標(biāo)題: Titlebook: Deep Learning Applications, Volume 2; M. Arif Wani,Taghi M. Khoshgoftaar,Vasile Palade Book 2021 The Editor(s) (if applicable) and The Aut [打印本頁(yè)] 作者: 習(xí)慣 時(shí)間: 2025-3-21 16:18
書(shū)目名稱Deep Learning Applications, Volume 2影響因子(影響力)
作者: 共同時(shí)代 時(shí)間: 2025-3-21 20:21 作者: gustation 時(shí)間: 2025-3-22 03:22 作者: hazard 時(shí)間: 2025-3-22 05:00
https://doi.org/10.1007/978-981-15-6759-9Deep Learning Architectures; Deep Learning Algorithms; Deep Learning Models; Convolutional Neural Netwo作者: Facilities 時(shí)間: 2025-3-22 10:42 作者: 憤憤不平 時(shí)間: 2025-3-22 16:12 作者: 憤憤不平 時(shí)間: 2025-3-22 17:04 作者: arrogant 時(shí)間: 2025-3-22 21:38 作者: 抵制 時(shí)間: 2025-3-23 04:04 作者: judiciousness 時(shí)間: 2025-3-23 07:43 作者: interference 時(shí)間: 2025-3-23 12:21 作者: Benzodiazepines 時(shí)間: 2025-3-23 17:55 作者: FLAX 時(shí)間: 2025-3-23 18:17 作者: Infelicity 時(shí)間: 2025-3-24 01:55 作者: 反感 時(shí)間: 2025-3-24 04:53
H. Kayapinar,H.-C. M?hring,B. Denkenaal GNSS receivers usually sample at 1?Hz, which is not sufficient to robustly and accurately track a vehicle in certain scenarios, such as driving on the highway, where the vehicle could travel at medium to high speeds, or in safety-critical scenarios. In addition, the GNSS relies on a number of sat作者: 個(gè)人長(zhǎng)篇演說(shuō) 時(shí)間: 2025-3-24 09:03
Wear Behavior in Microactuator Interfaceseep generative models can learn to generate realistic images approximating real-world distributions. In particular, the proper training of Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) enables them to perform semi-supervised image classification. Combining the power of t作者: 水土 時(shí)間: 2025-3-24 11:08
H. Kayapinar,H.-C. M?hring,B. Denkenand Mathematical analysis such as bifurcation study of dynamical systems. However, as far as we know, such efficient methods have seen relatively limited use in the optimization of neural networks. In this chapter, we propose a novel training method for deep neural networks based on the ideas from pa作者: 精美食品 時(shí)間: 2025-3-24 14:49 作者: Bumptious 時(shí)間: 2025-3-24 19:11
Syed V. Ahamed,Victor B. Lawrencee deep residual architectures. The technique proposed in this chapter achieves better accuracy compared to the state of the art for two separately hosted Retinal OCT image data-sets. Furthermore, we illustrate a real-time prediction system that by exploiting this deep residual architecture, consisti作者: Pedagogy 時(shí)間: 2025-3-25 01:50
Operational Environment for the HDSLnce of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multi-channel convolutional neural networks (CNN). Our method makes use of a 3D CNN fed with features previously extracted from each frame to generate a 作者: 有限 時(shí)間: 2025-3-25 06:42
Operational Environment for the HDSLr Motor Current Signal (MCS) is also gaining popularity. This paper uses MCS for the diagnosis of bearing inner raceway and outer raceway fault. Diagnosis using MCS is difficult as the fault signatures are buried beneath the noise in the current signal. Hence, signal-processing techniques are employ作者: FUSC 時(shí)間: 2025-3-25 10:12
https://doi.org/10.1007/978-1-4615-6291-7r panel arrays from satellite imagery. The networks are tested on real data and augmented data. Results indicate that deep learning segmentation networks work well for automatic solar panel detection from high-resolution orthoimagery.作者: 樹(shù)膠 時(shí)間: 2025-3-25 13:46 作者: 膠狀 時(shí)間: 2025-3-25 16:37
José Daniel García-Castro,Josefa Mulaing techniques are the typically used for analyzing past observations and to predict the future occurrences of events in a given RF environment. Machine learning (ML) techniques, having already proven useful in various domains, are also being sought for characterizing and understanding the RF enviro作者: 類人猿 時(shí)間: 2025-3-25 23:26 作者: intention 時(shí)間: 2025-3-26 03:16 作者: Benign 時(shí)間: 2025-3-26 06:23
H. Kayapinar,H.-C. M?hring,B. Denkena Several deep learning algorithms have been employed to learn the error drift for a better positioning prediction. We therefore investigate in this chapter the performance of Long Short-Term Memory (LSTM), Input Delay Neural Network (IDNN), Multi-Layer Neural Network (MLNN) and Kalman Filter (KF) fo作者: 治愈 時(shí)間: 2025-3-26 09:09
Wear Behavior in Microactuator Interfacesd training data from the computer vision and medical imaging domains demonstrate performance competitive to state-of-the-art semi-supervised models in simultaneous image generation and classification tasks.作者: 高歌 時(shí)間: 2025-3-26 13:05 作者: uveitis 時(shí)間: 2025-3-26 17:38
Deep Learning-Based Recommender Systems,inent parts of information in order to make better recommendations. The learned features are then incorporated into the learning process of MF. Comprehensive experiments on three real-world datasets have shown our method performs better than other state-of-the-art methods according to various evalua作者: 不安 時(shí)間: 2025-3-26 22:20
A Comprehensive Set of Novel Residual Blocks for Deep Learning Architectures for Diagnosis of Retine deep residual architectures. The technique proposed in this chapter achieves better accuracy compared to the state of the art for two separately hosted Retinal OCT image data-sets. Furthermore, we illustrate a real-time prediction system that by exploiting this deep residual architecture, consisti作者: 安定 時(shí)間: 2025-3-27 02:45
Three-Stream Convolutional Neural Network for Human Fall Detection,nce of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multi-channel convolutional neural networks (CNN). Our method makes use of a 3D CNN fed with features previously extracted from each frame to generate a 作者: Torrid 時(shí)間: 2025-3-27 07:53 作者: bronchiole 時(shí)間: 2025-3-27 10:34
Automatic Solar Panel Detection from High-Resolution Orthoimagery Using Deep Learning Segmentation r panel arrays from satellite imagery. The networks are tested on real data and augmented data. Results indicate that deep learning segmentation networks work well for automatic solar panel detection from high-resolution orthoimagery.作者: 文藝 時(shí)間: 2025-3-27 16:49
Training Deep Learning Sequence Models to Understand Driver Behavior,twork and the encoder–decoder model with attention were built and trained to analyze the effect of memory and attention on the computational expense and performance of the model. We compare the performance of these two complex networks to that of the MLP in estimating driver behavior. We show that o作者: 專橫 時(shí)間: 2025-3-27 20:12
Exploiting Spatio-Temporal Correlation in RF Data Using Deep Learning,ing techniques are the typically used for analyzing past observations and to predict the future occurrences of events in a given RF environment. Machine learning (ML) techniques, having already proven useful in various domains, are also being sought for characterizing and understanding the RF enviro作者: 斗爭(zhēng) 時(shí)間: 2025-3-28 00:26 作者: 陶器 時(shí)間: 2025-3-28 05:35 作者: Canyon 時(shí)間: 2025-3-28 08:52
Vehicular Localisation at High and Low Estimation Rates During GNSS Outages: A Deep Learning Approa Several deep learning algorithms have been employed to learn the error drift for a better positioning prediction. We therefore investigate in this chapter the performance of Long Short-Term Memory (LSTM), Input Delay Neural Network (IDNN), Multi-Layer Neural Network (MLNN) and Kalman Filter (KF) fo作者: 把手 時(shí)間: 2025-3-28 12:03 作者: 察覺(jué) 時(shí)間: 2025-3-28 14:51
Non-convex Optimization Using Parameter Continuation Methods for Deep Neural Networks,etween those techniques and deep neural networks. In particular, we illustrate a method that we call Natural Parameter Adaption Continuation with Secant approximation (NPACS). Herein we transform regularly used activation functions to their homotopic versions. Such a version allows one to decompose 作者: Intend 時(shí)間: 2025-3-28 21:04 作者: Dri727 時(shí)間: 2025-3-29 00:01 作者: LIEN 時(shí)間: 2025-3-29 04:55 作者: MORT 時(shí)間: 2025-3-29 09:11 作者: Heterodoxy 時(shí)間: 2025-3-29 12:32
Automatic Solar Panel Detection from High-Resolution Orthoimagery Using Deep Learning Segmentation oftops in a region or city and also for estimating the solar potential of the installed solar panels. Detection of accurate shapes and sizes of solar panels is a prerequisite for successful capacity and energy generation estimation from solar panels over a region or a city. Such an approach is helpf作者: exclamation 時(shí)間: 2025-3-29 16:42 作者: Anhydrous 時(shí)間: 2025-3-29 19:54
Exploiting Spatio-Temporal Correlation in RF Data Using Deep Learning,r our smartphones but also for other applications like surveillance, navigation, jamming, anti-jamming, radar to name a few areas of applications. These recent advances of wireless technologies in radio frequency?(RF) environments have warranted more autonomous deployments of wireless systems. With 作者: 無(wú)政府主義者 時(shí)間: 2025-3-30 01:29
Human Target Detection and Localization with Radars Using Deep Learning, The detection of human targets can assist in energy savings in commercial buildings, public spaces, and smart homes by automatic control of lighting, heating, ventilation, and air conditioning (HVAC). Such smart sensing applications can facilitate monitoring, controlling, and thus saving energy. Co作者: 比喻好 時(shí)間: 2025-3-30 05:54 作者: MAZE 時(shí)間: 2025-3-30 09:12 作者: 暖昧關(guān)系 時(shí)間: 2025-3-30 16:13
