Multimedia Retrieval Using Time Series Representation and Relevance Feedback

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.

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Machine Tool Component Health Identification with Unsupervised Learning

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp4030086 ◽

2020 ◽

Vol 4 (3) ◽

pp. 86

Author(s):

Thomas Gittler ◽

Stephan Scholze ◽

Alisa Rupenyan ◽

Konrad Wegener

Keyword(s):

Time Series ◽

Machine Tool ◽

Statistical Significance ◽

Clustering Algorithms ◽

Series Representation ◽

Feature Representation ◽

Machine Component ◽

Test Cycle ◽

Clustering Model ◽

Online Measurements

Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.

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Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints

Artificial Intelligence for Maximizing Content Based Image Retrieval ◽

10.4018/978-1-60566-174-2.ch007 ◽

2011 ◽

pp. 145-170 ◽

Cited By ~ 14

Author(s):

Chotirat “Ann” Ratanamahatana ◽

Eamonn Keogh ◽

Vit Niennattrakul

Keyword(s):

Time Series ◽

Image Retrieval ◽

Data Storage ◽

Distance Measure ◽

Series Representation ◽

Feedback System ◽

Multimedia Data ◽

Video Frames ◽

Computationally Intensive ◽

Dynamic Time

After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically increased in the database and data mining community. This includes videos, images, and handwriting, where we now have higher expectations in exploiting these data at hand. We argue however, that much of this work’s narrow focus on efficiency and scalability has come at the cost of usability and effectiveness. Typical manipulations are in some forms of video/image processing, which require fairly large amounts for storage and are computationally intensive. In this work, we will demonstrate how these multimedia data can be reduced to a more compact form, that is, time series representation, while preserving the features of interest, and can then be efficiently exploited in Content-Based Image Retrieval. We also introduce a general framework that learns a distance measure with arbitrary constraints on the warping path of the Dynamic Time Warping calculation. We demonstrate utilities of our approach on both classification and query retrieval tasks for time series and other types of multimedia data including images, video frames, and handwriting archives. In addition, we show that incorporating this framework into the relevance feedback system, a query refinement can be used to further improve the precision/recall by a wide margin.

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