An improved multiple-attractor cellular automata classifier with a tree frame based on CART

Fang, Min; Niu, Wenke; Zhang, Xiaosong

doi:10.1016/j.camwa.2013.05.032

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…A tree with | f DT |> N DT will overfit/overtrain the input data; learning too much from the noises of a given data. Overtraining the tree from a given data will not yield good predictions [6,7,8,9,10,11,12,13,14,15,16]. Two relations can be utilized in order to perform a partition; Gini index (eq.…”

Section: Decision Treesmentioning

confidence: 99%

Strategic Well Test Planning Using Random Forest

2014

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Optimizing a huge oilfield with hundreds of wells would involve thorough analysis. Here, the performance of a subset of those wells might be a critical factor. Therefore, it is important to obtain accurate information on that subset.Obtaining accurate information on an individual well is usually done via a proper well test analysis. Although performing a well test is relatively expensive, it can provide important information such as the skin, reservoir pressure (i.e. BHP), permeability, and nearby faults.In this study, we would like emphasize on the measurement of BHP. This emphasis is chosen due to the fact that the BHP from a subset of all wells can be a contributing factor towards the field oil recovery factor. Hence, the objective of this study is to find that subset of wells based on uncertainty studies conducted via reservoir simulations. Here, the uncertainty study involves performing 100 Latin Hypercube Monte Carlo (LHMC) sampling of the reservoir simulation input parameters. For each of the 100 runs, the uncertain input parameters and simulated BHP values/trends for each well are recorded. Thus, a dataset consists of the input parameters, simulated BHP for each well, and the field oil recovery factor as columns will be generated.The random forest (RF) algorithm was implemented in order to find the subset of wells mentioned. RF algorithm is simply a collection of decision trees (DT), where the results generated by RF were obtained by using a voting mechanism among the trees involved. Several trees were sampled from the generated forest, and reveal interactions between the monitored BHP value/trend with other parameters. Combination of LHMC and RF can also be seen a global approach to sensitivity analysis, since RF can reveal the rank of importance of the RF's independent variables based on a collection of uncertainty runs.In the exploration and production (E&P) applications, DT has been used extensively; appraising, real option valuation selecting an artificial lift method, predicting permeabilities and detecting fracture corridors . However, to the best of the authors' knowledge, RF had never been implemented in any E&P activities, particularly in strategic well test planning.

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Section: Decision Treesmentioning

confidence: 99%

Strategic Well Test Planning Using Random Forest

2014

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Design Automation for Tree-based Nearest Neighborhood–aware Placement of High-speed Cellular Automata on FPGA with Scan Path Insertion

Palchaudhuri

Sharma

Dhar

2021

ACM Trans. Des. Autom. Electron. Syst.

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Cellular Automata (CA) is attractive for high-speed VLSI implementation due to modularity, cascadability, and locality of interconnections confined to neighboring logic cells. However, this outcome is not easily transferable to tree-structured CA, since the neighbors having half and double the index value of the current CA cell under question can be sufficiently distanced apart on the FPGA floor. Challenges to meet throughput requirements, seamlessly translate algorithmic modifications for changing application specifications to gate level architectures and to address reliability challenges of semiconductor chips are ever increasing. Thus, a proper design framework assisting automation of synthesizable, delay-optimized VLSI architecture descriptions facilitating testability is desirable. In this article, we have automated the generation of hardware description of tree-structured CA that includes a built-in scan path realized with zero area and delay overhead. The scan path facilitates seeding the CA, state modification, and fault localization on the FPGA fabric. Three placement algorithms were proposed to ensure maximum physical adjacency amongst neighboring CA cells, arranged in a multi-columnar fashion on the FPGA grid. Our proposed architectures outperform implementations arising out of standard placers and behavioral designs, existing tree mapping strategies, and state-of-the-art FPGA centric error detection architectures in area and speed.

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An improved multiple-attractor cellular automata classifier with a tree frame based on CART

Cited by 2 publications

References 12 publications

Strategic Well Test Planning Using Random Forest

Strategic Well Test Planning Using Random Forest

Design Automation for Tree-based Nearest Neighborhood–aware Placement of High-speed Cellular Automata on FPGA with Scan Path Insertion

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