INTERACTION OF A SOLITARY WAVE AND A FRONT STEP SIMULATED BY LEVEL SET METHOD

As a new method, the Level Set method had been developed to compute the interface of two-phase flow. The basic mathematical theory and the detailed method to solve the free surface hydrodynamic problem had been investigated. By using the Level Set method, the transformation of a solitary wave over a front step was simulated. The results were in good agreement with laboratory experiments.

Reflection and Transmission of the Level Step to the Waves in a Linear and Continuous Layer Liquid

The reflection and transmission of the level step to the water waves in the linear and continuous layer liquid were studied in this paper. Based on the matching method of the eigenfunction and a Boussinesq approximation, the analytical expression of the diffraction was obtained and the computing methods of the reflection and transmission en- ergy of the level step were proposed. For the incident wave with a frequency greater than that of the flotage, there is only one mode of plane traveling wave in the flow field. In the range of this greater frequency, the linear and continuous delamination effect will affect the reflection and transmission energy of the floating rectangular box. When the fre- quency of the incident wave is less than the flotage frequency, the energy of the plane traveling wave with infinite modes in the flow field will change into the energy of different modes for each incident wave of different modes.

基于STEP-NC的闭环加工技术研究与实现

在研究闭环加工系统数据流需求和STEP-NC的基础上,提出了基于STEP-NC的闭环加工系统框架以及辅助实现STEP-NC执行和CAPP/CAM系统与CNC系统双向闭环的一种高层过程控制器,并对集成工艺规划、STEP-NC程序的后置处理、测量数据反馈与关联,以及工艺改进与优化等关键技术进行了研究与实现。开发并建立了一个闭环加工系统原型,通过STEP-NC标准测试件,验证了基于STEP-NC达到闭环加工的可行性与有效性。

LevelSet方法求解三维大幅晃动问题的分步有限元法

取与容器固结的坐标系,将外激励视为质量力,针对刚性容器内流体三维非线性大幅晃动问题,提出了在时间和空间上均具有二阶精度的两步Taylor-Galerk in分步有限元方法。该法首先显式地求解忽略压力梯度项的中间速度场,然后由压力泊松方程求得压力场,再根据求得的压力对速度场进行修正。求解中利用Level-Set方法隐式地追踪自由面,即在每个时间步求解Level-Set函数随水流的输运方程,并通过迭代的方法对其进行重新初始化,以保持为距离的函数,从而达到捕捉自由面的目的。控制方程统一采用简单方便且具有较高精度的两步Taylor-Galerk in格式进行离散,并通过两个算例验证了本文数值方法的正确性和有效性。

用Level Set算法模拟孤立波与前台阶的相互作用

进一步研究了Level Set方法的数学基础 ,研究了求解有自由水面水动力学问题的具体方法· 并在此基础上 ,对孤立波与前台阶相互作用这一问题进行了计算及研究 。

One-Step-Installation of Offshore Wind Turbine on Large-Scale Bucket-Top-Bearing Bucket Foundation

In 2010,the first offshore wind turbine with integrated installation was established in Qidong sea area of Jiangsu Province,China,which led to the implementation phase of one-step-installation technique based on the design and construction of large-scale bucket-top-bearing (LSBTB) bucket foundation.The critical technique of LSBTB bucket foundation included self-floating towing,penetration with adjustment of horizontal levelness,removability and one-step-installation.The process of one-step-installation included the prefabrication of LSBTB bucket foundation in onshore construction base,installation and debugging of wind power,overall water transportation of foundation and wind power system,and installation of foundation and offshore wind turbine on the appointed sea area.The cost of one-step-installation technique was about 5 000 Yuan/kW,which was 30%-50% lower than that of the existing technique.The prefabrication of LSBTB bucket foundation took about two months.During the one-step-installation process,the installation and debugging of wind power and overall water transportation need about one to two days in sea area within 35 m depth.After the proposed technique is industrialized,the cost will be further reduced,and the installation capacity is expected to be up to 500 wind turbines per year.

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.

pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 99% and its local accuracy is around 92% - 99%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGpos/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

Coseismic responses of groundwater levels in the Three Gorges well-network to the Wenchuan M_S8.0 earthquake

We systematically analyze coseismic responses and post-seismic characteristics of groundwater levels in the Three Gorges well-network to the Ms8.0 Wenchuan earthquake on 12 May 2008. The results indicate that these characteristics differ among wells. On the conditions of similar borehole configurations, the differences are associated with geological structural sites of wells, burial types of aquifers monitored, and transmissivities of aquifer systems. We explored coseismic and post-seismic step-rise and step-drop mechanical mechanisms and their implication to earthquake prediction. We validated the inference that the residual step-rise zone is a possible earthquake risk zone based on recent seismic activity on the Xiannüshan fault in the area.

Dynamic analysis of water level in the Wanquan Well and the precursory anomalies of earthquakes

According to the characteristics of frequent water level step-variation in the Wanquan Well, the step-variation frequency change on the time axis can be regarded as a sequence of step-variation. A study of the relationship between the process of development and evolution of the sequential step-variation and the regional seismicity indicated that the sequential step-variation anomalies are rather better in coincidence with the time, magnitude and frequency of regional seismic activities, which may predict the seismicity trend in months. Furthermore, on this basis, if the water level changes in a characteristic manner, the seismic regime during the time period from several days to 20 days or more may be evaluated. By using the method presented in this paper we had a prediction about the Baotou ML=6.6 earthquake on May 3, 1996. This event occurred within the predicted time span but with a less magnitude and a little location deviation.

一步法与二步法超声引导经皮肝穿刺肝总管置管引流术在晚期低位胆道梗阻的疗效及并发症分析

目的探究一步法与二步法超声引导经皮肝穿刺肝总管置管引流术(percutaneous transhepatic hepatic duct drainage,PTHD)治疗晚期低位胆道梗阻的疗效。方法2019年1月~2023年7月于本院接受姑息性穿刺减黄手术治疗的晚期低位胆道梗阻病人112例,采用随机数字表法分为两组,一步组56例,采用一步法PTHD手术;二步组56例,采用二步法PTHD手术。比较两组病人手术相关指标、术前及术后3天血清总胆红素(TBIL)、碱性磷酸酶(ALP)、丙氨酸氨基转移酶(ALT)、术后并发症发生情况及病人满意度。结果一步组术中出血量为(2.96±0.50)ml,与二步组的(3.02±0.53)ml比较,差异无统计学意义,术后48小时引流量分别为(555.84±90.51)ml和(560.52±104.23)ml,两组比较差异无统计学意义(P>0.05);一步组手术时间为(32.50±4.26)分钟,术后VAS评分为(3.72±1.57)分,二步组分别为(36.43±3.58)分钟和(4.53±1.34)分,穿刺成功率分别为96.43%和76.69%,两组比较差异有统计学意义(P<0.05);术后两组病人TBIL、ALP、ALT水平较术前均显著降低,差异有统计学意义(P<0.05),但一步组与二步组比较,差异无统计学意义(P>0.05);两组并发症发生率分别为3.57%和7.14%,差异无统计学意义(Fisher精确概率=0.679);一步组病人的满意度显著高于二步组病人(94.64%vs.75.00%),差异有统计学意义(P<0.05)。结论一步法PTHD术操作时间更短且穿刺成功率更高,病人疼痛较轻,可显著提高病人满意度。

一种基于开关电容的十三电平逆变器

针对现有多电平逆变器拓扑体积大、成本高和结构复杂的问题,文中提出一种基于开关电容的十三电平逆变器。所提拓扑由1个直流输入电源、3个电容器及14个开关器件组成,可实现6倍升压的十三电平输出,在具有较低成本和较小体积的同时减少了系统输出谐波的含量,且无需H桥即可实现逆变即可降低开关管的电压应力。所提逆变器无需任何辅助方法就能实现电容电压自平衡,简化控制的复杂度。文中通过与其他传统多电平逆变器的对比分析,验证了所提拓扑的优越性。仿真和样机平台的实验结果证明所提逆变器在稳态和动态情况下均具有良好的性能。

一种稳健的频率步进SAR距离向栅瓣抑制方法

频率步进合成孔径雷达(Synthetic Aperture Radar,SAR)的系统传递函数中存在幅度和相位误差(Magnitude Error and Phase Error,MEPE),通过宽带合成操作在距离宽带信号中引入了周期性MEPE,从而导致宽带合成后的高分辨率距离像(HRRP)中出现周期性栅瓣。经过后续SAR成像处理,这些周期性栅瓣在图像中表现为虚假目标,严重影响SAR图像目标的检测与识别。为此,基于SAR图像的栅瓣抑制(Grating Lobe Suppression,GLS)算法被提出,其基于点状目标假设,利用SAR图像中的强点目标实现图像栅瓣的有效抑制。然而,对于非点状目标场景,已有基于SAR图像的GLS算法的性能显著下降,而且会引起图像散焦。因此,文中提出了一种基于目标信息差异的GLS算法,取名为目标信息差异法。该算法不基于点状目标假设,通过估计宽带合成后的理想HRRP与实际HRRP之间的信息差异,稳健地估计出合成宽带信号中的周期性MEPE。通过补偿该周期性MEPE,该算法可以将频率步进SAR距离向栅瓣抑制到图像的背景水平。通过对不同GLS算法进行性能对比发现,所提GLS算法受图像信杂比影响小,且同时适用于非点状目标和点状目标场景,相比已有GLS算法具有明显的优势。实测数据处理结果证明了所提方法的有效性和相对已有GLS算法的优越性。

中低速磁浮诱发交通枢纽换乘中心振动响应研究

针对中低速磁浮诱发机场综合交通枢纽换乘中心(GTC)振动问题,建立磁浮列车-轨道梁耦合振动模型和轨道梁-GTC-土体有限元模型,采用两步法对车致GTC振动响应特征、传播规律和振级进行分析.磁浮列车采用多体动力学建模,轨道梁采用有限元法建模,两者之间通过线性化磁轨关系形成耦合振动模型,并通过与现场实测结果对比验证了模型的正确性.基于所建立的耦合振动模型,获取H型钢轨枕与承轨台之间的扣件力时程并将其施加在轨道梁-GTC-土体模型上,通过瞬态分析得到了GTC典型观测点的动力响应.结果表明:磁浮列车诱发振动沿高度方向传播时,结构柱和楼板的振动响应均随高度的增加先减小后增大,沿水平方向传播时,振动响应随距离的增大而快速衰减;GTC振动响应存在明显的倍频现象,即扣件力主频倍频处振动响应的功率谱密度曲线存在明显峰值;当车速为60 km/h时,车辆激励主频与轨道梁一阶竖向自振频率接近,此时观测点的1/3倍频程最大振级大于其他车速;当磁浮列车以60~120 km/h车速通过GTC时,各楼层最大Z振级均未超过规范限值,表明该机场GTC具有良好的整体刚度.

文档上下文异构表示的句子级关系抽取方法

关系抽取是指从文本中识别2个实体的关系。现有研究利用数据分组处理取得了良好的效果,但由于组内数据之间交互较少,因此大多忽略了组内数据之间的关联。此外,部分方法定义了较多种类的标注信息,从而导致标注信息冗余。针对上述问题,提出一种文档上下文异构表示的句子级关系抽取方法。设计基于异构图网络的文档上下文信息模块,将组内数据中的词和关系建模为图上的节点,然后通过消息传递机制将组内信息进行交互,充分表征组内数据间的关联关系;设计基于异构图网络的关系信息模块用于捕捉关系信息,其与文档上下文信息模块中异构图网络参数共享,从而节约了计算资源;设计融合标记策略,引入一种逻辑上的虚拟标签减少标签种类,缓解标注信息冗余问题。实验结果表明,所构建模型在NYT和WebNLG数据集上的F1值分别为93.2%和94.7%,在复杂场景下的8个子任务中,取得了6个子任务的最优表现,验证了所提方法的有效性。

ANPC三电平换流器高效电磁暂态建模仿真方法

在ANPC(有源中性点钳位型)三电平换流器小步长实时仿真研究中,现有电磁暂态建模方法在模型精度、计算效率、仿真规模等方面存在不足,为此,提出一种高效电磁暂态仿真模型。该模型基于ANPC三电平桥臂的离散化伴随电路,通过WARD等值理论消去内部节点,构造一个对外等效的多端口诺顿等效电路,替代桥臂进行网络的电磁暂态仿真求解,在不损失建模精度的前提下实现节点降维和仿真提速。在MATLAB/Simulink平台中搭建基于该等效模型的ANPC三电平换流器仿真系统,并与详细模型仿真结果进行对比,结果表明该等效模型能精确仿真稳态和暂态运行工况,并且具有较明显的速度提升。

地铁无人值守智慧车站建设研究

面对智慧技术快速发展和地铁运营降本增效压力,众多城市开始推进建设地铁智慧车站,但因智慧技术成熟度和完整性不足、支持性规范缺失、全寿命周期价值不明确等因素,距离实现全线全时段无人值守的全智慧型车站仍有较大差距。文章结合智慧技术发展趋势,根据智慧车站服务人员职责变化情况,提出智慧车站等级划分方案,将智慧车站分为人工服务车站、半自助服务车站、自助服务车站、集中值守车站、无人值守车站5个等级。并通过分析地铁车站各类工作场景,给出地铁无人值守智慧车站建设路径和分步有序建设步骤,为地铁智慧车站建设提供理论和实践参考。