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为了探索紫外-可见-近红外反射光谱测定油茶籽油掺伪量的方法，按照不同掺伪比例制备了244个油茶籽油掺伪大豆油、菜籽油、花生油、玉米油的样本，以自主搭建的实验平台采集所制备样本在200~1 100 nm范围内的反射光谱。将原始光谱进行Savitzky-Golay(SG)-连续小波变换（CWT）预处理后，利用Kennard-Stone(K-S)算法以2∶ 1的比例将样本划分成校正集和预测集。采用竞争性自适应重加权算法（CARS）、连续投影算法（SPA）、自主软收缩算法（BOSS）、迭代变量子集优化算法（IVSO）进行特征波长选择，分别建立基于支持向量机（SVM）、极限学习机（ELM）、随机森林（RF）的油茶籽油掺伪量快速预测模型，同时对特征波长的特性进行了研究。结果表明：原始光谱经过 SG-CWT（L5）预处理和 BOSS 特征波长筛选后，建立的基于SVM的油茶籽油掺伪量快速预测模型能够鉴别掺伪量为1%及以上的油茶籽油，该模型在十折交叉验证和网格搜索法下得到最佳惩罚因子（c）和核函数（γ）分别为5.278 0和0.108 8，其预测决定系数（R2P）、预测均方根误差（RMSEP）、预测平均绝对误差（MAEP）分别为0.998 5、0.013 4、0.010 2。特征波长聚集程度和陡度对模型预测结果存在一定影响。综上，建立的基于反射光谱的油茶籽油掺伪量快速预测模型预测误差小，预测效果较好。In order to explore the method of UV-Vis-NIR reflection spectroscopy to identify blended oil-tea camellia seed oil(CAO), 244 samples of CAO adulterated with soybean oil, rapeseed oil, peanut oil and corn oil were prepared according to different adulteration amounts, and the reflectance spectra of the prepared samples in the range of 200-1 100 nm were collected by an experimental platform built independently. After pretreating the raw spectra with SG-continuous wavelet transform (CWT), the samples were divided into correction and prediction sets using the Kennard-Stone (K-S) algorithm in a ratio of 2∶ 1. Competitive adapative reweighting sampling(CARS)algorithm, successive projections algorithm (SPA), bootstrapping soft shrinkage(BOSS) algorithm, and iteratively variable subset optimization (IVSO) algorithm were used for characteristic wavelength selection, and rapid identification models based on support vector machine (SVM), extreme learning machine (ELM), and random forest (RF) were established for CAO adulteration amount, respectively, and the characteristics of characteristic wavelength were studied. The results showed that the SVM model established after the SG-CWT (L5) pretreating and BOSS characteristic wavelength screening could discriminate the amount of adulteration 1% and above, and the model obtained the best penalty factor c ( 5.278 0) and kernel function γ (0.108 8) under the ten-fold cross-validation and grid search method, with R2P, RMSEP and MAEP of 0.998 5, 0.013 4 and 0.010 2, respectively. At the same time, the degree of aggregation and steepness of the characteristic wavelength had some influence on the model prediction results. In conclusion, the established rapid prediction model for the adulteration amount of oil-tea camellia seed oil based on reflection spectroscopy has low error and good prediction effect.

After the completion of the Three Gorges Dam, it increases the risk of inducing an earthquake. We use the GRACE Gravity Field Model to analyze the relationship between the operation of the Three Gorges Reservoir and the change of gravity field in western Sichuan. The research results indicate that the reservoir water level and the western Sichuan gravitational field are positively correlated. In the early stage of rising water level, the change of gravity field is not apparent, and the change of gravity field gradually increases with time. Therefore, the change of reservoir water level affects the gravity field in western Sichuan. The dynamic changes of the gravity field can reflect the Earth’s material change and deformation process and are closely related to earthquakes. Consequently, the Three Gorges Dam will indirectly affect the seismicity in western Sichuan by affecting the gravity field. The research provides valuable information for studying regional reservoir earthquake disasters and supports related policy decisions.

In this article, the integrity of the seismic catalog obtained (1970–2014, M > 2.8) was verified according to the Gutenberg–Richter relation, the appropriate minimum magnitude was determined, and the whole region was divided into five areas according to the geological structure background of the whole research object and the trend of the historical seismic zone. We applied multifractal analysis in each partition. The results showed that although in different geological backgrounds, before major earthquakes, the earthquake time series information dimension had different degrees of growth, and the parameter after the flame had different degrees of decline. To a certain extent, this reflected the seismic energy accumulation and release process. In addition, the variation of fractal parameters in scale-free regions of time series and spatial distribution series also indicated that these two kinds of sequences in different regions show the characteristics of a multifractal structure rather than a single and uniform fractal structure.

In this study, earthquakes with magnitudes above 3.0 on the Richter scale in southwest China from 1900 to 2013 are taken as research objects. The spatio-temporal migration characteristics of seismic activity in the area of concern are studied by using geostatistics method to explore the rules of seismic activity. From the perspective of geostatistics and based on z-value, eight major seismic zones in southwest China were studied zone-wise by using the geostatistics tool ArcGIS and the seismic analysis tool ZMAP to explore the spatial and temporal migration characteristics of earthquakes. It was found that each seismic zone had its own quiet period with relatively inactive seismic activity. Simultaneously, through the study of the time-varying characteristics of the seismic belt, it was found that the time-varying regularity of the seismic frequency of the fault belt with the same or related geological structure background is roughly consistent. In conclusion, the seismic activity in southwest China will still be active in the future.

Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P < 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control. [ABSTRACT FROM AUTHOR]