Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

The lower ionosphere response to its disturbances by powerful radio waves

The paper presents data from some campaigns at Sura heating facility in 2011–1016. The experiments on probing of the artificial disturbed region of the lower ionosphere were carried out at two observation sites. One of them was located near Vasil’sursk 1 km from Sura facility (56.1°N; 46.1°E) and the other site was located at the Observatory (55.85°N; 48.8°E) of Kazan State University, 170 km to the East. Investigation of the features of the disturbed region of the lower ionosphere based on its diagnostics by the methods of the vertical sounding and oblique backscattering is the main goal of this paper. Ionosphere disturbance was fulfilled by the effect of the powerful radio wave of the ordinary or extraordinary polarization emitted by transmitters of the Sura facility with effective radiated power ERP = 50–120 MW at the frequency of 4.3, 4.7 and 5.6 MHz. Pumping waves were emitted with period from 30 s to 15 min. The disturbed region of the ionosphere in Vasil’sursk was probed by the vertical sounding technique using the partial reflexion radar at the frequency of 2.95 and 4.7 MHz. For the oblique sounding of the disturbed region the modified ionosonde Cyclon-M, operating at ten frequencies from 2.01 to 6.51 MHz was used at the Observatory site. On many heating sessions simultaneous variations of the probing partial reflection signals in Vasil’sursk and backscattered signals in Observatory were observed at the height at 40–100 km below the reflection height of the pumping wave. These observations were correlated with the pumping periods of the Sura facility. Possible mechanisms of the appearance of the disturbance in the lower ionosphere and its effect on the probing radio waves are discussed.     

A comparative analysis of SLR,MLR, ANN,XGBoost and CNN for crop height estimation of sunflower using Sentinel-1 and Sentinel-2

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (?VH, ?VV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ?VH, ?VV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies.     

Deep reinforcement learning for six degree-of-freedom planetary landing

This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system capable of estimating the lander’s state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander’s estimated state directly to a commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system’s performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.     

利用连续推力的人工太阳同步轨道设计方法

针对人工太阳同步轨道的设计方法进行研究,通过施加法向连续推力调整升交点赤经（RAAN）变化率。首次推导了升交点赤经在变方向推力作用下的周期摄动平均值的精确计算公式,解决了已有近似方法对相关轨道参数的取值范围存在限制的问题,并给出了对应的轨道倾角周期摄动平均值计算公式。在分析J2项摄动对升交点赤经影响的基础上,给出了所需的法向连续推力幅值和一个轨道周期内对应的速度增量的计算方法。通过数值仿真,校验了计算公式的正确性,分析了实现人工太阳同步轨道的连续法向推力对轨道倾角的影响,给出了连续推力幅值随轨道参数的变化规律,并且提出了未来工程任务的应用建议。     

加工误差对压气机叶栅气动性能及稳定性影响的数据挖掘

为缓解加工误差影响评估过程中的"维数灾难",结合展向平均假设和基于高斯过程的Karhunen-Loève展开,提出了一种由法向加工误差导致的三维叶片表面几何不确定性降阶模型.通过给定加工误差分布的标准差函数求解几何不确定性降阶模型,并运用伪蒙特卡洛方法随机生成样本,最终训练人工神经网络预测了加工误差对高负荷直叶栅气动性...     

一些空间站活动的智能系统

空间站活动的自主性要求空间系统具有智能。本文就空间站的一些活动,尤其是针对材料生产所需的智能:空间视觉,空间机器人规划,智能敏感器,专家系统等提出初步的设计思想和技术实现途径。并对支持这些智能的空间站计算机系统作一构思。     

基于面向对象开发毛皮提花软件

主要介绍了基于面向对象的人造毛皮提花软件设计 ,利用基于面向对象的设计思想解决了图象颜色锐减、图象边界处理、图象滤波、工业用料成分分析及计算、对称画图、利用串行口上下位机图像数据传送以及绘图中的若干问题。本文介绍了软件的主要功能及基于面向对编程下的软件流程图。     

释放SF6气体扰动电离层时的人工气辉研究

在电离层高度释放SF₆气体能够显著扰动电离层.根据SF₆分子在电离层中的扩散方程,同时考虑其在电离层中主要的离子化学反应,研究了SF₆气体释放后电离层各粒子浓度的时空变化,计算了产生人工气辉的体发射系数和发射强度.结果表明,SF₆气体在电离层高度释放后,电子和O⁺的密度均有大幅度下降,主要的负离子成分由电子转变成SF₅^-;在释放过程中,主要产生777.4 nm和135.6 nm两种气辉,且前者的气辉强度远小于后者;电离层温度对气辉的强度有很大的影响.本文的数值计算与美国IMS/SF₆实验观测数据进行比较,结果近似,且通过数据比较还能准确推断出实验时当地的电离层温度.