Abstract:The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.
Through equipment monitoring, the uptimes of machines are enhanced in the industrial applications. The unpredicted failures risks are minimized by the proper equipment monitoring. The machine vibrations are increased caused by the failure modes. The vibration data requires effective analysis by the accurate assessment of the machine equipment. For fault feature selection and detection of faults in rotating equipment, the empirical knowledge is required. Low efficiency of the methods and motor speed control are the main drawbacks of the existing techniques. So the basic aim of this paper is the detection of rotating equipment faults by utilizing the vibration analysis. The motor vibration is analyzed and monitored using spectrum analysis. The spectral content are extracted and fed into the classifier like k-Nearest neighbors (KNN), back-propagation neural network BPNN, Sparse Representation Classifier (SRC), Support vector machine (SVM) and Random Forest (RF) for the type of failure prediction and analyze the unbalance condition (UNB), bearing faults (BDF), and broken rotor bars (BRB) faults. The RF classifier is better as compared to other classifiers in terms of accuracy, precision and recalls values by approximately 10.92 %, 11.03 % and 20.13 % respectively.
This paper proposes a digital watermarking algorithm based on two-way Arnold transformation and QR code. This algorithm uses the QR barcode as the watermark, and in its pretreatment process it uses the two-way Arnold transformation to broke up the QR code image into blocks which then coded and re-arranged in a specific order. Considering the role of Singular Value Decomposition in watermark embedding, here the watermark information is embedded into the low frequency coefficients of wavelet transform of the carrier image. Simulation results show that the algorithm is robust against attacks such as shearing, the JPEG compression and noise attacking.
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