An iterative feedrate optimization method for real-time NURBS interpolator

Non-structural traps and reservoir flanks are characterized by angular unconformities. Angular unconformity between dipping formation and sub-horizontal oil-water contact is common in the North Sea fields. This paper presents an approach to real-time inversion of LWD resistivity data for the scenario with angular unconformity. The approach utilizes artificial neural networks (ANNs) for calculating the tool responses in parametric surface-based 2D resistivity models. We propose a parametric model with two non-parallel boundaries suitable for scenarios with angular unconformity and pinch-out. Training of ANNs for this parametric model is performed using a database containing samples with the model parameters and corresponding tool responses. ANNs are the kernel of 2D inversion based on the Levenberg-Marquardt optimization method. To demonstrate applicability of our approach and compare with the results of 1D inversion, we analyze Extra Deep Azimuthal Resistivity tool responses in a 2D synthetic model. It is shown that 1D inversion determines either the position of the oil-water contact or dipping layers structure. At the same time, 2D inversion makes it possible to correctly reconstruct the positions of non-parallel boundaries. Performance of 2D inversion based on ANNs is suitable for real-time applications.

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Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

10.4018/978-1-6684-2408-7.ch008 ◽

2022 ◽

pp. 166-201

Author(s):

Asha Gowda Karegowda ◽

Devika G.

Keyword(s):

Neural Networks ◽

Real Time ◽

Parameter Optimization ◽

Heuristic Algorithms ◽

Search Space ◽

Optimization Method ◽

Classification Problems ◽

Local Optima ◽

Consistent Solution ◽

Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

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Body Bias Optimization for Real-Time Systems

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea10010008 ◽

2020 ◽

Vol 10 (1) ◽

pp. 8

Author(s):

Carlos C. Cortes Torres ◽

Ryota Yasudo ◽

Hideharu Amano

Keyword(s):

Real Time ◽

Computer Aided Design ◽

Optimization Method ◽

Design Guidelines ◽

Real Time Systems ◽

Total Energy Consumption ◽

Aided Design ◽

Time Systems ◽

Parameter Constraints ◽

Body Bias

The energy of real-time systems for embedded usage needs to be efficient without affecting the system’s ability to meet task deadlines. Dynamic body bias (BB) scaling is a promising approach to managing leakage energy and operational speed, especially for system-on-insulator devices. However, traditional energy models cannot deal with the overhead of adjusting the BB voltage; thus, the models are not accurate. This paper presents a more accurate model for calculating energy overhead using an analytical double exponential expression for dynamic BB scaling and an optimization method based on nonlinear programming with consideration of the real-chip parameter constraints. The use of the proposed model resulted in an energy reduction of about 32% at lower frequencies in comparison with the conventional model. Moreover, the energy overhead was reduced to approximately 14% of the total energy consumption. This methodology provides a framework and design guidelines for real-time systems and computer-aided design.

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ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

Acta Polytechnica ◽

10.14311/app.2016.56.0067 ◽

2016 ◽

Vol 56 (1) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Amanda Prorok ◽

M. Ani Hsieh ◽

Vijay Kumar

Keyword(s):

Real Time ◽

Large Scale ◽

Optimization Problem ◽

Optimization Method ◽

Transition Rates ◽

Trait Distribution ◽

Time Optimization ◽

Heterogeneous Robots ◽

Real Time Optimization ◽

Optimal Set

We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

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Real-Time Monte Carlo Optimization on FPGA for the Efficient and Reliable Message Chain Structure

Electronics ◽

10.3390/electronics8080866 ◽

2019 ◽

Vol 8 (8) ◽

pp. 866 ◽

Cited By ~ 1

Author(s):

Heoncheol Lee ◽

Kipyo Kim

Keyword(s):

Monte Carlo ◽

Real Time ◽

Communication Systems ◽

Optimization Method ◽

Chain Structure ◽

Processing Unit ◽

Monte Carlo Optimization ◽

Central Processing ◽

Field Programmable ◽

Programmable Gate Arrays

This paper addresses the real-time optimization problem to find the most efficient and reliable message chain structure in data communications based on half-duplex command–response protocols such as MIL-STD-1553B communication systems. This paper proposes a real-time Monte Carlo optimization method implemented on field programmable gate arrays (FPGA) which can not only be conducted very quickly but also avoid the conflicts with other tasks on a central processing unit (CPU). Evaluation results showed that the proposed method can consistently find the optimal message chain structure within a quite small and deterministic time, which was much faster than the conventional Monte Carlo optimization method on a CPU.

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