- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) - Kathmandu, Nepal (2019.11.4-2019.11.6)] 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) - Human Tracking of Single Laser Range Finder Using Features Extracted by Deep Learning
摘要： Human recognition using single laser range finder (LRF) is utilized for the task of following a target person such as a cargo transport robot. In these recognition methods, the approach is applied in which human-crafted features is inputted to the one-class classification model to identify whether it is a human or not. In this paper, we propose a method that introduce features extracted by deep learning. In this method, we create an encoder that can extract features from input data using PointNet-based autoencoder. In its experiment, the features extracted by encoder is compared with the human-crafted features, and these extraction process length of time is measured.
关键词： One-Class Classification,Point Cloud,Deep Learning,Laser Range Finder
更新于2025-09-23 15:21:01
-
[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Extraction of a Specific Land-Cover Class from Very High Spatial Resolution Imagery Using Positive and Unlabeled Learning with Convolutional Neural Networks
摘要： In remote sensing, supervised multiclass classifiers show a very promising performance in terms of classification accuracy. However, they require that all classes, in the study area, are labeled. In many applications, users may only be interested in specific land classes. When considering only one class, this referred to as One-Class classification (OC) problem. In this paper, we investigated the possibility of using Convolutional Neural Networks (CNN) within the Positive and Unlabeled Learning (PUL) framework for estimating the urban tree canopy coverage from very high spatial resolution aerial imagery. We also compared the proposed approach to the Binary CNN classification and to ensemble classifications based on various color-texture based features. The obtained classification accuracies show that PUL strategies provide competitive extraction results, especially the proposed CNN based one, due to the fact that PUL is a positive-unlabeled method in which large amounts of available unlabeled samples is incorporated into the training phase, allowing the classifier to model effectively the tree class.
关键词： convolutional neural networks,texture analysis,One-class classification,positive and unlabeled learning,ensemble classification
更新于2025-09-16 10:30:52
-
Automated active fault detection in fouled dissolved oxygen sensors
摘要： Biofilm formation causes bias in dissolved oxygen (DO) sensors, which hamper their usage for automatic control and thereby balancing energy- and treatment efficiency. We analysed if a dataset that was generated with deliberate perturbations, can automatically be interpreted to detect bias caused by biofilm formation. We used a challenging set-up with realistic conditions that are required for a full-scale application. This included automated training (adapting to changing normal conditions) and automated tuning (setting an alarm threshold) to assure that the fault detection (FD)-methods are accessible to the operators. The results showed that automatic usage of FD-methods is difficult, especially in terms of automatic tuning of alarm thresholds when small training datasets only represent the normal conditions, i.e. clean sensors. Despite the challenging set-up, two FD-methods successfully improved the detection limit to 0.5 mg DO/L bias caused by biofilm formation. We showed that the studied dataset could be interpreted equally well by simpler FD-methods, as by advanced machine learning algorithms. This in turn indicates that the information contained in the actively generated data was more vital than its interpretation by advanced algorithms.
关键词： Receiver operating characteristics,Monitoring,One-class classification,Active fault detection,Gaussian process regression
更新于2025-09-16 10:30:52