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申请号:201911316864.3 公开号:CN110927898A 主分类号:G02B6/42
摘要:【中文】本发明提供一种COB工艺的SFP28 SR光模块结构,属于光模块结构领域。本发明包括壳体、解锁机构、EMI屏蔽结构、电路板、设置在电路板一端的微光学组件,还包括与所述微光学组件插接的LC跳线,其中,所述壳体为尾端开放的腔体,所述壳体中部前端设有隔墙,所述隔墙上设有与微光学组件横截面配合的装配孔,所述壳体位于隔墙一侧后端设有用于安装电路板的安装腔,所述壳体设于隔墙另一侧的前端由壳体的四面围合成朝前端开口、并被中间隔板分割成对称的两部分的LC光接口,所述解锁机构设置在壳体前端,所述EMI屏蔽结构设置在与所述隔墙对应的壳体外围。本发明能有效降低高频信号泄漏,模块各项性能稳定。 【EN】The invention provides an SFP28 SR optical module structure of a COB process, and belongs to the field of optical module structures. The micro-optical module comprises a shell, an unlocking mechanism, an EMI shielding structure, a circuit board, a micro-optical module arranged at one end of the circuit board and an LC jumper wire inserted with the micro-optical module, wherein the shell is a cavity with an open tail end, a partition wall is arranged at the front end of the middle part of the shell, an assembling hole matched with the cross section of the micro-optical module is arranged on the partition wall, an installing cavity used for installing the circuit board is arranged at the rear end of one side of the shell, the front end of the shell, which is arranged at the other side of the partition wall, is surrounded by four sides of the shell to form an LC optical interface which is open towards the front end and is divided into two symmetrical parts by a middle partition plate, the unlocking mechanism is arranged at the front end of the shell, and the EMI shielding structure is. The invention can effectively reduce the leakage of high-frequency signals and has stable performance of modules.
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申请号:201911193657.3 公开号:CN110955998A 主分类号:G06F30/23
申请人:【中文】青岛科技大学【EN】Qingdao University Of Science And Technology 申请日:2019.11.28 公开日:2020.04.03
摘要:【中文】本发明属于数据处理技术领域,公开了一种基于GIS的大范围泥石流数值模拟及数值处理方法,通过现场观测获得地形数据,建立数值标高模型;采用有限差分法求解泥石流的连续方程和运动方程,利用地球信息系统GIS中的数字高程模型DEM,自动生成地形变量,利用数字高程模型中的栅格网作为有限差分的网格;利用野外试验或实际工程验证模型和进行参数调整;使用基于DEM栅格网的有限差分公式可以实现数值解。本发明在牛顿流体运动模型基础上,推导宾汉流体的连续方程和运动方程,尤其是宾汉流体启动条件,并将其深度积分以适用于有限差分网格模拟计算。并将宾汉流体模型与GIS耦合,便于利用大范围复杂地形的计算。 【EN】The invention belongs to the technical field of data processing, and discloses a large-range debris flow numerical simulation and numerical processing method based on a GIS (geographic information system). the method comprises the steps of obtaining topographic data through field observation, and establishing a numerical elevation model; solving a continuous equation and a motion equation of the debris flow by adopting a finite difference method, automatically generating a terrain variable by utilizing a digital elevation model DEM in a GIS (geographic information system), and using a grid network in the digital elevation model as a finite difference grid; verifying the model and adjusting parameters by using a field test or an actual project; the numerical solution can be achieved using finite difference equations based on DEM grid networks. The method is based on a Newtonian fluid motion model, derives a continuous equation and a motion equation of the Bingham fluid, particularly a Bingham fluid starting condition, and integrates the depth of the Bingham fluid starting condition to be suitable for finite difference grid simulation calculation. And the Bingham fluid model is coupled with the GIS, so that the calculation of large-range complex terrains is convenient to utilize.
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申请号:201911233437.9 公开号:CN110985651A 主分类号:F16H59/14
申请人:【中文】北京理工大学【EN】BEIJING INSTITUTE OF TECHNOLOGY 申请日:2019.12.04 公开日:2020.04.10
摘要:【中文】一种基于预测的自动变速器多参数融合换挡策略,包括如下步骤:基于车辆行驶工况的历史信息,通过所建立的深度神经网络算法模型(DNN),对未来短时域工况进行预测,基于模型预测架构(MPC),根据预测的行驶工况信息,通过动态规划滚动优化算法,对预测时域进行挡位寻优,得到使得在预测时域内代价函数最小的变速箱挡位控制序列;将动态规划滚动优化算法寻得的优化控制序列发送至各个低层控制器,控制变速箱以及电机等整车部件进行响应。实现驾驶员‑车辆‑环境闭环系统的智能化换挡,在保证动力性的前提下,实现车辆的经济性换挡。 【EN】A predictive-based multi-parameter fusion shift strategy for an automatic transmission, comprising the steps of: predicting a future short-time-domain working condition through an established deep neural network algorithm model (DNN) based on historical information of a vehicle running working condition, and optimizing gears in a prediction time domain through a dynamic programming rolling optimization algorithm according to predicted running working condition information based on a model prediction framework (MPC) to obtain a transmission gear control sequence with a minimum cost function in the prediction time domain; and sending the optimized control sequence searched by the dynamic programming rolling optimization algorithm to each low-level controller, and controlling the whole vehicle parts such as a gearbox, a motor and the like to respond. The intelligent gear shifting of a driver-vehicle-environment closed-loop system is realized, and the economical gear shifting of the vehicle is realized on the premise of ensuring the dynamic property.
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申请号:201610461795.5 公开号:CN106082874A 主分类号:C04B28/04(2006.01)I
申请人:黄河科技学院 申请日:2016.06.23 公开日:2016.11.09
摘要:本发明公开了一种高含量混杂纤维混凝土防弹防爆砖,由钢纤维、聚丙烯纤维、水泥、水、砂、碎石、硅灰、粉煤灰、高效减水剂按照下述质量百分比组成:钢纤维14.21%,聚丙烯纤维0.04%,水泥19.00%~19.80%,水5.40%~5.80%,砂17.00%~18.00%,碎石37.50%~38.50%,硅灰2.60%~3.00%,粉煤灰2.00%,高效减水剂0.45%。本发明优点在于具有优良的抗压、抗拉、抗弯、抗冲击侵彻、抗冲击韧性等动静力学性能的砖体,进而达到防弹、防爆之目的。
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申请号:202011144392.0 公开号:CN112199777A 主分类号:G06F30/15
申请人:中国科学院工程热物理研究所 申请日:2020.10.23 公开日:2021.01.08
摘要:本发明的适用于模化仿生前缘流场特征的方法,属于风电机叶片流场模拟方法的技术领域,解决现有技术的方法计算量大且耗时长的技术问题。该方法包括S101:基于CAE软件绘制光滑的机翼模型,在机翼模型上绘制仿生前缘的边缘结构,并在机翼模型上或在仿生前缘上添加网格;S102:基于CAE软件模拟气动力流场以获取仿生前缘对流场的作用力,将该作用力以动量原项替代仿生前缘结构;S103:通过作用在仿生前缘的作用力迭代求解的方法获取新的仿生前缘结构。本发明用以完善风电机叶片流场模拟的使用功能,满足人们对风电机叶片流场模拟耗时短且易于计算的要求。
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申请号:202510032271.3 公开号:CN119827448A 主分类号:G01N21/3563
申请人:生态环境部南京环境科学研究所 申请日:2025.01.09 公开日:2025.04.15
摘要:本发明属于生态环境监测技术领域,具体涉及一种疏林草地生态系统水分环境变化监测预警装置及方法,检测腔内底部设有红外发射器、侧壁设有红外接收器,侧腔内设有驱动红外接收器沿竖直方向移动的驱动组件;控制器模块用于接收红外接收器检测到的红外辐射数据,并对红外辐射数据进行处理和分析,以得出不同厚度土壤样本的水分含量和预警信息;显示模块用于接收水分含量和预警信息进行显示。本发明通过采用红外发射器和红外接收器对目标区域的疏林草地生态系统内取样的多个土壤样本进行水分含量检测,能对土壤样本的水分值进行实时的检测和传输,并能快速的在显示模块进行显示,为实现对生态系统水分环境变化进行及时、准确监测提供了条件。
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申请号:201911033392.0 公开号:CN110867250A 主分类号:G16H50/20
申请人:【中文】西安交通大学【EN】XI'AN JIAOTONG University 申请日:2019.10.28 公开日:2020.03.06
摘要:【中文】本发明公开了一种基于强鲁棒性特征选择的社交媒体自残行为检测方法,1)从网络社交媒体网站进行多维度的异质信息获取;2)对数据从文本、用户、时间和图片四个方面进行特征提取,构造自残内容数据集和正常内容数据集;3)通过l_2,1范数的loss函数和正则化项,构建基于强鲁棒性特征选择的有监督自残检测模型;4)对待检测的目标数据进行特征抽取,使用构建的检测模型进行自残检测。本发明所公开的面向社交媒体的自残检测方法,较传统的自残检测相比,可以更广泛的接触到自残主体、更深度的发掘自残主体的行为模式、更高效及时的发现自残行为,具有实际应用的优势。 【EN】The invention discloses a social media self-disabling behavior detection method based on strong robustness characteristic selection, which comprises the following steps of 1) obtaining multi-dimensional heterogeneous information from a network social media website; 2) extracting the characteristics of data from four aspects of texts, users, time and pictures to construct a self-residual content data set and a normal content data set; 3) constructing a supervised self-residual detection model selected based on the strong robustness characteristic through a loss function of l _2,1 norm and a regularization term; 4) and (4) extracting the characteristics of the target data to be detected, and performing self-residual detection by using the constructed detection model. Compared with the traditional self-residual detection, the self-residual detection method for the social media can be used for contacting with the self-residual subject more widely, exploring the behavior pattern of the self-residual subject more deeply, finding the self-residual behavior more efficiently and timely, and has the advantages of practical application.
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申请号:201810991878.4 公开号:CN110872096A 主分类号:B81B1/00
申请人:【中文】天津大学【EN】Tianjin University 申请日:2018.08.29 公开日:2020.03.10
摘要:【中文】本发明公开一种抗湿度干扰功能化硅纳米线气敏传感器及其制备方法,包括如下步骤:清洗p型硅片,将p型硅片放置氢氟酸和硝酸银混合水溶液中,p型硅片放置氢氟酸和过氧化氢的混合水溶液中,硝酸水溶液处理,将多孔硅纳米线进行红外氧化,多孔硅纳米线表面进行疏水化处理,制作双顶点电极制备得气敏传感器。本发明利用十八烷基三氯硅烷进行有机功能化处理构筑超疏水结构,改善室温抗湿干扰性能,该方法简单易行、效果好,形成的传感器器件在室温、湿度超过75%的环境中可对ppb级氮氧化物产生稳定的敏感响应。 【EN】The invention discloses a humidity interference resistant functional silicon nanowire gas sensor and a preparation method thereof, wherein the humidity interference resistant functional silicon nanowire gas sensor comprises the following steps: cleaning a p-type silicon wafer, placing the p-type silicon wafer in a mixed aqueous solution of hydrofluoric acid and silver nitrate, placing the p-type silicon wafer in a mixed aqueous solution of hydrofluoric acid and hydrogen peroxide, treating the p-type silicon wafer with a nitric acid aqueous solution, carrying out infrared oxidation on the porous silicon nanowire, carrying out hydrophobic treatment on the surface of the porous silicon nanowire, and manufacturing the double-vertex electrode to prepare the gas sensor. The invention utilizes octadecyl trichlorosilane to carry out organic functional treatment to construct a super-hydrophobic structure, improves the room temperature anti-moisture interference performance, has simple and easy operation and good effect, and can generate stable sensitive response to ppb level nitrogen oxide in the environment with room temperature and humidity over 75 percent.
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申请号:201810991929.3 公开号:CN110873733A 主分类号:G01N27/00
申请人:【中文】天津大学【EN】Tianjin University 申请日:2018.08.29 公开日:2020.03.10
摘要:【中文】本发明公开基于高性能电极的硅纳米线阵列基气体传感器及其制备方法,通过刻蚀法在硅片表面制备垂直于硅片表面的硅纳米线阵列,在硅纳米线阵列顶端设置旋涂层,并在旋涂层上设置欧姆接触的电极,旋涂层为氧化亚铜层。在有序硅纳米线阵列顶端形成Cu2O涂层互联结构,与在旋涂层上设置欧姆接触的电极,共同构成硅纳米线阵列传感器的顶端电极,通过Cu2O涂层互联结构引出所有硅纳米线的气敏响应信号。本发明的涂层电极结构在保证气体向纳米线阵列内部扩散的同时,可以有效引出几乎所有硅纳米线的气敏响应信号,显著提升传感器的响应灵敏度。 【EN】The invention discloses a silicon nanowire array base gas sensor based on a high-performance electrode and a preparation method thereof. Formation of Cu on top of ordered silicon nanowire arrays2An O coating interconnection structure, and an electrode with ohmic contact arranged on the spin coating layer, which together form a top electrode of the silicon nanowire array sensor and are connected with the top electrode through Cu2And the O coating interconnection structure leads out gas-sensitive response signals of all the silicon nanowires. The coating electrode structure can effectively extract gas-sensitive response signals of almost all silicon nanowires while ensuring that gas diffuses into the nanowire array, and remarkably improves the response sensitivity of the sensor.
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申请号:201911032367.0 公开号:CN110880017A 主分类号:G06K9/62
申请人:【中文】西安交通大学【EN】XI'AN JIAOTONG University 申请日:2019.10.28 公开日:2020.03.13
摘要:【中文】本发明公开了一种基于最优网络结构的网络社团发现方法,1)从原始网络结构中,面向网络社团发现生成最优网络结构;2)基于最优网络结构,为网络节点学习向量表示;3)基于网络节点表示,使用机器学习聚类算法对网络节点进行划分,从而得到网络的社团结构。本发明所公开的基于最优网络结构的网络社团发现方法,同现存的社团发现方法相比,通过数据驱动的策略可以自适应于现实世界中各种网络类型,更具有应用广泛性;同时,考虑并消除了原始网络结构中模糊社团结构的社团间边连接对社团发现的不利影响,通过更细粒度边连接关系的最优网络结构进行更准确、更稳健的社团发现,具有实际应用的优势。 【EN】The invention discloses a network community discovery method based on an optimal network structure, which comprises the following steps that 1) from an original network structure, the optimal network structure is generated facing network community discovery; 2) learning vector representations for the network nodes based on the optimal network structure; 3) and based on the network node representation, dividing the network nodes by using a machine learning clustering algorithm so as to obtain the community structure of the network. Compared with the existing community discovery method, the network community discovery method based on the optimal network structure disclosed by the invention can be self-adapted to various network types in the real world through a data-driven strategy, and has wider application; meanwhile, adverse effects of inter-community edge connection of fuzzy community structures in the original network structure on community discovery are considered and eliminated, more accurate and more stable community discovery is carried out through the optimal network structure of finer-granularity edge connection relations, and the method has the advantages of practical application.
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