汉字库图形点阵库液晶模块在激光告警接收机中的应用

在激光告警接收机中,为了直观显示激光的波长和入射方向,需要研究信息的显示技术.研究了字符点阵液晶显示模块(LCM12864ZK)在激光告警接收机中的应用,实现了2线串行接口方式与单片机89C2051通信,完成全中文人机交互图形界面.设计了简单方便的接口,编写了灵活的操作指令,其模块化的设计思想为多显示模并行应用提供方便.研究成果已经运用到激光告警设备中,能够实现快速显示敌方激光的波长与方向等信息.     

基于小波分析的子带特征提取与选择方法

在研究了目标图像多尺度小波分解特性的基础上,提出了基于小波多尺度分解子带主成分的特征提取的算法。该算法利用图像在不同尺度的小波变换域中能量局部集中性,选择各子带能量较集中的局部小波系数构成图像目标特征向量。这种特征包含图像目标的主要边缘、纹理、灰度、结构等多种信息。由于对图像目标的特征信息的分布没有任何限制,因而适用于多种类型的图像的特征提取,可以解决单一特征提取方法中必须面对的所提取特征不明显的难点。这种特征向量对噪声有较好的鲁棒性。     

基于免疫FNN算法的加热炉炉温优化控制

针对复杂钢坯加热过程,提出了一种免疫克隆进化模糊神经网络(ICE-FNN)控制算法。首先根据现场样本数据建立过程神经网络模型;然后基于该模型,采用模糊神经网络控制器(FNNC)规则优化算法,确定FNNC的最佳规则数;最后由FNNC的规则优化所得参数构造初始种群的一个解,采用免疫克隆进化(ICE)算法对FNNC参数优化。该算法具有全局寻优和局部求精能力,仿真结果证实了其有效性。     

卫星任务调度问题的约束规划模型

卫星任务规划与调度是空间资源管理的重要内容之一,其目的在于为卫星系统的任务计划编制提供科学合理的决策手段与依据。卫星任务调度问题的重要特点在于,调度任务存在可见时间窗口约束。只有在可见时间窗口内,调度任务才可能执行并完成。在进行合理假设的基础上,建立卫星任务调度问题的约束规划模型。对基本禁忌搜索算法进行改进,提出了模型求解的变邻域禁忌搜索算法。应用结果表明,约束规划模型的建立与求解是合理的。     

大延时网络中的主动队列管理机制

研究了网络延时对路由器主动队列管理机制的影响,分析了几种典型主动队列管理算法在大延时网络中的性能。在介绍了基于内模补偿的DC-AQM算法的优缺点之后,根据PID控制器延时补偿的Ziegler-Nichols设定方法,提出了ZNDC(Ziegler-Nichols delay compensation)AQM算法并进行了仿真实验验证,实验结果表明算法达到了预期的目标。     

气囊辅助RTM制备复合材料承力筒内腔尺寸控制技术

根据气囊充压压力与复合材料承力筒内径之间的变化关系,给出了成型复合材料承力筒内表面的气囊尺寸计算方程。分析了气囊充压压力增大对承力筒的壁厚、纤维含量和弯曲性能的影响。采用气囊辅助RTM工艺整体制备出复合材料承力筒。试验结果表明,气囊充压压力使复合材料承力筒的壁厚减薄,纤维体积含量增加,弯曲性能提高;选择适当的气囊充压压力可以制备出满足设计要求的复合材料承力筒。     

面向癫痫脑电的简化深度学习模型

针对脑电信号随机性强、动态变化迅速等特点,提出了一种简化深度学习模型研究癫痫脑电识别问题。提出的模型以一维卷积神经网络为基础,在结构方面简化了卷积层、池化层等以提高模型效率,在整体框架方面应用了Keras框架,在训练优化算法方面采用RMSProp算法作为模型优化算法,通过预定义的目标函数来进行损失估计,模型设计上加入了批标准化层和全局均值池化层。基于所提模型,从三个方面研究了癫痫脑电识别问题,即:利用经验模态分解,分别选取前三阶、前五阶、前七阶、前八阶的本征模态函数分量,在简化模型上进行对比分析;利用提出模型所具备的深度学习特点,直接识别原始脑电信号而无须特征提取环节;增加了三种不同方法分别提取7类特征,对相同的脑电数据进行对比分析。性能分析结果表明:对于五类不同的脑电信号,前三阶的本征模态函数分量的识别率达到92.1%,比其他几种处理方式识别率高;前八阶的本征模态分量识别率不及原始信号,表明人工数据处理时会给数据带来噪声; 所提出的简化深度学习模型能高效处理癫痫脑电识别问题,具备较高效率和较好性能。     

Damage of multi-layer spaced metallic target plates impacted by radial layered PELE

Three different kinds of PELE (the penetrator with lateral efficiency) were launched by ballistic artillery to impact the multi-layer spaced metal target plates. The impact velocities of the projectiles were measured by the velocity measuring system. The damage degree and process of each layer of target plate impacted by the three kinds of projectiles were analyzed. The experimental results show that all the three kinds of projectiles have the effect of expanding holes on the multi-layer spaced metal target plates. For the normal structure PELE(without layered) with tungsten alloy jacket and the radial layered PELE with tungsten alloy jacket, the diameters of holes on the second layer of plates are 3.36 times and 3.76 times of the diameter of the projectile, respectively. For radial layered PELE with W/Zr-based amorphous composite jacket, due to the large number of tungsten wires dispersed after the impact, the diameter of the holes on the four-layer spaced plates can reach 2.4 times, 3.04 times, 5.36 times and 2.68 times of the diameter of the projectile. Besides, the normal structure PELE with tungsten alloy jacket and the radial layered PELE whit tungsten alloy jacket formed a large number of fragments impact marks on the third target plate. Although the number of fragments penetrating the third target plate is not as large as that of the normal structure PELE, the area of dispersion of fragments impact craters on the third target plate is larger by the radial layered PELE. The radial layered PELE with W/Zr-based amorphous composite jacket released a lot of heat energy due to the impact of the matrix material, and formed a large area of ablation marks on the last three target plates.     

Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment

Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment, the evaluation information of alternatives given by decision makers is often fuzzy and uncertain. As a generalization of intuitionistic fuzzy set (IFSs) and Pythagoras fuzzy set (PFSs), q-rung orthopair fuzzy set (q-ROFS) is more suitable for expressing fuzzy and uncertain information. But, in actual multiple attribute decision making (MADM) problems, the weights of DMs and attributes are always completely unknown or partly known, to date, the maximizing deviation method is a good tool to deal with such issues. Thus, combine the q-ROFS and conventional maximizing deviation method, we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper. Firstly, we briefly introduce the basic concept of q-rung orthopair fuzzy sets (q-ROFSs) and q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs). Then, combine the maximizing deviation method with q-rung orthopair fuzzy information, we establish two new decision making models. On this basis, the proposed models are applied to MADM problems with q-rung orthopair fuzzy information. Compared with existing methods, the effectiveness and superiority of the new model are analyzed. This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers (q-ROFNs) and whose attributes are incomplete.     

Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources

Environmentally friendly energy resources open a new opportunity to tackle the problem of energy security and climate change arising from wide use of fossil fuels. This paper focuses on optimizing the allocation of the energy generated by the renewable energy system to minimize the total electricity cost for sustainable manufacturing systems under time‐of‐use tariff by clipping the peak demand. A rolling horizon approach is adopted to handle the uncertainty caused by the weather change. A nonlinear mathematical programming model is established for each decision epoch based on the predicted energy generation and the probability distribution of power demand in the manufacturing plant. The objective function of the model is shown to be convex, Lipchitz‐continuous, and subdifferentiable. A generalized benders decomposition method based on the primal‐dual subgradient descent algorithm is proposed to solve the model. A series of numerical experiments is conducted to show the effectiveness of the solution approach and the significant benefits of using the renewable energy resources.