一种基于神经网络的结构参数识别方法

提出了一种基于神经网络的结构参数识别方法，该方法以残余力向量作为结构参数识别的网络输入。针对训练样本在数据空间分布不均匀的问题，采用ＧＳＬ变换对训练样本数据进行预处理。从而提高了网络收敛速度及参数识别精度。本文算例说明了方法的有效性。     

基于投资的再保险定价模型

从系统的观点出发,把公司的赔付情况与投资收益相接合,对比例再保险与超额损失再保险,建立了在投资影响下的带跳的再保险模型,给出了基于投资的再保险定价公式,为公司厘订再保险费提供了新的方法.     

博爱县农田林网永续经营技术及效益分析

分析了博爱县农田林网形成后在生态、经济、社会等方面所产生的显著效益。总结了实现农田林网永续经营的具体方法。为平原地区农田林网的经营与管理提供了很好的依据。     

一种新颖的电流模式神经网络设计

将ＣＣⅡ引入神经网络，提出了一种新颖的电流模式神经网络，仿真结果表明，其动态特性优化电压模式网络。     

基于时延神经网络的木材干燥模型辨识的研究

木材干燥是一个复杂、多变的过程，传统的系统辨识方法很难建立一个理想的木材干燥模型。利用时延神经网络的特点，本文提出了基于时延神经网络的木材干燥模型辨识方法，并给出了辨识网络和结构。对辨识得到模型的仿真结果表明，利用时延神经网络所建立的模型是可靠且有效的。     

Stimulation of acetylcholinesterase activity by triiodothyronine in the brain of Singi fish,Heteropneustes fossilis (Bloch)

Three consecutive days of injections of triiodothyronine (T₃)(0.038, 0.075, 0.15 and 1.54 nmoles/g) significantly elevated the acetylcholinesterase (AchE) activity in the brain of Singi fish, Heteropneustes fossilis (Bloch). The higher doses of 0.075, 0.15 and 1.54 nmoles of T₃/g induced a greater increase in enzyme activity than 0.038 nmoles/g. A T₃ dose of 0.019 nmoles/g was found to be ineffective. The T₃ action on AchE activity was blocked by cycloheximide. Thiourea treatment for 30 days decreased the AchE activity below the control level. This reduced level of the enzyme activity was brought back even above the control level by T₃ injections. It is, therefore, suggested that thyroid hormone is involved in the sustenance of AchE activity in fish brain.     

生猪交易物流运作模式的分析

本文根据我国生猪产业发展的现状、生猪物流系统内部发展及运行环境,从物流主体的组织化程度、流通环节、信息的传播、资产专用性等角度来分析了我国现有三种生猪物流运作模式的特点,并在此基础上设计了适应未来生猪市场的以第四方电子交易平台为主导的新型物流模式。     

The image recognition of urban greening tree species based on deep learning and CAMP-MKNet model

The information of urban tree species resources is of vital significance to the planning and design of urban green spaces. Tree organs, such as the bark are used as the primary features of identifying tree species. However, traditional tree identification methods need to consume a lot of manpower and time costs. In addition, the application of machine image recognition technology to tree species recognition still has problems such as heavy data preprocessing workload, small number of tree species images, uneven distribution of categories, and low recognition accuracy. In order to promote the intelligent management of urban forestry and solve the above problems, it is necessary to establish an automatic image recognition model for urban greening tree species. We captured bark images of 21 urban afforestation tree species in their natural environment and constructed a dataset that was divided into a train set, validation set, and test set in the ratio of 7:1:2. Combining Channel Attention Module (CAM) with algorithms such as Spatial Pyramid Pooling (SPP) and Mixed Depthwise Dilated Convolutional Kernels. The core algorithm is Mixed Convolution Kernel (MK), and a CAMP-MKNet Convolutional Neural Network (CNN) is constructed as a bark image classification model for urban greening tree species. The overall accuracy of the generic models ranged from 41.06% to 82.03%, whereas the overall accuracy of the experimental CAMP-MKNet model was 84.25%, with lower prediction cost. Our study shows that the CAMP-MKNet CNN model with better prediction performance and computational cost and can provide crucial insights and technical support for developing automated urban tree species image recognition systems.     

疾病费用预测的建模分析

果肉自溶是影响龙眼采后果实贮运、货架寿命和贮藏品质的重要因素。本文综述了龙眼果肉自溶过程的组织结构变化、生理生化变化及微生物侵染与自溶的关系、果肉自溶相关的分子生物学等的研究进展,探讨龙眼果肉自溶的机理和常用的保鲜技术,并提出未来研究的方向,以期对龙眼果实采后贮运保鲜提供参考。