41,704 research outputs found
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
It is important to identify the change point of a system's health status,
which usually signifies an incipient fault under development. The One-Class
Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly
detection and hence could be used for identifying change points; however, it is
sometimes difficult to obtain a good OC-SVM model that can be used on sensor
measurement time series to identify the change points in system health status.
In this paper, we propose a novel approach for calibrating OC-SVM models. The
approach uses a heuristic search method to find a good set of input data and
hyperparameters that yield a well-performing model. Our results on the C-MAPSS
dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in
detecting change point in time series with fewer training data, compared to
state-of-the-art deep learning approaches. In our case study, the OC-SVM
calibrated by the proposed model is shown to be useful especially in scenarios
with limited amount of training data
Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches
In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping. Up
until recently, this has always dependent on using the human eye. Using
airborne lasers, it has become possible to quickly "see" more of the world in
many more dimensions. The resulting enormous point clouds serve as data sources
for applications far beyond the original mapping purposes ranging from flooding
protection and forestry to threat mitigation. In order to process these large
quantities of data, novel methods are required. In this contribution, we
develop models to automatically classify ground cover and soil types. Using the
logic of machine learning, we critically review the advantages of supervised
and unsupervised methods. Focusing on decision trees, we improve accuracy by
including beam vector components and using a genetic algorithm. We find that
our approach delivers consistently high quality classifications, surpassing
classical methods
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of binary networks vs. a single
-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.Comment: Accepted to IEEE/ACIS SNPD 2018, 6 pages, 3 figure
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