Academic literature on the topic 'HIDDEN NARKOV MODEL'

Cloud Computing has presented itself as a promising solution to new entrepreneurs as well as existing organizations for management of their IT needs at various levels. Many cloud service providers have exposed cloud services at cheap prices, which allow users at all levels of society to materialize their ideas and make them available across the globe. While the response has been overwhelming, the application areas where security of data is of utmost importance have not shown much interest. Hence incorporating dependable security measures in the cloud computing technology would be a good move since the aspect of security has turn out to be one of the main things to consider. In this thesis work we took an initiative as we adopted and/or interpolating Hidden Markov Model into the circles of Cloud Computing, as we exploited it’s capabilities in detecting the silent attack traces that dodge and/or bypass a set of methodical mechanisms intended to sense and prevent them into the cloud system. We modelled our hackers’ attack scenarios where we defined some states and observations. The hackers’ clusters have been taken to be the states, along with the observations which have been taken to a series of Virtual Machines on to which the attacker trades upon from the silent invasion through to the vulnerable and finally the target Virtual Machine. The modelling is given in such a way that, if we have a generated sequence of observed attacks, we must be able to find out the roots of done attackers from the grouped hackers.

This chapter surveys various proposals to interpret—that is, make sense of—quantum mechanics. We could attempt to think of quantum mechanics in purely instrumentalist terms, as an algorithm to predict observed experimental results. But this fits badly with scientific practice and is probably not viable. We could attempt to modify quantum mechanics itself to resolve the paradoxes, and there are some simple models that attempt to do that: some are ‘hidden-variable’ theories that add extra properties to the theory, some are ‘dynamical-collapse’ theories that modify the theory’s equations. But none of these models succeed in reproducing quantum theory’s predictions outside a relatively narrow range of applications. Or we could try to take the apparent indefiniteness of quantum mechanics literally, and interpret it as a theory of many parallel worlds. The correct interpretation of quantum mechanics remains controversial, but the search for understanding and interpretation of the theory has led to very substantial scientific results and is likely to lead to more.

This short introduction explains how Part II, “Visual Images,” engages with existing debates in visual international politics through chapters addressing the aesthetic turn in international relations (Chapter 4), visual securitization (Chapter 5), and ethical witnessing (Chapter 6). To make these arguments, it uses a range of visual images—photographs, documentary films, feature films, online videos, and visual art—to discuss visuality/visibility, ideology/affect, and cultural governance/resistance. Using these examples, Part II examines how visual culture studies and visual IR have used the visibility strategy to deconstruct visual images in order to reveal their hidden ideology. It argues that while exploring important issues, this research agenda is also limited by its hermeneutic mode of analysis and by its narrow focus on Euro-American images of security, war, and atrocity. It seeks to push beyond this verbally-inflected mode of analysis to see not just what images mean, but what they can “do” in provoking affective communities of sense. Part II thus employs comparative analysis and critical aesthetics to juxtapose concepts, practices, and experiences from different times and places.

For the frequency-domain spectral fatigue analysis, the probability mass function of stress range is essential for the assessment of the fatigue damage. The probability distribution of the stress range in the narrow-band process is known to follow the Rayleigh distribution, however the one in the wide-band process is difficult to define with clarity. In this paper, in order to assess the fatigue damage of a structure under wide band excitation, the probability mass function of the wide band spectrum was derived based on the artificial neural network, which is one of the most powerful universal function approximation schemes. To achieve the goal, the multi-layer perceptron model with a single hidden layer was introduced and the network parameters are determined using the least square method where the error propagates backward up to the weight parameters between input and hidden layer. To train the network under supervision, the varieties of different wide-band spectrums are assumed and the probability mass function of the stress range was derived using the rainflow counting method, and these artificially generated data sets are used as the training data. It turned out that the network trained using the given data set could reproduce the probability mass function of arbitrary wide-band spectrum with success.

Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.