ID 原文 译文
14045 传统卷积神经网络大量的计算及内存需求使嵌入式设备智能应用的开发成为挑战,为尝试将高度复杂的深度学习应用与性能有限的低成本嵌入式平台相结合,设计了一款小型嵌入式图像分类系统。 Traditional convolutional neural networks have a large demand of computation and memory‚which makes the development of embedded devices for intelligent applications become a challenge. In order to deploy highly complex deep learning applications into the low-cost embedded platforms with limited performance‚a small embedded system for image classification is designed.
14046 实验基于结构化稀疏学习算法在Caffe框架下构建结构稀疏卷积神经网络模型,将其部署在工业派(IndustriPi)最小化系统上,通过测试得到了85.5%的准确率和处理实时影像时不小于8帧/s的运行速度。 Based on the Structured Sparsity Learning(SSL) algorithm‚a sparse convolutional neural network model is constructed under the framework of Caffe and deployed on IndustriPi minimization system. Test results show that the accuracy of 85.5% and operating speed of more than 8 frames per second are achieved.
14047 与经典模型相比,通过稀疏学习后的网络模型很大程度上减少了计算量和内存占用率,提高了低成本嵌入式设备的运行速度。 Compared with classical models‚the sparse model can reduce computational amount and memory occupancy to a great extent‚and increase the embedded device operating speed.
14048 迎角、侧滑角是飞机重要的飞行状态参数,而大气数据系统在恶劣天气、大迎角或机动飞行情况下是难以准确测量出气流角等数据的。 Both attack angle and sideslip angle are important flight state parameters of aircraft. However‚it is difficult for the atmospheric data system to accurately measure the airflow angles under the conditions of adverse weather‚high attack angle or maneuvering flight.
14049 基于飞行数据,研究了一种飞机气流角的估计方法。 A method for estimation of aircraft airflow angle is studied based on flight data.
14050 考虑到飞行数据可能受到外部干扰发生数据突变、各数据采样频率不同以及飞行数据之间的噪声统计特性均未知等情况,建立飞机系统状态方程和量测方程,将非等间隔理论与基于极大似然准则的自适应卡尔曼滤波算法进行融合,以飞机转弯和爬升飞行为实例,施加外部干扰,对飞机迎角、侧滑角进行估计。 Taking the external disturbances‚the different data sampling frequencies‚and the unknown statistical characteristics of flight data into account‚the aircraft system state equation and measurement equation are established. The adaptive Kalman filtering algorithm based on the maximum likelihood criterion is integrated with the non-equal interval theory. Taking the turning and climbing flight of aircraft as an example‚external disturbance is applied to estimate the attack angle and sideslip angle.
14051 实验结果表明,该算法的估计精度和抗外部干扰的鲁棒性能均优于扩展卡尔曼滤波算法和无迹卡尔曼滤波算法。 Experimental results show that the algorithm has better estimation precision and higher robustness against external disturbance than the extended Kalman filtering algorithm and the unscented Kalman filtering algorithm.
14052 针对间歇采样转发干扰识别问题,提出一种多域识别融合算法。 To realize the recognition of intermittent sampling repeater jamming‚an algorithm for fusing multi-domain recognition results is proposed.
14053 首先分析了真实目标回波和间歇采样干扰(直接转发、重复转发、循环转发)4类信号在时域、频域和分数阶域的内部差异,并对各域进行奇异谱分析(SSA),得出的SSA结果可作为区分信号差异的依据。 Firstly‚the internal differences between four kinds of signals‚namely‚the echo of real targets and the interference of intermittent sampling(including ISDJ‚ISRJ and ISLJ) are analyzed in time domain‚frequency domain and fractional domain‚and a Singular Spectrum Analysis(SSA) is conducted for each domain. The results of SSA can be taken as a basis for distinguishing signal differences.
14054 其次构造时、频、分数阶域3个基分类器,提取4类信号时、频、分数阶域奇异谱峰度与斜度作为基分类器的特征因子进行训练验证。 Secondly‚three base classifiers of time domain‚frequency domain and fractional domain are constructed‚and the kurtosis and skewness of the singular spectrum are extracted and taken as the characteristic factors of the base classifiers for training and verification.