12,174 research outputs found
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation
Review of Methodologies to Assess Bridge Safety During and After Floods
This report summarizes a review of technologies used to monitor bridge scour with an emphasis on techniques appropriate for testing during and immediately after design flood conditions. The goal of this study is to identify potential technologies and strategies for Illinois Department of Transportation that may be used to enhance the reliability of bridge safety monitoring during floods from local to state levels. The research team conducted a literature review of technologies that have been explored by state departments of transportation (DOTs) and national agencies as well as state-of-the-art technologies that have not been extensively employed by DOTs. This review included informational interviews with representatives from DOTs and relevant industry organizations. Recommendations include considering (1) acquisition of tethered kneeboard or surf ski-mounted single-beam sonars for rapid deployment by local agencies, (2) acquisition of remote-controlled vessels mounted with single-beam and side-scan sonars for statewide deployment, (3) development of large-scale particle image velocimetry systems using remote-controlled drones for stream velocity and direction measurement during floods, (4) physical modeling to develop Illinois-specific hydrodynamic loading coefficients for Illinois bridges during flood conditions, and (5) development of holistic risk-based bridge assessment tools that incorporate structural, geotechnical, hydraulic, and scour measurements to provide rapid feedback for bridge closure decisions.IDOT-R27-SP50Ope
Underwater optical wireless communications in turbulent conditions: from simulation to experimentation
Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel.
This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated.
Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link.
Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
The Role of Transient Vibration of the Skull on Concussion
Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury
Water and Wastewater Digital Surveillance 1 for Monitoring and Early Detection of 2 the COVID-19 Hotspot: Industry 4.0
Abstract: There are a high number of COVID-19 cases per capita in the world that goes undetected including clinical diseases compatible with COVID-19. While the presence of the COVID-19 in untreated drinking water is possible, it is yet to be detected in the drinking-water supplies. COVID-19 viral fragments have been found in excrete, this call for wastewater monitoring and analysis (wastewater surveillance) of the potential health risk. This raises concern about the potential of the SARS-CoV-2 transmission via the water systems. The economic limits on the medical screening for the SARS-CoV-2 or COVID-19 worldwide are turning to wastewater-based epidemiology as great potential tools for assessing and management of the COVID-19 pandemic. Surveillance and tracking of the pathogens in the wastewater are key to the early warning system and public health strategy monitoring of the COVID-19. Currently, RT-qPCR assays is been developed for SARS-CoV-2 RNA specimen clinical testing and detection in the water system. Convectional wastewater treatment methods and disinfection are expected to eradicate the SAR-CoV-2. Chlorine, UV radiation, ozone, chloramine is been used to inactivate and disinfect the water treatment system against the SARS-CoV-2. Water management and design of the water infrastructure require major changes to accommodate climate change, water cycle, reimaging of digitalization, infrastructure and privacy protection. The water digital revolution, biosensors and nanoscale, contact tracing, knowledge management can accelerate with disruption of the COVID-19 outbreak (water-health-digital nexus)
Novel strategies for the modulation and investigation of memories in the hippocampus
Disruptions of the memory systems in the brain are linked to the manifestation of many neuropsychiatric diseases such as Alzheimer’s disease, depression, and post-traumatic stress disorder. The limited efficacy of current treatments necessities the development of more effective therapies. Neuromodulation has proven effective in a variety of neurological diseases and could be an attractive solution for memory disorders. However, the application of neuromodulation requires a more detailed understanding of the network dynamics associated with memory formation and recall. In this work, we applied a combination of optical and computational tools in the development of a novel strategy for the modulation of memories, and have expanded its application for interrogation of the hippocampal circuitry underlying memory processing in mice.
First, we developed a closed-loop optogenetic stimulation platform to activate neurons implicated in memory processing (engram neurons) with a high temporal resolution. We applied this platform to modulate the activity of engram neurons and assess memory processing with respect to synchronous network activity. The results of our investigation support the proposal that encoding new information and recalling stored memories occur during distinct epochs of hippocampal network-wide oscillations.
Having established the high efficacy of the modulation of engram neurons’ activity in a closed-loop fashion, we sought to combine it with two-photon imaging to enable high spatial resolution interrogation of hippocampal circuitry. We developed a behavioral apparatus for head-fixed engram modulation and the assessment of memory recall in immobile animals. Moreover, through the optimization of dual color two-photon imaging, we improved the ability to monitor activity of neurons in the subfields of the hippocampus with cellular specificity. The platform created here will be applied to investigate the effects of engram reactivation on downstream projections targets with high spatial and cell subtype specificity.
Following these lines of investigations will enhance our understanding of memory modulation and could lead to novel neuromodulation treatments for neurological disorders associated with memory malfunctioning
Microstructure and Microtexture Development in Grain Oriented Electrical Steel
The first Si-Fe electrical steel was produced in 1905, and the grain-oriented steel was discovered in 1930 after Goss demonstrated how optimal combinations of heat treatment and cold rolling could produce a texture giving Si-Fe strip good magnetic properties when magnetised along its rolling direction. This technology has reduced the power loss in transformers greatly and remains the basis of the manufacturing process today. Since then, many postulations reported on the mechanism on abnormal grain growth (AGG) which is the key for Si-Fe superior magnetic properties. However, none have provided a concrete understanding of this phenomenon. Identifying and classifying the driving force behind Goss abnormal grain growth is of industrial and academic importance to further optimise the manufacturing process and reduce losses. In the current investigation, the deviation from easy magnetisation direction was studied to find a correlation between crystallographic orientation and magnetic domain structure. Both deviation angles α: the angle between and in-plane rolling direction (RD), and β: the angle between and out-plane rolling direction were calculated using electron backscatter diffraction (EBSD) raw data. Further, EBSD combined with forescatter detector (FSD) is used to reveal the magnetic domain configuration within individual oriented grains. The magnetic domain patterns were directly imaged and correlated to the crystal orientation and α and β deviation angles. It was demonstrated that the size of the deviated orientation grains from ideal (110) Goss orientation is a critical microtexture parameter for the optimisation of magnetic property. It is concluded that the magnetic domain patterns and α and β angle of deviations are strongly correlated to the magnetic losses in GOES (grain oriented electrical steel).Furthermore, the effect of grain boundaries, grain size, heating rate and dislocation density on Goss abnormal grain growth was investigated using EBSD. It was found that in the early stages of secondary recrystallisation random grains grow and abnormal growth of Goss achieved in low heating rate. The advantage of Goss abnormal grain growth in secondary recrystallisation is lost while annealing at a high heating rate, and random orientation can grow abnormally. Also, statistical analysis of grain boundaries, including CSL (coincident site lattice), shows no distinct behaviour and high angle grain boundaries and CSL are not exclusive to Goss oriented grains. In addition, GND (geometrically necessary dislocation) and Taylor Factor showed to be randomly distributed around Goss grains, and the hypothesis of Goss grains grow by consuming high GND and Taylor Factor grains cannot be the reason for Goss abnormal grain growth. Neutron diffraction experiment was conducted at Rutherford Appleton Laboratory, ISIS facility at Oxford, UK using GEM beamline. It was demonstrated that Si atom positions in the solid solution disorder α-Fe cubic unit cell that cause lattice distortions and BCC symmetry reduction is the most influential factor in early stages of Goss AGG than what was previously thought to be dislocation related stored energy, grain boundary characteristics and grain size/orientation advantages. Finally, heat flux, heat flow direction, and strain effect on Goss abnormal grain growth investigated. It was found that heat flow direction greatly impacts the rate of abnormal grain growth of Goss. Also, strain areas can disrupt Goss AGG and promotes randomly oriented grains to grow abnormally
Phase Dynamics in Human Visuomotor Control - Health & Disease
In this thesis, comprised of four publications, I investigated phase dynamics of visuomotor control in humans during upright stance in response to an oscillatory visual drive. For this purpose, I applied different versions of a ‘moving room’ paradigm in virtual reality while stimulating human participants with anterior-posterior motion of their visual surround and analyzed their bodily responses.
Human balance control constitutes a complex interplay of interdependent processes. The main sensory contributors include vision, vestibular input, and proprioception, with a dominant role attributed to vision. The purpose of the balance control system is to keep the body’s center of mass (COM) within a certain spatial range around the current base of support. Ever-changing environmental circumstances along with sensory noise cause the body to permanently sway around its point of equilibrium. Considering this sway, the human body can be modelled as a (multi-link) inverted pendulum. To maintain balance while being exposed to perturbations of the visual environment, humans adjust their sway to counteract the perceived motion of their bodies. Neurodegenerative diseases like Parkinson’s impair balance control and thus are likely to affect these mechanisms. Hence, investigation of bodily responses to a visual drive gives insight into visuomotor control in health and disease.
In my first study, I introduced inter-trial phase coherence (ITPC) as a novel method to investigate postural responses to periodical visual stimulation. I found that human participants phase-locked the motion of their center of pressure (COP) to a 3-D dot cloud which oscillated in the anterior-posterior direction. This effect was equally strong for a low frequency of visual stimulation at 0.2 Hz and a high frequency of 1.5 Hz, the latter exceeding the previously assumed frequency range associated with coherent postural sway responses to periodical oscillations of the visual environment (moving room). Moreover, I was able to show that ITPC reliably captured responses in almost all participants, thereby addressing the common problem of inter-subject variability in body sway research. Based on the results of my first study, I concluded phase locking to be an essential feature in human postural control.
For the second study, I introduced a mobile and cost-effective setup to apply a visual paradigm consisting of a virtual tunnel which stretched in the anterior-posterior direction and oscillated back and forth at three distinct frequencies (0.2 Hz, 0.8 Hz, and 1.2 Hz). Because tracking of the COP alone neglects crucial information about how COM shifts are arranged across the body, I included additional full-body motion tracking here to evaluate sway of individual body segments. Using a modified measure of phase locking, the phase locking value (PLV), allowed me to find participants phase-locking not only their COP, but also additional segments of their body to the visual drive. While their COP exhibited a strong phase locking to all frequencies of visual stimulation, distribution of phase locking across the body underwent a shift as the frequency of the visual stimulation increased. For the lowest frequency of 0.2 Hz, participants phase-locked almost their entire body to the stimulus. At higher frequencies, this phase locking shifted towards the lower torso and hip, with subjects almost exclusively phase-locking their hip to the visual drive at the highest frequency of 1.2 Hz. Having introduced a novel and reliable measurement along with a mobile setup, these results allowed me to empirically confirm shifts in postural strategies previously proposed in the literature.
In the third study, a collaboration with the neurology department of the Universitätsklinikum Gießen und Marburg (UKGM), I used the same setup and paradigm as in the previous study and additionally derived the trajectory of the COM from a weighted combination of certain body segments. The aim was to investigate phase locking of body sway in a group of patients suffering from Parkinson’s disease (PD) to find potential means for an early diagnosis of the illness. For this purpose, I recruited a group of PD patients, an age-matched control group, and a group of young healthy adults. Even though the sway amplitude of PD patients was significantly larger than that of both other groups, they phase-locked their COP and COM in a similar manner as the control groups. However, considering individual body segments, the shift in PLV distribution differed between groups. While young healthy adults, analogous to the participants in the second study, exhibited a shift towards exclusive phase locking of their hips as frequency of the stimulation increased, both PD patients and age-matched controls maintained a rather homogeneous phase locking across their body. This suggested increased body stiffness, although being an effect of age rather than disease. Overall, I concluded that patients of early-to-mid stage PD exhibit impaired motor control, reflected in their increased sway amplitude, but intact visuomotor processing, indicated by their ability to phase-lock the motion of their body to a visual drive.
The fourth study, to which I contributed as second author, used experimental data collected from an additional visual condition in the course of the third study. This condition consisted of unpredictable back and forward motion of the simulated tunnel. Here, we investigated the velocity profiles of the COP and COM in response to the unpredictable visual motion and a baseline condition at which the tunnel remained static. We found PD patients to exhibit larger velocities of their COP and COM under both conditions when compared to the control groups. When examining the net increase that unpredictable motion had on the velocity of both parameters, we found a significantly higher increase in COP velocity for both PD patients and age-matched controls, but no increase in COM velocity in any of the groups. These results suggested that all groups successfully maintained their balance under unpredictable visual perturbations, but that PD patients and older adults required more effort to accomplish this task, as reflected by the increased velocity of their COP. Again, these results indicated an effect of age rather than disease on the observed postural responses.
In summary, using innovative phase-locking techniques and simultaneously tracking multiple body sway parameters, I was able to provide novel insight into visuomotor control in humans. First, I overcame previous issues of inconsistent sway parameters in groups of participants; Second, I found phase-locking to be an essential feature of visuomotor processing, which also allowed me to empirically confirm previously established theories of postural control; Third, through studies in collaboration with the neurology department of the UKGM, I was able to uncover new aspects of visuomotor processing in Parkinson’s, contributing to a better understanding of the sensorimotor aspects of the disease
- …