10 research outputs found
A Deep Learning based Explainable Control System for Reconfigurable Networks of Edge Devices
Edge devices that operate in real-world environments are subjected to unpredictable conditions caused by environmental forces such as wind and uneven surfaces. Since most edge systems exhibit dynamic properties, reinforcement learning can be a powerful tool for improving system accuracy. Successful maintenance of the position of a vehicle in such environments can be achieved with the aid of Deep Reinforcement Learning (DRL) that dynamically adjusts the Reconfigurable Wireless Network (RWN) response. Deep Neural Networks (DNNs) is often seen as black boxes, as neither the acquired knowledge nor the decision rationale can be explained. In this paper, we explain the process of a DNN on an autonomous dynamic positioning system by gauging reactions of the DNN to predefined constraints. We introduce a novel digitisation technique that reduces interesting patterns of time series data into single digits to obtain a cross comparable view of the conditions. By analysing the clusters formed on this cross comparable view, we discovered multiple intensities of environmental conditions spanning across 44\% of moderate conditions and 33\% and 23\% of harsh and mild conditions, respectively. Our analysis showed that the proposed system can provide stable responses to uncertain conditions by predicting randomness
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Human activity recognition (HAR) in wearable computing is typically based on
direct processing of sensor data. Sensor readings are translated into
representations, either derived through dedicated preprocessing, or integrated
into end-to-end learning. Independent of their origin, for the vast majority of
contemporary HAR, those representations are typically continuous in nature.
That has not always been the case. In the early days of HAR, discretization
approaches have been explored - primarily motivated by the desire to minimize
computational requirements, but also with a view on applications beyond mere
recognition, such as, activity discovery, fingerprinting, or large-scale
search. Those traditional discretization approaches, however, suffer from
substantial loss in precision and resolution in the resulting representations
with detrimental effects on downstream tasks. Times have changed and in this
paper we propose a return to discretized representations. We adopt and apply
recent advancements in Vector Quantization (VQ) to wearables applications,
which enables us to directly learn a mapping between short spans of sensor data
and a codebook of vectors, resulting in recognition performance that is
generally on par with their contemporary, continuous counterparts - sometimes
surpassing them. Therefore, this work presents a proof-of-concept for
demonstrating how effective discrete representations can be derived, enabling
applications beyond mere activity classification but also opening up the field
to advanced tools for the analysis of symbolic sequences, as they are known,
for example, from domains such as natural language processing. Based on an
extensive experimental evaluation on a suite of wearables-based benchmark HAR
tasks, we demonstrate the potential of our learned discretization scheme and
discuss how discretized sensor data analysis can lead to substantial changes in
HAR
Applications of high-frequency telematics for driving behavior analysis
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsProcessing driving data and investigating driving behavior has been receiving an
increasing interest in the last decades, with applications ranging from car insurance
pricing to policy-making. A popular way of analyzing driving behavior is to move
the focus to the maneuvers as they give useful information about the driver who is
performing them.
Previous research on maneuver detection can be divided into two strategies, namely,
1) using fixed thresholds in inertial measurements to define the start and end of specific
maneuvers or 2) using features extracted from rolling windows of sensor data
in a supervised learning model to detect maneuvers. While the first strategy is not
adaptable and requires fine-tuning, the second needs a dataset with labels (which is
time-consuming) and cannot identify maneuvers with different lengths in time.
To tackle these shortcomings, we investigate a new way of identifying maneuvers
from vehicle telematics data, through motif detection in time-series. Using a publicly
available naturalistic driving dataset (the UAH-DriveSet), we conclude that motif
detection algorithms are not only capable of extracting simple maneuvers such as accelerations,
brakes, and turns, but also more complex maneuvers, such as lane changes
and overtaking maneuvers, thus validating motif discovery as a worthwhile line for
future research in driving behavior.
We also propose TripMD, a system that extracts the most relevant driving patterns
from sensor recordings (such as acceleration) and provides a visualization that allows
for an easy investigation. We test TripMD in the same UAH-DriveSet dataset and show
that (1) our system can extract a rich number of driving patterns from a single driver
that are meaningful to understand driving behaviors and (2) our system can be used
to identify the driving behavior of an unknown driver from a set of drivers whose
behavior we know.Nas últimas décadas, o processamento e análise de dados de condução tem recebido
um interesse cada vez maior, com aplicações que abrangem a área de seguros de
automóveis até a atea de regulação. Tipicamente, a análise de condução compreende a
extração e estudo de manobras uma vez que estas contêm informação relevante sobre
a performance do condutor.
A investigação prévia sobre este tema pode ser dividida em dois tipos de estratégias,
a saber, 1) o uso de valores fixos de aceleração para definir o início e fim de cada
manobra ou 2) a utilização de modelos de aprendizagem supervisionada em janelas
temporais. Enquanto o primeiro tipo de estratégias é inflexível e requer afinação dos
parâmetros, o segundo precisa de dados de condução anotados (o que é moroso) e não
é capaz de identificar manobras de diferentes durações.
De forma a mitigar estas lacunas, neste trabalho, aplicamos métodos desenvolvidos
na área de investigação de séries temporais de forma a resolver o problema de deteção
de manobras. Em particular, exploramos área de deteção de motifs em séries temporais
e testamos se estes métodos genéricos são bem-sucedidos na deteção de manobras.
Também propomos o TripMD, um sistema que extrai os padrões de condução mais
relevantes de um conjuntos de viagens e fornece uma simples visualização. TripMD é
testado num conjunto de dados públicos (o UAH-DriveSet), do qual concluímos que
(1) o nosso sistema é capaz de extrair padrões de condução/manobras de um único
condutor que estão relacionados com o perfil de condução do condutor em questão e (2)
o nosso sistema pode ser usado para identificar o perfil de condução de um condutor
desconhecido de um conjunto de condutores cujo comportamento nos é conhecido
FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS
Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences.
Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction.
In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures.
Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction.
Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope