1,727,900 research outputs found
A Modified inverse integer Cholesky decorrelation method and the performance on ambiguity resolution
No full textOne of the research focuses in the integer least squares problem is the decorrelation technique to reduce the number of integer parameter search candidates and improve the efficiency of the integer parameter search method. It remains as a challenging issue for determining carrier phase ambiguities and plays a critical role in the future of GNSS high precise positioning area. Currently, there are three main decorrelation techniques being employed: the integer Gaussian decorrelation, the Lenstra–Lenstra–Lovász (LLL) algorithm and the inverse integer Cholesky decorrelation (IICD) method. Although the performance of these three state-of-the-art methods have been proved and demonstrated, there is still a potential for further improvements. To measure the performance of decorrelation techniques, the condition number is usually used as the criterion. Additionally, the number of grid points in the search space can be directly utilized as a performance measure as it denotes the size of search space. However, a smaller initial volume of the search ellipsoid does not always represent a smaller number of candidates.\ud
This research has proposed a modified inverse integer Cholesky decorrelation (MIICD) method which improves the decorrelation performance over the other three techniques. The decorrelation performance of these methods was evaluated based on the condition number of the decorrelation matrix, the number of search candidates and the initial volume of search space. Additionally, the success rate of decorrelated ambiguities was calculated for all different methods to investigate the performance of ambiguity validation. \ud
The performance of different decorrelation methods was tested and compared using both simulation and real data. The simulation experiment scenarios employ the isotropic probabilistic model using a predetermined eigenvalue and without any geometry or weighting system constraints. MIICD method outperformed other three methods with conditioning improvements over LAMBDA method by 78.33% and 81.67% without and with eigenvalue constraint respectively. The real data experiment scenarios involve both the single constellation system case and dual constellations system case. Experimental results demonstrate that by comparing with LAMBDA, MIICD method can significantly improve the efficiency of reducing the condition number by 78.65% and 97.78% in the case of single constellation and dual constellations respectively. It also shows improvements in the number of search candidate points by 98.92% and 100% in single constellation case and dual constellations case.\u
Statistical/climatic models to predict and project extreme precipitation events dominated by large-scale atmospheric circulation over the central-eastern China
Full text linkGlobal warming has posed non-negligible effects on regional extreme precipitation changes and increased the uncertainties when meteorologists predict such extremes. More importantly, floods, landslides, and waterlogging caused by extreme precipitation have had catastrophic societal impacts and led to steep economic damages across the world, in particular over central-eastern China (CEC), where heavy precipitation due to the Meiyu-front and typhoon activities often causes flood disaster. There is mounting evidence that the anomaly atmospheric circulation systems and water vapor transport have a dominant role in triggering and maintaining the processes of regional extreme precipitation. Both understanding and accurately predicting extreme precipitation events based on these anomalous signals are hot issues in the field of hydrological research.
In this thesis, the self-organizing map (SOM) and event synchronization were used to cluster the large-scale atmospheric circulation reflected by geopotential height at 500 hPa and to quantify the level of synchronization between the identified circulation patterns with extreme precipitation over CEC. With the understanding of which patterns were associated with extreme precipitation events, and corresponding water vapor transport fields, a hybrid deep learning model of multilayer perceptron and convolutional neural networks (MLP-CNN) was proposed to achieve the binary predictions of extreme precipitation. The inputs to MLP-CNN were the anomalous fields of GP at 500 hPa and vertically integrated water vapor transport (IVT). Compared with the original MLP, CNN, and two other machine learning models (random forest and support vector machine), MLP-CNN showed the best performance. Additionally, since the coarse spatial resolution of global circulation models and its large biases in extremes precipitation estimations, a new precipitation downscaling framework that combination of ensemble-learning and nonhomogeneous hidden Markov model (Ensemble-NHMM) was developed, to improve the reliabilities of GCMs in historical simulations and future projection. The performances of downscaled precipitation from reanalysis and GCM datasets were validated against the gauge observations and also compared with the results of traditional NHMM. Finally, the Ensemble-NHMM downscaling model was applied to future scenario data of GCM. On the projections of change trends in precipitation over CEC in the early-, medium- and late- 21st centuries under different emission scenarios, the possible causes were discussed in term of both thermodynamic and dynamic factors. Main results are enumerated as follows.
(1) The large-scale atmospheric circulation patterns and associated water vapor transport fields synchronized with extreme precipitation events over CEC were quantitatively identified, as well as the contribution of circulation pattern changes to extreme precipitation changes and their teleconnection with the interdecadal modes of the ocean. Firstly, based on the nonparametric Pettitt test, it was found that 23% of rain gauges had significant abrupt changes in the annual extreme precipitation from 1960 to 2015. The average change point in the annual extreme precipitation frequency and amount occurred near 1989. Complex network analysis showed that the rain gauges highly synchronized on extreme precipitation events can be clustered into four clusters based on modularity information. Secondly, the dominant circulation patterns over CEC were robustly identified based on the SOM. From the period 1960–1989 to 1990–2015, the categories of identified circulation patterns generally remain almost unchanged. Among these, the circulation patterns characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. An obvious water vapor channel originating from the northern Indian Ocean driven by the southwesterly airflow was observed for the representative circulation patterns (synchronized with extreme precipitation). Finally, the circulation pattern changes produced an increase in extreme precipitation frequency from 1960–1989 to 1990–2015. Empirical mode decomposition of the annual frequency variation signals in the representative circulation pattern showed that the 2–4 yr oscillation in the annual frequency was closely related to the phase of El Niño and Southern Oscillation (ENSO); while the 20–25 yr and 42–50 yr periodic oscillations were responses to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation.
(2) A regional extreme precipitation prediction model was constructed. Two deep learning models-MLP and CNN were linearly stacked and used two atmospheric variables associated with extreme precipitation, that is, geopotential height at 500 hPa and IVT. The hybrid model can learn both the local-scale information with MLP and large-scale circulation information with CNN. Validation results showed that the MLP-CNN model can predict extreme or non-extreme precipitation days with an overall accuracy of 86%. The MLP-CNN also showed excellent seasonal transferability with an 81% accuracy on the testing set from different seasons of the training set. MLP-CNN significantly outperformed over other machine learning models, including MLP, CNN, random forest, and support vector machine. Additionally, the MLP-CNN can be used to produce precursor signals by 1 to 2 days, though the accuracy drops quickly as the number of precursor days increases.
(3) The GCM seriously underestimated extreme precipitation over CEC but showed convincing results for reproducing large-scale atmospheric circulation patterns. The accuracies of 10 GCMs in extreme precipitation and large-scale atmospheric circulation simulations were evaluated. First, five indices were selected to measure the characteristics of extreme precipitation and the performances of GCMs were compared to the gauge-based daily precipitation analysis dataset over the Chinese mainland. The results showed that except for FGOALS-g3, most GCMs can reproduce the spatial distribution characteristics of the average precipitation from 1960 to 2015. However, all GCMs failed to accurately estimate the extreme precipitation with large underestimation (relative bias exceeds 85%). In addition, using the circulation patterns identified by the fifth-generation reanalysis data (ERA5) as benchmarks, GCMs can reproduce most CP types for the periods 1960–1989 and 1990–2015. In terms of the spatial similarity of the identified CPs, MPI-ESM1-2-HR was superior.
(4) To improve the reliabilities of precipitation simulations and future projections from GCMs, a new statistical downscaling framework was proposed. This framework comprises two models, ensemble learning and NHMM. First, the extreme gradient boosting (XGBoost) and random forest (RF) were selected as the basic- and meta- classifiers for constructing the ensemble learning model. Based on the top 50 principal components of GP at 500 hPa and IVT, this model was trained to predict the occurrence probabilities for the different levels of daily precipitation (no rain, very light, light, moderate, and heavy precipitation) aggregated by multi-sites. Confusion matrix results showed that the ensemble learning model had sufficient accuracy (>88%) in classifying no rain or rain days and (>83%) predicting moderate precipitation events. Subsequently, precipitation downscaling was done using the probability sequences of daily precipitation as large-scale predictors to NHMM. Statistical metrics showed that the Ensemble-NHMM downscaled results matched best to the gauge observations in precipitation variabilities and extreme precipitation simulations, compared with the result from the one that directly used circulation variables as predictors. Finally, the downscaling model also performed well in the historical simulations of MPI-ESM1-2-HR, which reproduced the change trends of annual precipitation and the means of total extreme precipitation index.
(5) Three climate scenarios with different Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs) were selected to project the future precipitation change trends. The Ensemble-NHMM downscaling model was applied to the scenario data from MPI-ESM1-2-HR. Projection results showed that the CEC would receive more precipitation in the future by ~30% through the 2075–2100 period. Compared to the recent 26-year epoch (1990–2015), the frequency and magnitude of extreme precipitation would increase by 21.9–48.1% and 12.3–38.3% respectively under the worst emission scenario (SSP585). In particular, the south CEC region is projected to receive more extreme precipitation than the north. Investigations of thermodynamic and dynamic factors showed that climate warming would increase the probability of stronger water vapor convergence over CEC. More wet weather states due to the enhanced water vapor transport, as well as the increased favoring large-scale atmospheric circulation and the strengthen pressure gradient would be the factors for the increased precipitation
王君实散文研究 = Wang Jun Shi prose research
No full text20世纪30年代末，马华文学随着越来越多中国文人的南来，逐渐走向勃兴，特别是1937年至1942年迎来了黄金时期。文艺的兴起，自然也离不开当时的时代背景。1937年，正是中国面临内忧外患的时期，不少中国作家参与到抗战救亡的活动中，其中一部分前往海外呼吁华侨救助抗战，为民族事业尽一份力，如人们所熟知的胡愈之和郁达夫等。
王君实亦是从中国南来的作家之一，也是第一个殉难的马华青年文艺作者。狮城在1942年初沦陷并更名为昭南岛，“反日分子”就成了日军头号打击的对象，很快王君实被汉奸出卖，为此他选择跳楼牺牲。但他的事迹与作品似乎随着时间的流逝已被世人逐渐遗忘。因此，笔者想通过拙作让世人对王君实的事迹和作品有进一步的了解并着重探讨那段尘封已久的红色记忆。
As more and more members of the literary class migrated south in the late 1930s, Malayan Chinese Literature began to develop rapidly. Thus, between 1937 and 1942, the period was heralded as a golden age. However, it is essential to note that we cannot view the growth of literature in isolation from the societal forces simmering in the background.
The 1937 was a terrible time marked by foreign threats and domestic trouble for China. Thus, many Chinese authors joined the rank and file of compatriots who participated in the war resistance and reformation movement, these authors some of them travelled overseas to solicit support from the Chinese Diaspora to protect their homeland, and some of the more notable authors within these categories include people like Yu Dafu and Hu Yuzhi.
Wang Jun Shi is also one of the members of the “south migrating author” from China and the first Malayan Chinese Youth Author to die as a martyr for the anti-war cause. At the beginning of 1942, Singapore fell and became renamed Syonon-to. Thus, the anti-war resistance movement became the primary target for extermination by the Japanese army. Not long after, Wang Jun Shi was ratted out by an informant; in response, Wang Jun Shi chose to end his life by jumping down a building.
However, over time, his prose and legacy have become forgotten by our generation. Thus, I hope my thesis can help to spread awareness of Wang Jun Shi’s prose and legacy; alongside that, I wish to reconstruct and analyse the crimson memories of that period, which have long been buried in time.
Keywords: Wang Jun Shi; Malayan Chinese Literature; Nationalism；Nanyang ColoursBachelor's degre
The genus Terthrothrips Karny (Thysanoptera: Phlaeothripidae) from China with one new species
No full textWang, Jun, Tong, Xiaoli (2011): The genus Terthrothrips Karny (Thysanoptera: Phlaeothripidae) from China with one new species. Zootaxa 2745: 63-67, DOI: 10.5281/zenodo.20278
A new species of Urothrips (Thysanoptera, Phlaeothripidae) from canopy of monsoon forest in China
No full textLu, Wenmin, Wang, Jun (2019): A new species of Urothrips (Thysanoptera, Phlaeothripidae) from canopy of monsoon forest in China. Zootaxa 4614 (1): 191-194, DOI: 10.11646/zootaxa.4614.1.1
The genus Anaphothrips (Thysanoptera, Thripidae) in China, with three new species
No full textCui, Yanze, Wang, Jun (2019): The genus Anaphothrips (Thysanoptera, Thripidae) in China, with three new species. Zootaxa 4700 (2): 246-258, DOI: https://doi.org/10.11646/zootaxa.4700.2.
CUHK electronic theses & dissertations collection
No full textWang Jun."February 2003."Thesis (Ph.D.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (p. 116-124).Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Mode of access: World Wide Web.Abstracts in English and Chinese
Hoplothrips species recorded from China (Thysanoptera, Phlaeothripidae), with one new species from Yunnan
No full textMound, Laurence A., Wang, Jun (2020): Hoplothrips species recorded from China (Thysanoptera, Phlaeothripidae), with one new species from Yunnan. Zootaxa 4758 (3): 596-599, DOI: https://doi.org/10.11646/zootaxa.4758.3.1
Terthrothrips parvus Okajima 2006
No full textTerthrothrips parvus Okajima, 2006 Terthrothrips parvus Okajima, 2006: 616 Material examined. China: Guangdong Province, Zhaoqing, Dinghushan National Nature Reserve (23 ° 10 '03"N, 112 ° 32 '06"E), 1 female, 1 male (macropterous), 14.vi. 1986, Xiaoli Tong; Hainan Province, Ledong, Jianfengling National Nature Reserve (18 ° 44 ' 24 "N, 108 ° 51 ' 48 "E), 4 males, 31.x. 1986, Xiaoli Tong; Diaoluoshan Nature Reserve (18 ° 43 'N, 109 ° 52 'E), 1 male (micropterous), 5.xii. 2008, Wang Jun. Distribution. China (Guangdong, Hainan); Japan.Published as part of Wang, Jun & Tong, Xiaoli, 2011, The genus Terthrothrips Karny (Thysanoptera: Phlaeothripidae) from China with one new species, pp. 63-67 in Zootaxa 2745 on page 64, DOI: 10.5281/zenodo.20278
- …
