ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the...ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the extension of ESA. In Ex ESA, the original approach is extended to a 2-pass process and the ratio of different word lengths is introduced as the third type of information combined with cohesion and separation. A maximum strategy is adopted to determine the best segmentation of a character sequence in the phrase of Selection. Besides, in Adjustment, Ex ESA re-evaluates separation information and individual information to overcome the overestimation frequencies. Additionally, a smoothing algorithm is applied to alleviate sparseness. The experiment results show that Ex ESA can further improve the performance and is time-saving by properly utilizing more information from un-annotated corpora. Moreover, the parameters of Ex ESA can be predicted by a set of empirical formulae or combined with the minimum description length principle.展开更多
A recombinant inbred line （RIL） population of F8 and F9 generations derived from a cross between a typical indica rice （Qishanzhan） and a typical japonica rice （Akihikari） was used to study the difference betwee...A recombinant inbred line （RIL） population of F8 and F9 generations derived from a cross between a typical indica rice （Qishanzhan） and a typical japonica rice （Akihikari） was used to study the difference between morphological differentiation based on phenotype characters and genetic differentiation using indica and japonica specific SSR markers, and to evaluate the relationship between vascular bundle characters and morphological and genetic differentiations. The results showed that the frequency distributions of morphological and genetic differentiations were all inclined to japonica type in the filial generation. The population was more inclined to japonica type based on genetic differentiation than on morphological differentiation. The consistent degrees of classification based on the Cheng’s index, the ratio of large vascular bundle number to small vascular bundle number in panicle neck （RLSVB） and the ratio of large vascular bundle number in the second internode from the top to that in the panicle neck （RLVB） were all about 50% compared with the genetic differentiation, and the consistent degree of the total scores of the Cheng’s index combined with the vascular bundle number ratios was significantly increased to about 80% compared with the genetic differentiation. Therefore, the vascular bundle characters could be used as a helpful supplement for subspecies classification.展开更多
Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research top...Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research topics of flood prevention.Recently,the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation,low cost,and zero need of manual reading.However,there are two mainly shortcomings of the existing imagebased water level recognition methods:(1)severely affected by light intensity and(2)low accuracy of water level recognition for stained water gauges.To solve these two problems,this paper proposes a water level recognition method in consideration of complex scenarios.This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask,and then uses the YOLOv5 object detection network to extract the letter“E”on the water gauge.Based on the character sequence inspection strategy,the algorithm dynamically compensates for the missed detection of characters of stained water gauges.Through a large number of experiments,the proposed water level measurement method has good robustness in complex scenarios,meeting the needs of flash flood defense.展开更多
基金supported in part by National Science Foundation of China under Grants No. 61303105 and 61402304the Humanity & Social Science general project of Ministry of Education under Grants No.14YJAZH046+2 种基金the Beijing Natural Science Foundation under Grants No. 4154065the Beijing Educational Committee Science and Technology Development Planned under Grants No.KM201410028017Beijing Key Disciplines of Computer Application Technology
文摘ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the extension of ESA. In Ex ESA, the original approach is extended to a 2-pass process and the ratio of different word lengths is introduced as the third type of information combined with cohesion and separation. A maximum strategy is adopted to determine the best segmentation of a character sequence in the phrase of Selection. Besides, in Adjustment, Ex ESA re-evaluates separation information and individual information to overcome the overestimation frequencies. Additionally, a smoothing algorithm is applied to alleviate sparseness. The experiment results show that Ex ESA can further improve the performance and is time-saving by properly utilizing more information from un-annotated corpora. Moreover, the parameters of Ex ESA can be predicted by a set of empirical formulae or combined with the minimum description length principle.
基金supported by the National Basic Research Program of China (Grant No.2009CB126007)the ‘948’ Project of China
文摘A recombinant inbred line （RIL） population of F8 and F9 generations derived from a cross between a typical indica rice （Qishanzhan） and a typical japonica rice （Akihikari） was used to study the difference between morphological differentiation based on phenotype characters and genetic differentiation using indica and japonica specific SSR markers, and to evaluate the relationship between vascular bundle characters and morphological and genetic differentiations. The results showed that the frequency distributions of morphological and genetic differentiations were all inclined to japonica type in the filial generation. The population was more inclined to japonica type based on genetic differentiation than on morphological differentiation. The consistent degrees of classification based on the Cheng’s index, the ratio of large vascular bundle number to small vascular bundle number in panicle neck （RLSVB） and the ratio of large vascular bundle number in the second internode from the top to that in the panicle neck （RLVB） were all about 50% compared with the genetic differentiation, and the consistent degree of the total scores of the Cheng’s index combined with the vascular bundle number ratios was significantly increased to about 80% compared with the genetic differentiation. Therefore, the vascular bundle characters could be used as a helpful supplement for subspecies classification.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC3006700。
文摘Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research topics of flood prevention.Recently,the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation,low cost,and zero need of manual reading.However,there are two mainly shortcomings of the existing imagebased water level recognition methods:(1)severely affected by light intensity and(2)low accuracy of water level recognition for stained water gauges.To solve these two problems,this paper proposes a water level recognition method in consideration of complex scenarios.This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask,and then uses the YOLOv5 object detection network to extract the letter“E”on the water gauge.Based on the character sequence inspection strategy,the algorithm dynamically compensates for the missed detection of characters of stained water gauges.Through a large number of experiments,the proposed water level measurement method has good robustness in complex scenarios,meeting the needs of flash flood defense.
