Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

数控机床直线进给轴温度测点优化研究

为分析数控机床直线进给系统的温度场,需要布置一定数量的温度测点来进行温度数据的采集。然而,测点的位置和数目都会对研究结果产生直接影响。为实现准确布置温度测点,文章提出一种基于统计学理论进行改进的Canopy-FCM-GRA温度测点优化模型。以某数控机床X向直线进给轴为例,首先根据测得的实验数据确定预聚类数,然后通过模糊矩阵和灰色关联度系数筛选出相应的温度敏感点,最后基于SVR理论分别建立温度测点优化前和优化后的温度-热误差预测模型,通过比较两个模型精度来验证温度测点优化的有效性。结果表明,温度测点优化效果良好,通过优化后的测温点可准确探究进给系统的热特性。

The IOCAS intermediate coupled model(IOCAS ICM) and its real-time predictions of the 2015–2016 El Nio event

The tropical Pacific is currently experiencing an El Nifio event. Various coupled models with different degrees of complexity have been used to make real-time E1 Nifio predictions, but large uncertainties exist in the inten- sity forecast and are strongly model dependent. An intermediate coupled model （ICM） is used at the Institute of Oceanology, Chinese Academy of Sciences （IOCAS）, named the IOCAS ICM, to predict the sea surface temper- ature （SST） evolution in the tropical Pacific during the 2015-2016 E! Nifio event. One unique feature of the IOCAS ICM is the way in which the temperature of subsurface water entrained in the mixed layer （Te） is parameterized. Observed SST anomalies are only field that is utilized to initialize the coupled prediction using the IOCAS ICM. Examples are given of the model＇s ability to predict the SST conditions in a real-time manner. As is commonly evident in E1 Nifio- Southern Oscillation predictions using coupled models, large discrepancies occur between the observed and pre- dicted SST anomalies in spring 2015. Starting from early summer 2015, the model can realistically predict warming conditions. Thereafter, good predictions can be made through the summer and fall seasons of 2015. A transition to normal and cold conditions is predictecl to occur in rote spring 2016. Comparisons with other model predictions are made and factors influencing the prediction performance of the IOCAS ICM are also discussed.

基于粒子群算法优化最小二乘支持向量机在基坑施工过程中周围环境沉降预测研究

随着城市化进程的加快,越来越多的深基坑的周围环境日趋复杂,近年来预测模型在基坑施工过程中周围环境的沉降预测应用日益广泛,在预测精度方面取得了较为有效的成果,但模型的预测精度取决于参数选择,传统的参数选择往往基于试算法,该方法无确定的参数选择目标且计算体量过大。因此,本文提出一种基于粒子群优化算法来确定LSSVM参数的方法,计算结果表明通过PSO算法选择LSSVM参数,进而提高模型的预测精度和计算速度是切实可行的。

联合大气角动量函数的VAR模型用于日长变化实时快速预报

日长变化的实时快速预报具有重要的实用价值和科学研究意义,大气角动量函数的轴向分量与日长变化有强相关性,传统预报方法对日常变化的实时快速预报精度不高。经过实验分析,联合大气角动量的向量自回归模型对日长变化进行实时快速预报方法,相对于传统的预报模型,预报精度显著提高。

基于J2EE+ResNet的医院智能财务报销管理系统设计与实现

为了简化医院发票报销流程,提高财务管理效率,基于J2EE平台和改进ResNet模型构建医院智能化财务报销管理系统。系统在HTML、Java、JDBC、SQL Server 2019技术支持下,划分为3个技术层和5个功能模块。针对不同类型发票的报销问题,提出改进ResNet模型对发票进行分类识别,从而解决传统卷积神经网络存在过度拟合的缺陷。通过对比各种模型的使用效果,改进ResNet模型参数少,识别准确率更高,在训练时间和测试时间上也相对较少。对系统进行高并发访问情况下的性能检测,结果表明,系统在100人、点击率800次/s的高并发应用情况下,平均响应时间能保持在35 s左右,稳定性较好,可在实际工作中予以应用。

改进灰色模型在海上交通综合安全指数预测应用

为提高海上交通综合安全指数的预测精度,弥补传统预测方法的不足,构造一种改进的灰色预测模型。在对传统的灰色预测模型GM(1,1)的基本原理和预测精度进行阐述的基础上,引入弱化算子序列对灰色预测模型进行改进,设计一种改进的灰色预测模型。以2004—2013年海上交通综合安全指数4项指标历史数据为基础,进行传统灰色模型预测和改进灰色模型预测,并绘制出2种预测模型的实际值和预测值的拟合曲线。结果表明:改进灰色模型的预测精度和拟合曲线都比传统灰色模型要好,能真实反映海上交通综合安全指数的发展趋势,预测结果具有一定的可靠性和实用性。

改进灰色理论在海上交通事故预测中的应用

为了提高海上交通事故预测精度,实现对海上交通事故的准确预测,构造一种改进的灰色预测模型,对传统的灰色预测模型GM(1,1)基本原理和预测精度进行阐述,建立灰色预测模型;引入弱化算子序列对灰色预测模型进行改进,在已有的二阶弱化算子研究的基础上,设计一种改进的灰色预测模型,很大程度上提高了传统灰色预测模型的精度;以2004—2014年海上交通事故数为基础,进行传统灰色模型预测、二阶弱化模型预测和改进模型预测,并绘制出3种预测模型的实际值和预测值的拟合曲线图;结果表明:改进模型的4项预测精度指标和拟合曲线都较另外2种模型要好,能够真实反映海上交通事故的未来发展趋势。

基于改进遗传算法的矿井人员定位新算法

为了提高矿井人员定位精度,根据矿井巷道基站布设的实际情况,以无线信号传输理论模型为基础,提出周期性获取信号传输路径衰减指数的加权平均值,通过分析基站与标识卡之间的通讯约束条件,并借助改进的遗传算法提出矿井人员定位新算法,再利用MATLAB软件在相同条件下与加权质心算法和直接测距算法进行仿真对比。研究结果表明:新算法定位精度较高,同时3种定位算法的定位精度受巷道宽度影响较大。新算法不仅降低了人员定位的误差,而且具有较强的稳定性,适合各类复杂的巷道环境,为矿井人员精确定位提供了新方法。

预测模型在对流层延迟计算中的应用研究

为了消弱对流层延迟影响,提高GPS定位的精度,探讨了利用预测模型计算对流层延迟的可行性。建立了计算对流层延迟的多元线性回归模型、组合预测模型、灰色模型和BP神经网络模型,并提出了一种基于可靠度的组合模型权系数确定方法。结合邳州、新沂两个CORS站的观测数据和地面气象数据,利用4种预测模型进行对流层延迟预测实验。实验结果表明:在48 h的预测时段内,4种模型预测对流层延迟的精度分别为10,15,25和30 mm。其中多元线性回归模型预测效果最佳,在已知学习样本真值的情况下,其预测精度达到1 cm,较传统对流层延迟改正模型精度提高约50%。

电涡流传感器误差修正技术研究

采用自回归分布滞后模型的软件补偿法对电涡流传感器进行误差修正,提高了电涡流传感器的测量精度。该方法具有成本低、效果明显的特征。通过与多元线性回归模型比较,验证了自回归分布滞后模型对传感器精度提升的优势。

基于GPS数据的MODIS大气可降水量反演精度提高模型

水汽含量是大气的最重要变量之一。本文对地基GPS数据反演的PWV(Precipitable Water Vapor,可降水量,单位mm或cm)和MODIS数据反演的PWV结果进行了对比研究,得出了两者线性相关的结论。并对地基GPS数据反演的PWV和探空数据反演的PWV进行对比研究,验证了地基GPS数据反演PWV的精度在mm级别的结论。最后,本文应用最小二乘回归法,构建了一种基于GPS数据的MODIS大气PWV实时精度提高模型。

最小二乘外推与ARIMA(P,1,0)组合模型在极移短期预报中的应用

将ARIMA（P,1,0）模型应用到极移随机性部分序列的预报中,提出了利用最小二乘外推与ARIMA（P,1,0）的组合模型来对整体极移序列进行短期预报。并与其他方法对极移1~5天的短期预报精度进行了对比,结果证明了该模型在极移短期预报上的有效性与优越性。

优化背景值的GM(1,1)模型组合改进

针对提高模型的拟合效果.在GM(1,1)模型基础上,提出改进方案生成紧邻生成序列.利用组合预测思想,结合BP神经网络预测算法对改进后的灰色模型做进一步的优化修正,同时引用新陈代谢灰色模型理论成为BP-GM(1,1)等维新息模型.该模型充分利用了两种预测方法的优势,适当的减小了单个模型预测时的误差.数据拟合结果表明:BP-GM(1,1)模型具有更好的拟合精度,该模型应用范围更广.

基于A星算法的无人机路径规划优化模型研究

在分析传统无人机路径规划方法应用局限的基础上,针对传统A星算法启发函数单一、拐点冗余,以及效率不够高等系列问题,以虚拟城市地理环境为研究背景,通过提出面向起讫节点方位角的A星算法加速优化、启发式搜索中动态加权重构评价函数的A星算法加速优化和顾及Floyd算法插点策略的A星算法路径优化等层次递进的算法优化策略,解决了对A星算法搜索方向的量化约束、搜索权重数值的优化重构及冗余拐点的插点删除等技术问题,实现了改进后的A星算法运行效率的显著提高及最短路径判定准确性的明显提升。

提高运载火箭模态频率预示精度的方法与模型修正策略

针对运载火箭质量变化后模态参数预示问题,提出火箭有限元模型修正以振型吻合为主的修正策略和提高模态频率预示精度的频率补偿算法。通过理论分析指出,运载火箭模型修正频率误差对飞行器质量变化的灵敏度主要取决于振型吻合程度,在有限元模型修正不能同时兼顾频率与振型时,可以适当放宽频率修正误差,确保振型的一致性,然后在利用修正模型预示质量变化后的火箭模态频率时进行误差补偿即可很大程度上消除修正误差,确保频率预示精度。通过算例研究表明:采用补偿算法可以不同程度地消除频率修正误差,而补偿效果与振型的吻合程度有直接关系,验证了提出模型的修正策略的正确性与频率补偿方法的有效性。

提高水力机械模型效率试验精度方法探讨

在总结中国水利水电科学研究院水力机械模型试验台提高水轮机某些试验精度方法的基础上,根据对测试误差的来源分析,从完善测试设备原位标定系统和方法、减小标定误差及减小随机误差等3个方面提出了提高试验精度的措施方法。

基于跟踪偏差角和改进迭代误差修正的光伏功率预测优化策略

随着光伏发电逐渐接入电网,电网对光伏功率的预测精度也提出了更高的要求。针对双轴太阳能自动跟踪系统实际运行中存在的跟踪偏差角以及采用传统迭代误差修正思想进行多模型预测时存在的误差异常值,提出一种基于跟踪偏差角和改进迭代误差修正的光伏功率预测模型。首先,建立改进迭代误差修正模型;然后,利用改进迭代误差修正模型对功率预测进行一层优化;最后,结合跟踪偏差角完成对光伏功率预测的二层优化。仿真结果表明,所建立的光伏功率预测模型能使功率预测精度提高5%左右,弥补了单一模型存在的不足。