architecture for underwater object recognition. It uses the deep encoding-decoding network for extracting the discriminative features from the noisy low-light underwater images. To create the deconvolutional layers for classification, we apply the deconvolution kernel with a matched feature map, instead of full connection, to solve the problem of dimension disaster and low accuracy. Moreover, we introduce data augmentation and transfer learning technologies to solve the problem of data starvation. For experiments, we investigated the public datasets with our proposed method and the state-of-the-art methods. The results show that our work achieves significant accuracy. This work provides new underwater technologies applied for ocean exploration.展开更多
文摘Based on the theory of language transfer in second language acquisition, this study explored the dynamic acquisition of English double object construction by Chinese EFL learners through comparative analysis. Altogether 120 subjects participated in this experiment and were required to take the proofreading exercise in limited time. The experimental results showed that Chinese EFL learners at different levels of proficiency acquired the core subclass of double object construction better than peripheral ones;meanwhile, learners at higher levels of proficiency outperformed those at lower levels, especially in the peripheral types. Relevant theoretical interpretations were given thereafter to the above research findings, with the hope to shed some light on the learning of double object construction by Chinese EFL learners.
文摘利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代理模型优化方法。首先,依据物理结构之间的几何相似性构建易于精确解析化的相似电机;随后,建立相似电机设计变量-优化目标的解析映射模型并开展灵敏度分析;进而,对设计变量分层,将变量空间划分为高-低灵敏度子空间,以提高相似电机迁移结果与原型优化结果的一致性。少变量的高灵敏度参数空间通过原电机有限元分析(finite element analysis,FEA)数据建立常规代理模型进行优化,而多变量的低灵敏度参数空间则基于充足的相似电机解析数据并结合少量原型电机FEA数据,利用迁移学习训练多重保真代理模型完成最终优化。所提方法突破了精确解析模型拓扑限制,降低了结构复杂电机解析建模难度,并通过分层优化策略结合多重保真迁移显著提升高维优化效率,在保证精度前提下大幅减少计算量。该方法已用于内置式交替极永磁游标电机多目标优化,样机试验验证了有效性。
文摘The effects of rod falling and moving, external flow field, boiling film and radiation were investigated on fluid flow and heat transfer of AISI 4140 steel horizontal rod during direct quenching by mathematical modeling. The flow field and heat transfer in quenching tank were simulated by computational fluid dynamics （CFD） method considering falling and moving of rods during process. Therefore, modeling of flow field was done by a fixed-mesh method for general moving objects equations, and then, energy equation was solved with a numerical approach so that effeet of boiling film heat flux was considered as a source term in energy equation for solid-liquid boundary. Simulated results were verified by comparing with published and experimental data and there was a good agreement between them. Also, the effects of external forced flow and film boiling were investigated on heat flux output, temperature distribution and heat transfer coefficient of rod. Also simulated results determined optimum quenching time for this process.
基金supported by the Jilin Science and Technology Development Plan Project (Nos. 20160209006GX, 20170309001GX and 20180201043GX)
文摘Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively applied for underwater environment observation. Different from the conventional methods, video technology explores the underwater ecosystem continuously and non-invasively. However, due to the scattering and attenuation of light transport in the water, complex noise distribution and lowlight condition cause challenges for underwater video applications including object detection and recognition. In this paper, we propose a new deep encoding-decoding convolutional architecture for underwater object recognition. It uses the deep encoding-decoding network for extracting the discriminative features from the noisy low-light underwater images. To create the deconvolutional layers for classification, we apply the deconvolution kernel with a matched feature map, instead of full connection, to solve the problem of dimension disaster and low accuracy. Moreover, we introduce data augmentation and transfer learning technologies to solve the problem of data starvation. For experiments, we investigated the public datasets with our proposed method and the state-of-the-art methods. The results show that our work achieves significant accuracy. This work provides new underwater technologies applied for ocean exploration.
