418 research outputs found
The Challenges and Issues Facing the Deployment of RFID Technology
Griffith Sciences, School of Information and Communication TechnologyFull Tex
Intelligent Sensor Networks
In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts
Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management
Deep Learning Methods for Human Activity Recognition using Wearables
Wearable sensors provide an infrastructure-less multi-modal sensing method. Current
trends point to a pervasive integration of wearables into our lives with these devices
providing the basis for wellness and healthcare applications across rehabilitation,
caring for a growing older population, and improving human performance.
Fundamental to these applications is our ability to automatically and accurately
recognise human activities from often tiny sensors embedded in wearables. In this
dissertation, we consider the problem of human activity recognition (HAR) using
multi-channel time-series data captured by wearable sensors.
Our collective know-how regarding the solution of HAR problems with wearables has
progressed immensely through the use of deep learning paradigms. Nevertheless, this
field still faces unique methodological challenges. As such, this dissertation focuses on
developing end-to-end deep learning frameworks to promote HAR application opportunities
using wearable sensor technologies and to mitigate specific associated challenges. In our
efforts, the investigated problems cover a diverse range of HAR challenges and spans
from fully supervised to unsupervised problem domains.
In order to enhance automatic feature extraction from multi-channel time-series
data for HAR, the problem of learning enriched and highly discriminative activity
feature representations with deep neural networks is considered. Accordingly, novel
end-to-end network elements are designed which: (a) exploit the latent relationships
between multi-channel sensor modalities and specific activities, (b) employ effective
regularisation through data-agnostic augmentation for multi-modal sensor data
streams, and (c) incorporate optimization objectives to encourage minimal intra-class
representation differences, while maximising inter-class differences to achieve more
discriminative features.
In order to promote new opportunities in HAR with emerging battery-less sensing
platforms, the problem of learning from irregularly sampled and temporally sparse readings
captured by passive sensing modalities is considered. For the first time, an efficient
set-based deep learning framework is developed to address the problem. This
framework is able to learn directly from the generated data, bypassing the need for
the conventional interpolation pre-processing stage. In order to address the multi-class window problem and create potential solutions
for the challenging task of concurrent human activity recognition, the problem of
enabling simultaneous prediction of multiple activities for sensory segments is considered.
As such, the flexibility provided by the emerging set learning concepts is further
leveraged to introduce a novel formulation of HAR. This formulation treats HAR
as a set prediction problem and elegantly caters for segments carrying sensor data
from multiple activities. To address this set prediction problem, a unified deep HAR
architecture is designed that: (a) incorporates a set objective to learn mappings from
raw input sensory segments to target activity sets, and (b) precedes the supervised
learning phase with unsupervised parameter pre-training to exploit unlabelled data
for better generalisation performance.
In order to leverage the easily accessible unlabelled activity data-streams to serve
downstream classification tasks, the problem of unsupervised representation learning from
multi-channel time-series data is considered. For the first time, a novel recurrent
generative adversarial (GAN) framework is developed that explores the GAN’s latent
feature space to extract highly discriminating activity features in an unsupervised
fashion. The superiority of the learned representations is substantiated by their
ability to outperform the de facto unsupervised approaches based on autoencoder
frameworks. At the same time, they rival the recognition performance of fully
supervised trained models on downstream classification benchmarks.
In recognition of the scarcity of large-scale annotated sensor datasets and the
tediousness of collecting additional labelled data in this domain, the hitherto unexplored
problem of end-to-end clustering of human activities from unlabelled wearable data is
considered. To address this problem, a first study is presented for the purpose of
developing a stand-alone deep learning paradigm to discover semantically meaningful
clusters of human actions. In particular, the paradigm is intended to: (a) leverage
the inherently sequential nature of sensory data, (b) exploit self-supervision from
reconstruction and future prediction tasks, and (c) incorporate clustering-oriented
objectives to promote the formation of highly discriminative activity clusters. The
systematic investigations in this study create new opportunities for HAR to learn
human activities using unlabelled data that can be conveniently and cheaply collected
from wearables.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
Design and Simulation of RFID-Enabled Aircraft Reverse Logistics Network via Agent-Based Modeling
Reverse Logistics (RL) has become increasingly popular in different industries especially aerospace industry over the past decade due to the fact that RL can be a profitable and sustainable business strategy for many organizations. However, executing and fulfilling an efficient recovery network needs constructing appropriate logistics system for flows of new, used, and recovered products.
On the other hand, successful RL network requires a reliable monitoring and control system. A key factor for the success and effectiveness of RL system is to conduct real-time monitoring system such as radio frequency identification (RFID) technology. The RFID system can evaluate and analyze RL performance timely so that in the case of deviation in any areas of RL, the appropriate corrective actions can be taken in a quick manner. An automated data capturing system like RFID and computer simulation techniques such as agent-based (AB), system dynamic (SD) and discrete event (DE) provide a reliable platform for effective RL tracking and control, as they can respectively decrease the time needed to obtain data and simulate various scenarios for suitable best corrective actions. The functionality of the RL system can be noticeably elevated by integrating these two systems and techniques. Besides, each computer simulation approach has its own benefits for understanding the RL network from different aspects. Therefore, in this study, after designing and constructing the RL system through the real case study from Bell Helicopter Company with the aid of unified modeling language (UML), three simulation techniques were proposed for the model. Afterwards the results of all three simulation approaches (AB, SD and DE) were compared with considering two scenarios of RL RFID-enabled and RL without RFID. The computer simulation models were developed using “AnyLogic 7.1” software.
The results of the research present that with exploiting RFID technology, the total disassembly time of a single helicopter was decreased. The comparison of all three simulation methods was performed as well.
Keywords: Reverse logistics (RL), RFID, aerospace industry, agent-based simulation, system dynamic simulation, discrete event simulation, AnyLogi
A review of laser scanning for geological and geotechnical applications in underground mining
Laser scanning can provide timely assessments of mine sites despite adverse
challenges in the operational environment. Although there are several published
articles on laser scanning, there is a need to review them in the context of
underground mining applications. To this end, a holistic review of laser
scanning is presented including progress in 3D scanning systems, data
capture/processing techniques and primary applications in underground mines.
Laser scanning technology has advanced significantly in terms of mobility and
mapping, but there are constraints in coherent and consistent data collection
at certain mines due to feature deficiency, dynamics, and environmental
influences such as dust and water. Studies suggest that laser scanning has
matured over the years for change detection, clearance measurements and
structure mapping applications. However, there is scope for improvements in
lithology identification, surface parameter measurements, logistic tracking and
autonomous navigation. Laser scanning has the potential to provide real-time
solutions but the lack of infrastructure in underground mines for data
transfer, geodetic networking and processing capacity remain limiting factors.
Nevertheless, laser scanners are becoming an integral part of mine automation
thanks to their affordability, accuracy and mobility, which should support
their widespread usage in years to come
Optimising mobile laser scanning for underground mines
Despite several technological advancements, underground mines are still largely relied on visual inspections or discretely placed direct-contact measurement sensors for routine monitoring. Such approaches are manual and often yield inconclusive, unreliable and unscalable results besides exposing mine personnel to field hazards. Mobile laser scanning (MLS) promises an automated approach that can generate comprehensive information by accurately capturing large-scale 3D data. Currently, the application of MLS has relatively remained limited in mining due to challenges in the post-registration of scans and the unavailability of suitable processing algorithms to provide a fully automated mapping solution. Additionally, constraints such as the absence of a spatial positioning network and the deficiency of distinguishable features in underground mining spaces pose challenges in mobile mapping.
This thesis aims to address these challenges in mine inspections by optimising different aspects of MLS: (1) collection of large-scale registered point cloud scans of underground environments, (2) geological mapping of structural discontinuities, and (3) inspection of structural support features. Firstly, a spatial positioning network was designed using novel three-dimensional unique identifiers (3DUID) tags and a 3D registration workflow (3DReG), to accurately obtain georeferenced and coregistered point cloud scans, enabling multi-temporal mapping. Secondly, two fully automated methods were developed for mapping structural discontinuities from point cloud scans – clustering on local point descriptors (CLPD) and amplitude and phase decomposition (APD). These methods were tested on both surface and underground rock mass for discontinuity characterisation and kinematic analysis of the failure types. The developed algorithms significantly outperformed existing approaches, including the conventional method of compass and tape measurements. Finally, different machine learning approaches were used to automate the recognition of structural support features, i.e. roof bolts from point clouds, in a computationally efficient manner. Roof bolts being mapped from a scanned point cloud provided an insight into their installation pattern, which underpinned the applicability of laser scanning to inspect roof supports rapidly. Overall, the outcomes of this study lead to reduced human involvement in field assessments of underground mines using MLS, demonstrating its potential for routine multi-temporal monitoring
Mechatronic Systems
Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools
Saving Our Bacon: Applications of Deep Learning for Precision Pig Farming
PhD ThesisThe research presented in this thesis focussed on how deep learning can be applied to the field of
agriculture to enable precision livestock farming for pigs. This refers to the use of technology
to automatically monitor, predict, and manage livestock. Increased consumer awareness of the
welfare issues facing animals in the farming industry, combined with growing demand for high-quality produce, has resulted in a need for providing farmers with tools to improve and simplify
animal care. The concept of precision livestock farming tackles these requirements, as it makes
it possible to treat animals as individuals, rather than as batches. This translates to tailored care
for each animal and the potential for higher-quality produce. As deep learning has shown rapidly
increasing potential in recent years, this research explored and evaluated various architectures
for applications in two distinct areas within pig farming. We began by demonstrating how deep
learning methods can be used to monitor and model the environmental conditions in which pigs
are living in order to forecast oncoming respiratory disease. Implementing this approach can
mean earlier intervention than if simplify looking for clinical symptoms. However, as not all
diseases are caused by environmental conditions, we also implemented and evaluated a full
workflow for the localisation and tracking of individual pigs. This made it possible to extract
behavioural metrics to better understand the wellbeing of each pig. Overall, this research shows
that deep learning can be used to advance the agriculture industry towards better levels of care,
which is valuable for all stakeholders
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