Deep Learning Methods for Visual Fault Diagnostics of Dental X-ray Systems

Dental X-ray systems go through rigorous quality assurance protocols following their production and assembly. The protocols include tests, which address the image quality and find certain errors or artifacts that may be present in the images. Detecting faults from the images require human effort, experience, and time. Recent advances in deep learning have proven them to be successful in image classification, object detection, machine translation. The applications of deep learning can be extended to fault detection in X-ray systems. This thesis work consists of surveying, applying, and developing state-of-art deep learning approaches for detection of visual faults or artifacts in the dental X-ray systems. In this thesis, we have shown that deep learning methods can detect geometry and collimator artifacts from X-ray images efficiently and rapidly. This thesis is a precursor for further development of deep learning methods to include detection of wide range of faults and artifacts in X-ray systems to ease quality assurance, calibration, and device maintenance

Experimental and numerical modelling investigations into coal mine rockbursts and gas outbursts

Rockbursts and gas outbursts are a longstanding hazard in underground coal mining due to their sudden occurrences and high consequences. These hazards are becoming prominent due to the increase in mining depth, difficult mining conditions, and adverse gas pressure conditions. Several researchers have proposed different theories, mechanisms, and indices to determine the rockbursts and gas outbursts liability but most of them focus on only some aspects of the complex engineering system for the ease to represent them using partial differential equations. They have often ignored the dynamics of changing mining environment, coal seam heterogeneity and stochastic variations in the rock properties. Most of the indices proposed were empirical and their suitability to different mining conditions is largely debated. To overcome the limitations of previous theories, mechanisms and indices, a probabilistic risk assessment framework was developed in this research to mathematically represent the complex engineering phenomena of rockbursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity occurring in the coal seam to respect their geological origin. The dynamically changing mining conditions during a longwall top coal caving mining (LTCC) was extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among several parameters, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rockbursts and gas outbursts. The results obtained from the probabilistic risk assessment analysis incorporate the variations occurring due to lithological heterogeneity and give a probability for the occurrence of rockbursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used.Open Acces

Modeling of opencast mines using Surpac and its optimization

In this developing world, the demand for raw materials is increasing at a steady rate, in order to bridge the gap between supply and demand, technological advancement and automation in production method is needed. Since the raw materials are non-replenishable in nature and have took millions of years in their formation, so these resources should be judiciously used with maximum extraction level and aiming for zero mining waste, while adhering to all safety and regulatory norms. In this project, an effort have been made to estimate the resource using Surpac software for ore modeling and optimization algorithm are used for optimizing the shape of the pit and in ultimate pit design to ensure maximum extraction of the deposit. If the available mineral resources are not properly utilized then the cost of production will increase and hence company may lose in this competitive environment. So to ensure that efficient utilization of available resources in terms of shovels and dumpers and other face machinery available, a C++ program have been developed which can dynamically allocate the dumper to the nearest available shovel obeying various constraints to ensure that the production target is reached and the process is fully optimized

Z-Drawing: A Flying Agent System for Computer-assisted Drawing

We present a drone-based drawing system where a user's sketch on a desk is transformed across scale and time, and transferred onto a larger canvas at a distance in real-time. Various spatio-temporal transformations like scaling, mirroring, time stretching, recording and playing back over time, and simultaneously drawing at multiple locations allow for creating various artistic effects. The unrestricted motion of the drone promises scalability and a huge potential as an artistic medium

Comprehensive Simultaneous Shipboard and Airborne Characterization of Exhaust from a Modern Container Ship at Sea

We report the first joint shipboard and airborne study focused on the chemical composition and water-uptake behavior of particulate ship emissions. The study focuses on emissions from the main propulsion engine of a Post-Panamax class container ship cruising off the central coast of California and burning heavy fuel oil. Shipboard sampling included micro-orifice uniform deposit impactors (MOUDI) with subsequent off-line analysis, whereas airborne measurements involved a number of real-time analyzers to characterize the plume aerosol, aged from a few seconds to over an hour. The mass ratio of particulate organic carbon to sulfate at the base of the ship stack was 0.23 ± 0.03, and increased to 0.30 ± 0.01 in the airborne exhaust plume, with the additional organic mass in the airborne plume being concentrated largely in particles below 100 nm in diameter. The organic to sulfate mass ratio in the exhaust aerosol remained constant during the first hour of plume dilution into the marine boundary layer. The mass spectrum of the organic fraction of the exhaust aerosol strongly resembles that of emissions from other diesel sources and appears to be predominantly hydrocarbon-like organic (HOA) material. Background aerosol which, based on air mass back trajectories, probably consisted of aged ship emissions and marine aerosol, contained a lower organic mass fraction than the fresh plume and had a much more oxidized organic component. A volume-weighted mixing rule is able to accurately predict hygroscopic growth factors in the background aerosol but measured and calculated growth factors do not agree for aerosols in the ship exhaust plume. Calculated CCN concentrations, at supersaturations ranging from 0.1 to 0.33%, agree well with measurements in the ship-exhaust plume. Using size-resolved chemical composition instead of bulk submicrometer composition has little effect on the predicted CCN concentrations because the cutoff diameter for CCN activation is larger than the diameter where the mass fraction of organic aerosol begins to increase significantly. The particle number emission factor estimated from this study is 1.3 × 10^(16) (kg fuel)^(−1), with less than 1/10 of the particles having diameters above 100 nm; 24% of particles (>10 nm in diameter) activate into cloud droplets at 0.3% supersaturation

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201

Investigation of active anti-roll bars and development of control algorithm

Active anti-roll bars have recently found greater acceptance among premium car manufacturers and optimal application of this technology has emerged as an important field of research. This thesis investigates the potential of implementing active anti-roll bars in a passenger vehicle with the purpose of increasing customer value. For active anti-roll bars, customer value is defined in terms of vehicle’s ride comfort and handling performance. The objective with this thesis is to demonstrate this value through development of a control algorithm that can reflect the potential improvement in ride comfort and handling. A vehicle with passive anti-roll bars is simulated for different manoeuvres to identify the potential and establish a reference for the development of a control algorithm and for the performance of active anti-roll bars. While ride is evaluated using single-sided cosine wave and single-sided ramps, handling is evaluated using standardized constant radius, frequency response and sine with dwell manoeuvres.The control strategy developed implements a combination of sliding mode control, feed forward and PI-controllers. Simulations with active anti-roll bars showed significant improvement in ride and handling performance in comparison to passive anti-roll bars. In ride comfort, the biggest benefit was seen in the ability to increase roll damping and isolating low frequency road excitations. For handling, most significant benefits are through the system’s ability of changing the understeer behaviour of the vehicle and improving the handling stability in transient manoeuvres. Improvement in the roll reduction capability during steady state cornering is also substantial. In conclusion, active anti-roll bars are undoubtedly capable of improving both ride comfort and handling performance of a vehicle. Although the trade-off between ride and handling performance is significantly less, balance in requirements is critical to utilise the full potential of active anti-roll bars. With a more comprehensive control strategy, they also enable the vehicle to exhibit different driving characteristics without the need for changing any additional hardware

Investigation of active anti-roll bars and development of control algorithm

Active anti-roll bars have recently found greater acceptance among premium car manufacturers and optimal application of this technology has emerged as an important field of research. This thesis investigates the potential of implementing active anti-roll bars in a passenger vehicle with the purpose of increasing customer value. For active anti-roll bars, customer value is defined in terms of vehicle’s ride comfort and handling performance. The objective with this thesis is to demonstrate this value through development of a control algorithm that can reflect the potential improvement in ride comfort and handling. A vehicle with passive anti-roll bars is simulated for different manoeuvres to identify the potential and establish a reference for the development of a control algorithm and for the performance of active anti-roll bars. While ride is evaluated using single-sided cosine wave and single-sided ramps, handling is evaluated using standardized constant radius, frequency response and sine with dwell manoeuvres.The control strategy developed implements a combination of sliding mode control, feed forward and PI-controllers. Simulations with active anti-roll bars showed significant improvement in ride and handling performance in comparison to passive anti-roll bars. In ride comfort, the biggest benefit was seen in the ability to increase roll damping and isolating low frequency road excitations. For handling, most significant benefits are through the system’s ability of changing the understeer behaviour of the vehicle and improving the handling stability in transient manoeuvres. Improvement in the roll reduction capability during steady state cornering is also substantial. In conclusion, active anti-roll bars are undoubtedly capable of improving both ride comfort and handling performance of a vehicle. Although the trade-off between ride and handling performance is significantly less, balance in requirements is critical to utilise the full potential of active anti-roll bars. With a more comprehensive control strategy, they also enable the vehicle to exhibit different driving characteristics without the need for changing any additional hardware

Investigation of active anti-roll bars and development of control algorithm

Active anti-roll bars have recently found greater acceptance among premium car manufacturers and optimal application of this technology has emerged as an important field of research. This thesis investigates the potential of implementing active anti-roll bars in a passenger vehicle with the purpose of increasing customer value. For active anti-roll bars, customer value is defined in terms of vehicle’s ride comfort and handling performance. The objective with this thesis is to demonstrate this value through development of a control algorithm that can reflect the potential improvement in ride comfort and handling. A vehicle with passive anti-roll bars is simulated for different manoeuvres to identify the potential and establish a reference for the development of a control algorithm and for the performance of active anti-roll bars. While ride is evaluated using single-sided cosine wave and single-sided ramps, handling is evaluated using standardized constant radius, frequency response and sine with dwell manoeuvres.The control strategy developed implements a combination of sliding mode control, feed forward and PI-controllers. Simulations with active anti-roll bars showed significant improvement in ride and handling performance in comparison to passive anti-roll bars. In ride comfort, the biggest benefit was seen in the ability to increase roll damping and isolating low frequency road excitations. For handling, most significant benefits are through the system’s ability of changing the understeer behaviour of the vehicle and improving the handling stability in transient manoeuvres. Improvement in the roll reduction capability during steady state cornering is also substantial. In conclusion, active anti-roll bars are undoubtedly capable of improving both ride comfort and handling performance of a vehicle. Although the trade-off between ride and handling performance is significantly less, balance in requirements is critical to utilise the full potential of active anti-roll bars. With a more comprehensive control strategy, they also enable the vehicle to exhibit different driving characteristics without the need for changing any additional hardware

Calixarenes and Their Biomimetic Applications

The synthetic models for the structures, spectroscopic properties and catalytic activities of metalloprotein active sites have been reviewed. Calixarenes were used as new biomimetic catalysts because of their advantage of providing preorganiiation of the catalytic group, which can bind the substrate dynamically that results in fast turnover and fast release of the products. Functional and structural models based on calixarenes are presented and in addition importance of molecular recognition and non-covalent interactions e.g. hydrogen bonding and their role in biological systems are discussed with the help of synthetic systems