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With the increasing popularity of electric vehicles, the number of electric vehicle users in industrial parks is increasing, and their charging and discharging behaviors pose great challenges to the planning and operation of park integrated energy system (PIES). A two-layer optimal scheduling of PIES considering the charging and discharging willingness of electric vehicles is proposed. Firstly, a charging and discharging willingness model is established based on factors such as dynamic real-time electricity price, battery charge capacity, battery loss compensation, and additional participation incentives. An improved electric vehicle charging and discharging model is obtained on this basis. A two-layer optimal scheduling model is established with the goal of minimizing the charging cost of the car, and the inner model is transformed into the constraints of the outer model through the Karush-Kuhn-Tucker (KKT) condition, so as to quickly and stably solve the single-layer model. Finally, the simulation solution is performed, and three different scenarios are set up. The proposed model is compared with the general charging and discharging willingness model. The effectiveness and feasibility of the two-layer optimal scheduling of PIES proposed in this paper are verified.

Aiming at the practical problem of the difficulty in identifying associated users when abnormal line loss occurs in the station area, a method for judging and accurately locating the line loss abnormality in the station area based on the correlation measurement algorithm is proposed. Firstly, the optimal clustering number of the data set is determined by the gap statistics-contour coefficient fusion algorithm, and on this basis, the dichotomous K-means++ is used to construct the station area line loss standard library. Secondly, the station area line loss anomaly identification is completed based on the standard library and then the abnormal time is determined. The Spearman correlation coefficient (SCC) and Euclidean-discrete Frand#233;chet distance (E-DFD) of each user's power and line loss during the abnormal time is calculated. And based on SCC and E-DFD, a comprehensive evaluation index to analyze user relevance is estabilished. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to sort the comprehensive evaluation index values to achieve precise positioning of abnormally associated users. The calculation example uses real field data in a certain area to analyze, and the results show that the method proposed in this paper has better performance in clustering effectiveness, calculation time, and identification accuracy.

In order to reduce the damage of apple harvesting robot to fruits and achieve compliant picking, an adaptive variable parameter impedance control method for apple harvesting robot compliant picking is proposed in this paper. Firstly, the Burgers viscoelastic model is used to characterize the rheological properties of apples and study the variation of mechanical properties of apple grasping at different speeds. Then, a force-based impedance control system is designed. On this basis, aiming at the influence of impedance controller parameters on contact force, three impedance parameters self-tuning functions are constructed to complete the design of an improved force-based impedance control system based on the hyperbolic secant function. The simulation and experimental results show that the proposed control makes the desired force smoother, and its overshoot is about 2.3%. The response speed is faster, and the adjustment time of contact force is shorter of about 0.48 s. The contact force overshoot is about 2%, which is 37.5% less than that of the traditional force-based impedance control. This research improves the control performance for apple harvesting robot compliant picking.

A method of character recognition based on Hough transform and RBF neural network is proposed through research on weight accumulation algorithm of Hough transform. According to the feature of characters’ structure by using the duality of point-line Hough transform was done. In this method, the number of the points on the same line in parameter space and the position coordinates of the elements in image mapping space were taken to RBF neural network recognition system as characteristic input vector. It reduced the dimension of character feature vector and reflected the overall distribution of character lattice and the essential feature of character shape. The simulation results indicated there were some merits in this improved method: capability of recognition is strong, the quantity of calculation is small, and the speed of calculation is quick.

Sex chromosomes evolved from autosomes many times across the eukaryote phylogeny. Several models have been proposed to explain this transition, some involving male and female sterility mutations linked in a region of suppressed recombination between X and Y (or Z/W, U/V) chromosomes. Comparative and experimental analysis of a reference genome assembly for a double haploid YY male garden asparagus (Asparagus officinalis L.) individual implicates separate but linked genes as responsible for sex determination. Dioecy has evolved recently within Asparagus and sex chromosomes are cytogenetically identical with the Y, harboring a megabase segment that is missing from the X. We show that deletion of this entire region results in a male-to-female conversion, whereas loss of a single suppressor of female development drives male-to-hermaphrodite conversion. A single copy anther-specific gene with a male sterile Arabidopsis knockout phenotype is also in the Y-specific region, supporting a two-gene model for sex chromosome evolution.