This document describes using radial basis function networks (RBF) for well log data inversion. It proposes modifying a conventional two-layer RBF and introducing a three-layer RBF. Simulation experiments show the three-layer 10-27-9-10 RBF model achieves the smallest error in testing simulated well log data. When applied to real well log data, the 10-27-9-10 RBF model provides an acceptable inversion of true formation conductivity from apparent conductivity measurements.
I am Boris M. I am a Computer Science Assignment Help Expert at programminghomeworkhelp.com. I hold an MSc. in Programming, McGill University, Canada. I have been helping students with their homework for the past 8years. I solve assignments related to Computer Science.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Computer Science assignments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
I am Boris M. I am a Computer Science Assignment Help Expert at programminghomeworkhelp.com. I hold an MSc. in Programming, McGill University, Canada. I have been helping students with their homework for the past 8years. I solve assignments related to Computer Science.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Computer Science assignments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
I am Samantha H. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, the University of Alberta Canada. I have been helping students with their assignments for the past 14 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignment.
Homomorphic Lower Digit Removal and Improved FHE Bootstrapping by Kyoohyung Hanvpnmentor
Kyoohyung Han is a PhD student in the Department of Mathematical Science at the Seoul National University in Korea. These are the slides from his presentation at EuroCrypt 2018.
GENERIC APPROACH FOR VISIBLE WATERMARKINGEditor IJCATR
In this paper generic image watermarking technique is used for the copyright protection of color images. Watermarking
with monochrome and translucent images based on One-to-One compound mapping of the values of the image pixels, which provide us
the recovered image without any loss. Both the translucent full color and Opaque monochrome images are used in this paper. Two-fold
monotonically increasing compound mapping is used to get more typical visible watermarks in the image. Measures have been taken to
protect it from hackers.
USING ADAPTIVE AUTOMATA IN GRAMMAR-BASED TEXT COMPRESSION TO IDENTIFY FREQUEN...ijcsit
Compression techniques allow reduction in the data storage space required by applications dealing with large amount of data by increasing the information entropy of its representation. This paper presents an adaptive rule-driven device - the adaptive automata - as the device to identify recurring sequences of symbols to be compressed in a grammar-based lossless data compression scheme.
I am Samantha H. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, the University of Alberta Canada. I have been helping students with their assignments for the past 14 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignment.
Homomorphic Lower Digit Removal and Improved FHE Bootstrapping by Kyoohyung Hanvpnmentor
Kyoohyung Han is a PhD student in the Department of Mathematical Science at the Seoul National University in Korea. These are the slides from his presentation at EuroCrypt 2018.
GENERIC APPROACH FOR VISIBLE WATERMARKINGEditor IJCATR
In this paper generic image watermarking technique is used for the copyright protection of color images. Watermarking
with monochrome and translucent images based on One-to-One compound mapping of the values of the image pixels, which provide us
the recovered image without any loss. Both the translucent full color and Opaque monochrome images are used in this paper. Two-fold
monotonically increasing compound mapping is used to get more typical visible watermarks in the image. Measures have been taken to
protect it from hackers.
USING ADAPTIVE AUTOMATA IN GRAMMAR-BASED TEXT COMPRESSION TO IDENTIFY FREQUEN...ijcsit
Compression techniques allow reduction in the data storage space required by applications dealing with large amount of data by increasing the information entropy of its representation. This paper presents an adaptive rule-driven device - the adaptive automata - as the device to identify recurring sequences of symbols to be compressed in a grammar-based lossless data compression scheme.
Presentation of my NSERC-USRA funded summer research project given at the Canadian Undergraduate Mathematics Conference (CUMC) 2014.
Please refer to the project site: http://jessebett.com/Radial-Basis-Function-USRA/
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
High Speed radix256 algorithm using parallel prefix adderIJMER
A finite impulse response (FIR) filter computes its output using multiply and accumulate
operations. In the present work, a FIR filter based on novel higher radix-256 and RB arithmetic is
implemented. The use of radix-256 booth encoding reduces the number of partial product rows in any
multiplication by 8 fold. In the present work inputs and coefficients are considered of 16-bit. Hence, only
two partial product rows are obtained in RB form for each input and coefficient multiplications. These
two partial product rows are added using carry free RB addition. Finally the RB output is converted back
to natural binary (NB) form using RB to NB converter. The performance of proposed multiplier
architecture for FIR filter is compared with computation sharing multiplier (CSHM)
Acquisition of Long Pseudo Code in Dsss SignalIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Fixed Point Realization of Iterative LR-Aided Soft MIMO Decoding AlgorithmCSCJournals
Multiple-input multiple-output (MIMO) systems have been widely acclaimed in order to provide high data rates. Recently Lattice Reduction (LR) aided detectors have been proposed to achieve near Maximum Likelihood (ML) performance with low complexity. In this paper, we develop the fixed point design of an iterative soft decision based LR-aided K-best decoder, which reduces the complexity of existing sphere decoder. A simulation based word-length optimization is presented for physical implementation of the K-best decoder. Simulations show that the fixed point result of 16 bit precision can keep bit error rate (BER) degradation within 0.3 dB for 8×8 MIMO systems with different modulation schemes.
AI optimizing HPC simulations (presentation from 6th EULAG Workshop)byteLAKE
See our presentation from the 6th International EULAG Users Workshop. We talked about taking HPC to the "Industry 4.0" by implementing smart techniques to optimize the codes in terms of performance and energy consumption. It explains how Machine Learning can dynamically optimize HPC simulations and byteLAKE's software autotuning solution.
Find out more about byteLAKE at: www.byteLAKE.com
Iterative Soft Decision Based Complex K-best MIMO DecoderCSCJournals
This paper presents an iterative soft decision based complex multiple input multiple output (MIMO) decoding algorithm, which reduces the complexity of Maximum Likelihood (ML) detector. We develop a novel iterative complex K-best decoder exploiting the techniques of lattice reduction for 8×8 MIMO. Besides list size, a new adjustable variable has been introduced in order to control the on-demand child expansion. Following this method, we obtain 6.9 to 8.0 dB improvement over real domain K-best decoder and 1.4 to 2.5 dB better performance compared to iterative conventional complex decoder for 4th iteration and 64-QAM modulation scheme. We also demonstrate the significance of new parameter on bit error rate. The proposed decoder not only increases the performance, but also reduces the computational complexity to a certain level.
Iterative Soft Decision Based Complex K-best MIMO DecoderCSCJournals
This paper presents an iterative soft decision based complex multiple input multiple output (MIMO) decoding algorithm, which reduces the complexity of Maximum Likelihood (ML) detector. We develop a novel iterative complex K-best decoder exploiting the techniques of lattice reduction for 8×8 MIMO. Besides list size, a new adjustable variable has been introduced in order to control the on-demand child expansion. Following this method, we obtain 6.9 to 8.0 dB improvement over real domain K-best decoder and 1.4 to 2.5 dB better performance compared to iterative conventional complex decoder for 4th iteration and 64-QAM modulation scheme. We also demonstrate the significance of new parameter on bit error rate. The proposed decoder not only increases the performance, but also reduces the computational complexity to a certain level.
1. Well Log Data Inversion Using Radial Basis Function Network Kou-Yuan Huang,Li-Sheng Weng Department of Computer Science National Chiao Tung University Hsinchu, Taiwan kyhuang@cs.nctu.edu.tw and Liang-Chi Shen Department of Electrical & Computer Engineering University of Houston Houston, TX
12. Review of well log data inversion Lin, Gianzero, and Strickland used the least squares technique, 1984. Dyos used maximum entropy, 1987. Martin, Chen, Hagiwara, Strickland, Gianzero, and Hagan used 2-layer neural network, 2001. Goswami, Mydur, Wu, and Hwliot used a robust technique,2004. Huang, Shen, and Chen used higher order perceptron, IEEE IGARSS, 2008.
13.
14. Hush and Horne, 1993,used RBF network for functional approximation.
17. Properties of RBF RBF is a supervised training model. The 1st layer used the K-means clustering algorithm todetermine the K nodes. The activation function of the 2nd layer was linear. f(s)=s. f ’(s)=1. The 2ndlayer used the Widrow-Hoff learning rule.
21. Output of the 1st layer: response of Gaussian basis function𝑜𝑖=exp−(𝐱−𝐦𝑖)𝑇(𝐱−𝐦𝑖)2σ𝑖2
22. Training in the 2nd layer Widrow-Hoff’s learning rule. Error function 𝐸=12𝑗=1𝐽(𝑑𝑗−𝑜𝑗)2 Use gradient descent method to adjust weights∆ 𝑤𝑗𝑖𝑡=𝑤𝑗𝑖𝑡+1−𝑤𝑗𝑖𝑡=−η𝜕𝐸𝜕𝑤𝑗𝑖 =η𝑑𝑗−𝑜𝑗𝑓𝑗′𝑠𝑗𝑜𝑖=η𝑑𝑗−𝑜𝑗𝑜𝑖 f(s)=s. 𝑓′(𝑠)=1
35. Perceptron training in the 2nd layer Activation function at the 2nd layer: sigmoidal 𝑜𝑗=𝑓𝑠𝑗= 11+𝑒−𝑆𝑗 Error Function 𝐸=12𝑗=1𝐽(𝑑𝑗−𝑜𝑗)2 Delta learning rule(Rumelhart, Hinton, and Williams, 1986): use gradient descent method to adjust weights ∆𝑤𝑗𝑖𝑡=𝑤𝑗𝑖𝑡+1−𝑤𝑗𝑖𝑡=−η𝜕𝐸𝜕𝑤𝑗𝑖= η𝑑𝑗−𝑜𝑗𝑓𝑗′(𝑠𝑗)𝑜𝑖
46. Generalized delta learning rule (Rumelhart, Hinton, and Williams, 1986) Adjust weights between the 2nd layer and the 3rd layer 𝑤𝑘𝑗𝑡+1=𝑤𝑘𝑗𝑡+∆𝑤𝑘𝑗𝑡 ∆𝑤𝑘𝑗(𝑡)=𝜂 𝑑𝑘−𝑜𝑘𝑓𝑘′𝑠𝑘𝑜𝑗=𝜂𝛿𝑘𝑜𝑗 𝛿𝑘= 𝑑𝑘−𝑜𝑘𝑓𝑘′𝑠𝑘 Adjust weights between the 1st layer and the 2nd layer, ∆𝑤𝑗𝑖(𝑡)=𝜂 𝑘=1𝐾𝛿𝑘𝑤𝑘𝑗𝑓𝑗′𝑠𝑗𝑜𝑖=𝜂𝛿𝑗𝑜𝑖 𝛿𝑗= 𝑘=1𝐾𝛿𝑘𝑤𝑘𝑗𝑓𝑗′𝑠𝑗 Adjust weights with momentum term: ∆𝑤𝑘𝑗𝑡=𝜂𝛿𝑘𝑡𝑜𝑗𝑡+𝛽∆𝑤𝑘𝑗𝑡−1 ∆𝑤𝑗𝑖𝑡=𝜂𝛿𝑗𝑡𝑜𝑖𝑡+𝛽∆𝑤𝑗𝑖(𝑡−1)
56. Experiments: on simulated well log data In the simulation, there are 31 well logs. Professor Shenat University of Houston worked on theoretical calculation. Each well log has the apparent conductivity (Ca) as the input, and the true formation conductivity (Ct) as the desired output. Well logs #1~#25 are for training. Well logs #26~#31 are for testing.
63. Input data length and # of training patterns from 25 training well logs
64. Optimal cluster number of training patternsExample: for input data length 10 PFS vs. K. For input N=10, the optimal cluster number K is 27.
65. Optimal cluster number of training patterns in 10 cases Set up 10 two-layer RBF models. Compare the testing errors of 10 models to select the optimal RBF model.
67. Parameter setting in the experiment Parameters in RBF training Learning rate η : 0.6 Momentum coefficient 𝛽: 0.4 (in 3-layer RBF) Maximum iterations: 20,000 Error threshold: 0.002. Define mean absolute error (MAE): Pis the pattern number, K is the output nodes. MAE=1𝑃𝐾𝑝=1𝑃𝑘=1𝐾𝑑𝑝𝑘−𝑜𝑝𝑘
68.
69. Inversion testing using 10-27-10 two-layer RBF Inverted Ct of log #26 by network 10-27-10 (MAE= 0.051753). Inverted Ct of log #27 by network 10-27-10 (MAE= 0.055537).
70. Inverted Ct of log #28 by network 10-27-10 (MAE= 0.041952). Inverted Ct of log #29 by network 10-27-10 (MAE= 0.040859).
71. Inverted Ct of log #31 by network 10-27-10 (MAE= 0.050294). Inverted Ct of log #30 by network 10-27-10 (MAE= 0.047587).
86. Inversion testing using 10-27-9-10 three-layer RBF Inverted Ct of log 27 by network 10-27-9-10 (MAE= 0.059158) Inverted Ct of log 26 by network 10-27-9-10 (MAE= 0.041526)
87. Inverted Ct of log 28 by network 10-27-9-10 (MAE= 0.046744) Inverted Ct of log 29 by network 10-27-9-10 (MAE= 0.043017)
88. Inverted Ct of log 30 by network 10-27-9-10 (MAE= 0.046546) Inverted Ct of log 31 by network 10-27-9-10 (MAE= 0.042763)
89. Testing error of each well log using 10-27-9-10 three-layer RBF model Average error: 0.046625
90. Average testing error of each three-layer RBF model in simulation Experiments using RBFs with different number of hidden nodes. 10-27-9-10 get the smallest average error in testing. So it is selected to the real data application.
103. Select 10-27-9-10 optimal RBF model for real data inversion. After convergence in training, input 10 real data to the RBF model to get the 10 output data, then input 10 data of the next segment to get the next 10 output data, repeatedly.
114. 3-layer RBF has better inversion than 2-layer RBF because more layers can do more nonlinear mapping.In the simulation, the optimal 3-layer model is 10-27-9-10. It can get the smallest average mean absolute error in the testing. The trained 10-27-9-10 RBFmodel is applied to the real well log data inversion. The result is acceptable and good. It shows that the RBF model can work on well log data inversion. Errors are different at experiments because initial weights are different in the network. But the order or percentage of errors can be for comparison in the RBF performance.