Protect privacy of deep classification networks by exploiting their generative power

Machine Learning - Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a...     

Identifying epidemic spreading dynamics of COVID-19 by pseudocoevolutionary simulated annealing optimizers

Neural Computing and Applications - At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration...     

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

Neural Computing and Applications - In real-world application of affective brain–computer interface (aBCI), individual differences across subjects and non-stationary characteristics of...     

A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

基于D-FNN的混沌铁磁谐振系统非线性补偿控制方法

针对电网混沌铁磁谐振系统产生的混沌现象,提出基于动态模糊神经网络的混沌铁磁谐振系统非线性补偿控制方法。该方法采用动态模糊神经网络来逼近系统的非线性部分,消除了过电压的混沌现象,将系统稳定到目标位置,实现了对系统的非线性补偿控制。Matlab仿真结果表明,基于动态模糊神经网络的非线性补偿控制方法控制结果正确,响应快速。     

飞机振动特性激光测量与分析

飞机振动特性测量与分析,对于飞机识别和探测系统的设计与评估是至关重要的;在简要介绍了振动特性测量理论之后,针对外场环境条件,设计了飞机振动特性激光测量装置和测量方法,并经过实验室验证;在全尺寸飞机试验与测量过程中,获得了有效的原始测量数据,计算出飞机振动特征量:平均中心频率和二阶中心矩;同时指出了在飞机振动测量中应注意的问题;试验表明,飞机振动特性测量与分析方法正确、有效。     

攻击型无人机切换控制系统容错算法研究

攻击型无人机单侧发射或投放重型武器时,可将飞行控制系统看作是一个切换控制系统。针对常见的常值型和时变型两种执行器故障,提出了一种自适应观测器设计方法,同时结合状态反馈控制设计了容错控制器。通过Lyapunov函数推出了观测器的误差是关于故障界输入状态实际稳定的充分条件,通过平均驻留时间方法证明了切换控制系统是全局输入状态实际稳定的。某型攻击型无人机仿真实例表明,此方法能在准确估计故障大小的基础上,有效保持切换飞行控制系统的性能,应用前景十分广阔。     

纯电动汽车太阳能辅助空调控制系统的硬件设计

纯电动汽车车载动力电池是其唯一的动力源且很有限,辅助设备消耗的电能减少了纯电动汽车的续驶里程,尤其是空调,所以开发高效的纯电动汽车空调系统是纯电动汽车能够被市场接受的关键。将纯电动汽车顶盖布满太阳电池,可以使空调系统的制冷能力增强,同时还能增加纯电动汽车的行驶距离。对小型纯电动汽车的太阳能辅助空调系统进行研究,设计出适合该空调的自动控制系统,可为纯电动汽车创造出一个更加舒适的驾驶和乘座环境。     

考虑多元可控负荷协调的配网鲁棒优化经济调度模型

为有效应对大规模分布式可再生能源和可控负荷接入配网后在经济优化调度方面所带来的挑战,本文建立考虑多元可控负荷协调的配网鲁棒优化经济调度模型.首先考虑电池储能系统、电动汽车及可参与需求侧管理的电负荷的能量转移特性,建立多元可控负荷可调模型;进而考虑配网中多元可控负荷的调峰能力及动态约束以运行成本最低建立配网鲁棒经济优化调度模型;最后,以蒙东某配网实际数据为例,在改进IEEE 14节点配电系统上进行调度仿真分析,验证本文所提出模型可充分利用多元可控负荷的调节能力,有效提升配网运行经济性.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.