Establishing an eyeball-weight relationship for Litopenaeus vannamei using machine vision technology

This study aimed to establish a shrimp eyeball-weight relationship model for Litopenaeus vannamei using machine vision technology. A total of 295 shrimp were sampled from a recirculating aquaculture system (RAS). The long-axis length (d), body length (L), and body weight (W) of each individual was measured. The long axis length of the shrimp eyeball was identified and measured using machine vision technology. Continuous fitting and piecewise fitting models were used to construct the eyeball-weight relationship model for L. vannamei. The continuous fitting relationship model was described as: W = 38.865d^2.7914, while the piecewise model was described as: d < 2 mm, W = 0.0326d^3.7363, R² = 0.9288; 2 mm ≤ d < 3.9 mm, W = 0.0401d^3.104, R² = 0.9629; 3.9 mm ≤ d < 5.8 mm, W = 0.0421d^3.0311, R² = 0.9216; 5.8 mm < d, W = 0.103d^2.6226, R² = 0.9457. The root mean square error (RMSE) of the piecewise fitting model (0.0244, 0.1575, 0.5034, 0.7072) was smaller than the continuous fitting model (0.8229). The correlation coefficient (R²) of the piecewise model (0.9288, 0.9629, 0.9216, and 0.9457) was similar to that of the continuous fitting model (R² = 0.9621). The results indicated that the piecewise fitting model is suitable for calculating the biomass of L. vannamei in RAS and provides a novel way of estimating the biomass of L. vannamei cultured in RAS. The piecewise fitting model can also provide the foundation of evaluating the production of shrimp using underwater image recognition in intelligent aquaculture systems.     

Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network

Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability.     

基于热致液晶弹性体的仿生心脏泵的设计

为设计一款仿生心脏功能较佳的心室泵,采用柔性、热致伸缩的热致形变弹性体代替心肌,根据螺旋心室心肌带理论,选择心肌带的左心室段,并选取了Hilbert平面填充曲线作为热致导体在热致形变弹性体中的走行方式,设计出与真实心脏类似的心室泵结构.通过试验测量了所设计的心室泵的射血分数与心率,结果显示该心室泵的搏出量高于自然心脏,而其心率远低于自然心脏.相对于机械泵,所设计的心室泵结构与真实心脏类似,具有收缩能力强,射血分数高的优点,且不易破坏血细胞,生物相容性较好.不足之处在于,热致动液晶弹性体变形响应慢,暂时还未能达到正常心脏的心率.论证了构建双稳态结构,提高仿生心室泵效率的可能性,以及对于心肌纤维走向较为复杂的其他心肌带段,双轴拉伸机械雕刻对其仿生的可能性.此研究可为容积心脏泵的研发提供新的思路,也可以对右心衰竭等心脏疾病起到辅助治疗的作用.     

KRH-Ⅱ型智能固态微生物发酵成套装置的研制

以机械结构优化设计为基础,运用pH值、水分活度(湿度)、发酵温度等参数检测手段,结合模糊控制、神经网络以及多变量系统的解耦控制等多种智能控制方法,研究实现发酵过程多变量解耦的模糊神经网络控制技术,设计出一种从液体制种到灭菌、接种、混合、发酵、干燥和包装新概念的固态成套发酵装置.实际应用结果表明:固态发酵的快速灭菌、快速冷却、过程多参数信息调控与过程优化技术,大大提高了固态发酵设备机械化和自动化程度.     

基于组合神经网络的农用车轮胎号识别方法

为了保证轮胎号识别系统具有较高的识别正确率和置信度，对轮胎号字符识别方法进行了研究。对大量的农用车轮胎号字符图像样本进行各类特征量提取，针对每类特征量建立各自的子BP网络进行训练，并将各训练好的子网络进行组合形成并行组合神经网络，按照等权平均或投票选举决策得出最终识别结果。并行组合神经网络的连接数较传统庞大单级神经网络少，训练和识别的速度要快，大量的轮胎号字符样本识别试验表明，并行组合神经网络的识别正确率和置信度都较传统BP网络得到提高。     

Two new anthraquinone dimers from the fruit bodies of Bulgaria inquinans

Two new dimeric anthraquinone derivatives, bulgareone A(1) and bulgareone B(2), were isolated from the fruit bodies of Bulgaria inquinans. Their structures were elucidated on the basis of extensive spectroscopic analysis. The in vitro cytotoxic activity of compounds 1 and 2 was assayed. They displayed inhibitive activities against human cancer cell lines HL60 and K562.     

基于轻量级SSD模型的夜间金蝉若虫检测

为实现夜间树上金蝉若虫的快速准确检测,该研究以自然环境图像数据集为研究对象,结合近距离实际应用场景,考虑到嵌入式系统的模型小型化和计算过程轻量化,在保持精度指标基本不变的前提下,基于适度削减模型深度、宽度的思路对已有目标检测网络MobileNet-SSD提出改进。具体措施包括：删除骨干网络末端的小尺寸特征图卷积层,逐级裁剪模型整体宽度、适当增加中高层卷积深度,在目标检测的分类层和预测框的回归层中使用深度可分离卷积代替传统3×3卷积等措施,先后获取3种改进的精简模型以进行比较。夜间图像测试结果表明,在基本保持网络性能的前提下,改进后的模型大小及计算量均呈现大幅减小,其中最优模型大小从原MobileNet-SSD的15.22 MB减少到1.51 MB,模型的浮点运算量也由原先的1.13×109减少到1.26×108,其平均准确率达90.46%,平均交并比达83.52%,F1分数达92.35%,GPU上的检测速度达179.3帧/s,CPU上的检测速度达到6.49帧/s,与改进前的模型相比具有更好的综合性能,白天图像的试验结果也显示出较好的泛化性能。该文提出的改进模型在大幅减少模型大小及其计算量的同时使模型性能保持在一个较高的水平,更适合部署在移动终端等资源受限设备上,可为金蝉的人工养殖提供有益参考。     

基于深度图像和生猪骨架端点分析的生猪步频特征提取

为高效提取生猪的行走快慢特征,以微软公司Kinect作为图像采集设备,采集生猪运动深度图像序列。在对各帧深度图像进行骨架提取、剪枝的基础上,采用基于路径相似性骨架图匹配法确定生猪前后肢骨架端点,进一步以骨架端点所属骨架枝子集像素值特征判定端点远近侧属性。以生猪前后肢远、近侧端点的帧间相对坐标变化建立了生猪运动模型,提出了通过帧间坐标变化点集拟合正弦曲线计算生猪行走完整步的方法。最后,通过计算序列完整步与序列采集时间长度比值提取生猪步频特征。通过对采集的28个生猪运动深度图像序列及其镜像序列共56个图像序列进行的试验,表明该文提出方法的正确率达到82.1%。该项研究对于开展生猪异常步态分析,进一步建立生猪多源特征融合的计算机视觉异常监测系统,提高生猪异常行为预警可靠性具有重要意义。     

高光谱图像技术在掺假大米检测中的应用

为了有效判别出优质大米中是否掺入劣质大米,该文研究了一种针对大米掺假问题的快速、无损检测方法。从市场上购买了东北长粒香大米和江苏溧水大米,按纯东北长粒香大米、3∶1、2∶2、1∶3和纯江苏溧水大米共5个掺合水平进行大米试验样本的制备。利用可见-近红外高光谱图像采集系统(390~1050 nm)获取了200个大米样本的高光谱图像。采用ENVI软件确定高光谱图像的感兴趣区域(region of interest,ROI),并提取出所有样本在ROI内的平均高光谱数据。采用支持向量机(support vector machine,SVM)建立全光谱波段下的大米掺假判别模型,径向基(radial basis function,RBF)核函数模型交叉验证准确率为93%、预测集正确率为98%。由于高光谱信息量大、冗余性强且受噪声的影响较大,该文采用主成分分析方法(principal component analysis,PCA)分别对大米高光谱图像和高光谱数据进行处理,从特征选择和特征提取2个角度对原始高光谱数据进行处理,通过主成分权重系数图选择了531.1、702.7、714.3、724.7、888.2和930.6 nm 6个特征波长,通过留一交叉验证法(leave-one-out cross-validation,LOOCV)确定并提取出PCA降维后的最优主成分数(number of principal component,PCs)为9。最后分别将优选出的特征波长和提取出的最优主成分数作为模型的输入,建立SVM模型。试验结果表明,基于特征波长SVM模型的交叉验证准确率为95%、预测集正确率为96%,基于最优主成分数SVM模型的交叉验证准确率为94%、预测集正确率为98%。该研究结果表明,该文建立的基于特征波长和基于最优主成分数的SVM模型均具有较优的预测性能,且利用高光谱图像技术对大米掺假问题进行检测是可行的。     

电动拖拉机PC-DSPM电机电子变极策略与分析

针对田间作业和转场运输等不同工况时的行驶速度需求,该研究提出使用双凸极变极永磁（pole-changing doubly-salient permanent magnet,PC-DSPM）电机作为电动拖拉机的驱动电机,通过变极获得驱动电机的多种机械特性。基于气隙磁场调制理论,将PC-DSPM电机气隙磁场中的主要工作谐波分为2组,采用电子变极改变电枢绕组连接方式,从而选择不同组别的气隙磁场谐波参与机电能量转换,形成PC-DSPM电机3种运行模式。根据不同模式下的转矩-转速曲线,选取电机在恒功率区的2个变极切换点,并据此将拖拉机的运行速度划分为0～7.7、7.7～10.5和10.5～32.7 km/h共3个区间。为实现平滑变极,构建自抗扰控制和跟踪微分器的PC-DSPM电机变极策略。与采用自抗扰控制的阶跃响应变极相比,虽然2个切换点处的变极时间分别延长至400和600 ms,但变极过程中dq轴电流过渡平稳,转矩波动分别下降8.5%和11.8%,转速恒定在920和1 250 r/min。研究结果可为实现电动拖拉机多工况高效运行及变极永磁电机平滑切换提供理论参考。