Determination of combined effect of ampli- fication and attenuation of soft rock site us- ing digital seismic data

In the paper, for the application of stochastic simulation of ground motion, we put forward a method to determine ″the combined effect of amplification and attenuation″ (combined effect for short) of soft rock site by using digital seismic data of moderate and small earthquakes. Our approach aims at solving the problem of the combined effect of soft rock site, which is difficult to determine in most regions of China because fewer measures were done for S-wave velocity structure. The combined effect of soft rock site can be determined by using the approach recom- mended by us. An example is given to discuss the practical application of the method.

Phylogeny of subclass Scuticociliatia(Protozoa,Ciliophora) using combined data inferred from genetic,morphological,and morphogenetic evidence

Gene sequence-based genealogies of scuticociliates are different from those produced by morphological analyses.For this reason,11 representative scuticociliates and two ambiguously related genera were chosen to test the ability of combined phylogenetic analyses using both gene sequences and morphological/morphogenetic characteristics.Analyses of both the SSrRNA gene sequences and the combined datasets revealed a consistent branching pattern.While the terminal branches and the order level relationships were generally well resolved,the family level relationships remain unresolved.However,two other trees based on ITS1-5.8S-ITS2 region sequences and morphological/morphogenetic characters showed limited information,due to a lack of informative sites in these two datasets.Our data suggest,however,that the combined analysis of morphological/morphogenetic characters and gene sequences did produce some changes to the phylogenetic estimates of this group.

An adaptive over/under data combination method

The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.

Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

Multi-area combined economic/emission dispatch(MACEED)problems are generally studied using analytical functions.However,as the scale of power systems increases,ex isting solutions become time-consuming and may not meet oper ational constraints.To overcome excessive computational ex pense in high-dimensional MACEED problems,a novel data-driven surrogate-assisted method is proposed.First,a cosine-similarity-based deep belief network combined with a back-propagation(DBN+BP)neural network is utilized to replace cost and emission functions.Second,transfer learning is applied with a pretraining and fine-tuning method to improve DBN+BP regression surrogate models,thus realizing fast con struction of surrogate models between different regional power systems.Third,a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization poli cy is proposed to execute MACEED optimization to obtain scheduling decisions.The proposed method not only ensures the convergence,uniformity,and extensibility of the Pareto front,but also greatly reduces the computational time.Finally,a 4-ar ea 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.

Contrast-enhanced ultrasound in association with serum biomarkers for differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma

BACKGROUND Combined hepatocellular-cholangiocarcinoma(CHC)is a rare type of primary liver cancer.Due to its complex histopathological characteristics,the imaging features of CHC can overlap with those of hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC).AIM To investigate the possibility and efficacy of differentiating CHC from HCC and ICC by using contrast-enhanced ultrasound(CEUS)Liver Imaging Reporting and Data System(LI-RADS)and tumor biomarkers.METHODS Between January 2016 and December 2019,patients with histologically confirmed CHC,ICC and HCC with chronic liver disease were enrolled.The diagnostic formula for CHC was as follows:(1)LR-5 or LR-M with elevated alphafetoprotein(AFP)and carbohydrate antigen 19-9(CA19-9);(2)LR-M with elevated AFP and normal CA19-9;or(3)LR-5 with elevated CA19-9 and normal AFP.The sensitivity,specificity,accuracy and area under the receiver operating characteristic curve were calculated to determine the diagnostic value of the criteria.RESULTS After propensity score matching,134 patients(mean age of 51.4±9.4 years,108 men)were enrolled,including 35 CHC,29 ICC and 70 HCC patients.Based on CEUS LI-RADS classification,74.3%(26/35)and 25.7%(9/35)of CHC lesions were assessed as LR-M and LR-5,respectively.The rates of elevated AFP and CA19-9 in CHC patients were 51.4%and 11.4%,respectively,and simultaneous elevations of AFP and CA19-9 were found in 8.6%(3/35)of CHC patients.The sensitivity,specificity,positive predictive value,negative predictive value,accuracy and area under the receiver operating characteristic curve of the aforementioned diagnostic criteria for discriminating CHC from HCC and ICC were 40.0%,89.9%,58.3%,80.9%,76.9%and 0.649,respectively.When considering the reported prevalence of CHC(0.4%-14.2%),the positive predictive value and NPV were revised to 1.6%-39.6%and 90.1%-99.7%,respectively.CONCLUSION CHCs are more likely to be classified as LR-M than LR-5 by CEUS LI-RADS.The combination of the CEUS LI-RADS classification with serum tumor markers shows high specificity but low sensitivity for the diagnosis of CHC.Moreover,CHC could be confidently excluded with high NPV.

Research on Data Combination for Phased-Array Ground Penetrating Radar

To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.

Determination of Light Rare Earths by a Spectrophoto-metry Based on the Linear Combination of Multiwavelength Data

The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that the weighted combination absorbance attained is only in direct proportion to the concentration of the analysed component and independent of coexisting interferents.The accuracy of the analytical results is improved greatly for the analysis of light rare earths with the coexistence of heavy rare earths.The analyti- cal error from the reagent blank and co-coloration of light and heavy rare earths have also been overcome. The greatly improved linearity and additivity of absorbance are obtained.

Combination of structure tensor and tilt angle in the edge detection of potential field data

Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the combination of structure tensor and tilt angle to detect the edges of the sources,which can display the edges of shallow and deep bodies simultaneously. Through tests on synthetic potential field data,it is obvious that the proposed edge detection methods can display the sources edges more clearly and precisely,compared with other commonly used methods. The application on real potential field data shows similar result,obtaining the edges of layers and faults clearly. In addition,another advantage of the new method is its insensitivity to noise.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

基于贪心组合优化的分布极端不平衡分类算法

现有针对不平衡数据分类的研究主要从重采样、特征、代价和算法等4个角度展开,方法多样,但针对极端不平衡的数据分布仍缺乏有效算法.本文的目标是通过结合各种算法的特性获取一个最优性能的组合算法.本文假设算法间的组合满足次模函数性质,并采用贪心的组合优化方法.具体而言,选择深度森林算法为基础,依次组合最优重采样方法、以异常检测思想的特征提取方法对数据进行的特征处理方法或基于贝叶斯优化的最优代价敏感矩阵方法.在3种组合算法中选择分类性能最优的算法组合,再次组合其余角度的方法,判断分类性能是否再次提升.实验选择两组极端不平衡数据——真实饮用水数据和UCI数据库中的page-blocks数据进行验证.结果表明,基于贪心优化对算法间进行组合,在3轮迭代后得到的算法组合,较单一算法其分类性能能有进一步的提升.

融合多源数据挖掘信息的非轴对称端壁/叶身联合成型设计空间知识挖掘

为厘清联合成型设计空间各造型方法对涡轮叶栅性能提升的贡献,将基于总变差分析的全局敏感性分析方法与平行坐标系及直方图、散点图等可视化技术相结合,提出了将多源数据挖掘信息进行融合的设计空间知识挖掘框架,并利用该框架对某小展弦比叶栅端壁/叶身联合成型设计空间进行知识挖掘。结果表明:叶栅气动损失增减对基元型线微调十分敏感;非轴对称端壁所带来的损失减少量与型线微调接近,但设计空间内不同非轴对称端壁样本性能波动较小,导致其重要性被全局敏感性分析方法低估;虽然单独采用弯叶片难以整体降低小展弦比叶栅损失,但将弯、型线微调和非轴对称端壁造型相结合可获得“1+1+1>3”的性能增益;在端壁/叶身联合成型设计空间内,将基元型线朝着后加载方向微调,并采用喉部附近下凹较深的非轴对称端壁造型及反弯叶片设计,可获得气动损失较小的叶栅设计。研究结果明晰了端壁/叶身联合成型设计空间优化解特征,可为类似小展弦比叶栅设计优化提供借鉴。

基于数据挖掘的胆石症动物模型特点及应用分析

目的对现有胆石症动物模型进行整理归纳,探索可更好反映中西医临床病证特点,满足中医药临床与基础研究发展需要的动物模型。方法检索中国知网、万方、维普、SinoMed、PubMed等数据库,对国内外胆石症相关实验研究的动物模型进行搜集和整理,对模型的动物种类、造模方法、造模周期、检测指标和阳性药等数据进行统计和分析。结果纳入的128篇文献中,胆石症模型的动物种类以豚鼠、家兔和C57BL/6小鼠为主。使用最多的造模方法为高脂饲料法,饲料喂养周期集中在8周。高频检测指标为成石率、总胆固醇、磷脂、总胆红素、总胆汁酸等。较多使用的干预方式为中药复方、西药和单味中药或提取物,西药干预以熊去氧胆酸(Ursodeoxycholic acid,UDCA)为主,中药干预以中药复方为主,还包括针灸、运动、饮食等其他干预方式。实验设立阳性对照药多为UDCA。结论随着胆石症动物模型的不断完善和发展,目前有较多种造模方法都较好地模拟了胆石症中西医临床表现特点,但也存在一定局限性。该文分析结果可为胆石症模型的选择、应用和完善提供参考。

基于商用密码的水利重要数据点面结合安全保护方法

为保障数字孪生流域建设过程中的重要数据安全,针对破解海量高精度数据汇集面临的数据窃取、泄露、损毁等问题隐患开展研究,提出网络安全基础—数据分类分级—数据安全防护的数字孪生流域数据安全总体防护方案。针对数据安全传输、存储、使用中的安全薄弱点,提出全面通用防护和应用内加密相结合的点面结合加密保护方法,利用商用密码技术对数据进行机密性、完整性、可用性保护。本研究方法已在全国水利一张图等重要系统中得到应用,可实现数据安全保护和便捷高效应用平衡,广泛适用于数字孪生流域建设中的水利重要数据全生命周期保护。

基于数实结合的测控装备健康监测系统设计与实现

针对测控业务领域装备种类多、型号杂、布站分散、技术保障人力不足、远程技术协助手段落后等实际问题,结合测控装备自身特点和运维保障规律,提出了采用数字技术构建功能级数字测控装备,并利用数字装备和实体装备间物联网络使数实装备状态数据一致,实现基于数实结合的测控装备健康监测,实现装备状态数实同步,并可利用数实同步有效的评估装备状态、进行故障诊断和健康趋势预估,实现了运维保障数字化。

基于通道组合-数据对齐-多尺度全局CNN的MI-EEG分类

由于运动想象脑机接口(MI-BCI)范式不需要视觉刺激,应用MI-BCI范式在提高人机交互系统舒适度方面具有重要意义。为实现辅助设备的异步控制,提高模型的鲁棒性,减少通道使用数量以降低BCI系统输入的复杂性,提出一种基于通道组合(channel combination,CC)-数据对齐(euclidean space data alignment,EA)-多尺度全局卷积神经网络(multiscale global convolutional neural network,MGCNN)的运动想象脑电分类方法。通过引入大脑静息状态下的脑电信号,扩展MI-BCI输出指令集;利用CC将22通道脑电数据重构为左右对称通道加中间通道的3通道形式,重构后的数据经过EA方法规范后作为网络输入;构建多尺度卷积模块与全局卷积模块,并行提取脑电信号的局部特征和ERS/ERD全局特征;利用迁移学习提升模型的解码能力。结果表明:该方法在BCI Competition IV 2a数据集上达到了99.28%的平均准确率和0.99的Kappa值,提高了运动想象脑电分类精度,为在线异步运动想象脑机接口的应用与发展作出了贡献。

电网优化调度的模型-数据-知识融合方法研究评述及展望

随着新型电力系统时变不确定因素日益增多和非线性更为增强,电网调度决策的复杂度剧增,充分利用物理模型、数据驱动和调度员知识经验的互补特性,有望实现电网优化调度决策效率和场景适应性的提升。首先,从不确定性调度、数据驱动和知识驱动3方面分别梳理了电网优化调度相关新发展;其次,分析了模型-数据-知识融合的内涵,将3者融合架构分为主从驱动模式和对等驱动模式,分别评述了国内外学者的相关研究工作;最后,针对模型-数据-知识融合在电网优化调度中的应用现状分析了存在问题,并从模型-数据-知识融合效果的量化评价、调度可信知识的主动筛选和演绎、多模式融合的电网调度可靠智能决策和自主趋优演化4个方面对未来研究方向进行了展望。

数据与知识联合驱动的人工智能方法在电力调度中的应用综述

数据与知识联合驱动的人工智能方法是利用数据驱动和知识驱动各自优势、取长补短形成的一种更优的联合方法。文中对联合驱动方法的特点和现状进行了分析,并对其电力调度重点应用场景进行了分类统计。总结了各类场景中联合方法的创新性、相较于单一方法的提升以及应用优势。归纳出了电力调度数据与知识联合驱动方法的3种联合建模方式:数据驱动优化知识驱动、知识驱动辅助数据驱动、数据驱动与知识驱动混合模式。最后,从准确性和高效性的平衡、有效性、可靠性等5个维度讨论了联合建模时存在的挑战性问题,对联合驱动方法在电力调度中的适用场景进行了总结及展望。

基于一维卷积神经网络的物联网终端非干扰身份识别方法

针对物联网终端身份识别过程易受干扰的问题,设计了基于一维卷积神经网络的物联网终端非干扰身份识别方法.通过物联网终端内置的加速度传感器收集不同步态的加速度数据,并采用平滑与分窗处理进行预处理.利用步态数据融合模型分析用户在不同行为位置组合下的数据,再利用随机森林模型预测物联网终端的位置与用户行为.将行为与位置数据融合,得到步态融合数据,再将融合数据输入至构建的一维卷积神经网络模型中,经过迭代训练后输出用户的身份识别结果.实验结果显示,基于一维卷积神经网络的物联网终端非干扰身份识别方法在合适的模型参数条件下能够准确识别物联网终端用户身份.

基于网络搜索数据的GDP组合预测研究

网络搜索数据(Web Search Data, WSD)是研究宏观经济现象的重要微观信息依据。从需求、供给与政策环境等方面选取和筛选关键词来合成网络搜索指数,采用金枪鱼群(Tuna Swarm Optimization, TSO)算法优化的最小二乘支持向量回归(Least Squares Support Vector Regression, LSSVR)模型,对GDP进行预测。结果表明,网络搜索指数与GDP具有强相关性,合成的网络搜索指数能较好地反映GDP的波动走势;网络搜索数据的加入使得预测结果呈现出强时效性,预测效果及预测精度都取决于对最优模型的选择,引入参数智能优化算法可提高模型的预测性能。提出的TSO-LSSVR&WSD模型充分利用网络搜索数据及组合预测优势,提高了GDP的预测精度和时效性,可应用于宏观经济指标预测中。

企业数据商业秘密保护的困境与制度完善

【目的】企业数据的法律属性、权利归属、保护路径都存在认定难题,在现有法律制度的基础框架下进行新的完善十分必要。【方法】结合当前数字化发展现状,明确企业数据商业秘密的认定要件与相关标准,破解权利人举证难的困境。【结果】当下对于企业数据的商业秘密保护缺乏相关立法,现行保护制度与保护措施不力,对企业数据作为商业秘密的司法认定也相对困难。【结论】建议积极推进企业数据商业秘密保护的专门立法,补足与完善现行企业数据保护的相关法律法规。采取民刑并举的保护思路,提升企业数据商业秘密保护案件处理的有效性。