Forecasting Groundwater Level Based on Relevance Vector Machine

2010 ◽  
Vol 121-122 ◽  
pp. 43-47 ◽  
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao
Keyword(s):  
Groundwater Level ◽  
Kernel Method ◽  
Nonlinear Problems ◽  
Relevance Vector Machine ◽  
Kernel Functions ◽  
Experimental Results ◽  
The Real

Relevance Vector Machine (RVM) is a novel kernel method based on sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, and the relevance vectors are automatically determined and have fewer parameters. In this paper, the RVM model is applied to forecasting groundwater level. The experimental results show the final RVM model achieved is sparser, the prediction precision is higher and the prediction values are in better agreement with the real values. It can be concluded that this technique can be seen as a very promising option to solve nonlinear problems such as forecasting groundwater level.

Research on the Pressure Fluctuation in Draft Tube Based on the Sparse Bayesian

2010 ◽  
Vol 108-111 ◽  
pp. 696-699
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Ji Gao Zhang
Keyword(s):  
Pressure Fluctuation ◽  
Draft Tube ◽  
Kernel Method ◽  
Relevance Vector Machine ◽  
Kernel Functions ◽  
Hydropower Station ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Actual Situation ◽  
Unit Discharge

Relevance Vector Machine (RVM) is a novel kernel method based on Sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, the relevance vectors automatically determined and fewer parameters. In view of the actual situation that the corresponding space curved surface which expressed the characteristics of pressure fluctuation is too complex to be analyzed, the RVM regression model for describing the characteristics which is nonlinear relationship among pressure fluctuation value，unit rotation and unit discharge is established，and it is applied to hydropower station. Comparing with Support Vector Machine (SVM), the experimental results show the final RVM model achieved is sparser, the prediction precision is higher and the prediction values are in better agreement with the real values.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

scholarly journals A Multi-Resolution Approach to GAN-Based Speech Enhancement

2021 ◽  
Vol 11 (2) ◽  
pp. 721
Author(s):  
Hyung Yong Kim ◽  
Ji Won Yoon ◽  
Sung Jun Cheon ◽  
Woo Hyun Kang ◽  
Nam Soo Kim
Keyword(s):  
Speech Enhancement ◽  
Optimal Solution ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
The Real ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Speech Characteristics ◽  
Conventional Methods ◽  
Convex Property

Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

scholarly journals Hydraulic Tomography for Estimating the Diffusivity of Heterogeneous Aquifers Based on Groundwater Response to Tidal Fluctuation in an Artificial Island in Taiwan

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jet-Chau Wen ◽  
Hong-Ru Lin ◽  
Tian-Chyi Jim Yeh ◽  
Yu-Li Wang ◽  
Keng-Li Lin ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Hydraulic Properties ◽  
Tidal Fluctuation ◽  
Heterogeneous Aquifers ◽  
Hydraulic Diffusivity ◽  
The Real ◽  
Groundwater Levels ◽  
Hydraulic Tomography ◽  
Groundwater Response ◽  
Spectrum Ratio

This study investigated the hydraulic properties of the heterogeneous aquifers of an artificial island (Yunlin Offshore Industrial Park) in Taiwan. The research was based on the groundwater level response affected by tidal fluctuation using the hydraulic tomography (HT) to analyze the hydraulic diffusivity (α). Specifically, the power spectrum ratio of groundwater and tidal fluctuations derived from the Gelhar solution was used to estimate α in homogeneous aquifers; this, however, could not be applied in the artificial island. Next, the spatial distribution of the groundwater level response affected by tidal fluctuation was analyzed and found to be irregular, proving the existence of hydrogeological heterogeneity in the artificial island. Furthermore, the results of the estimated α using the HT showed low error and high correlation, 0.41 m2/hr and 0.83, respectively, between the optimal estimated heterogeneous and reference α fields in the synthetic aquifer. Last, the HT was used in the real tested scenario. By comparing the predicted groundwater levels of the optimal estimated heterogeneous α field and the observed groundwater levels of the real aquifer, it was found that the correlation was higher than 0.99. Therefore, the HT can be used to obtain the optimal estimated heterogeneous α field in the artificial island.

scholarly journals Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668529 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Bat Algorithm ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Machine Method ◽  
Training Samples ◽  
Kernel Parameter

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

Semi-supervised learning for raindrop removal on a single image

2021 ◽  
pp. 1-9
Author(s):  
Juan Zhang ◽  
Wenbin Guo
Keyword(s):  
Supervised Learning ◽  
Experimental Results ◽  
Real Image ◽  
Single Image ◽  
The Real ◽  
Rainy Weather

This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.

Semantic Analysis of Bloggers Experiences as a Knowledge Source of Average Human Morality

2015 ◽  
pp. 73-95 ◽  
Author(s):  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Real World ◽  
Semantic Analysis ◽  
Experimental Results ◽  
Artificial Agents ◽  
Knowledge Source ◽  
The Real ◽  
Long Run

This chapter introduces an approach and methods for creating a system that refers to human experiences and thoughts about these experiences in order to ethically evaluate other parties', and in a long run, its own actions. It is shown how applying text mining techniques can enrich machine's knowledge about the real world and how this knowledge could be helpful in the difficult realm of moral relativity. Possibilities of simulating empathy and applying proposed methods to various approaches are introduced together with discussion on the possibility of applying growing knowledge base to artificial agents for particular purposes, from simple housework robots to moral advisors, which could refer to millions of different experiences had by people in various cultures. The experimental results show efficiency improvements when compared to previous research and also discuss the problems with fair evaluation of moral and immoral acts.

scholarly journals Impact on Stability by the Use of Memory in Traub-Type Schemes

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 274
Author(s):  
Francisco I. Chicharro ◽  
Alicia Cordero ◽  
Neus Garrido ◽  
Juan R. Torregrosa
Keyword(s):  
Order Of Convergence ◽  
Nonlinear Problems ◽  
Higher Order ◽  
Lower Number ◽  
Dynamical Analysis ◽  
The Real ◽  
Original Scheme ◽  
Interpolation Polynomials ◽  
With Memory ◽  
Number Of Iterations

In this work, two Traub-type methods with memory are introduced using accelerating parameters. To obtain schemes with memory, after the inclusion of these parameters in Traub’s method, they have been designed using linear approximations or the Newton’s interpolation polynomials. In both cases, the parameters use information from the current and the previous iterations, so they define a method with memory. Moreover, they achieve higher order of convergence than Traub’s scheme without any additional functional evaluations. The real dynamical analysis verifies that the proposed methods with memory not only converge faster, but they are also more stable than the original scheme. The methods selected by means of this analysis can be applied for solving nonlinear problems with a wider set of initial estimations than their original partners. This fact also involves a lower number of iterations in the process.

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