An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)algorithm.The research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,respectively.These results demonstrate the model’s effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model’s superior inversion accuracy over other methods.

Study on the wave extreme parameters of the Bohai Sea with the numerical simulation using SWAN

Wave fields in Bohai Sea from 1985 to 2004 were simulated using SWAN wave model by inputting high-resolution hindcast wind fields dataset. Comparisons of wave heights between simulation and observation show good agreement in general. According to the annual extreme values of simulation, this paper gives wave extreme parameters with different return-period for all computation grids in Bohai sea.

Wave Characteristics and Extreme Parameters in the Bohai Sea

This paper is aimed at the whole Bohai Sea, as the complement and improvement of wave characteristics and extreme parameters. Wave fields were simulated in the Bohai Sea by using wave model SWAN from 1985 to 2004. The input data based on the hindcast of high-resolution wind fields from RAMS and water level fields from POM, which have been tested and verified well. Comparisons of significant wave heights between simulation and station observations show a good agreement in general. By statistical analysis, the wave characteristics such as significant wave heights, dominant wave directions and their seasonal variations are discussed. In addition, main wave extreme parameters and directional extreme values particularly for 100-year return period are investigated.

Aeroengine Performance Parameter Prediction Based on Improved Regularization Extreme Learning Machine

Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.

Alternation in F-wave parameters of median nerve from unaffected extremity in stroke patients with hemiplegia under dynamic state

BACKGROUND: For many years, the extremities of stroke patients are divided into affected side and unaffected side according to clinical symptoms and body signs. Moreover, previous rehabilitation function training is developed simply aiming to the dysfunction manifested by unaffected extremity. Problems of unaffected extremity are always ignored, such as left- and right- side connection dysfunction, abnormal muscular tension of unaffected side and so on. OBJECTIVE: To observe neurophysiological change characteristics of unaffected extremity of stroke patients with hemiplegia by electromyographical method. DESIGN: Case-control observation. SETTING: First Hospital, Jilin University. PARTICIPANTS: Eighty stroke patients with hemiplegia confirmed by skull CT or MRI, who firstly hospitalized in the Department of Neurology, First Hospital, Jilin University between July 2004 and March 2005, were retrieved. They were scored > 8 points in Glasgow Coma Scale and had stable vital sign. Nineteen normal persons who received healthy examination in the clinic were involved in normal control group. Following the classification criteria of Brunnstrom's Recovery Stages of Stroke (BRSS), 80 stroke patients with hemiplegia were assigned into 3 groups: BRSS Ⅰ-Ⅱ group (n =36), BRSS Ⅲ-Ⅳ group (n =23) and BRSSⅤ-Ⅵ (n =21). METHODS: F-wave parameters of median nerve of unaffected extremity were detected by electromyographical technique. The recording electrode (muscular belly of abductor pollicis brevis) and reference electrode (first finger bone) were connected with grounding electrode. Stimulating electrode was placed in the median part of wrist joint with stimulation intensity of 130% that of threshold stimulation, stimulation frequency of 2 Hz, current pulse width of 0.2 ms, time course of 5 ms and sensitivity of 2 mV. The F-wave of median nerve of affected extremity under the resting stage (static status) and that of unaffected extremity under the maximum resistant contracted state were detected in order. The amplitude and appearance percentage of F wave were recorded. MAIN OUTCOME MEASURES: Comparison of F-wave parameters of median nerve between the unaffected extremity of stroke patients with hemiplegia and the extremity of control subjects under different status. RESULTS: All the patients accomplished the detection, and all of them participated in the final analysis. ①Under dynamic status, the amplitude and appearance percentage of F wave of unaffected extremity of patients in BRSS Ⅲ-Ⅳ group were significantly higher than those in the normal control group, respectively[(0.803 9±0.157 3) mV vs. (0.406 7±0.170 3) mV; (0.856 1±0.266 8)% vs. (0.650 0±0.197 6)%, P < 0.05]. Under static status, there were no significant differences in F-wave parameters of median nerve in the unaffected extremity of patients between BRSS Ⅰ-Ⅱ group and BRSS Ⅴ-Ⅵ group (P > 0.05). ②F-wave parameters of median nerve of unaffected extremity of patients in BRSS Ⅰ-Ⅱ group and BRSS Ⅴ-Ⅵ group under dynamic statewere higher than those under static status, without significant difference (P > 0.05), while the amplitude and appearance percentage of F wave of median nerve of unaffected extremity of patients in BRSS Ⅲ-Ⅳ group under dynamic statewere significantly higher than those under static state[(0.803 9±0.157 3) mV vs. (0.391 7±0.131 6) mV; (0.856 1±0.266 8 )% vs.(0.639 1 ±0.259 4)%,P < 0.05]. ③ There was no significant difference in F wave parameters among groups under static state(P > 0.05). However, under dynamic status, the amplitude and appearance percentage of F wave parameters of median nerve of unaffected extremity of patients in BRSS Ⅲ-Ⅳ group [(0.803 9±0.157 3) mV,(0.856 1±0.266 8)%] were significantly lower than those in the other two groups [(0.395 1±0.148 8),(0.437 1±0.157 6) mV;(0.612 5±0.232 8)%,(0.657 1±0.232 5)%,P < 0.05]. CONCLUSION: With the development of disease condition and the increase of muscular tension at anesthetic side, combination motor of affected extremity is caused following movement and muscular tension enhances to non-anesthetic-side. Therefore, F-wave parameters increase under dynamic status.

Prediction of length-of-day using extreme learning machine

Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

Effects of Different Electromagnetic Fields on Circadian Rhythms of Some Haematochemical Parameters in Rats

Objective To investigate the effects of different electromagnetic fields on some haematochemical parameters of circadian rhythms in Sprague-Dawley rats. Methods The study was carried out in 18 male and 18 female rats in good health conditions exposed to 50 Hz magnetic sinusoid fields at the intensity of 1000 μT, 100 μT, and 0 μT （control group） respectively, and in 18 male and 18 female rats in good health conditions exposed to 1.8 GHz electromagnetic fields at the intensity of 50 V/m, 25 V/m and 0 V/m （control group）, respectively. Following haematochemical parameters for glucose, triglycerides, and total cholesterol were measured. Results Different effects of electromagnetic fields on circadian rhythms of both male and female rats were observed. Different changes occurred in some haematochemical parameters for glucose, triglycerides, and total cholesterol （P〈0.05）. Conclusion Exposure to different electromagnetic fields is responsible for the variations of some haematochemical parameters in rats.

Least squares fitting of coordinate parameters model

This paper starts with untime-diversification of the time-diversification deformation model and gives displacement distribution model of untime-diversification and simplifies further the study of deformation model. The paper discusses the problem of least squares fitting of coordinate parameters model—parameters of deformation model. During discussion, the basic means of cubic B splines and two steps of multidimensional disorder datum fitting are adopted which can make fitting function calculated mostly approximate coordinate parameters model and it can make calculation easier.

Estimation of Extreme Flows in Nkana River to Verify the Adequacy of Naming’ongo Bridge Waterway

The response by the government of Tanzania to food security and poverty alleviation in the Naming’ongo area in Mbozi District has been to develop Naming’ongo irrigation scheme as well as construct a bridge across River Nkana to connect the farms and other parts of the district to facilitate a reliable transportation of the produce to the market. The Australian Water Balance Model that was calibrated by using 10 years data from a nearby sub-catchment of Mbarali. The Naming’ongo Sub-catchment was delineated form a 30 m digital elevation model. The observed rainfall was obtained from Mbozi Meteorological station. The study approximated the peak flows in River Nkana for a return period of 50 years to be slightly above 560 m3/s. This was considered to be adequate for the proposed structure. The study recommends that when undertaking human activities such as deforestation and cultivation an account for soil and environmental conservation should be considered. While it is necessary to establish a monitoring system within the catchment, the designs of future hydraulic structures should incorporate stream flow measuring facilities.

Best Linear Unbiased Estimators of Location and Scale Ranked Set Parameters under Moving Extremes Sampling Design

In the current paper,the best linear unbiased estimators(BLUEs)of location and scale parameters from location-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under moving extremes ranked set sampling(MERSS).Explicit mathematical expressions of these estimators and their variances are derived.Their relative efficiencies with respect to the minimum variance unbiased estimators(MVUEs)under simple random sampling(SRS)are compared for the cases of some usual distributions.The numerical results show that the BLUEs under MERSS are significantly more efficient than the MVUEs under SRS.

基于GPD理论和百分位数阈值法的轮轨力极值估计与动力系数研究

在高速铁路无砟轨道结构设计和检算时,列车荷载设计值是关键的设计参数之一。基于GPD理论,研究全波段不平顺激励下的轮轨力极值估计方法,并与脉冲激励下的结果对比,为无砟轨道结构设计和相关规范的完善提供依据。结果表明:采用百分位数法选取阈值时,应对样本分簇以提高样本之间的独立性和轮轨力极值估计的精度;提出结合百分位数阈值的形状参数筛选法进行轮轨力极值估计,确定了每簇样本量大小和形状参数区间;样本量宜取3×10^(5)~5×10^(5)个,百分位数阈值宜取50%~98%,且以轮轨力极值估计值的均值作为最终的轮轨力极值估计值;列车速度为250~400 km/h的列车荷载设计值动力系数分别为2.2、2.4、2.6和2.8。

船用双燃料柴油机多参数优化匹配分析

为了减少实验次数,寻找最佳参数匹配,使用正交实验设计方法,选取异戊醇掺混比、喷油提前角、进气温度、扫气压力4个参数的各4个水平进行仿真实验,以NO质量分数和指示功率作为实验的评价指标进行正交设计,运用数据极差分析寻求最佳参数组合。结果表明,得到的最优参数为:异戊醇掺混比为体积分数40%,喷油提前角为22.6°,进气温度为315.15 K,扫气压力为0.233 MPa。在此参数组合下得到最终生成的NO质量分数为0.01102%,指示功率为52.10 kW。可见,在动力性损耗较小的情况下可以实现较高的异戊醇掺混比。

基于改进灰狼优化核极限学习机的疾病诊断模型

为提高疾病诊断的效率,本文提出一种改进的灰狼优化算法与核极限学习机的混合模型。通过引入一种新的机制提高灰狼优化算法的探索与利用能力,改进的灰狼优化算法在进行特征选择的同时,也对核极限学习机的2个关键参数进行优化,模型在2个疾病数据集上进行实验验证。实验结果显示:提出的模型在准确率、敏感性、特异性等评价指标方面相对于其他混合模型高出约1%~2%,带特征选择的优化模型相对于没有特征选择的模型在评价指标上也高出约1%~2%。结果表明提出的模型具有一定的优势。

基于极限学习机模型参数优化的光伏功率区间预测技术

提出一种基于极限学习机(ELM)模型参数优化的光伏功率区间预测技术.首先,提出加权欧氏距离作为光伏功率预测区间评估指标,筛选历史样本单元并优化ELM训练集;然后,提出ELM参数混合寻优算法,利用精英保留策略遗传算法与分位数回归优化ELM模型隐层输入及输出权重与偏置参数,并采用训练后的模型预测光伏功率区间;最后,基于光伏电站与气象站历史数据构建实际算例,预测光伏功率区间,并与其他方法得到的结果进行对比.算例结果表明:所提方法在增加区间预测可信度的同时,能较大程度提高区间预测准确度.

郑州“7·20”极端暴雨的双偏振雷达回波与风场特征分析

为了细致地了解郑州极端暴雨的雷达回波特征与内部动力特征间的联系,利用地面自动站、探空、欧洲中期再分析资料以及双偏振雷达,分析了中尺度对流系统发展和成熟阶段的回波和雷达反演的风场结构,揭示了对流系统内粒子相态特征,动力特征等宏微观特征。主要研究结果如下:反射率因子特征为南北向的带状区域逐渐汇集为团状;径向速度特征为低层辐合和风向随高度顺转的强垂直风切变;基于雷达偏振量的分析,给出了对流系统局部结构,进一步的研究表明:差分反射率狭窄的强弧状区域可有效地指示对流系统内的强上升运动,其为极端降水系统的持续性发展提供了动力强迫;剖面上差分反射率柱与差分相位常数柱在空间分布上呈现出分离现象,两者体现出对流系统内的强上升运动,为暴雨的发展与增强提供了强有力的支持。

长江流域极端降水特征时空变化分析

基于长江流域224个气象站点1960—2018共59 a的日降水数据,采用Mann-Kendall(M-K)趋势检验、线性拟合方法分析长江流域极端降水时间变化特点;确定长江流域最适用于广义极值分布(Generalized Extreme Value Distribution, GEV)参数估计的方法,分析GEV尾部特征,解析长江流域极端降水空间特征。结果表明:(1)长江流域的极端降水总体上呈上升趋势,呈上升趋势地区主要是长江中下游区域,极端降水量增长速率最快的站点主要分布在长江下游,长江中下游流域极端降水量可能在未来会进一步增长,暴雨洪水发生频率可能会提高;(2)L-Moment方法更适用于估计GEV分布参数,对于降水量较大的极端降水序列,L-Moment的稳定性更高,极端降水极值序列分布为Fréchet的站点占比约为70%,表明长江流域大部分站点极端降水极值分布厚尾特征明显,极端降水量波动较大;(3)长江流域极端降水在不同重现期水平下呈现出明显的空间分布差异,不同极端降水序列的极端降水量均呈现出从上游到下游逐渐递增的趋势,长江中下游极端降水量增速快,波动大,不同重现期降水量更大。因此,长江中下游的防洪能力需要进一步加强。

超高速激光熔覆技术的最新研究进展:关键技术特点及优势,设备研发及其技术参数

超高速激光熔覆技术是近年新开发的表面涂层技术,本质是通过改变粉末的熔化位置,使粉末在工件上方与激光交汇发生熔化,随之均匀涂覆在工件表面,从而具有优质高效、绿色低成本、高质量和高适应性、低热输入和低激光功率、低稀释率和高性能等特点。首先,简要介绍了超高速激光熔覆关键技术特点及技术优势、国内外超高速激光熔覆装备发展现状;其次,结合最新研究成果重点阐述了影响超高速激光熔覆制备的涂层最终组织结构与性能的关键技术参数(所采用的熔覆材料、激光功率、搭接率、光斑直径、熔覆速度、送粉量和送粉压力等);最后,详细介绍了超高速激光熔覆制备的涂层质量检测参数,包括熔覆层厚度、结合强度、孔隙率、稀释率、表面粗糙度、硬度、耐磨性和耐腐蚀性等熔覆效果。通过综述超高速激光熔覆技术的特点、优势以及关键技术参数,以期为超高速激光熔覆技术的进一步改进与提升、设备研制等方面取得更多进展提供借鉴和指导。

基于ISSA-ELM模型的温室环境参数预测研究

温室环境系统具有非线性、多变量和强耦合的特点,传统的温室模型难以预测其真实环境。采用极限学习机、BP神经网络和支持向量机三种模型对温室温度、湿度和光照强度进行了预测分析,结果显示极限学习机模型预测值与温室环境实时参数最为相近。为提高温室环境参数的预测精度,采用改进的麻雀搜索算法对极限学习机模型进行优化,预测的环境参数与天津某温室实测数据吻合较好,证实了所提出预测模型用于温室环境调控的可行性。

基于深度学习的全景片自动牙位标识

根据国际牙科联盟系统的全景片影像实际特征,提出一种基于参数优化的用于自动牙齿检测和标号分类的方法。运用先进的深度学习方法构建创新以及实用的三阶段牙科全景片牙齿标识方法。使用全景片图像将其分为几个阶段,以SqueezeNet的基于掩膜区域卷积神经网络作为基线模型进行特征提取过程,使用燕群优化算法进行参数优化,应用基于SoftMax分类器的牙齿预测和加权极限学习机的阶段分类模型确定牙齿编号类别标签,在图像数据集上进行评估,所提方法具有性能竞争力。

多源数据驱动的轧机振动预测及可解释性分析

为研究轧制过程动态工艺参数对轧机振动的影响规律,改善现有研究中机理模型精度较低且数据模型缺乏可解释性的问题,采用极端梯度提升(Extreme Gradient Boosting,XGBoost)算法建立基于多源数据的轧机振动预测模型,并使用SHapley Additive exPlanations(SHAP)框架对预测模型进行解释。通过与其他预测模型相比,XGBoost预测模型可以利用工艺参数实现对轧机运行状态的高精度预测。基于SHAP框架解释的结果表明,出入口厚度、轧制力、轧制速度对轧机振动影响较大,后张力对轧机振动影响较小。研究为提高轧机设备与工艺参数的匹配度,实现将工业数据应用于轧机振动预测和分析提供理论基础。