作者: 邪惡的你 時(shí)間: 2025-3-21 23:56
Forecasting Tropical Instability Waves Based on Artificial Intelligence,esearch fields inspire us to develop a deep neural network-based DL ocean forecasting model that is driven only by the time series of gridded sea surface temperature (SST) data. The model forecasted the SST pattern variations in the eastern equatorial Pacific Ocean, where a well-known prevailing oce作者: 神圣將軍 時(shí)間: 2025-3-22 03:25 作者: 單純 時(shí)間: 2025-3-22 04:48
,Satellite Data-Driven Internal Solitary Wave Forecast Based on?Machine Learning Techniques,emand yet with little progress in recent years. Numerical simulations and empirical methods are mainly used to forecast the propagation of ISWs but suffer from different issues, such as computational cost, time inefficiency, or low accuracy. Accumulating satellite imagery has provided a solid and fu作者: narcissism 時(shí)間: 2025-3-22 12:20 作者: 溫和女孩 時(shí)間: 2025-3-22 16:20 作者: Bombast 時(shí)間: 2025-3-22 18:41
,Detecting Tropical Cyclogenesis Using Broad Learning System from?Satellite Passive Microwave Observe radiometer’s brightness temperature (TB) data, a tropical cyclogenesis prediction model is trained with the broad learning system (BLS). In contrast to the high computational demand of deep networks, BLS is a flatted one with fast training speed. Meanwhile, the incremental learning ability of BLS 作者: 玉米棒子 時(shí)間: 2025-3-23 00:46
Tropical Cyclone Monitoring Based on Geostationary Satellite Imagery,s and property. Forecasters and emergency responders both rely on accurate TC location and intensity estimates. In this chapter, two deep convolutional neural networks (CNNs) were designed for locating TC centers (CNN-L) and estimating their intensities (CNN-I) from the brightness temperature data o作者: AGATE 時(shí)間: 2025-3-23 02:55 作者: largesse 時(shí)間: 2025-3-23 06:52
Detection and Analysis of Mesoscale Eddies Based on Deep Learning,ddies have signals on sea surface height (SSH) images, sea surface temperature (SST) images. Previous studies developed automatic eddy identification methods based on SSH or SST. However, single remote sensing data cannot adequately characterize mesoscale eddies. To improve the accuracy and efficien作者: anaphylaxis 時(shí)間: 2025-3-23 11:42 作者: Ingredient 時(shí)間: 2025-3-23 15:51 作者: DRAFT 時(shí)間: 2025-3-23 19:16
Detection and Analysis of Marine Green Algae Based on Artificial Intelligence, Based on the 1,055/4,071 pairs of labelled samples, the model reached an accuracy of 97.51 (99.83)% and an Intersection over Union (IoU) of 42.62 (88.09)% for the MODIS (SAR) images. We processed satellite images containing . using the DL model and drew an exciting finding: since SAR (MODIS) detect作者: 無所不知 時(shí)間: 2025-3-23 22:31
,Automatic Waterline Extraction of?Large-Scale Tidal Flats from?SAR Images Based on?Deep Convolutioning operations such as grey-value thresholding due to speckle noise. Both the signal returned from the sea surface and the exposed tidal flats vary drastically from different sea conditions. This chapter develops an automatic method for extracting the waterline accurately and efficiently from Sentin作者: 彎腰 時(shí)間: 2025-3-24 03:35 作者: 靈敏 時(shí)間: 2025-3-24 06:50 作者: Dictation 時(shí)間: 2025-3-24 14:33
ext decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.?.978-981-19-6377-3978-981-19-6375-9作者: LAVA 時(shí)間: 2025-3-24 14:53 作者: 殖民地 時(shí)間: 2025-3-24 19:00 作者: Immunoglobulin 時(shí)間: 2025-3-25 01:36
https://doi.org/10.1007/978-0-387-32793-8lassify a series of SAR images from freezing to melting of Bering Strait. The results are compared with the 1 km products of the National Snow and Ice Data Center. Results show that the DAU-Net is capable of dealing with complex sea conditions, showing good robustness and applicability.作者: 我不明白 時(shí)間: 2025-3-25 03:57 作者: EXPEL 時(shí)間: 2025-3-25 11:11 作者: Concerto 時(shí)間: 2025-3-25 15:02
Sea Ice Detection from SAR Images Based on Deep Fully Convolutional Networks,lassify a series of SAR images from freezing to melting of Bering Strait. The results are compared with the 1 km products of the National Snow and Ice Data Center. Results show that the DAU-Net is capable of dealing with complex sea conditions, showing good robustness and applicability.作者: micturition 時(shí)間: 2025-3-25 18:26
Book‘‘‘‘‘‘‘‘ 2023. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark p作者: overbearing 時(shí)間: 2025-3-25 22:30 作者: 先兆 時(shí)間: 2025-3-26 01:45 作者: 琺瑯 時(shí)間: 2025-3-26 06:32 作者: 起草 時(shí)間: 2025-3-26 11:56 作者: 退出可食用 時(shí)間: 2025-3-26 16:14
Explorations in Managerial Economics the three development stages of artificial intelligence. Then, we discuss four commonly used deep learning architectures. Finally, we elaborate on the common application scenarios of deep learning technology.作者: 四指套 時(shí)間: 2025-3-26 20:46 作者: Adulate 時(shí)間: 2025-3-26 23:47 作者: cocoon 時(shí)間: 2025-3-27 02:31
The Natural Language of Random Processes,e mean absolute errors of length and width estimated by the SSENet are 7.88 and 2.23?m, respectively. Compared to the old mean square error loss function, the new MSSE reduces the length’s MAE by approximately 1 m. SSENet demonstrates its robustness over a variety of training/testing sets.作者: 記憶 時(shí)間: 2025-3-27 08:50 作者: 有權(quán) 時(shí)間: 2025-3-27 11:16
,Detecting Tropical Cyclogenesis Using Broad Learning System from?Satellite Passive Microwave Observd 2506 non-TC samples during 2005–2006, which are . images extracted from Special Sensor Microwave Imager TB data. The proposed model successfully detects tropical cyclogenesis with a total testing accuracy of 86.83%, a testing hit rate (HT) of 81.14%, and a testing false alarm ratio (FAR) of 11.18%.作者: SCORE 時(shí)間: 2025-3-27 16:32
,Automatic Waterline Extraction of?Large-Scale Tidal Flats from?SAR Images Based on?Deep Convolutionlat’s digital elevation model (DEM) of Subei Bank automatically using the waterline method. The results indicates that our DCNN model not only has appreciable performance for extraction of waterline in SAR images under complex imaging conditions but also excellent potential for rapid analysis of tidal flat topography evolution.作者: MURAL 時(shí)間: 2025-3-27 21:51 作者: Stress-Fracture 時(shí)間: 2025-3-28 00:17 作者: Serenity 時(shí)間: 2025-3-28 04:50 作者: 固定某物 時(shí)間: 2025-3-28 09:33 作者: 變色龍 時(shí)間: 2025-3-28 11:59 作者: STALL 時(shí)間: 2025-3-28 18:10 作者: 使腐爛 時(shí)間: 2025-3-28 19:18
Explorations in Mathematical Physicsven the surface limitation of remote sensing techniques and expenses of in-situ observations, the sparse information of the ocean’s three-dimensional structure has hindered the studies about the global ocean. In this chapter, a neural network (NN) is described, which is capable to learn the relation作者: FLIC 時(shí)間: 2025-3-29 02:05 作者: Licentious 時(shí)間: 2025-3-29 04:00 作者: Consensus 時(shí)間: 2025-3-29 09:20 作者: 熱烈的歡迎 時(shí)間: 2025-3-29 11:26
Explorations in Mathematical Physicsddies have signals on sea surface height (SSH) images, sea surface temperature (SST) images. Previous studies developed automatic eddy identification methods based on SSH or SST. However, single remote sensing data cannot adequately characterize mesoscale eddies. To improve the accuracy and efficien作者: promote 時(shí)間: 2025-3-29 19:06
Variational Calculus and Field Theory,ct coastal inundation from multi-temporal and dual-polarimetric SAR data with good and robust performance. Two specially-designed adaptations for SAR image flooding mapping are included in the SARCFMNet model: (1) radar remote sensing physics-driven input information design; and (2) regularization s作者: 反復(fù)無常 時(shí)間: 2025-3-29 20:57
https://doi.org/10.1007/978-0-387-32793-8-Net. The encoder is a ResNet-34 model. The position and channel attention modules are integrated into the DAU-Net to improve performance. The Sentinel-1A SAR images are used as experimental data. The model’s input contains three channels: VV polarized information, VH polarized information, and the 作者: 暴露他抗議 時(shí)間: 2025-3-30 01:00
The Elegance and Power of Tensor Notation, Based on the 1,055/4,071 pairs of labelled samples, the model reached an accuracy of 97.51 (99.83)% and an Intersection over Union (IoU) of 42.62 (88.09)% for the MODIS (SAR) images. We processed satellite images containing . using the DL model and drew an exciting finding: since SAR (MODIS) detect作者: CRACK 時(shí)間: 2025-3-30 05:55
Variational Calculus and Field Theory,ing operations such as grey-value thresholding due to speckle noise. Both the signal returned from the sea surface and the exposed tidal flats vary drastically from different sea conditions. This chapter develops an automatic method for extracting the waterline accurately and efficiently from Sentin作者: aesthetic 時(shí)間: 2025-3-30 09:29 作者: Debility 時(shí)間: 2025-3-30 13:38 作者: 懶惰民族 時(shí)間: 2025-3-30 20:17
Xiaofeng Li,Fan WangThis book is open access, which means that you have free and unlimited access.Includes abundant case studies to guide the readers on how to utilize AI in assisting oceanography research.Reveals multi-作者: Heart-Rate 時(shí)間: 2025-3-30 21:21 作者: 材料等 時(shí)間: 2025-3-31 04:06 作者: ALLEY 時(shí)間: 2025-3-31 06:14 作者: 現(xiàn)存 時(shí)間: 2025-3-31 11:54 作者: 煩擾 時(shí)間: 2025-3-31 13:26 作者: 咽下 時(shí)間: 2025-3-31 20:42 作者: FIR 時(shí)間: 2025-3-31 21:55
Tropical Cyclone Monitoring Based on Geostationary Satellite Imagery,nsity estimate using a combination of four channels of data as input. We added a focal_loss function to the CNN model to mitigate the negative effect of the severely imbalanced distribution of TC category samples. The accuracy increases to 88.9% if we convert the multi-classification problem to a bi
