Wen L¹, SK Dong¹, YY Li¹, C Pulver², XY Li¹, JJ Shi³, YL Wang³, YS Ma³, DM Liu⁴

Phyton-International Journal of Experimental Botany, Vol.82, pp. 45-54, 2013, DOI:10.32604/phyton.2013.82.045

Abstract The alpine grassland in the Qinghai-Tibet Plateau is an extensive rangeland ecosystem sustaining a sparse population of traditional nomadic pastoralists in China. However, global climate change and anthropologic disturbances have severely degraded the alpine grasslands, and the consequences of this degradation are largely unknown. Forage is the only food source for livestock in the alpine region, and livestock is the major income source for nomadic herders. Therefore, it is critical to assess the forage quantity and quality along the current grassland degradation gradient. In this study, we examined the botanical composition, biomass of different functional groups, and forage grass nutritive… More >

Shang ZH^1,2, SG Xu¹

Phyton-International Journal of Experimental Botany, Vol.81, pp. 75-79, 2012, DOI:10.32604/phyton.2012.81.075

Abstract Pedicularis kansuensis is a dominating poisonous weed, and it might have allelopathic effects on other native grasses in alpine meadows. An experiment was conducted to examine a range of concentrations of aqueous whole plant extracts (25, 12.5, 2.5, 1.25, 0.25 and 0.0 g/L) of P. kansuensis, prepared at the flowering stage on seed germination and seedling growth of two native grasses (Poa pratensis and Elymus nutans). High concentrations of aqueous extracts of P. kansuensis inhibited seed germination and seedling growth of P. pratensis (p<0.05). Most aqueous extracts of P. kansuensis had a stimulatory (p<0.05) effect on E. nutans. Our results… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866

Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More >

Zhijuan Wang^1,2, Xiaobin Zhao^1,2, Wei Song^1,*, Antai Wang³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 213-225, 2019, DOI:10.32604/cmc.2019.05690

Abstract Readability is a fundamental problem in textbooks assessment. For low re-sources languages (LRL), however, little investigation has been done on the readability of textbook. In this paper, we proposed a readability assessment method for Tibetan textbook (a low resource language). We extract features based on the information that are gotten by Tibetan segmentation and named entity recognition. Then, we calculate the correlation of different features using Pearson Correlation Coefficient and select some feature sets to design the readability formula. Fit detection, F test and T test are applied on these selected features to generate a new readability assessment formula. Experiment… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1223-1235, 2019, DOI:10.32604/cmc.2019.05636

Abstract Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single… More >

Xiaodong Yan^1,2, Wei Song^1,2,*, Xiaobing Zhao^1,2, Anti Wang³

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 707-719, 2019, DOI:10.32604/cmc.2019.05157

Abstract We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the… More >

Xi Kan¹, Yonghong Zhang^2,*, Linglong Zhu², Liming Xiao², Jiangeng Wang³, Wei Tian⁴, Haowen Tan⁵

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 49-68, 2018, DOI:10.32604/cmc.2018.02376

Abstract Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow, cloud and snow-free land. Moreover,… More >