著者

Tana Bao Tao Gao Banzragch Nandintsetseg Mei Yong Erdemtu Jin

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

vol.17, pp.145-150, 2021 (Released:2021-08-27)

参考文献数

42

被引用文献数

13

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In this study, we investigated the spatiotemporal variations of border-crossing dust events (DEs), including floating, blowing dust, and dust storms between Mongolia (MG) and Inner Mongolia (IM), China using the ground-based observations from 91 synoptic stations across the Mongolian Plateau during 1977-2018. We defined the intensity of DEs (progressive and recessive) depending on the dust impact area (number of stations affected by dust) by dividing them into three categories: DEs, transported dust events (T-DEs), and severe transported dust events (ST-DEs). The results revealed that during 1977-2018, the frequency of DEs in MG was two times higher than in IM. Simultaneously, the frequency of DEs (dominated by dust storms) increased in MG, whereas IM experienced a decrease in DEs (prevalent types of blowing dust). The T-DEs occurred 2.4 times higher than the ST-DEs over Mongolian Plateau. For the border-crossing DEs, transported dust storms were the dominant type. During 1977-1999, approximately 86% of DEs in IM originated from MG; however, this was decreased to 60% in the 2000s (2000-2018). The intensity of the border-crossing DEs originated from MG and the recessive T-DEs increased significantly since the 2000s, which were more significant than the progressive type.

著者

Tana Bao Tao Gao Banzragch Nandintsetseg Mei Yong Erdemtu Jin

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

pp.2021-026, (Released:2021-07-21)

被引用文献数

13

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In this study, we investigated the spatiotemporal variations of border-crossing dust events (DEs), including floating, blowing dust, and dust storms between Mongolia (MG) and Inner Mongolia (IM), China using the ground-based observations from 91 synoptic stations across the Mongolian Plateau during 1977-2018. We defined the intensity of DEs (progressive and recessive) depending on the dust impact area (number of stations affected by dust) by dividing them into three categories: DEs, transported dust events (T-DEs), and severe transported dust events (ST-DEs). The results revealed that during 1977-2018, the frequency of DEs in MG was two times higher than in IM. Simultaneously, the frequency of DEs (dominated by dust storms) increased in MG, whereas IM experienced a decrease in DEs (prevalent types of blowing dust). The T-DEs occurred 2.4 times higher than the ST-DEs over Mongolian Plateau. For the border-crossing DEs, transported dust storms were the dominant type. During 1977-1999, approximately 86% of DEs in IM originated from MG; however, this was decreased to 60% in the 2000s (2000-2018). The intensity of the border-crossing DEs originated from MGand the recessive T-DEs increased significantly since the 2000s, which were more significant than the progressive type.

著者

Xi ZHANG Yanan ZHANG Tao GAO Yong FANG Ting CHEN

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E106-D, no.5, pp.625-634, 2023-05-01

被引用文献数

1

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The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.