Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles （UCAVs） is a complicated global optimum problem. Particle Swarm Optimization （PSO） is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Diseretization （CPTD） method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.     

Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network

This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network （VANET）. A novel bionic swarm intelligence algorithm, which is called ant colony algorithm, was introduced into a traditional ad-hoc route algorithm named AODV. Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units, the parameters in ant colony system were modified to enhance the performance of the route selection probability rules. When the vehicle moves into the range of several different roadsides, it could build the route by sending some route testing packets as ants, so that the route table can be built by the reply information of test ants, and then the node can establish the optimization path to send the application packets. The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm, especially when the vehicle is in higher speed or the number of nodes increases.     

微粒群优化神经网络及其在环境评价中的运用

农业项目环境影响综合评价是目前新的研究领域,随着农业项目的增加,其环境影响的研究愈来愈重要.以某农业项目为例,运用PSO-BP进行农业项目环境评价;仿真和实验表明:微粒群优化神经网络,能够克服神经网络收敛速度慢,陷入局部最小的缺点;微粒群优化算法涉及的参数不多,但是微粒群优化结果是比较理想的.     

基因枪转化的bar基因表达盒在水稻中遗传行为复杂

基因枪介导基因表达盒(仅包括启动子、编码区和终止子)转化是基因枪转化植物的新趋势,它能消除质粒载体主干序列对转基因植物的不利影响。本文研究了基因枪转化的bar基因表达盒在转基因水稻T1～T3世代中的遗传行为。结果发现:作为筛选标记的bar基因表达盒在水稻基因组中多拷贝整合,遗传分离行为复杂,还出现了Basta抗感分离比在35:1～144:1之间的"假纯合体",但50%转基因株系中(5/10)bar基因可作为一个显性基因按孟德尔方式稳定遗传至自交T2代。虽然bar基因为多拷贝整合,30%的转基因株系(3/10)在自交低世代(T1)能获得纯合体。Southern杂交分析发现,多拷贝的bar基因表达盒倾向于连接成转基因串联子整合在水稻基因组内。我们发现在Basta抗性正常分离的株系后代中bar基因表达盒Southern杂交模式能稳定遗传,但异常分离的株系后代中bar基因表达盒的一些拷贝发生了丢失。我们推测,bar基因表达盒在水稻中遗传分离行为的复杂原因可能是bar基因表达盒多拷贝整合、基因丢失和基因表达互作。     

Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results were obtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R²) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.     

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

We present two methods for observing bumblebee choice behavior in an enclosed testing space. The first method consists of Radio Frequency Identification (RFID) readers built into artificial flowers that display various visual cues, and RFID tags (i.e., passive transponders) glued to the thorax of bumblebee workers. The novelty in our implementation is that RFID readers are built directly into artificial flowers that are capable of displaying several distinct visual properties such as color, pattern type, spatial frequency (i.e., “busyness” of the pattern), and symmetry (spatial frequency and symmetry were not manipulated in this experiment). Additionally, these visual displays in conjunction with the automated systems are capable of recording unrewarded and untrained choice behavior. The second method consists of recording choice behavior at artificial flowers using motion-sensitive high-definition camcorders. Bumblebees have number tags glued to their thoraces for unique identification. The advantage in this implementation over RFID is that in addition to observing landing behavior, alternate measures of preference such as hovering and antennation may also be observed. Both automation methods increase experimental control, and internal validity by allowing larger scale studies that take into account individual differences. External validity is also improved because bees can freely enter and exit the testing environment without constraints such as the availability of a research assistant on-site. Compared to human observation in real time, the automated methods are more cost-effective and possibly less error-prone.     

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks

In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five factors was established. The PA signals and peak-to-peak values (PPVs) of 625 rabbit whole blood were obtained under 625 influence combinations. Due to the accurate measurement of BGC limited by the overlap PA signals, wavelet neural network (WNN) was utilized to train the PPVs of blood glucose for 500 rabbit blood. The mean square error (MSE) of BGC for 125 testing blood was approximately 6.5782 mmol/L. To decrease the MSE, the parameters of WNN were optimized by particle swarm optimization (PSO), that is, PSO-WNN algorithm was employed. Under the optimal parameters, MSE of BGC was decreased to approximately 0.48005 mmol/L. To further improve the prediction accuracy of BGC, an improved nonlinear dynamic inertia weight (NDIW) strategy of PSO was proposed, and compared with other two kinds of dynamic inertia weight strategies. Under the optimal parameters, the MSE of BGC was decreased to approximately 0.2635 mmol/L. The comparison of nine algorithms demonstrate that the PA technique combined with PSO-WNN and the improved NDIW strategy is significant in the quantitative measurement of blood glucose influenced by multiple factors.