福建省高素质农民发展跟踪分析

为反映福建省高素质农民的发展状况,探索高素质农民培育的更有效途径,采用比较分析方法,跟踪调查2015—2018年福建省高素质农民的总量、性别比例、年龄结构、文化程度等数据,并以此为基础,重点分析农业新型经营主体家庭农场经营者、农民合作社负责人等群体的发展情况,同时比较研究福建省高素质农民重点群体之间的发展特点。从文化程度、培育供求、产学矛盾、政策扶持等方面深入剖析当前高素质农民发展的制约因素,从加快推进福建省高素质农民培育工作、构建高素质农民终身学习体系、探索培育新型农业经营模式、推进线上线下融合培育、创设高素质农民扶持政策等方面提出相应的对策建议。     

农用无人机移动补给平台自主降落算法与试验

为实现根据无人机作业位置改变中途补充能源或喷洒物的起降地点,增加无人机有效作业时间,提高无人机作业效率,设计了无人机移动补给平台。通过研究农用无人机自主降落过程,提出一种基于模糊逻辑和比例积分微分(Proportional-integral and derivative,PID)分段控制的农用无人机跟踪降落算法,该算法既拥有PID算法的高精度,又兼顾模糊控制算法响应速度快、超调量小、鲁棒性强的优点。目标轨迹跟踪预测由粒子滤波器跟踪算法和轨迹拟合算法相结合进行求解。仿真和现场试验表明,与单一的PID算法和模糊逻辑算法相比,分段控制算法能够把农用无人机对移动补给平台的跟踪误差缩小到6. 7 cm以内,在移动补给平台上的降落精度控制在7. 2 cm以内。     

崎岖山地环境履带机器人降维变系数控制方法研究

针对崎岖山地环境下自走式履带机器人自走姿态波动大、跟踪精度低等问题,研究了三维崎岖路面履带机器人控制方法。通过分析机器人在二维平整路面与三维崎岖路面的运动学模型,建立了降维运动学几何模型;设计了一种基于降维变系数的滑模控制方法,实现三维崎岖路面履带机器人的运动控制,并进行了平整路面与崎岖路面的路径跟踪仿真与试验。仿真结果表明,平整路面仿真中,行驶方向误差逐渐减小并趋近于0,侧向位置误差在±0. 2 m内波动,并可在1 s内完成姿态调整;崎岖路面仿真中,三轴位置误差均控制在±0. 1 m范围内,同样可在1 s内完成姿态调整。路径跟踪田间试验结果表明,平整路面和崎岖路面机器人跟踪稳定后的横向误差分别为-2. 9～8. 8 cm、-14. 3～21. 5 cm,姿态误差分别控制在±2°、±5°内,能够满足实际跟踪需求。     

SCINTIGRAPHIC TRACKING OF MESENCHYMAL STEM CELLS AFTER PORTAL,SYSTEMIC INTRAVENOUS AND SPLENIC ADMINISTRATION IN HEALTHY BEAGLE DOGS

Mesenchymal stem cells have been proposed to treat liver disease in the dog. The objective of this study was to compare portal, systemic intravenous and splenic injections for administration of mesenchymal stem cells to target the liver in healthy beagle dogs. Four healthy beagle dogs were included in the study. Each dog received mesenchymal stem cells via all three delivery methods in randomized order, 1 week apart. Ten million fat‐derived allogeneic mesenchymal stem cells labeled with Technetium‐99m (99mTc)‐hexamethyl‐propylene amine oxime(HMPAO) were used for each injection. Right lateral, left lateral, ventral, and dorsal scintigraphic images were obtained with a gamma camera equipped with a low‐energy all‐purpose collimator immediately after injection and 1, 6, and 24 h later. Mesenchymal stem cells distribution was assessed subjectively using all four views. Pulmonary, hepatic, and splenic uptake was quantified from the right lateral view, at each time point. Portal injection resulted in diffuse homogeneous high uptake through the liver, whereas the systemic intravenous injection led to mesenchymal stem cell trapping in the lungs. After splenic injection, mild splenic retention and high homogeneous diffuse hepatic uptake were observed. Systemic injection of mesenchymal stem cells may not be a desirable technique for liver therapy due to pulmonary trapping. Splenic injection represents a good alternative to portal injection. Scintigraphic tracking with 99mTc‐HMPAO is a valuable technique for assessing mesenchymal stem cells distribution and quantification shortly after administration. Data obtained at 24 h should be interpreted cautiously due to suboptimal labeling persistence.     

仿人机器人头部设计与目标跟踪运动控制

针对仿人机器人视觉目标跟踪任务需求,设计了一种高精度、灵巧型仿人机器人头部系统,构建了视觉感知模块与3自由度机械臂运动机构。利用虚拟连杆方法对仿人机器人头部进行运动学建模,将目标跟踪问题转换为机械臂的运动学逆解问题,通过计算机构可操作度与条件数,显示机器人头部对目标跟踪任务具有良好的运动学灵活性。在此基础上,基于梯度投影法建立了目标跟踪运动学逆解与规避关节极限位置的同步优化算式,并针对视觉测量系统带宽较窄、容易造成较大跟踪误差的问题,提出了在两次视觉测量间隔内用轨迹预测的方法获得目标位置的估计值,以提高跟踪精度。仿真与实验结果表明,在测量盲区内采用轨迹预测方法,可将跟踪精度提高约80%,实现高精度目标跟踪。所提出的仿人机器人头部及控制方法,对动态目标具有良好的跟踪性能。     

基于自适应趋近律的采摘机器人关节滑模控制研究

利用离散趋近律设计的滑模控制可有效抑制滑模控制中的抖动现象,但其抑制抖动的效果与离散趋近律的设计参数及系统的采样时间有着密切的关系。为此,在分析指数趋近律抖动的基础上,提出了一种自适应滑模控制器,Mat LAB能够进一步的提高基于趋近律的滑模控制器的抖动抑制能力。为了验证本文所提出方法的有效性,在Mat LAB中实现了该算法,并利用其来控制采摘机器人关节的伺服电机。仿真结果表明:本文所提的算法具有较好的位置跟踪能力,进一步地抑制了指数趋近律滑模控制中存在的抖动现象,提高了系统的性能。     

基于分数阶微分视频融合的森林烟火检测算法

森林火灾检测是国内外林业应用研究的重要课题之一。及时准确地检测到森林火灾，对于森林健康及环境安全意义重大。现有的利用视频技术检测森林火灾的方法大多针对单一波段，如可见光波段或红外波段的视频信息进行分析，然而在实际应用过程中，由于森林环境复杂，基于单一波段视频信息检测火灾的结果欠佳。现阶段，基于多个波段的森林火灾检测方法非常少。本文综合利用红外及可见光视频特征，提出了一种基于分数阶微分视频融合的森林烟火检测算法，将分数阶微分理论引入红外视频和可见光视频融合中，利用分数阶微分算子对两个波段视频进行融合，然后利用背景去除法检测融合视频中的异常帧，且对异常帧图像及其与背景帧的差分图像分别进行图像分割，最终得到检测出的森林烟火区域。采用空间频率、平均梯度、森林火灾检测准确率和森林火灾检测时间误差度4个测度对本文算法和基于区域能量融合算法、基于窗口方差融合算法、基于HSI变换融合算法进行定量分析和比较。结果表明，本文算法的融合视频的融合效果最佳，并且森林火灾检测准确率和森林火灾检测时间误差均明显优于其他3种算法，说明本文提出的算法具有较好的有效性和准确性，为森林火灾检测提供了有利的新途径。      

A computer vision system to analyse the swimming behaviour of farmed fish in commercial aquaculture facilities: A case study using cage-held Atlantic salmon

Knowledge of fish behaviour plays an important role in aquaculture farm management. Video systems are the most common and cost-effective way of observing behaviours in commercial aquaculture operations. However long term observation is not feasible due to a limited ability to analyse footage manually. This paper describes preliminary findings obtained via computer vision software that was developed to automatically analyse fish movement and behaviours in aquaculture sea cages. Results show that the system is capable of detecting fish shapes in video recordings and from these shapes quantifying changes in swimming speed and direction continuously throughout the day. Also variations between days were detected and these may have been associated with the daily shift in the tidal cycle. The system has the potential to act as an alarm to farm operators, informing them about unusual fish behaviours on a continuous, real-time basis. It also has potential to assist in the evaluation of fish welfare.