Tropical forests in many areas of Central and South America experience strong seasonality in climatic variables such as rainfall, solar radiation, wind speed, and relative humidity. Such seasonality is typical of the mangrove forests we study along the Caribbean coast of Panama. Tied to this environmental variation are changes in leaf phenology and physiology that can affect the spectral properties of leaves and thus our ability to discriminate canopies of differing species composition. The goals of this study were twofold. First, we compared the efficacy of three different classification methods for discriminating mangrove canopies, including a back-propagation, feed-forward neural network classifier with two hidden layers of 24 and 12 neurons (hereafter, BP:24:12), a newly developed clusteringbased neural network classifier (CBNN), and a maximum likelihood classifier (MLC). Comparisons were made with and without added textural information. Our second aim was to compare the absolute and relative discrimination abilities of these methods when applied to images of the same forest acquired in different seasons. Two sets of Ikonos images acquired in February (dry season) and May (early wet season) 2004 were analyzed in this study. When only spectral information was considered, MLC and CBNN discriminated differences in canopy species composition with higher accuracy than the BP:24:12 method. When second-order textural information was also taken into account, CBNN outperformed MLC and presented the best classification accuracy, i.e., kappa value equaled 0.93. Analyses of the wet season (May) image were consistently more accurate in discriminating mangrove canopies of differing species composition than analyses of the dry season (February) image, regardless of the classification method or the inclusion of textural information.
School bullying is a common social problem, which affects children both mentally and physically, making the prevention of bullying a timeless topic all over the world. This paper proposes a method for detecting bullying in school based on activity recognition and speech emotion recognition. In this method, motion and voice data are gathered by movement sensors and a microphone, followed by extraction of a set of motion and audio features to distinguish bullying incidents from daily life events. Among extracted motion features are both time-domain and frequency-domain features, while audio features are computed with classical MFCCs. Feature selection is implemented using the wrapper approach. At the next stage, these motion and audio features are merged to form combined feature vectors for classification, and LDA is used for further dimension reduction. A BPNN is trained to recognize bullying activities and distinguish them from normal daily life activities. The authors also propose an action transition detection method to reduce computational complexity for practical use. Thus, the bullying detection algorithm will only run, when an action transition event has been detected. Simulation results show that the combined motion-audio feature vector outperforms separate motion features and acoustic features, achieving an accuracy of 82.4% and a precision of 92.2%. Moreover, with the action transition method, the computation cost can be reduced by half.
Estimation for weather-related failure probability of overhead transmission lines is essential in the reliability assessment of a power system. This paper analyzes the outage and weather data of 110 kV overhead transmission lines in the Guangxi Zhuang Autonomous Region of China during 2011-2014. The result reveals obvious uneven distributions of outage events for time and space due to the spatial and temporal variation of severe weather. Based on the results, an estimation method is proposed in this paper. Split and aggregation is used to smooth the outage and weather data. The poisson model is adopted in our method to investigate the statistic characteristics of transmission line outage events. Regression analysis is applied to obtain the correlation between the weather intensity and history failure rate. Furthermore the method proposed is validated against the empirical outage data.
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