773 research outputs found
Evaluation of spatial-temporal anomalies in the analysis of human movement
The dissemination of Internet of Things solutions, such as smartphones, lead to the
appearance of devices that allow to monitor the activities of their users. In manufacture,
the performed tasks consist on sets of predetermined movements that are exhaustively
repeated, forming a repetitive behaviour. Additionally, there are planned and unplanned events on manufacturing production lines which cause the repetitive behaviour to stop. The execution of improper movements and the existence of events that might prejudice the productive system are regarded as anomalies.
In this work, it was investigated the feasibility of the evaluation of spatial-temporal
anomaly detection in the analysis of human movement. It is proposed a framework capable of detecting anomalies in generic repetitive time series, thus being adequate to handle Human motion from industrial scenarios. The proposed framework consists of (1) a new unsupervised segmentation algorithm; (2) feature extraction, selection and dimensionality reduction; (3) unsupervised classification based on DBSCAN used to distinguish normal and anomalous instances.
The proposed solution was applied in four different datasets. Two of those datasets
were synthetic and two were composed of real-world data, namely, electrocardiography
data and human movement in manufacture. The yielded results demonstrated not only
that anomaly detection in human motion is possible, but that the developed framework
is generic and, with examples, it was shown that it may be applied in general repetitive
time series with little adaptation effort for different domains.
The results showed that the proposed framework has the potential to be applied in
manufacturing production lines to monitor the employees movements, acting as a tool to detect both planned and unplanned events, and ultimately reduce the risk of appearance of musculoskeletal disorders in industrial settings in long-term
Flexible Time Series Matching for Clinical and Behavioral Data
Time Series data became broadly applied by the research community in the last decades after
a massive explosion of its availability. Nonetheless, this rise required an improvement
in the existing analysis techniques which, in the medical domain, would help specialists
to evaluate their patients condition. One of the key tasks in time series analysis is pattern
recognition (segmentation and classification). Traditional methods typically perform subsequence
matching, making use of a pattern template and a similarity metric to search
for similar sequences throughout time series. However, real-world data is noisy and variable
(morphological distortions), making a template-based exact matching an elementary
approach. Intending to increase flexibility and generalize the pattern searching tasks
across domains, this dissertation proposes two Deep Learning-based frameworks to solve
pattern segmentation and anomaly detection problems.
Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is
proposed, learning to distinguish, point-by-point, desired sub-patterns from background
content within a time series. The proposed framework was validated in two use-cases:
electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed
two conventional matching techniques, being capable of notably detecting the
targeted cycles even in noise-corrupted or extremely distorted signals, without using any
reference template nor hand-coded similarity scores.
Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction
ability of Variational Autoencoders and a local similarity score to identify
non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH
Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results
indicated competitiveness relative to recent techniques, achieving detection AUC scores
of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica
nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este
aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria
os especialistas na avaliação da condição dos seus pacientes. Um dos principais
tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação).
Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em
subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade
para procurar por subsequências similares ao longo de séries temporais. Todavia, dados
do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência
exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo
aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura
de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep
Learning para solucionar problemas de segmentação de padrões e deteção de anomalias.
Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução
proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de
fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais
eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou
duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente,
mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de
nenhum padrão de referência nem métricas de similaridade codificadas manualmente.
A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a
capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade
local para identificar anomalias desconhecidas. A proposta foi validada na identificação
de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e
ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando
métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)
Features Extraction from Time Series
Time series can be found in various domains like medicine, engineering, and finance. Generally speaking, a time series is a sequence of data that represents recorded values of a phenomenon over time. This thesis studies time series mining, including transformation and distance measure, anomaly or anomalies detection, clustering and remaining useful life estimation.
In the course of the first mining task (transformation and distance measure), in order to increase the accuracy of distance measure between transformed series (symbolic series), we introduce a novel calculation of distance between symbols. By integrating this newly defined method to symbolic aggregate approximation and its extensions, the experimental results show this proposed method is promising.
During the process of the second mining task (anomaly or anomalies detection), for the purpose of improving the accuracy of anomaly or anomalies detection, we propose a distance measure method and an anomalies detection calculation. These proposed methods, together with previous published anomaly detection methods, are applied to real ECG data selected from MIT-BIH database. The experimental results show that our proposed outperforms other methods.
During the course of the third mining task (clustering), we present an automatic clustering method, called AT-means, which can automatically carry out clustering for a given time series dataset: from the calculation of global average time series to the setting of initial centres and the determination of the number of clusters. The performance of the proposed method was tested on 10 benchmark time series datasets obtained from UCR database. For comparison, the K-means method with three different conditions are also applied to the same datasets. The experimental results show the proposed method outperforms the compared K-means approaches.
During the process of the fourth mining task (remaining useful life estimation), all the original data are transformed into low-dimensional space through principal components analysis. We then proposed a novel multidimensional time series distance measure method, called as multivariate time series warping distance (MTWD), for remaining useful life estimation. This whole process is tested on the CMAPSS (Commercial Modular Aero Propulsion System Simulation) datasets and the performance is compared with two existing methods. The experimental results show that the estimated remaining useful life (RUL) values are closer to real RUL values when compared with the comparison methods.
Our work contributes to the time series mining by introducing novel approaches to distance measure, anomalies detection, clustering and RUL estimation. We furthermore apply our proposed methods and related methods to benchmark datasets. The experimental results show that our methods are better than previously published methods in terms of accuracy and efficiency
Analiza i predviđanje toka vremenskih serija pomoću “Case-BasedReasoning” tehnologije.
This thesis describes one promising approach where a problem of time series analysis and prediction was solved by using Case Based Reasoning (CBR) technology. Foundations and main concepts of this technology are described in detail. Furthermore, a detailed study of different approaches in time series analysis is given. System CuBaGe (Curve Base Generator) - A robust and general architecture for curve representation and indexing time series databases, based on Case based reasoning technology, was developed. Also, a corresponding similarity measure was modelled for a given kind of curve representation. The presented architecture may be employed equally well not only in conventional time series (where all values are known), but also in some non-standard time series (sparse, vague, non-equidistant). Dealing with the non-standard time series is the highest advantage of the presented architecture.U ovoj doktorskoj disertaciji prikazan je interesantan i perspektivan pristup rešavanja problema analize i predviđanja vremenskih serija korišćenjem Case Based Reasoning (CBR) tehnologije. Detaljno su opisane osnove i glavni koncepti ove tehnologije. Takođe, data je komparativna analiza različitih pristupa u analizi vremenskih serija sa posebnim osvrtom na predviđanje. Kao najveći doprinos ove disertacije, prikazan je sistem CuBaGe (Curve Base Generator) u kome je realizovan originalni način reprezentacije vremenskih serija zajedno sa, takođe originalnom, odgovarajućom merom sličnosti. Robusnost i generalnost sistema ilustrovana je realnom primenom u domenu finansijskog predviđanja, gde je pokazano da sistem jednako dobro funkcioniše sa standardnim, ali i sa nekim nestandardnim vremenskim serijama (neodređenim, retkim i neekvidistantnim)
- …