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In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-level morphological active contour (MMAC) algorithm to identify and delineate tree crowns in mountainous forest based on rasterized airborne lidar data. MMAC is a generalized tree crown mapping algorithm which can accommodate multiple heads in a crown as well as overlapping crowns. The MMAC algorithm comprises three steps: bottom up erosion (BUE) which identifies stand candidates, top down dilation (TDD) which estimates the crown periphery, and an active contour model (ACM) which delineates crown contours. Three sample plots were selected in Alishan National Scenic Area, Taiwan, (predominantly alder, sugi, and red cypress) for evaluation of the algorithm. When compared with ground survey data, the algorithm achieved an average detection accuracy of 24 percent omission error and 13 percent commission error in identifying individual trees in mountainous forest stands. Detection accuracy is potentially related to stand density. [ABSTRACT FROM AUTHOR]

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments. • We utilize an improved SMOTE that could decrease the impact of data imbalance that exists in the credit data. • We propose to leverage the credit feature pruning algorithm and memetic optimization algorithm. • We propose an automated credit scoring strategy (ACSS) method with a cost-effective neural architecture search (C-NAS) scheme. [ABSTRACT FROM AUTHOR]