Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated re...Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated reluctance to use machine-harvested cotton. The first objective was to determine how the fiber quality was affected by the ginning and lint cleaning and how the fiber damage during levels of lint cleaning changed. The second objective was to determine the optimum number of lint cleaners for machine-harvested cotton based on fiber damage. Cotton samples were collected from 13 fields and processed in seven ginneries between 2013 and 2015. The results indicated that ginning and lint cleaning didn＇t have significant effect on fiber strength and significantly affected both fiber length and short fiber index. Fiber length was reduced by more than 1.00 mm from six of 13 fields after lint cleaning, then the damage rate on short fiber index from 11 of 13 fields was more than 20%. The third lint cleaning caused great fiber damage, reducing fiber length by 0.35 mm and increasing short fiber index by 0.65%. So, the lint should be cleaned by one lint cleaner in the Xinjiang, however, the stage of lint cleaning was sometimes omitted when the foreign matter content of lint was little.展开更多
In the patents pavilion of the Fifth Asia-Pacific Fair, the attention of numerous visitors was riveted by an onthe-spot demonstration. Standing before them was a machine with a stainless steel casing partly in a conta...In the patents pavilion of the Fifth Asia-Pacific Fair, the attention of numerous visitors was riveted by an onthe-spot demonstration. Standing before them was a machine with a stainless steel casing partly in a container of clear water. The operator pressed a button and ripples appeared on the surface of the water. The operator took watch chain s and jewellery from the visitors and put them in展开更多
In this article, research was conducted to improve Linter machines that remove short fibers remaining in ginned cotton seeds at cotton ginneries. The study examined the effect of changing the dimensions of the brush d...In this article, research was conducted to improve Linter machines that remove short fibers remaining in ginned cotton seeds at cotton ginneries. The study examined the effect of changing the dimensions of the brush drum, guide and mesh surface in the cleaning device proposed for the linting machine on the movement of the peg and the cleaning efficiency, and the highest level of efficiency in separating impurities from the peg was determined. During the study, the main factors influencing the effective operation of the improved linting machine were identified, the limits of their values were determined, and studies were carried out using the mathematical modeling method. As a result, at the values of the given coefficients, efficient operation of the improved linting machine was observed, that is, the lint cleaning efficiency reached 55.1%.展开更多
The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the per...The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the performance of NMT is greatly reduced,and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model.However,not all languages have large-scale and high-quality bilingual corpus resources available.In these cases,improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results.This paper proposes a new method to improve the quality of data by using data cleaning,data expansion,and other measures to expand the data at the word and sentence-level,thus improving the richness of the bilingual data.The long short-term memory(LSTM)language model is also used to ensure the smoothness of sentence construction in the process of sentence construction.At the same time,it uses a variety of processing methods to improve the quality of the bilingual data.Experiments using three standard test sets are conducted to validate the proposed method;the most advanced fairseq-transformer NMT system is used in the training.The results show that the proposed method has worked well on improving the translation results.Compared with the state-of-the-art methods,the BLEU value of our method is increased by 2.34 compared with that of the baseline.展开更多
基金supported by the National Key Technology R&D Program of China (2014BAD09B03)the National Natural Science Foundation of China (31560366)
文摘Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated reluctance to use machine-harvested cotton. The first objective was to determine how the fiber quality was affected by the ginning and lint cleaning and how the fiber damage during levels of lint cleaning changed. The second objective was to determine the optimum number of lint cleaners for machine-harvested cotton based on fiber damage. Cotton samples were collected from 13 fields and processed in seven ginneries between 2013 and 2015. The results indicated that ginning and lint cleaning didn＇t have significant effect on fiber strength and significantly affected both fiber length and short fiber index. Fiber length was reduced by more than 1.00 mm from six of 13 fields after lint cleaning, then the damage rate on short fiber index from 11 of 13 fields was more than 20%. The third lint cleaning caused great fiber damage, reducing fiber length by 0.35 mm and increasing short fiber index by 0.65%. So, the lint should be cleaned by one lint cleaner in the Xinjiang, however, the stage of lint cleaning was sometimes omitted when the foreign matter content of lint was little.
文摘In the patents pavilion of the Fifth Asia-Pacific Fair, the attention of numerous visitors was riveted by an onthe-spot demonstration. Standing before them was a machine with a stainless steel casing partly in a container of clear water. The operator pressed a button and ripples appeared on the surface of the water. The operator took watch chain s and jewellery from the visitors and put them in
文摘In this article, research was conducted to improve Linter machines that remove short fibers remaining in ginned cotton seeds at cotton ginneries. The study examined the effect of changing the dimensions of the brush drum, guide and mesh surface in the cleaning device proposed for the linting machine on the movement of the peg and the cleaning efficiency, and the highest level of efficiency in separating impurities from the peg was determined. During the study, the main factors influencing the effective operation of the improved linting machine were identified, the limits of their values were determined, and studies were carried out using the mathematical modeling method. As a result, at the values of the given coefficients, efficient operation of the improved linting machine was observed, that is, the lint cleaning efficiency reached 55.1%.
基金This research was supported by the National Natural Science Foundation of China(NSFC)under the grant(No.61672138).
文摘The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the performance of NMT is greatly reduced,and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model.However,not all languages have large-scale and high-quality bilingual corpus resources available.In these cases,improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results.This paper proposes a new method to improve the quality of data by using data cleaning,data expansion,and other measures to expand the data at the word and sentence-level,thus improving the richness of the bilingual data.The long short-term memory(LSTM)language model is also used to ensure the smoothness of sentence construction in the process of sentence construction.At the same time,it uses a variety of processing methods to improve the quality of the bilingual data.Experiments using three standard test sets are conducted to validate the proposed method;the most advanced fairseq-transformer NMT system is used in the training.The results show that the proposed method has worked well on improving the translation results.Compared with the state-of-the-art methods,the BLEU value of our method is increased by 2.34 compared with that of the baseline.
文摘探究教师注意力对于评估课堂教师行为具有极其重要的研究价值。然而,现有的教师注意力识别算法存在无法应对极端头部姿态角度等问题。为此,提出一种基于6DRep Net360模型的教师注意力状态识别算法,提升极端角度中头部姿态估计算法的准确性。相较于传统的依赖条件判断来分类教师注意力状态的方法,设计一种基于支持向量机(SVM)的教师注意力分类模型,对复杂头部姿态角度进行注意力状态的精准识别。为进一步解决算法稳定性和准确性带来的误差数据,提出基于滑动窗口的数据清洗算法,有效提高整体识别结果的真实性和可靠性。通过在构建的CCNUTeacherS tat e数据集上进行一系列的算法评估,实验结果表明,所提出的教师注意力识别算法在CCNUTeacherS tate数据集上达到了90.67%的准确率。
