Autonomous Mission Replanning Method for Imaging Satellites Considering Real-Time Weather Conditions

2016 ◽  
Vol 13 (10) ◽  
pp. 6967-6973 ◽  
Author(s):  
Yongming He ◽  
Lei He ◽  
Yuan Wang ◽  
Yu Xiao ◽  
Yingwu Chen ◽  
...  
Keyword(s):  
Real Time ◽  
Input Data ◽  
Meteorological Factors ◽  
Weather Conditions ◽  
Earth Observation ◽  
Mission Planning ◽  
Satellite Mission ◽  
Actual Observation ◽  
Weather Changes ◽  
Observation Results

During the observations made by imaging satellites, meteorological factors are likely to change frequently. The vagaries of weather conditions and significant effects on the actual observation results mean that there is an urgent need to apply more intelligence to satellite mission planning. Thus, this paper describes an autonomous replanning method for imaging satellites that considers the real-time weather conditions. Considering the characteristics of different input data, this method replans the low-yield task set and fine-tunes others to improve profitability. Moreover, the proposed method can heuristically select the appropriate adjustment rule to achieve autonomous satellite mission planning. A series of simulations with various task quantities and in different environments shows that the proposed method can respond effectively to real-time weather changes, and can steadily improve the total profits in a variety of weather conditions during Earth observation activities.

A Review on Machine Learning for Earth Observation Satellite Mission Planning

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiliang Li ◽  
Mingyong Jiang ◽  
Da Ran ◽  
Fengjie Zheng
Keyword(s):  
Machine Learning ◽  
Solution Process ◽  
Field Research ◽  
Earth Observation ◽  
Mission Planning ◽  
Planning Problem ◽  
Satellite Mission ◽  
The Earth ◽  
Integral Process ◽  
Earth Observation Satellite

: Mission planning is an integral process that enables earth observation missions and defines their success rate. This paper identifies key problems and opportunities on machine learning for earth observation satellite mission planning. Firstly, the description elements, classification methods, solution process and solution difficulties of the mission planning problem of earth observation satellite are described. Secondly, the current research status of machine learning for the earth observation satellite mission planning is summarized and analyzed. Finally, the problems of current research are analyzed, and the prospect of new field research is given in light of the development needs.

Influence of meteorological factors on potato yield in the conditions of the Right-Bank Forest-steppe

2018 ◽  
Vol 1 (94) ◽  
pp. 55-61
Author(s):  
R.O. Myalkovsky
Keyword(s):  
Meteorological Factors ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Potato Yield ◽  
High Yield ◽  
Forest Steppe ◽  
Potato Tubers ◽  
Middle Aged ◽  
Lower Yield ◽  
The Right

Goal. The purpose of the research was to determine the influence of meteorological factors on potato yield in the conditions of the Right Bank Forest-steppe of Ukraine. Methods.Field, analytical and statistical. Results.It was established that among the mid-range varieties Divo stands out with a yield of 42.3 t/ha, Malin white – 39.8 t/ha, and Legend – 37.1 t/ ha. The most favourable weather and climatic conditions for the production of potato tubers were for the Divo 2011 variety with a yield of 45.9 t/ha and 2013 – 45.1 t/ha. For the Legenda variety 2016, the yield of potato tubers is 40.6 t/ha and 2017 – 43.2 t/ha. Malin White 2013 is 41.4 t/ha and 2017 42.1 t/ha. The average varieties of potatoes showed a slightly lower yield on average over the years of research. However, among the varieties is allocated Nadiyna – 40.3 t/ha, Slovyanka – 37.2 t/ ha and Vera 33.8 t/ha. Among the years, the most high-yielding for the Vera variety was 2016 with a yield of 36.6 t/ha and 2017 year – 37.8 t/ha. Varieties Slovyanka and Nadiyna 2011 and 2012 with yields of 42.6 and 44.3 t/ha and 46.5 and 45.3 t/ha, respectively. Characterizing the yield of potato tubers of medium-late varieties over the years of research, there was a decrease in this indicator compared with medium-early and middle-aged varieties. However, the high yield of the varieties of Dar is allocated – 40.0 t/ha, Alladin – 33.6 t/ha and Oxamit 31.3 t/ha. Among the years, the most favourable ones were: for Oxamit and Alladin – 2011 – 33.5 and 36.5 t/ha, and 2017 – 34.1 and 36.4 t/ha, respectively. Favourable years for harvesting varieties were 2011 and 2012 with yields of 45.7 and 45.8 t/ha. Thus, the highest yield of potato tubers on average over the years of studies of medium-early varieties of 41.2-43.3 t / ha were provided by weather conditions of 2011 and 2017 years, medium-ripe varieties 41.0-41.1 - 2012 and 2011, medium- late 37,6-38,5 t / ha - 2012 and 2011, respectively.

REAL CONDITION AND PROSPECTS OF DEVELOPMENT OF THE RADAR STATIONS OF THE COUNTER BATTERY FIGHTINGV

2019 ◽  
Vol 1 (12) ◽  
pp. 30-40
Author(s):  
V. Нolovan ◽  
V. Gerasimov ◽  
А. Нolovan ◽  
N. Maslich
Keyword(s):  
Comparative Analysis ◽  
Real Time ◽  
Weather Conditions ◽  
Time Of Day ◽  
Current Position ◽  
Real Condition ◽  
Leading Role ◽  
Artillery Shell

Fighting in the Donbas, which has been going on for more than five years, shows that a skillful counter-battery fight is an important factor in achieving success in wars of this kind. Especially in conditions where for the known reasons the use of combat aviation is minimized. With the development of technical warfare, the task of servicing the counter-battery fight began to rely on radar stations (radar) to reconnaissance the positions of artillery, which in modern terms are called counter-battery radar. The principle of counter-battery radar is based on the detection of a target (artillery shell, mortar mine or rocket) in flight at an earlier stage and making several measurements of the coordinates of the current position of the ammunition. According to these data, the trajectory of the projectile's flight is calculated and, on the basis of its prolongation and extrapolation of measurements, the probable coordinates of the artillery, as well as the places of ammunition falling, are determined. In addition, the technical capabilities of radars of this class allow you to recognize the types and caliber of artillery systems, as well as to adjust the fire of your artillery. The main advantages of these radars are:  mobility (transportability);  inspection of large tracts of terrain over long distances;  the ability to obtain target's data in near real-time;  independence from time of day and weather conditions;  relatively high fighting efficiency. The purpose of the article is to determine the leading role and place of the counter-battery radar among other artillery instrumental reconnaissance tools, to compare the combat capabilities of modern counter-battery radars, armed with Ukrainian troops and some leading countries (USA, China, Russia), and are being developed and tested in Ukraine. The method of achieving this goal is a comparative analysis of the features of construction and combat capabilities of modern models of counter-battery radar in Ukraine and in other countries. As a result of the conducted analysis, the directions of further improvement of the radar armament, increasing the capabilities of existing and promising counter-battery radar samples were determined.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals UAVs fleet mission planning robust to changing weather conditions

2020 ◽  
Vol 53 (2) ◽  
pp. 10518-10524
Author(s):  
Grzegorz Bocewicz ◽  
Grzegorz Radzki ◽  
Izabela Nielsen ◽  
Marcin Witczak ◽  
Banaszak Zbigniew
Keyword(s):  
Weather Conditions ◽  
Mission Planning

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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