1,226 research outputs found
Management of threats and errors in normal operations of assistant controllers : a thesis presented in partial fulfilment of the requirements for the degree of Master in Aviation at Massey University
"To err is indeed human, so to err is normal" Human errors are usually pronounced in accident or incident reports. Seldom does one pay enough attention to these errors during daily normal operations as these either go unnoticed or unreported for whatsoever the reasons may be. Therefore, the causes of these errors and also the system threats prevalent in the daily operations may not be fully contained. On the other hand, problematic situations that are successfully tackled by human skills are quite often treated as less important than they really are. The job of an assistant controller (AC) is one of the important domains in air traffic management (ATM). The AC work together with air traffic controllers as team members and they do have direct and indirect contributions to the safe, orderly and efficient flow of air traffic. In this study, the threats, errors and potential undesired states occurring with AC during normal operations will be recorded by a methodology, which is new to Hong Kong Air Traffic Control (ATC). This methodology, called Normal Operations Safety Observation (NOSO), is built on the Threat and Error Management (TEM) framework. The results will generate a broad outline on what sorts of threats, errors and undesired states an AC can be facing during normal operations. The relative frequencies of occurrence of these conditions will be presented separately in tables and figures. The AC's potential vulnerabilities and capabilities to cope with these threats, errors and undesired states will be discussed together with a suggested ranking. It is envisaged that an analysis of the data collected will aid the development and evaluation of safety defence measures in ATM and further support the applicability of this data collection methodology in other ATM operations and subsequent researches. KEYWORDS:- Normal Operations Safety Observation, Threat and Error Management, Safety Management, Air Traffic Control
The Competition for Shortest Paths on Sparse Graphs
Optimal paths connecting randomly selected network nodes and fixed routers
are studied analytically in the presence of non-linear overlap cost that
penalizes congestion. Routing becomes increasingly more difficult as the number
of selected nodes increases and exhibits ergodicity breaking in the case of
multiple routers. A distributed linearly-scalable routing algorithm is devised.
The ground state of such systems reveals non-monotonic complex behaviors in
both average path-length and algorithmic convergence, depending on the network
topology, and densities of communicating nodes and routers.Comment: 4 pages, 4 figure
Functional Imaging of Malignant Gliomas with CT Perfusion
The overall survival of patients with malignant gliomas remains dismal despite multimodality treatments. Computed tomography (CT) perfusion is a functional imaging tool for assessing tumour hemodynamics. The goals of this thesis are to 1) improve measurements of various CT perfusion parameters and 2) assess treatment outcomes in a rat glioma model and in patients with malignant gliomas. Chapter 2 addressed the effect of scan duration on the measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS). Measurement errors of these parameters increased with shorter scan duration. A minimum scan duration of 90 s is recommended. Chapter 3 evaluated the improvement in the measurements of these parameters by filtering the CT perfusion images with principal component analysis (PCA). From computer simulation, measurement errors of BF, BV, and PS were found to be reduced. Experiments showed that CT perfusion image contrast-to-noise ratio was improved. Chapter 4 investigated the efficacy of CT perfusion as an early imaging biomarker of response to stereotactic radiosurgery (SRS). Using the C6 glioma model, we showed that responders to SRS (surviving \u3e 15 days) had lower relative BV and PS on day 7 post-SRS when compared to controls and non-responders (P \u3c 0.05). Relative BV and PS on day 7 post-SRS were predictive of survival with 92% accuracy. Chapter 5 examined the use of multiparametric imaging with CT perfusion and 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) to identify tumour sites that are likely to correlate with the eventual location of tumour progression. We developed a method to generate probability maps of tumour progression based on these imaging data. Chapter 6 investigated serial changes in tumour volumetric and CT perfusion parameters and their predictive ability in stratifying patients by overall survival. Pre-surgery BF in the non-enhancing lesion and BV in the contrast-enhancing lesion three months after radiotherapy had the highest combination of sensitivities and specificities of ≥ 80% in predicting 24 months overall survival. iv Optimization and standardization of CT perfusion scans were proposed. This thesis also provided corroborating evidence to support the use of CT perfusion as a biomarker of outcomes in patients with malignant gliomas
Empirical studies on the network of social groups: the case of Tencent QQ
Participation in social groups are important but the collective behaviors of
human as a group are difficult to analyze due to the difficulties to quantify
ordinary social relation, group membership, and to collect a comprehensive
dataset. Such difficulties can be circumvented by analyzing online social
networks. In this paper, we analyze a comprehensive dataset obtained from
Tencent QQ, an instant messenger with the highest market share in China.
Specifically, we analyze three derivative networks involving groups and their
members -- the hypergraph of groups, the network of groups and the user network
-- to reveal social interactions at microscopic and mesoscopic level. Our
results uncover interesting behaviors on the growth of user groups, the
interactions between groups, and their relationship with member age and gender.
These findings lead to insights which are difficult to obtain in ordinary
social networks.Comment: 18 pages, 9 figure
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
Self-sustained clusters and ergodicity breaking in spin models
Self-sustained spin clusters are analytically linked to ergodicity breaking in fully connected Ising and Sherrington-Kirkpatick (SK) models, relating the less understood spin space to the well understood state space. This correspondence is established through the absence of clusters in the paramagnetic phase, the presence of one dominant cluster in the Ising ferromagnet, and the formation of nontrivial clusters in SK spin glass. Yet unobserved phenomena are also revealed such as a first order phase transition in cluster sizes in the SK ferromagnet. The method could be adapted to investigate other spin models
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