A new method is presented which combines a deterministic analytical method and a probabilistic measure to classify rock types on the basis of their hyperspectral curve shape. This method is a supervised learning algor...
详细信息
A new method is presented which combines a deterministic analytical method and a probabilistic measure to classify rock types on the basis of their hyperspectral curve shape. This method is a supervised learning algorithm using Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function. The OAD covariance function makes use of the properties of the Spectral Angle Mapper (SAM) which is used frequently for classifying hyperspectral data. Results show that it is possible to identify and classify rocks in an `One vs. One' and an `One vs. All' approach using the entire spectral curve (0.35-2.5 μm). The results show an average classification accuracy of 98% and an F-score of 92% for the new method in an `One vs. All' approach. Slightly higher classification accuracy and F-measure for the new method can be achieved for the `One vs. One' binary approach. This paper extends the ideas of the deterministic SAM method to a probabilistic framework and enables data fusion with similar and disparate kinds of sensors. This paper demonstrates a superior classification performance of the new probabilistic method over the classical SAM.
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based en...
详细信息
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems lately. The diversification of classifiers performed by the methods presented in this work is obtained by varying the feature set each classifier uses, and also at one point, alternating the intrinsic parameters for the training algorithm. We show results obtained with the established genetic algorithm GEFS and our recently developed approach called BSFS, which has a lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results compare the outcomes from the two methods mentioned, and present a modification in one of them that improved the accuracy, reinforcing the motivation for the usage of that method.
The concept of a digital factory is based on the coexistence of the real factory and a virtual factory, being a complete representation of the real factory's data. This includes information on resources, processes...
详细信息
ISBN:
(纸本)9781615676668
The concept of a digital factory is based on the coexistence of the real factory and a virtual factory, being a complete representation of the real factory's data. This includes information on resources, processes and operational data along with the ability to simulate factory devices and production processes. This paper focuses especially on the operative production planning and control within the virtual factory setting and presents an effort to implement this concept for small and medium enterprises (SME's) in the print and media industry. Although originally designed for the printing sector, the system can be customized to meet other industries needs, given through its very universal representation of resources, processes and orders. The digital factory implementation iProPlan - intelligent production planning - offers both an advanced research environment and a high educational benefit for the production planning and control laboratory.
Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (GED). In this paper, data transformat...
详细信息
ISBN:
(纸本)9783642043932
Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (GED). In this paper, data transformations are optimized instead. This is equivalent to searching for GEDs, but can be applied to any learning algorithm, even if it does not use distances explicitly. Two optimization techniques have been used: a simple Local Search (LS) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). CMA-ES is an advanced evolutionary method for optimization in difficult continuous domains. Both diagonal and complete matrices have been considered. Results show that in general, complete matrices found by CMA-ES either outperform or match both Local Search, and the classifier working on the original untransformed data.
The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. The focus has turned to adding more humanlike characteristics into computer game agents....
详细信息
This paper describes a Conscious Tutoring System (CTS) capable of dynamic fine-tuned assistance to users. We put forth the integration of an Episodic learning mechanism within CTS that allows it to first establish, th...
详细信息
In this study, a dynamic power management method based on reinforcement learning is proposed to improve the energy utilization for energy harvesting wireless sensor networks. Simulations of the proposed method on wire...
详细信息
Knowledge engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different fields. Knowledge engineering techniques offer the fol...
详细信息
Supervised classification is a spot/task of data mining which consists in building a classifier from a set of examples labeled by their class (learning step) and then predicting the class of new examples with a classi...
详细信息
A predictor based on interacting multiple model (IMM) algorithm is proposed to forecast hourly travel time index (TTI) data in the paper. It is the first time to propose the approach to time series prediction. Seven b...
详细信息
暂无评论