Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Obs...Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.展开更多
The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated ...The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.展开更多
对航空器各运行阶段轨迹预测是规划飞行路径、维护航空器运行安全等任务的关键技术,在保障航空器安全高效运行等方面具有重要意义。航空器从跑道入口上空50英尺到脱离跑道期间属于高速运行阶段,对其着陆阶段运行轨迹进行预测可预知其脱...对航空器各运行阶段轨迹预测是规划飞行路径、维护航空器运行安全等任务的关键技术,在保障航空器安全高效运行等方面具有重要意义。航空器从跑道入口上空50英尺到脱离跑道期间属于高速运行阶段,对其着陆阶段运行轨迹进行预测可预知其脱离跑道位置,辅助管制员对地面滑行航空器进行路径规划,从而减少航空器之间的运行冲突。论文将长短期记忆(Long Short Term Memory,LSTM)循环神经网络与ANN(Artificial Neural Network)相结合,构建航空器着陆阶段滑行轨迹预测模型。预测结果与同类型预测算法进行对比后发现,ANN-LSTM模型预测准确性更高,可实现对航空器着陆阶段轨迹的更好预测。展开更多
文摘Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.
基金supported by the Ministry of Education,Culture,Research,and Technology Directorate General of Higher Education,Research,and Technology grant number[2147/UN2621/PN/2022].
文摘The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.
文摘对航空器各运行阶段轨迹预测是规划飞行路径、维护航空器运行安全等任务的关键技术,在保障航空器安全高效运行等方面具有重要意义。航空器从跑道入口上空50英尺到脱离跑道期间属于高速运行阶段,对其着陆阶段运行轨迹进行预测可预知其脱离跑道位置,辅助管制员对地面滑行航空器进行路径规划,从而减少航空器之间的运行冲突。论文将长短期记忆(Long Short Term Memory,LSTM)循环神经网络与ANN(Artificial Neural Network)相结合,构建航空器着陆阶段滑行轨迹预测模型。预测结果与同类型预测算法进行对比后发现,ANN-LSTM模型预测准确性更高,可实现对航空器着陆阶段轨迹的更好预测。
