Markov Chain Neural Networks

Awiszus, Maren; Rosenhahn, Bodo

doi:10.1109/cvprw.2018.00293

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…The Markov chain is a particular type of Markov process in which the system state selects only discrete values, and it is based on the probability matrix. The future of this process does not depend on the way it was taken in the past, but only on its current position (Awiszus and Rosenhahn, 2018;Kratochv´ıl, 2018). Accordingly, an accidental string of variables {X t , t } is called a Markov chain if for all state values i 0 , i 1 , i 2 , ..., i t , then…”

Section: Markov Chain Theorymentioning

confidence: 99%

Drill Bit Selection in a Formation With Different Sedimentary Facies Using the Markov Chain: A Case Study at One of the Oil Fields in the South of Iran

Rad¹,

Negahban²,

Tokhmechi³

et al. 2021

MBPB

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The selection of a drill bit is an essential issue in well planning. Furthermore, identification and evaluation of sedimentary rocks before well drilling plays a crucial role in choosing the drill bit. Moreover, the Markov chain as a stochastic model is one of the powerful methods for identifying lithological units, which is based on the calculation of the transition probability matrix or transition matrix. The Markov chain experiences transitions from one state (a situation or set of values) to another according to specified probabilistic rules. In this paper, the Markov chain was implemented for bit selection in a formation with different sedimentary facies (such as the Dashtak Formation). Therefore, the proper drill bit was proposed by utilizing the transition matrix of rock facies and the available bits. This process was carried out in two wells where the thicknesses of the Dashtak Formation are 960 meters and 1410 meters. Consequently, the results indicate that the Markov chain is a practical method for selecting bits in a sequence of rock facies based on an acceptable matching between the reality mode (the used bits in the well) and the Markov chain results. Besides, in the case of using an improper bit in a well, and using a bit in a washing and reaming operation, there were differences between the used bits and the Markov chain outputs.

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Section: Markov Chain Theorymentioning

confidence: 99%

Drill Bit Selection in a Formation With Different Sedimentary Facies Using the Markov Chain: A Case Study at One of the Oil Fields in the South of Iran

Rad¹,

Negahban²,

Tokhmechi³

et al. 2021

MBPB

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show abstract

“…While Machine Learning (ML) solutions demonstrated an ability to perform successfully in system modeling tasks [ 98 , 99 , 100 , 101 ], a minimal amount of studies applied those for the modeling of blockchain-based systems. For instance, the Markov chain neural networks concept introduced by Awiszus and Rosenhahn [ 102 ] has great potential in the application of Markov chains for blockchain modeling. At the same time, the Markov Decision Process Extraction Network (MPEN) [ 103 ] can potentially be utilized in order to extract automatically minimal relevant aspects of the dynamics from observations to model a Markov decision process.…”

Section: Current Challenges and Future Prospectsmentioning

confidence: 99%

Blockchain Evaluation Approaches: State-of-the-Art and Future Perspective

Smetanin

Ometov

Komarov

et al. 2020

Sensors

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The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.

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“…In this work, we propose a novel end-to-end model to incorporate attention and semantic correlations for visual relationship detection. The novelties of our work are: (1) We design a spatial attention model which constrains the network focusing more on the most important regions. (2) We introduce a new spatial feature extraction model which significantly improves the detection performance.…”

Section: Contributionsmentioning

confidence: 99%

“…In recent years, deep learning technology has achieved great success in computer vision tasks, such as object detection techniques [13,26,27], pose estimation [32], tracking [10], AI games [1,29]. However, visual scene understanding remains open challenging tasks.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Attention for Visual Relationship Detection

Liao

Yang

et al. 2019

Lecture Notes in Computer Science

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Visual relationship detection targets on predicting categories of predicates and object pairs, and also locating the object pairs. Recognizing the relationships between individual objects is important for describing visual scenes in static images. In this paper, we propose a novel end-to-end framework on the visual relationship detection task. First, we design a spatial attention model for specializing predicate features. Compared to a normal ROI-pooling layer, this structure significantly improves Predicate Classification performance. Second, for extracting relative spatial configuration, we propose to map simple geometric representations to a high dimension, which boosts relationship detection accuracy. Third, we implement a feature embedding model with a bi-directional RNN which considers subject, predicate and object as a time sequence. We evaluate our method on three tasks. The experiments demonstrate that our method achieves competitive results compared to state-of-the-art methods.

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Markov Chain Neural Networks

Cited by 20 publications

References 19 publications

Drill Bit Selection in a Formation With Different Sedimentary Facies Using the Markov Chain: A Case Study at One of the Oil Fields in the South of Iran

Drill Bit Selection in a Formation With Different Sedimentary Facies Using the Markov Chain: A Case Study at One of the Oil Fields in the South of Iran

Blockchain Evaluation Approaches: State-of-the-Art and Future Perspective

Exploiting Attention for Visual Relationship Detection

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