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Convex Analysisの二,三の進展について

Deep learning on image denoising: An overview.

This paper presents a hybrid particle swarm optimization algorithm (HPSO) as a modern optimization tool to solve the discrete optimal power flow (OPF) problem that has both discrete and continuous optimization variables. The problem is classified as constrained mixed integer nonlinear programming with multimodal characteristics. The objective functions considered are the system real power losses, fuel cost, and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents nondifferentiable and nonconvex regions that challenge most gradient-based optimization algorithms. The proposed algorithm makes use of the PSO, known for its global searching capabilities, to allocate the optimal control settings while Newton-Raphson algorithm minimizes the mismatch of the power flow equations. A hybrid inequality constraint handling mechanism that preserves only feasible solutions without the need to augment the original objective function is incorporated in the proposed approach. To demonstrate its robustness, the proposed algorithm was tested on the IEEE 30-bus system with six generating units. Several cases were investigated to test and validate the consistency of detecting optimal or near optimal solution for each objective. Results are compared to solutions obtained of MATPOWER software outcomes that employs sequential quadratic programming algorithm to solve the OPF. The impact of the proposed inequality constraint handling method in improving the HPSO performance is illustrated. Furthermore, a study of HPSO parameters tuning with regard to the OPF problem is presented and analyzed.

Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects

Image denoising review: From classical to state-of-the-art approaches

Velocity analysis can be a time-consuming task when performed manually. Methods have been proposed to automate the process of velocity analysis, which, however, typically requires significa...

Automatic velocity analysis using convolutional neural network and transfer learning

Deep learning has been successfully applied to image denoising. In this study, we take one step forward by using deep learning to suppress random noise in poststack seismic data from the aspects of network architecture and training samples. On the one hand, poststack seismic data denoising mainly aims at 3-D seismic data. We designed an end-to-end 3-D denoising convolutional neural network (3-D-DnCNN) that takes raw 3-D cubes as input in order to better extract the features of the 3-D spatial structure of poststack seismic data. On the other hand, denoising images with deep learning require noisy–clean sample pairs for training. In the field of seismic data processing, researchers usually try their best to suppress noise by using complex processes that combine different methods, but clean labels of seismic data are not available. In addition, building training samples in field seismic data has become an interesting but challenging problem. Therefore, we propose a training sample selection method that contains a complex workflow to produce comparatively ideal training samples. Experiments in this study demonstrate that deep learning can directly learn the ability to denoise field seismic data from selected samples. Although the building of the training samples may occur through a complex process, the experimental results of synthetic seismic data and field seismic data show that the 3-D-DnCNN has learned the ability to suppress the Gaussian noise and super-Gaussian noise from different training samples. Moreover, the 3-D-DnCNN network has better denoising performance toward arc-like imaging noise. In addition, we adopt residual learning and batch normalization in order to accelerate the training speed. After network training is satisfactorily completed, its processing efficiency can be significantly higher than that of conventional denoising methods.

Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network

Random noise suppression in seismic data: What can deep learning do?

As one type of important seismic geometric attributes, the seismic volumetric dip is extensively used to assist interpretation of horizons, faults, and other geologic structures in 3-D seismic data. In this paper, we mainly focus on estimating seismic volumetric dip robustly and try to reduce the influences of amplitude’s lateral changes, faults, and other discontinuous structures. We first use the instantaneous phase (IP) as one fundamental data set to reduce the influence of amplitude’s lateral variation. Second, we construct structure tensor (ST) on IP and apply eigendecomposition on corresponding ST covariance matrix to obtain three eigenvalues and corresponding eigenvectors. Then, the seismic volumetric dip can be calculated from the dominant eigenvector, and a similarity measure can be constructed based on these three eigenvalues. Third, based on the similarity measure, we reduce the influence of fault on dip estimation by using multiwindow technology if the analyzing window spans a fault. Finally, we applied our method to three synthetic data examples and two field data examples. The results of seismic volumetric dip and curvature estimation verify that the proposed method has better antinoise and antifault performance comparing with the corresponding sophisticated method in commercial software and the conventional ST-based method.

Robust Seismic Volumetric Dip Estimation Combining Structure Tensor and Multiwindow Technology

The concept of bids sensitivities is presented, i.e. the first-order derivatives of nodal prices, generation outputs, unit' profits and transmission line power with respect to each unit's bids. The bids that each generator submits are the coefficient of its quadratic cost function. The bids' sensitivities are derived based on the interior-point optimal power flow (IPOPF) model, then an IPOPF-based bidding model is used for individual supplier to generate its optimal bids in the electricity generation auction market. The objective function of this bidding model is to maximise the individual supplier's profit and its constraints are the OPF problem, i.e. each generator submits its optimal bid by taking into account not only its own profit maximisation but also the system securities, and so on. This optimisation model is tested in the IEEE 30-bus system and some results are presented.

Bidding strategies based on bid sensitivities in generation auction markets

The industry treats the distributed acoustic sensing (DAS) system, which uses an optical fiber cable in vertical seismic profiling (VSP) data acquisition, as a new and fast-growing technolo...

Xiaokai Wang

Papers

Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network

Random noise suppression in seismic data: What can deep learning do?

Robust Seismic Volumetric Dip Estimation Combining Structure Tensor and Multiwindow Technology

Bidding strategies based on bid sensitivities in generation auction markets

Distributed acoustic sensing coupling noise removal based on sparse optimization