基于单片机的小气候信息采集系统便携机设计

农业生产技术的快速发展,迫切需要一种适用于普及的、可靠、有效的针对农田小气候信息参数的实时采集系统,提高人们对作物生长与小气候之间关系的认识,为农业生产服务。本文介绍了基于PIC单片机的小气候信息采集系统便携机部分设计,文中详细描述了小气候信息采集方法和系统软硬件结构。便携机系统实现了包括温湿度、光照度、降雨量、蒸发量、大气压等15种小气候参数的自动、实时监测。通过实践证明该便携机设计合理、性能可靠,作为小气候信息采集系统的主体部分,是适合目前农田小气候信息自动监测的普及型设备。     

基于PRI谱的双门限雷达信号分选算法及其硬件平台设计

针对修正的PRI变换法无法分选重频参差雷达信号的缺点,提出了一种基于PRI谱的双门限雷达信号分选算法.该算法先对PRI谱设定双门限检测各个准PRI,再结合组合查看及序列搜索提取各个PRI.同时,构建了一种预分选、主分选结合的新型分选平台.预分选利用带有PowerPC的FPGA构架片上系统,完成对脉冲雷达信号的实时测量、AOA分类及数据存储;主分选采用DSP专职完成其他维参数的分选算法.谊分选新算法在保留抑制PRI子谐波优点的同时,对重频固定、抖动和参差的雷达脉冲信号都有较好的检测结果.视频信号源的大量测试表明,谊分选平台对普通雷达、PRI抖动和参差雷达、频率捷变雷达及一些特殊雷达均具有较好的分选能力.     

一种多模冗余结构存储器系统的容错设计研究

某型飞机代码转换器存储器用于存储ARINC419总线数据的地址,针对存储器数据易丢失影响数据传输的故障,提出了采用多模冗余结构的存储器系统容错设计方案;首先研究了三模冗余结构的可靠性,分析了存储器采用冗余结构设计的可行性,在此基础上,给出了多模冗余结构存储器实现方法和逻辑电路,并对其容错性进行了分析;实际工程应用表明,采用多模冗余结构存储器设计提高了国产化代码转换器的可靠性,降低了该设备的故障率。     

转子系统早期碰摩故障特征提取方法研究

航空发动机转子系统是发动机的核心部件,针对发动机转子系统早期碰摩故障难以检测的特点,通过对碰摩机理分析及早期故障特征的研究,提出了基于小波变换对原始信号降噪和虚拟仪器对转子系统振动数据采集相结合的方法,对转子系统早期碰摩故障特征进行提取,并在转子试验台上进行了实验验证。结果表明,采用LabVIEW程序采集转子系统振动数据并存储,然后应用小波变换进行数据分析和处理,能够对转子早期碰摩故障特征进行有效地提取。     

Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing Process

During the operation of a grinding circuit (GC) in mineral processing plant the main purpose of control and optimal operation is to control the product quality index, namely the product particle size, into its technically desired ranges. Moreover, the grinding production rate needs to be maximized. However, due to the complex dynamic characteristics between the above two indices and the control loops, such control objectives are difficult to achieve using existing control methods. The complexity is reflected by the existence of process heavy nonlinearities, strong coupling and large time variations. As a result, the lower level loop control with human supervision is still widely used in practice. However, since the setpoints to the involved control loops cannot be accurately adjusted under the variations of the boundary conditions, the manual setpoints control cannot ensure that the actual production indices meet with technical requirements all the time. In this paper, an intelligent optimal-setting control (IOSC) approach is developed for a typical two-stage GC so as to optimize the production indices by auto-adjusting on line the setpoints of the control loops in response to the changes in boundary conditions. This IOSC approach integrates case-based reasoning (CBR) pre-setting controlling, neural network (NN)-based soft-sensor and fuzzy adjusting into one efficient control model. Although each control element is well known, their innovative combination can generate better and more reliable performance. Both industrial experiments and applications show the validity and effectiveness of the proposed IOSC approach and its bright application foreground in industrial processes with similar features.     

Impulsive controller design for singular networked control systems with packet dropouts

This paper considers the problem of impulsive time-delay control for singular networked impulsive control systems(SNICSs) and uncertain SNICSs both with network-induced delay and packet dropouts. The parameter uncertainty is assumed to be norm bounded. The problem to be addressed is the design of robust impulsive time-delay feedback controllers such that the exponential stability of the resulting closed-loop system is guaranteed for admissible uncertainties. By applying Lyapunov function theory and Halanay Lemma, impulsive time-delay controller is derived through solving LMIs. Numerical examples are provided to demonstrate the application of the proposed method.     

Properties of Cr:LiSrAIF(6) crystals for laser operation

We have performed several physical and optical measurements on the Cr:LiSAF (LiSrAlF(6)) laser material that are relevant to its laser performance, including thermal and mechanical properties, water durabilities, and Auger upconversion constants. The expansion coefficient, Young's modulus, fracture toughness, thermal conductivity, and heat capacity are all used to determine an overall thermomechanical figure of merit for the crystal. An investigation of the water durability suggests that the cooling solution should be maintained at pH = 7 to ameliorate problems associated with water dissolution. The Auger constant was found to become much more significant at higher Cr doping, in which excited-state migration leads to a substantial increase in the upconversion rate. We propose a design for a 50-W Cr:LiSAF laser system that is based on a detailed knowledge of all the relevant material parameters.     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

Effect of Nano Potassium Fertilizer on Cucumber Amino Acid Component,Volatile Metabolite Components and GLN Family Genes Expression

Wireless Personal Communications - To investigate the effect of nano-potassium silicate on the expression of glutamine (GLN) family gene, amino acid (AAs) componentand volatile metabolites (VMs)...     

Global contextual guided residual attention network for salient object detection

High-level semantic features and low-level detail features matter for salient object detection in fully convolutional neural networks (FCNs). Further integration of low-level and high-level features increases the ability to map salient object features. In addition, different channels in the same feature are not of equal importance to saliency detection. In this paper, we propose a residual attention learning strategy and a multistage refinement mechanism to gradually refine the coarse prediction in a scale-by-scale manner. First, a global information complementary (GIC) module is designed by integrating low-level detailed features and high-level semantic features. Second, to extract multiscale features of the same layer, a multiscale parallel convolutional (MPC) module is employed. Afterwards, we present a residual attention mechanism module (RAM) to receive the feature maps of adjacent stages, which are from the hybrid feature cascaded aggregation (HFCA) module. The HFCA aims to enhance feature maps, which reduce the loss of spatial details and the impact of varying the shape, scale and position of the object. Finally, we adopt multiscale cross-entropy loss to guide network learning salient features. Experimental results on six benchmark datasets demonstrate that the proposed method significantly outperforms 15 state-of-the-art methods under various evaluation metrics.