摘要：
Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN). In order to form a continuous frame sequence as the standard input for SFLN, a sliding window algorithm of short frame length is suitable for differentiate the Mel-spectrogram of bird sound. The vertical 3D filter in the linear layer moves linearly along the continuous frame and cover its full frequency band. The weight is initialized to a Gaussian distribution to attenuate the high-and low-frequency noise, thereby extracting the long-and short-term features of the continuous frame of the bird sound. Finally, the GRU network is connected and used as a classifier to directly output the prediction results. Four kinds of bird sound from the xeno-canto website are tested to evaluate the influences of different parameters of sliding window on the effect of SFLN-based classification. In the comparison experiment, the mean average precision (MAP) achieves the highest value of 0.97.
摘要：
In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.
作者机构：
[吴淇; 周国雄; 陈爱斌] School of Computer and Information Engineering, Central South University of Forestry &, Technology, Changsha, 410004, China;[吴淇] School of Information Science and Engineering, Hunan University, Changsha, 410082, China
通讯机构：
School of Computer and Information Engineering, Central South University of Forestry & Technology, Changsha, China
关键词：
Automatic control of teaching experiment;Furnace;Networks
摘要：
The rapid development of information technology has become the important factors that influence economy and people life, automatic control principle course teaching has extensive theoretical and practical value, thus to explore to adapt to the new situation of the automatic control principle course teaching become a problem to be solved. Automatic control theory teaching experiment system based on network was proposed, it is the use of multimedia courseware and the resistance furnace temperature control system is set up network teaching platform, not only more convenient teacher prepares a lesson, class teaching content more rich vivid, but also students can better understand and communicate through the network has been to learn knowledge, and can be learned through simulation test, for the follow-up professional courses to lay a good foundation.
