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基于SVMD-BO-BiTCN的超短期光伏发电功率预测

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分布式能源 2024年第5期9卷 22-31页

作者：何瑨麟郝建新苏成飞屠壮壮中国民航大学电子信息与自动化学院天津市东丽区300300 中国民航大学工程训练中心天津市东丽区300300 中国民航大学航空工程学院天津市东丽区300300 中国民航大学安全科学与工程学院天津市东丽区300300

光照的间歇性使光伏发电功率波动性较大,导致光伏发电功率的预测准确率较低。为此,提出一种基于连续变分模态分解(successive variational mode decomposition,SVMD)、贝叶斯优化(Bayesian optimization,BO)算法和双向时序卷积网络(bidi... 详细信息

光照的间歇性使光伏发电功率波动性较大,导致光伏发电功率的预测准确率较低。为此,提出一种基于连续变分模态分解(successive variational mode decomposition,SVMD)、贝叶斯优化(Bayesian optimization,BO)算法和双向时序卷积网络(bidirectional temporal convolutional network,BiTCN)的超短期光伏发电功率预测模型,以提高预测精度。首先,通过SVMD将原始光伏发电功率分解为多个功率分量和功率残差,以获得多个波动性小的序列;然后,使用改进的BiTCN代替单向时序卷积网络(temporal convolutional network,TCN),完成低耗时下SVMD分解结果的双向特征提取与预测;之后,使用BO算法高效寻找BiTCN超参数,从而提高BiTCN对各功率分量和功率残差的预测精度;最后,求和并重构预测结果,实现超短期光伏发电功率预测。实验证明,该模型与单一的TCN模型相比,均方根误差(root mean square error,RMSE)减小了35.18%,决定系数提升了4.82%。

关键词：光伏发电发电功率预测深度学习模型连续变分模态分解(SVMD)

新型KTN衬底上生长金刚石及其光催化性能研究

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金刚石与磨料磨具工程 2024年第1期44卷 9-14页

作者：栾玉翰李天蔚王旭平郝建新赵洪阳付秋明陶洪高登马志斌武汉工程大学材料科学与工程学院武汉430205 齐鲁工业大学(山东省科学院) 新材料研究所济南250000

采用微波等离子体化学气相沉积技术,以钽铌酸钾(KTa1-xNbxO3,简称KTN)作为衬底,生长高质量金刚石薄膜。用X射线衍射仪、扫描电子显微镜和拉曼光谱仪观察并分析金刚石薄膜的表面形貌和微观结构,并研究样品的光催化性能。结果表明:金刚石... 详细信息

采用微波等离子体化学气相沉积技术,以钽铌酸钾(KTa1-xNbxO3,简称KTN)作为衬底,生长高质量金刚石薄膜。用X射线衍射仪、扫描电子显微镜和拉曼光谱仪观察并分析金刚石薄膜的表面形貌和微观结构,并研究样品的光催化性能。结果表明:金刚石生长过程中形成了碳化钽过渡层;随着生长时间延长金刚石质量不断提高;所有样品均表现出良好的降解罗丹明B的能力,其中生长时间为12 h的样品对罗丹明B的降解效率提高了0.29倍,与生长时间为3 h的样品相比其降解效率提高了1.6倍。本研究为生长多晶金刚石提供新型衬底,并探索金刚石在光催化方面的应用。

关键词：金刚石铌酸钽钾光催化微波等离子体化学气相沉积(MPCVD)

Dynamic network embedding via incremental skip-gram with negative sampling

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Science China(Information Sciences) 2020年第10期63卷 89-107页

作者： hao PENG jianxin LI hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

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永磁同步电动机电磁振动噪声的研究

防爆电机 2016年第5期51卷 1-7页

作者：黄克峰王金全郝建新陈静静解放军理工大学国防工程学院国防电力与智能化教研中心江苏南京210007

电机的噪声大小是永磁同步电动机的重要性能之一,降低电机本身的电磁噪声是降低噪声的重要途径。通过解析法推导了负载条件下永磁电动机电磁激振力的解析表达式,并对激振力进行了分析,结果表明:永磁电动机的极对数、定子槽数和控制方式... 详细信息

电机的噪声大小是永磁同步电动机的重要性能之一,降低电机本身的电磁噪声是降低噪声的重要途径。通过解析法推导了负载条件下永磁电动机电磁激振力的解析表达式,并对激振力进行了分析,结果表明:永磁电动机的极对数、定子槽数和控制方式等是影响电磁振动噪声的主要因素。最后利用有限元法对2极18槽永磁电动机进行了仿真,结果验证了理论分析的正确性。

关键词：永磁同步电动机电磁噪声激振力有限元法

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线性四元树到边界链码的一种新转换算法

计算机辅助工程 1997年第2期6卷 47-51页

作者：颜飞翔蔡宣平郝建新国防科技大学电子工程学院

在图象编码过程中，往往需要将四元树转换为边界链码。针对这一问题，本文提出了一种线性四元树到边界链码的新转换算法。与以往一些算法相比，该算法实现起来非常简单而又不失通用性。在该算法中，四元树上所有大于最小黑结点的黑结点... 详细信息

在图象编码过程中，往往需要将四元树转换为边界链码。针对这一问题，本文提出了一种线性四元树到边界链码的新转换算法。与以往一些算法相比，该算法实现起来非常简单而又不失通用性。在该算法中，四元树上所有大于最小黑结点的黑结点被递归划分成最小黑结点大小，随后采用一种堆栈的方式实现结点的邻接寻找。

关键词：区域表示线性4元树边界链码图象编码图形学

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

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International Journal of Mining Science and Technology 2024年第6期34卷 821-836页

作者： hao Zhang Xueyang Xing Yiteng Du Tingchun Li jianxin Yu Qingwen Zhu College of Civil Engineering and Architecture Shandong University of AeronauticsBinzhou 256600China Shandong Key Laboratory of Civil Engineering Disaster Prevention and Mitigation Shandong University of Science and TechnologyQingdao 266590China School of Civil Engineering Henan Polytechnic UniversityJiaozuo 454003China

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy *** water decoupling coefficients can adjust the contributions of the stress wave and quasi-static ***,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling *** this study,a theoretical model of blasting fracturing partitioning is *** mechanical mechanism and determination method of the optimal decoupling coefficient are *** reliability is verified through model experiments and a field *** results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then *** ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed *** quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling *** optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical *** with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

关键词： Water decoupling blasting Blasting dynamic-static action Optimal decoupling coefficient Adjustment mechanism

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

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China Communications 2016年第5期13卷 141-150页

作者： hao Peng jianxin Li Bo Li M.Hassan Arif School of Computer Science and Engineering Beihang University

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

关键词： compressed network traffic network security multiple pattern matching skip scanning depth of boundary

A rapid and effective method for alloy materials design via sample data transfer machine learning

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npj Computational Materials 2023年第1期9卷 2083-2094页

作者： Lei Jiang Zhihao Zhang hao Hu Xingqun He Huadong Fu jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing100083 BeijingChina Beijing Laboratory of Metallic Materials and Processing for Modern Transportation Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing100083 BeijingChina Key Laboratory for Advanced Materials Processing(MOE) University of Science and Technology Beijing100083 BeijingChina

One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing ***,a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data.A new type of aluminum alloy(E2 alloy)with ultra strength and high toughness previously developed by the authors is used as an *** optimal three-stage solution-aging treatment process(T66R)was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum *** realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously,which is of great significance for lightweight of high-end ***,the microstructure analysis clarifies the mechanism of alloy performance *** study shows that transferring the existing alloy data is an effective method to design new alloys.

关键词： alloy rapid toughness

阻燃剂增韧剂玻璃纤维对挤出型薄壁阻燃增强PC材料的阻燃等级力学性能和抗翘曲性能的影响

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广东化工 2022年第7期49卷 59-61页

作者：张宗豪郝建鑫陈晓强陈林钱进明广州市聚赛龙工程塑料股份有限公司广东广州510945 芜湖聚赛龙新材料科技有限公司安徽芜湖241100

研究了磷酸酯阻燃剂、全氟丁基磺酸钾、有机倍半硅氧烷类阻燃剂、溴代三嗪、六苯氧环三磷腈5种阻燃剂对阻燃增强聚碳酸酯(PC)材料薄壁阻燃性能的影响;研究了MBS、ABS高胶粉、EMA类弹性体3种增韧剂对阻燃增强PC材料力学性能的影响;研究... 详细信息

研究了磷酸酯阻燃剂、全氟丁基磺酸钾、有机倍半硅氧烷类阻燃剂、溴代三嗪、六苯氧环三磷腈5种阻燃剂对阻燃增强聚碳酸酯(PC)材料薄壁阻燃性能的影响;研究了MBS、ABS高胶粉、EMA类弹性体3种增韧剂对阻燃增强PC材料力学性能的影响;研究了PC专用普通短切玻璃纤维、PC专用扁平短切玻璃纤维对阻燃增强PC材料抗翘曲性能的影响。实验表明,六苯氧环三磷腈对增强阻燃PC薄壁阻燃效率比溴代三嗪、磷酸酯阻燃剂、有机倍半硅氧烷类阻燃剂和全氟丁基磺酸钾要好,当六苯氧环三磷腈添加量为7.5wt%,阻燃增强PC材料的阻燃等级可达到UL 94 0.75 mmV-0等级;与ABS高胶粉和MBS相比,EMA类弹性体对阻燃增强PC材料的综合力学性能更好,当EMA类弹性体的添加量为2 wt%时,阻燃增强PC材料的韧性最好;PC专用扁平短切玻璃纤维对挤出板抗翘曲性能比PC专用普通短切玻璃纤维要好。

关键词：聚碳酸酯PC 阻燃增强抗翘曲六苯氧环三磷腈