A review of wind speed and wind power forecasting with deep neural networks
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …
has attracted increasing attention. However, intermittent electricity generation resulting from …
Triplet-center loss for multi-view 3d object retrieval
Most existing 3D object recognition algorithms focus on leveraging the strong discriminative
power of deep learning models with softmax loss for the classification of 3D data, while …
power of deep learning models with softmax loss for the classification of 3D data, while …
Abnormal event detection and localization via adversarial event prediction
We present adversarial event prediction (AEP), a novel approach to detecting abnormal
events through an event prediction setting. Given normal event samples, AEP derives the …
events through an event prediction setting. Given normal event samples, AEP derives the …
Driver drowsiness detection using condition-adaptive representation learning framework
We propose a condition-adaptive representation learning framework for driver drowsiness
detection based on a 3D-deep convolutional neural network. The proposed framework …
detection based on a 3D-deep convolutional neural network. The proposed framework …
Abnormal event detection using adversarial predictive coding for motion and appearance
In this paper, we propose adversarial predictive coding (APC), a novel method for detecting
abnormal events. Abnormal event detection (AED) is to identify unobserved events from a …
abnormal events. Abnormal event detection (AED) is to identify unobserved events from a …
A spatial mapping algorithm with applications in deep learning-based structure classification
T Corcoran, R Zamora-Resendiz, X Liu… - arXiv preprint arXiv …, 2018 - arxiv.org
Convolutional Neural Network (CNN)-based machine learning systems have made
breakthroughs in feature extraction and image recognition tasks in two dimensions (2D) …
breakthroughs in feature extraction and image recognition tasks in two dimensions (2D) …
Ranking-based triplet loss function with intra-class mean and variance for fine-grained classification tasks
J Bhattacharya, RK Sharma - Soft Computing, 2020 - Springer
This paper proposed a deep ranking model for triplet selection to efficiently learn similarity
metric from top ranked images. A modified distance criterion described in the current work …
metric from top ranked images. A modified distance criterion described in the current work …
Geometric features for voxel-based surface recognition
D Yarotsky - arXiv preprint arXiv:1701.04249, 2017 - arxiv.org
We introduce a library of geometric voxel features for CAD surface recognition/retrieval
tasks. Our features include local versions of the intrinsic volumes (the usual 3D volume …
tasks. Our features include local versions of the intrinsic volumes (the usual 3D volume …
Hybrid 3D surface description with global frames and local signatures of histograms
Z Shen, X Ma, X Zeng - 2018 24th International Conference on …, 2018 - ieeexplore.ieee.org
This paper presents a novel 3D descriptor named Frame-SHOT to combine global structural
frame with local surface information. Global feature descriptors are generally more …
frame with local surface information. Global feature descriptors are generally more …