V带智能CAD系统结构与神经网络应用

结合 Ｖ 带传动智能 Ｃ Ａ Ｄ 系统的总体结构、系统回溯等问题,通过 Ａ Ｒ Ｔ 网络在 Ｖ 带传动智能 Ｃ Ａ Ｄ 系统中的一个具体应用实例,讨论了该系统的结构和网络应用问题     

基于多传感器的雷达对抗侦察数据融合算法

为提升复杂电磁环境下雷达对抗侦察系统的可靠性及抗干扰能力, 提出了一种基于多传感器融合的综合权值估计算法。首先由各传感器对原始数据进行分批估计得到局部期望及方差, 然后利用四分位离散度剔除异常数据, 接着针对局部估计结果通过测度算子以及自适应融合估计算法分别确定各传感器权重, 最后利用综合权重进行加权得到融合结果。仿真结果表明, 所提算法在不同情况下均能得到较高的融合精度, 较现有算法具有更好的适应能力及准确性。     

光网络体系结构的演进

随着光通信技术的迅猛发展,光通信仅仅提供光纤传输通道向具有网络组织、控制和管理的光传送网络发展。同时,信息化的驱使,通信网络的体系构架也由适应主导业务以话音为主,向适应高速大容量和高质量服务的数据业务为主的方向发展。对光网络的发展演进历程、发展趋势、热点问题作了较为详细的讨论。     

Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.     

POI Neural-Rec Model via Graph Embedding Representation

With the booming of the Internet of Things(Io T) and the speedy advancement of Location-Based Social Networks(LBSNs), Point-Of-Interest(POI) recommendation has become a vital strategy for supporting people's ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user's check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models,structural deep network Graph embedding Neural-network Recommendation system(SG-Neu Rec) and Deepwalk on Graph Neural-network Recommendation system(DG-Neu Rec) to improve POI recommendation. combined with embedding representation from social and geographical graph information(called SG-Neu Rec and DG-Neu Rec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.     

高斯Wasserstein距离改进轻量YOLOv7模型的遥感影像道路交叉口检测

YOLOv7是目前目标检测任务中性能较优的模型,但在处理遥感影像中的道路交叉口时,出现目标背景复杂、先验框定位误差以及模型训练参数量增多的问题。针对复杂场景的道路交叉口提出一种结合归一化高斯Wasserstein距离与轻量级YOLOv7的遥感影像道路交叉口检测模型。首先,使用归一化高斯Wasserstein距离与CIoU(complete-IoU)进行先验框定位损失函数的改进,以提高网络模型对于目标尺寸的鲁棒性;其次,在加强网络特征提取模块中加入三维注意力机制,实现网络处理的特征优化;最后,在主干特征提取网络与加强特征提取网络中加入改进的FasterNet模块,提升网络模型的训练速度,减少了模型训练的参数。实验结果表明,改进后的YOLOv7网络模型相比原网络模型,漏检测情况得到明显改善,准确率(precision,P)、召回率(recall,R)、平均准确率(average precision, AP)和F₁分别提升了6.2%、4.9%、6.7%、6.5%,对道路交叉口的检测效果优于原网络模型。其成果对不同环境的影像具有较强适应能力,为道路交叉口检测的发展提供了参...     

基于改进尖点突变级数法的高温环境下人体生理状态综合评价

热应激会降低工作效率并导致热相关疾病。为了量化高温环境下环卫工人的热应激水平,本文建立一个生理状态安全评价指标(physiological state safety evaluation index, PSEI)。首先,引入尖点突变理论分析不同热环境下人体生理状态变化趋势。其次,基于信息熵和变权理论计算生理指标的权重,并利用尖点突变级数法建立人体生理状态评价体系。最后,提出新评价指标PSEI以量化生理状态安全水平,并在此基础上确定PSEI的安全等级。结果表明:在不同环境条件和生理状态下,生理指标的权重是变化的;平均皮肤温度、耳膜温度、收缩压和心率的权重范围分别为0.09-0.64、0.08-0.41、0.07-0.74和0.06-0.33;PSEI的安全等级为:非常安全(0.948, 1]、相对安全(0.886, 0.948]、中等(0.798, 0.886]、相对危险(0.649, 0.798]和非常危险[0, 0.649]。PSEI可为高温环境下环卫工人提供实时的生理状态安全评估和健康预警。     

Sampled-Data Stabilization of a Class of Stochastic Nonlinear Markov Switching System with Indistinguishable Modes Based on the Approximate Discrete-Time Models

This paper investigates the stabilization issue for a class of sampled-data nonlinear Markov switching system with indistinguishable modes. In order to handle indistinguishable modes, the authors reconstruct the original mode space by mode clustering method, forming a new merged Markov switching system. By specifying the difference between the Euler-Maruyama(EM) approximate discrete-time model of the merged system and the exact discrete-time model of the original Markov switching system, the authors prove that the sampled-data controller, designed for the merged system based on its EM approximation, can exponentially stabilize the original system in mean square sense. Finally, a numerical example is given to illustrate the effectiveness of the method.     

电磁弹射用变极距直线电机电感参数分析

为了分析极距变化型直线电机的驱动特性,通过建模仿真方法研究了变极距直线感应电机绕组电感特性。设计极距变化型长初级短次级直线感应电机,永磁体次级位于双边初级中,通过连接装置连接待弹射物体,完成弹射加速任务。直线感应电机初级绕组线圈采用分段供电模式,提高电磁弹射过程能源利用率。仿真分析极距值对初级线圈绕组自感和互感参量影响。建立有限元仿真分析模型,分析极距变化型直线感应电机磁场情况。结果表明绕组自感值和互感值在高频区域趋向于恒定。可见增加电磁弹射频率有利于电磁弹射任务的完成,模型的分析为变极距直线感应电机运用奠定理论基础。     

北半球冰冻圈变化及其对气候环境的影响

冰冻圈是气候系统的重要组成部分,其变化与影响已受到了全社会的普遍关注。本文介绍了北半球冰川、冻土、积雪、海冰近几十年来的时空变化规律,发现夏季在北极中央区出现海冰密集度极低的重要现象,阐明了冰川变化对水资源的影响,明确了积雪、海冰、冻土变化对气候的重要作用,并指出了今后需要进一步研究的问题。