Yanbo Shen^1,2, Yang Gao³, Yueming Hu^1,2, Xin Yao⁴, Wenzheng Yu^4,*, Yubing Zhang⁴

Energy Engineering, Vol.121, No.1, pp. 43-57, 2024, DOI:10.32604/ee.2023.041921 - 27 December 2023

Abstract The Tibet Plateau is one of the regions with the richest solar energy resources in the world. In the process of achieving carbon neutrality in China, the development and utilization of solar energy resources in the region will play an important role. In this study, the gridded solar resource data with 1 km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model. On this basis, the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years (1991–2020) are finely evaluated, and the annual global… More >

Shuting Chang^1,2, Guanzhao Zhang^2,3, Nengjun Sun², Xinpeng Yuan⁴, Qingting Wang⁵, Lin Zhu⁶, Feiyue Zhang⁷, Yu Xiu⁴, Yang Dong⁸, Yonghong Chen⁹, Zhanpeng Zhao¹⁰, Xiao Liu⁴, Qiang Shao¹¹, Xiaofeng Xu¹², Anshun Wang¹³, Mengjiao Li¹⁴, Bo Li^2,*

Congenital Heart Disease, Vol.17, No.6, pp. 709-716, 2022, DOI:10.32604/chd.2022.018657 - 11 October 2022

Abstract Background: This study aimed to illustrate the prevalence of CHD by screening children in extremely high-altitude areas (over 4000 m to even 5000 m above sea level) and explore an aid model for early diagnosis and treatment for the Tibetan population. Methods: A total of 2242 students from different schools in Ngamring County, Xigaze city, Tibet from September 2019 to September 2020 were selected for screening. The students were examined through the inquiry of their current medical history and family history, cardiac auscultation and a physical examination, in order to screen out the suspected cases of… More >

Kuntharrgyal Khysru^1,*, Jianguo Wei^1,2, Jianwu Dang³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4897-4907, 2022, DOI:10.32604/cmc.2022.027591 - 28 July 2022

Abstract In China, Tibetan is usually divided into three major dialects: the Am-do, Khams and Lhasa dialects. The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan. Although this dialect has its own specific historical and social conditions and development, there have been different degrees of communication with other ethnic groups, but all the abovementioned dialects developed from the same language: Tibetan. This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language, which optimizes the problems existing in previous research. Audio data of… More >

AnJian-CaiRang^1,2, Dawei Song^3,4,*

Journal on Artificial Intelligence, Vol.4, No.2, pp. 85-98, 2022, DOI:10.32604/jai.2022.029141 - 18 July 2022

Abstract Sorting the Tibetan language quickly and accurately requires first identifying the component elements that make up Tibetan syllables and then sorting by the priority of the component. Based on the study of Tibetan text structure, grammatical rules and syllable structure, we present a structure-based Tibetan syllable recognition method that uses syllable structure instead of grammar. This method avoids complicated Tibetan grammar and recognizes the components of Tibetan syllables simply and quickly. On the basis of identifying the components of Tibetan syllables, a Tibetan syllable sorting algorithm that conforms to the language sorting rules is proposed.… More >

Xiaodong Yan^1,2, Yiqin Wang^1,2, Wei Song^1,2,*, Xiaobing Zhao^1,2, A. Run³, Yang Yanxing⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1769-1781, 2022, DOI:10.32604/cmc.2022.027301 - 18 May 2022

Abstract Text summarization creates subset that represents the most important or relevant information in the original content, which effectively reduce information redundancy. Recently neural network method has achieved good results in the task of text summarization both in Chinese and English, but the research of text summarization in low-resource languages is still in the exploratory stage, especially in Tibetan. What’s more, there is no large-scale annotated corpus for text summarization. The lack of dataset severely limits the development of low-resource text summarization. In this case, unsupervised learning approaches are more appealing in low-resource languages as they… More >

Zhijie Wang¹, Yue Zhao^1,*, Licheng Wu¹, Xiaojun Bi¹, Zhuoma Dawa², Qiang Ji³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 629-639, 2022, DOI:10.32604/cmc.2022.027092 - 18 May 2022

Abstract As one of Chinese minority languages, Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently. This, along with the relatively small Tibetan corpus, has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model. This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data. We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects: modeling unit selection, transfer learning method, and source language selection. Experimental results show that the More >

Yuan Sun^1,2,*, Chaofan Chen^1,2, Andong Chen³, Xiaobing Zhao^1,2

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3203-3213, 2021, DOI:10.32604/cmc.2021.016517 - 06 May 2021

Abstract As the dual task of question answering, question generation (QG) is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph. The QG task is of great significance to question answering systems, conversational systems, and machine reading comprehension systems. Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks. However, the task of Tibetan QG is rarely mentioned. The key factor impeding its development is the lack of a public Tibetan QG dataset. Faced with this challenge, the present paper first collects… More >

Feng Liu^1,2,3, Zilan Ma^1,2,3, Marcos A. Caraballo-Ortiz⁴, Hui Zhang⁵, Xu Su^1,2,3, Yuping Liu^1,2,3,*

Phyton-International Journal of Experimental Botany, Vol.89, No.4, pp. 1083-1090, 2020, DOI:10.32604/phyton.2020.011239 - 09 November 2020

Abstract Pedicularis longiflora var. tubiformis (Orobanchaceae) is an abundant parasitic herb mainly found in the Xiaopohu wetland of the Qinghai Lake Basin in Northwestern China. The species has an important local medicinal value, and in this study, we evaluated the chemical profile of its stems, leaves and seeds using mass spectrometry. Dried samples of stems, leaves and seeds were grinded, weighted, and used for a series of extractions with an ultrasonic device at room temperature. The chemical profiles for each tissue were determined using Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid ChromatographyMass Spectrometry (LC-MS). Twenty-seven amino acids and organic… More >

Runzhe Tao^{1, *}, Yonghong Zhang¹, Lihua Wang¹, Pengyan Cai¹, Haowen Tan²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526 - 16 September 2020

Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution… 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 >