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21.
高效率地使用工程车辆是工程项目管理中节约成本的有效方法,无人监管环境下工程车辆的工况识别,是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别,提出一种基于GRU循环神经网络的工程车辆工况识别方法,通过对工程车辆在不同工况下产生的音频信号进行分析,从中提取Mel倒谱系数作为主要特征,构建GRU循环神经网络模型进行训练和识别。实验结果表明,该方法可以实现对工程车辆工况的有效识别。 相似文献
22.
Xia-Bing Zhou Zhong-Qing Wang Xing-Wei Liang Min Zhang Guo-Dong Zhou 《计算机科学技术学报》2022,37(5):1146-1160
There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models. 相似文献
23.
Gui-Yang Luo Yi-Jing Gu Yuan Liu Zi-Liang Chen Yong-lin Huo Fu-Zhong Wu Yi Mai Xin-Yi Dai Yong Deng 《Ceramics International》2021,47(8):11332-11339
LiFePO4 modified by N-doped graphene (NG) with a three-dimensional conductive network structure was synthesized via a one-step in situ hydrothermal method. The effects of N amount of NG on the phase structure, morphology, and electrochemical properties of LiFePO4 are investigated in this study. X-ray diffraction (XRD) results show that doping suitable N amounts in NG do not alter the crystal structure of LiFePO4, and scanning electron microscopy (SEM) images show that NG can slightly reduce the particle size of LiFePO4. The high-resolution transmission electron microscopy (HRTEM) results show that the LiFePO4 particles are well covered and connected by NG. The electrochemical performance confirms that LiFePO4 modified by 20% N-doped graphene (named LFP/NG-4) displays a perfect specific capacity of 166.6 mAh·g?1 at a rate of 0.2C and can reach 125 mAh·g?1 at a rate of 5 C. Electrochemical impedance spectroscopy (EIS) results illustrate that the charge transfer resistance value of the LFP/NG-4 composite is only 58.6 Ω, which is very low compared with LiFePO4. Cyclic voltammetry (CV) tests indicate that the addition of 20% N-doped graphene can effectively reduce electrode polarization and improve reversibility. The LFP/NG-4 composite with a three-dimensional conductive network structure can be regarded as a promising cathode material for Li-ion batteries. 相似文献
24.
At present, the synthesis of body temperature triggering shape memory polymers usually requires elaborate structural design, which limits their wide application. Herein, starting from bio-based Eucommia ulmoides gum (EUG), a series of EUG/silica hybrids (ESHs) are prepared through a facile one-pot process, in which EUG is epoxied and then self-crosslinked with SiO2 by epoxy ring-open reaction. Varying the amount of H2O2, the shape memory transition temperature (Ttrans) of ESHs is adjusted to 47.4–36.6 ℃, which is close to human body temperature (37 ℃). Among them, ESH-17 exhibited the best body temperature triggering shape memory ability (Ttrans = 36.6 ℃), which can restore the permanent shape within 60 s at 37 ℃ with a shape fixity ratio of 99% and shape recovery ratio near 100%. In addition, the shape memory mechanism is discussed and shows some application scenarios of ESHs. The as-produced materials can be used as smart biomaterials such as self-tightening sutures, self-sealing root canal filling materials, and so on. 相似文献
25.
配电网停电会造成电力系统供配电可靠性以及服务质量下降,研究基于地理信息系统(GIS)单线图的配网停电单模拟操作应用。利用网格长度作为基本单位建立坐标系,以选取起始点与终止点为基础,通过四参数法将GIS坐标映射至图纸网格内,实现配网内设备初步布局,将杆塔、站房和整体均匀分布作为优化目标,设置多目标优化目标函数实现GIS单线图最终优化。选取某电力公司配网作为单模拟操作应用对象,模拟结果表明,单模拟操作配网停电后,该配网各负荷点年故障率、次平均停电时间以及年停电时间均有所减少,可有效提升配网的供配电可靠性。 相似文献
26.
To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity. 相似文献
27.
Yu-Ting Liu Xian-Bin Li Hui Zheng Nian-Ke Chen Xue-Peng Wang Xu-Lin Zhang Hong-Bo Sun Shengbai Zhang 《Advanced functional materials》2021,31(21):2009803
Phase change memory (PCM) is an emerging non-volatile data storage technology concerned by the semiconductor industry. To improve the performances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge-Sb-Te (GST) alloy based on experimental “trial-and-error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high-throughput screening strategy consisting of criteria related to PCM characteristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge2Bi2Te5, GeAs4Te7, GeAs2Te4, so on and other candidates that have not been reported, such as TlBiTe2, TlSbTe2, CdPb3Se4, etc. Compared with GST, materials with close cohesive energy include AgBiTe2, TlSbTe2, As2Te3, TlBiTe2, etc., indicating possible low power consumption. Through further melt-quenching molecular dynamic calculation and structural/electronic analyses, Ge2Bi2Te5, CdPb3Se4, MnBi2Te4, and TlBiTe2 are found suitable for optical/electrical PCM applications, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big-data applications. 相似文献
28.
29.
《Ceramics International》2022,48(6):7748-7758
Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al2O3 nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al2O3 nanocomposites with different Al2O3 concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al2O3 nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength. 相似文献
30.