Group method of data handling to predict scour depth around bridge piers

In this study, group method of data handling network with quadratic polynomial was used to predict scour depth around bridge piers. Effective parameters on scour phenomena include sediment size, geometry of bridge pier, and upstream flow conditions. Different shapes of piers have been utilized to develop the GMDH network. Back propagation algorithm was performed to train the GHMD network which updated weighting coefficients of quadratic polynomial in each iteration of the training stage. The GMDH performed with the lowest errors of training and testing stages for cylindrical pier. Also, Richardson and Davis, Johnson’s equations produced relatively good performances for different types of piers. Finally, the results indicated that GMDH could be provided more accurate prediction than those obtained using traditional equations.     

Soft computing for prediction of river pipeline scour depth

This study presents gene-expression programming (GEP) as an alternative soft computing tool for the prediction of scour below underwater pipeline across river. Actual laboratory measurements were used for the model development. The scour depth was formulated in terms of several influencing parameters. The results indicate that GEP is a very promising approach to predict the river pipeline scour depth.     

进化数据分组处理算法研究进展

先对进化人工神经网络的理论研究和运用现状进行了分析,在此基础上,分别分析了各种进化数据分组处理算法研究的现状,最后结合进化数据分组处理算法研究现状提出了一些新的进化算法。     

Prediction of local scour around bridge piers using the ANFIS method

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles’ initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville’s formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

     

Cluster ensemble framework based on the group method of data handling

Cluster ensemble is a powerful method for improving both the robustness and the stability of unsupervised classification solutions. This paper introduced group method of data handling (GMDH) to cluster ensemble, and proposed a new cluster ensemble framework, which named cluster ensemble framework based on the group method of data handling (CE-GMDH). CE-GMDH consists of three components: an initial solution, a transfer function and an external criterion. Several CE-GMDH models can be built according to different types of transfer functions and external criteria. In this study, three novel models were proposed based on different transfer functions: least squares approach, cluster-based similarity partitioning algorithm and semidefinite programming. The performance of CE-GMDH was compared among different transfer functions, and with some state-of-the-art cluster ensemble algorithms and cluster ensemble frameworks on synthetic and real datasets. Experimental results demonstrate that CE-GMDH can improve the performance of cluster ensemble algorithms which used as the transfer functions through its unique modelling process. It also indicates that CE-GMDH achieves a better or comparable result than the other cluster ensemble algorithms and cluster ensemble frameworks.     

Compressibility indices of saturated clays by group method of data handling and genetic algorithms

Compression index (C _c) and recompression index (C _r) are used to estimate the consolidation settlement of fine-grained soils. As the determination of these indices from oedometer test is relatively time-consuming, in present research group method of data handling-type neural network optimized using genetic algorithms is used to estimate the compressibility indices (C _c and C _r) of saturated clays. C _c and C _r were modeled as a function of three variables including the initial void ratio (e ₀), liquid limit (LL) and specific gravity (G _s). Three hundred data sets collected from multiple sites in the province of Mazandaran, Iran, were used for the training and testing of the models. The predicted compressibility indices were compared with those of experimentally measured values to evaluate the performances of the proposed models. The results showed that appreciable improvement toward other correlations has been achieved. At the end, sensitivity analyses of the obtained models were carried out to evaluate the influence of input parameters on model outputs and showed that e ₀ and LL are the most influential parameters on C _c and C _r, respectively. Also, it has been demonstrated that the compressibility indices predicted by models are considerably influenced by changing measured G _s (uncertainty). In other words, the mean absolute percent error values increase greatly by G _s variation. Therefore, it needs more accuracy to measure this parameter in the laboratory.

     

Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE–GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE–GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE–GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE–GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems.     

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water scour condition, existing methods do not always produce reasonable results for scour predictions. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and presents an alternative to the regression in the form of artificial neural networks, ANNs, and adaptive neuro-fuzzy inference system, ANFIS. Two ANNs model, feed forward back propagation, FFBP, and radial basis function, RBF, were utilized to predict the depth of the scour hole. Two combinations of input data were used for network training; the first input combination contains six-dimensional variables, which are flow depth, mean velocity, critical flow velocity, grain mean diameter, pile diameter, distance between the piles (gap), besides the number of piles normal to the flow and the number of piles in-line with flow, while the second combination contains seven non-dimensional parameters which is a composition of dimensional parameters. The training and testing experimental data on local scour at pile groups are selected from several precious references. Networks’ results have been compared with the results of empirical methods that are already considered in this study. Numerical tests indicate that FFBP-NN model provides a better prediction than the other models. Also a sensitivity analysis showed that the pile diameter in dimensional variables and ratio of pile spacing to pile diameter in non-dimensional parameters are the most significant parameters on scour depth.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Self-organizing background subtraction using color and depth data

Background subtraction from color and depth data is a fundamental task for video surveillance applications that use data acquired by RGBD sensors. We present a method that adopts a self-organizing neural background model previously adopted for RGB videos to model the color and depth background separately. The resulting color and depth detection masks are combined to guide the selective model update procedure and to achieve the final result. Extensive experimental results and comparisons with several state-of-the-art methods on a publicly available dataset show that the exploitation of depth information allows achieving much higher performance than just using color, accurately handling color and depth background maintenance challenges.

     

Constraint handling in genetic algorithms using a gradient-based repair method

Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embedded into a simple GA as a special operator. Experiments using 11 benchmark problems are presented and compared with the best known solutions reported in the literature. Our results are competitive, if not better, compared to the results reported using the homomorphous mapping method, the stochastic ranking method, and the self-adaptive fitness formulation method.     

A modified slacks‐based ranking method handling negative data in data envelopment analysis

Performance ranking for a set of comparable decision‐making units (DMUs) with multiple inputs and outputs is an important and often‐discussed topic in data envelopment analysis (DEA). Conventional DEA models distinguish efficient units from inefficient ones but cannot further discriminate the efficient units, which all have a 100% efficiency score. Another weakness of these models is that they cannot handle negative inputs and/or outputs. In this paper, a new modified slacks‐based measure is proposed that works in the presence of negative data and provides quantitative data that helps decision makers obtain a full ranking of DMUs in situations where other methods fail. In addition, the new method has the properties of unit invariance and translation invariance, and it can give targets for inefficient DMUs to guide them to achieve full efficiency. Two numerical examples are analysed to demonstrate the usefulness of the new method.     

Prediction of penetration depth in a plunging water jet using soft computing approaches

The flow characteristics of the plunging water jets can be defined as volumetric air entrainment rate, bubble penetration depth, and oxygen transfer efficiency. In this study, the bubble penetration depth is evaluated based on four major parameters that describe air entrainment at the plunge point: the nozzle diameter (D _N), jet length (L _j), jet velocity (V _N), and jet impact angle (θ). This study presents artificial neural network (ANN) and genetic expression programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of the bubble penetration depth by plunging water jets. A new formulation for prediction of penetration depth in a plunging water jets is developed using GEP. The GEP-based formulation and ANN approach are compared with experimental results, multiple linear/nonlinear regressions, and other equations. The results have shown that the both ANN and GEP are found to be able to learn the relation between the bubble penetration depth and basic water jet properties. Additionally, sensitivity analysis is performed for ANN, and it is found that D _N is the most effective parameter on the bubble penetration depth.     

Inequality constraint handling in genetic algorithms using a boundary simulation method

Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes a boundary simulation method to address inequality constraints for GAs. This method can efficiently generate a feasible region boundary point set to approximately simulate the boundary of the feasible region. Based on the results of the boundary simulation method, GAs can start the genetic search from the boundary of the feasible region or the feasible region itself directly. Furthermore, a series of genetic operators that abandon or repair infeasible individuals produced during the search process is also proposed. The numerical experiments indicate that the proposed method can provide competitive results compared with other studies.     

Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.     

一种混合云环境下安全高效的群数据共享方法

在混合云数据共享中,用户量大,属性多,导致用户端的计算量随着属性数目的增多而增大,影响着群用户之间的云数据共享效率,并且还存在用户的身份隐私及相关属性容易被泄露的风险。针对这些问题进行研究,提出了一种适合混合云环境下安全高效的群数据共享方法。该方法通过使用匿名技术、属性隐藏和计算外包技术相结合,保障用户的身份隐私和属性的安全,降低用户端的计算量。安全性与性能分析和实验结果表明,该方法具有较好的安全性和效率。     

Prediction method for energy consumption per ton of fused magnesium furnaces using data driven and mechanism model

This paper gives an overview of early development of nonlinear disturbance observer design technique and the disturbanceobserver based control (DOBC) design. Some critical points raised in the development of the methods have been reviewed anddiscussed which are still relevant for many researchers or practitioners who are interested in this method. The review is followedby the development of a new type of nonlinear PID controller for a robotic manipulator and its experimental tests. It is shown that,under a number of assumptions, the DOBC consisting of a predictive control method and a nonlinear disturbance observer couldreduce to a nonlinear PID with special features. Experimental results show that, compared with the predictive control method,the developed controller significantly improves performance robustness against uncertainty and friction. This paper may triggerfurther research and interests in the development of DOBC and related methods, and building up more understanding betweenthis group of control methods with comparable ones (particularly control methods with integral action).     

Evaluation of high-resolution,true-colour,aerial imagery for mapping bathymetry in a clear-water river without ground-based depth measurements

This study evaluates the potential of the hydraulically assisted bathymetry (HAB-2) model coupled with true-colour, three-band, aerial imagery to map bathymetry in the clear-water McKenzie River, Oregon, USA, in the absence of ground-based depth measurements. It is the most rigorous test of the HAB-2 model to date. Correlation-coefficient (r ²) values for sonar depths versus modelled depths are 0.40 for 2007, 10 cm resolution imagery. Overall depth trends follow those of sonar data, except in areas where there are shadows, riffles or obstructions that block the view of the river (e.g. overhanging trees, bridges). Low-pass filtering of the image to remove film granularity does little to improve the results, although an Olympic filter improves the r ² value from 0.40 to 0.48. The moderate fit of the model results to sonar data in 2007 may also result from the 28–39 day gap between sonar and image acquisition, during which time, the discharge changed. HAB-2 depth estimates for the 2008 0.5 m imagery fit the depth measurements more closely (r ² = 0.89). The better fit may reflect the collection of ground and image data at approximately the same time and discharge, as well as coarser spatial resolution, which created less sensitivity to changes in substrate size and colour. The results suggest that the best depth-estimate results for the HAB-2 model are for depths ranging from 0.25 and 1.5 m. Use of digital imagery collected with digital cameras should also improve accuracies. Results indicate that the HAB-2 is useful for characterizing the approximate depths throughout the river channel if one avoids shadows, riffles and obstructions.