TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

Quantifying Eulerian Eddy Leakiness in an Idealized Model

An idealized eddy‐resolving ocean basin, closely resembling the North Pacific Ocean, is simulated using MITgcm. We identify rotationally coherent Lagrangian vortices (RCLVs) and sea surface height (SSH) eddies based on the Lagrangian and Eulerian framework, respectively. General statistical results show that RCLVs have a much smaller coherent core than SSH eddies with the ratio of radius is about 0.5. RCLVs are often enclosed by SSH anomaly contours, but SSH eddy identification method fails to detect more than half of RCLVs. Based on their locations, two types of eddies are classified into three categories: overlapping RCLVs and SSH eddies, nonoverlapping SSH eddies, and nonoverlapping RCLVs. Using Lagrangian particles, we examine the processes of leakage and intrusion around SSH eddies. For overlapping SSH eddies, over the lifetime, the material coherent core only accounts for about 25% and about 50% of initial water leak from eddy interior. The remaining 25% of water can still remain inside the boundary, but only in the form of filaments outside the coherent core. For nonoverlapping SSH eddies, more water leakage (about 60%) occurs at a faster rate. Guided by the number and radius of SSH eddies, fixed circles and moving circles are randomly selected to diagnose the material flux around these circles. We find that the leakage and intrusion trends of moving circles are quite similar to that of nonoverlapping SSH eddies, suggesting that the material coherence properties of nonoverlapping SSH eddies are not significantly different from random pieces of ocean with the same size

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Computer Vision Applications for Autonomous Aerial Vehicles

Undoubtedly, unmanned aerial vehicles (UAVs) have experienced a great leap forward over the last decade. It is not surprising anymore to see a UAV being used to accomplish a certain task, which was previously carried out by humans or a former technology. The proliferation of special vision sensors, such as depth cameras, lidar sensors and thermal cameras, and major breakthroughs in computer vision and machine learning fields accelerated the advance of UAV research and technology. However, due to certain unique challenges imposed by UAVs, such as limited payload capacity, unreliable communication link with the ground stations and data safety, UAVs are compelled to perform many tasks on their onboard embedded processing units, which makes it difficult to readily implement the most advanced algorithms on UAVs. This thesis focuses on computer vision and machine learning applications for UAVs equipped with onboard embedded platforms, and presents algorithms that utilize data from multiple modalities. The presented work covers a broad spectrum of algorithms and applications for UAVs, such as indoor UAV perception, 3D understanding with deep learning, UAV localization, and structural inspection with UAVs. Visual guidance and scene understanding without relying on pre-installed tags or markers is the desired approach for fully autonomous navigation of UAVs in conjunction with the global positioning systems (GPS), or especially when GPS information is either unavailable or unreliable. Thus, semantic and geometric understanding of the surroundings become vital to utilize vision as guidance in the autonomous navigation pipelines. In this context, first, robust altitude measurement, safe landing zone detection and doorway detection methods are presented for autonomous UAVs operating indoors. These approaches are implemented on Google Project Tango platform, which is an embedded platform equipped with various sensors including a depth camera. Next, a modified capsule network for 3D object classification is presented with weight optimization so that the network can be fit and run on memory-constrained platforms. Then, a semantic segmentation method for 3D point clouds is developed for a more general visual perception on a UAV equipped with a 3D vision sensor. Next, this thesis presents algorithms for structural health monitoring applications involving UAVs. First, a 3D point cloud-based, drift-free and lightweight localization method is presented for depth camera-equipped UAVs that perform bridge inspection, where GPS signal is unreliable. Next, a thermal leakage detection algorithm is presented for detecting thermal anomalies on building envelopes using aerial thermography from UAVs. Then, building on our thermal anomaly identification expertise gained on the previous task, a novel performance anomaly identification metric (AIM) is presented for more reliable performance evaluation of thermal anomaly identification methods

Defining the 3D geometry of thin shale units in the Sleipner reservoir using seismic attributes

Acknowledgments The seismic interpretation and image processing was carried out in the SeisLab facility at the University of Aberdeen (sponsored by BG BP and Chevron). Seismic imaging analysis was performed using GeoTeric (ffA), and analysis of seismic amplitudes was performed in Petrel 2015 (Schlumberger). We would like to thank the NDDC (RG11766-10) for funding this research and Statoil for the release of the Sleipner field seismic dataset utilized in this research paper and also Anne-Kari Furre and her colleagues for their assistance. We also thank the editor, Alejandro Escalona and the two anonymous reviewers for their constructive and in depth comments that improved the paper.Peer reviewedPostprin

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable