![Loading...](https://link.springer.com/static/c4a417b97a76cc2980e3c25e2271af3129e08bbe/images/pdf-preview/spacer.gif)
-
Chapter and Conference Paper
Semantic 3D CAD and Its Applications in Construction Industry – An Outlook of Construction Data Visualization
In response to the need of using electronic design data directly in construction management applications, many CAD developers have started implementing semantic data models in their CAD products using industry...
-
Chapter and Conference Paper
Global Exponential Stability of Recurrent Neural Networks with Time-Dependent Switching Dynamics
In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estim...
-
Chapter and Conference Paper
Finding Intrinsic and Extrinsic Viewing Parameters from a Single Realist Painting
In this paper we studied the geometry of a three-dimensional tableau from a single realist painting – Scott Fraser’s Three way vanitas (2006). The tableau contains a carefully chosen complex arrangement of object...
-
Chapter and Conference Paper
Robust Learning-Based Annotation of Medical Radiographs
In this paper, we propose a learning-based algorithm for automatic medical image annotation based on sparse aggregation of learned local appearance cues, achieving high accuracy and robustness against severe dise...
-
Chapter and Conference Paper
An Image Encryption Algorithm Based on Small Permutation Array Combining
In traditional chaotic map based image encryption algorithm, the encryption performance is determined by the permutation generating speed, and due to short periodical problem led by the finite precision effect...
-
Chapter and Conference Paper
Adaptive Backstep** Neural Control for Switched Nonlinear Stochastic System with Time-Delay Based on Extreme Learning Machine
In this paper, for a class of switched stochastic nonlinear systems with time-varying delays, the output feedback stabilization problem is addressed based on single hidden layer feed-forward network (SLFN) and...
-
Chapter and Conference Paper
Damage Pattern Recognition of Refractory Materials Based on BP Neural Network
The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the refractory materials based on the BP (back propagation) Neural Network ...
-
Chapter and Conference Paper
Displacement Prediction Model of Landslide Based on Ensemble of Extreme Learning Machine
Based on time series analysis, total accumulative displacement of landslide is divided into the trend component displacement and the periodic component displacement according to the response relation between d...
-
Chapter and Conference Paper
Distance Metric Learning-Based Conformal Predictor
In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and o...
-
Chapter and Conference Paper
Study on Landslide Deformation Prediction Based on Recurrent Neural Network under the Function of Rainfall
Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people’s life and property. In this paper, a method based on r...
-
Chapter and Conference Paper
Classifying Stem Cell Differentiation Images by Information Distance
The ability of stem cells holds great potential for drug discovery and cell replacement therapy. To realize this potential, effective high content screening for drug candidates is required. Analysis of images ...
-
Chapter and Conference Paper
Local Clustering Conformal Predictor for Imbalanced Data Classification
The recently developed Conformal Predictor (CP) can provide calibrated confidence for prediction which is out of the traditional predictors’ capacity. However, CP works for balanced data and fails in the case ...
-
Chapter and Conference Paper
A New Closed Loop Method of Super-Resolution for Multi-view Images
In this paper, we propose a closed loop method to resolve the multi-view super-resolution problems. Given that the input is one high-resolution view along with its neighboring low-resolution views, our method ...
-
Chapter and Conference Paper
Generalized Regression Neural Networks with K-Fold Cross-Validation for Displacement of Landslide Forecasting
This paper proposes a generalized regression neural networks (GRNNS) with \(K\) -fold cross-validation (GRNNSK) for pr...
-
Chapter and Conference Paper
A Kernel ELM Classifier for High-Resolution Remotely Sensed Imagery Based on Multiple Features
Better interpretation about the contents in high-resolution remote sensing images can be obtained by using multiple features of various types. In order to process large image data sets with high feature dimens...
-
Chapter and Conference Paper
Multi-step Predictions of Landslide Displacements Based on Echo State Network
Time series prediction theory and methods can be applied to many practical problems, such as the early warning of landslide hazard. Most already existing time series prediction methods cannot be effectively ap...
-
Chapter and Conference Paper
Semi-supervised Non-negative Local Coordinate Factorization
Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1)...
-
Chapter and Conference Paper
Two-Dimensional Euler PCA for Face Recognition
Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional...
-
Chapter and Conference Paper
Non-negative Low-Rank and Group-Sparse Matrix Factorization
Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grou** structure from a...
-
Chapter and Conference Paper
Event Detection with Convolutional Neural Networks for Forensic Investigation
Traditional approaches rely on domain expertise to acquire complicated features. Meanwhile, existing Natural Language Processing (NLP) tools and techniques are not competent to extract information from digital...