Studying the genetic diversity and population structure of natural forest populations is essential for evaluating their ability to survive under future environmental changes and establishing conservation strategies. Pinus koraiensis is a conifer species with high ecological and economic value in Northeast China. However, its natural forests have been greatly reduced in recent years, mostly due to over exploitation and over utilization. Here, we evaluated the genetic diversity and population structure of seven populations of P. koraiensis located throughout its native distribution. A total of 204 samples were genotyped with nine polymorphic nuclear SSR (simple sequence repeat) markers. The results showed high genetic diversity in all populations, with an average expected heterozygosity of 0.610, and the northern-most populations (Dailin (DL) and Fenglin (FL)) showed slightly higher diversity than the other five populations. The level of genetic differentiation among populations was very low (FST = 0.020). Analysis of molecular variance (AMOVA) showed that only 2.35% of the genetic variation existed among populations. Moreover, STRUCTURE analysis clearly separated the seven populations into two clusters. Populations DL and FL from the Xiaoxinganling Mountains comprised cluster I, while cluster II included the five populations from the Changbai Mountains and adjacent highlands. Our research on the genetic diversity and population structure of P. koraiensis in natural forests of China can provide a basis for the implementation of programs for the conservation and utilization of P. koraiensis genetic resources in the future.

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

A continuous calibration system for high voltage current transformers is presented in this paper. The sensor of this system is based on a kind of electronic instrument current transformer, which is a clamp-shape air core coil. This system uses an optical fiber transmission system for its signal transmission and power supply. Finally the digital integrator and fourth-order convolution window algorithm as error calculation methods are realized by the virtual instrument with a personal computer. It is found that this system can calibrate a high voltage current transformer while energized, which means avoiding a long calibrating period in the power system and the loss of power metering expense. At the same time, it has a wide dynamic range and frequency band, and it can achieve a high accuracy measurement in a complex electromagnetic field environment. The experimental results and the on-site operation results presented in the last part of the paper, prove that it can reach the 0.05 accuracy class and is easy to operate on site.

A high accurate electronic instrument transformer calibration system is introduced in this paper. The system uses the fourth-order convolution window algorithm for the error calculation method. Compared with Fast Fourier Transform, which is recommended by standard IEC-60044-8 (Electronic current transformers), it has higher accuracy. The relative measuring errors caused by asynchronous sampling could be reduced effectively without any special hardware technique adopted. The results show that the ratio error caused by asynchronous sampling can be reduced to 10 -4, and the phase error can be reduced to 10 -3 degrees when the deviation of frequency is within ±0.5 Hz. The present method of measurement processing is achieved by a high-accuracy USB multifunction data acquisition (DAQ) card and virtual measurement devices, with low cost, short exploitation period and high stability.