网络中心战及其对装备精确保障的几点启示

介绍了网络中心战的产生背景及发展历史,研究归纳了网络中心战的概念及内涵,分析了网络中心战原理,探讨了网络中心战对实现装备精确保障的一些启示,提出了我军在实现装备精确保障过程中借鉴网络中心战理论的构想及应注意的问题.     

用神经网络建立反后坐装置参数对其性能的影响模型

详细分析火炮反后坐装置影响因素基础上,用人工神经网络建立了完整的反后坐装置参数对其性能的影响模型,运用算例证明了神经网络在反后坐装置中应用的可行性。所提出的提灌法使网络的学习更加灵活,收敛更快。     

微波DS扩频技术在军事通信中的应用

分析了直接序列扩频通信技术的基本技术特性和优点,典型产品的性能,微波扩频无线网络的组成方法及应用.重点论述了微波直接序列扩频无线网络在军事通信中的应用、存在的问题及解决方法.     

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.     

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.     

基于灰色神经网络的装甲部队油料消耗预测

油料消耗量的精确预测直接影响装甲部队后勤保障能力的提升,而传统预测模型精度不高,应用范围也有一定的局限,难以满足信息化战争精确保障的需要。提出一种装甲部队油料消耗预测的组合模型,对历史油料消耗数据和油耗影响因素进行统计分析,求出各影响因素与油耗量的关联度作为权重系数;通过改进GM(1,1)模型预测某部队下一次军事行动的油耗量;用GM(1,1)模型的预测值、加权后的各影响因素值和油耗实际值训练网络,对下一次想定的军事行动油耗量进行预测。通过平均相对误差计算表明,组合预测模型比单一的GM(1,1)模型预测精度高,能够较好地指导部队进行下一步的油料供管工作。     

网络多媒体技术结合案例教学法在《心理健康教育》教学中的应用

科学信息的快速发展给高校心理健康教育提出了新的挑战,网络多媒体相结合案例教学的教育模式已成为高校教育模式发展的趋势。文章阐述了在《心理健康教育》教学中运用网络多媒体技术结合案例教学的心得体会,分析了网络多媒体结合案例教学的优势。     

用遗传算法实现雷达网目标分配

通过对雷达网目标分配问题的深入研究,建立了数学模型.为了获得发现概率最大的目标分配方案,引入了遗传算法来对模型求解,并给出了实现的步骤和方法.最后经过验证,证明该方法应用在雷达网目标分配问题上切实可行.     

基于UKF-SOFNN的近距机动目标跟踪滤波算法

对于多目标杂波环境中的机动目标跟踪,由于目标集群中各个目标间的空间距离可能小于探测器的空间分辨率,因而可能出现误跟、诱饵欺骗与杂波虚警等一系列严重后果。对此,提出一种综合运用UKF(不敏卡尔曼滤波)和SOFNN(自组织模糊神经网络)的UKF-SOFNN滤波跟踪算法,将机动目标模型视作严格的非线性系统,利用UKF-SOFNN对非线性参数的辨识能力提高对锁定机动目标的跟踪能力。仿真实例表明,该算法能有效地辨识目标群中的目标,并进行可靠的跟踪。     

网络环境下著作权合理使用之思考

网络技术的发展改变了人们使用作品的方式,给著作权合理使用制度带来了巨大挑战。在网络环境下,著作权合理使用的范围需要重新界定。为了保护权利人的经济利益,且避免不合理的权利滥用和过度垄断,有必要将网络环境下著作权合理使用的部分情形转化为给付报酬但无需事前征得同意的法定许可。