Search results

The purpose of this paper is to improve the accuracy and signal-to-noise ratio (SN) of a crop nitrogen sensor.

The accuracy and wide adaptability of two spectral calibration methods for a crop nitrogen sensor based on standard reflectivity gray plates and standard detector, respectively, were compared.

The calibration method based on standard detector could significantly improve the measurement accuracy and the SN of this crop nitrogen sensor. When compared with the method based on standard gray plates, the measurement accuracy and the SN of the crop nitrogen sensor calibrated based on the standard detector method improved by 50 and 10 per cent, respectively.

This research analysed the calibration problems faced by the crop nitrogen sensor (type CGMD302) based on standard gray plates, and proposed a sensor calibration method based on a standard detector. Finally, the results of the two calibration methods were compared in terms of measurement accuracy and the SN of the crop nitrogen sensor.

Considering the importance of China as a global economic power and the emphasis placed on human resources in a knowledge economy, the findings of no less than 30 articles on diversity management in that country seem inadequate given the growing importance of diversity in the workplace. Analysis of those articles reveals that most of the research focuses on firms located on the eastern coast. Moreover, while cataloging the types of industry and ownership covered provides a broad overview, specific industries and ownership types require further examination.

Searches were conducted in both English and Chinese databases using the keyword search phrase of “diversity management and China”. The criteria for including an article were as follows: 1) an emphasis on diversity management within the business environment; 2) a focus on applications of diversity management within the People’s Republic of China, thus excluding Taiwan; and 3) a research-based or conceptual orientation. The search was further limited by using the “abstract” as a limiter under the assumption that if the concepts were important, the author(s) would have used that terminology in the abstract.

Gender emerged as a major concern along with residential status; racial and ethnic differences, on the other hand, cultural and/or other influences on diversity management received limited attention. Both qualitative and quantitative research methods were used by the various authors, but exploratory methods such as grounded theory saw minimal use. With the little research done on diversity management in China, it is difficult to assess whether or Chinese firms are fully using its available workforce. China must embrace diversity management practices with a view to achieving competitive advantages as well as equality and harmony in the workplace.

This is one of the first published reviews of articles from both Chinese and English databases that delves into the issue of diversity management in China.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.