Xiaoguang Zhou

Application of Object Detection Algorithm in Identification of Rice Weevils and Maize Weevils

Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to the identification of the stored-grain pests. We apply for the first time an object detection model to identify rice weevils and maize weevils, which have always been a challenge in the field of the research of stored-grain pests because of their very similar appearance. To conduct our initial study, we created a pre-training dataset of 4000 images and a object detection dataset of 1600 images. In the experiments, we used Faster R-CNN and R-FCN as object detectors and used VGG16, ResNet101 and Inception-ResNet-v2 as feature extractors. In detail, we pre-trained the object detection models on our pre-training dataset, and fine tuned with our object detection dataset. Finally, we demonstrate that the final object detection model outperforms our baseline and shows a nice detection effect with a high accuracy. It is worth noting that our research will have a revelatory influence on stored-grain pest control and grain storage.

A support vector machine approach for AF classification from a short single-lead ECG recording

OBJECTIVE In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum features, and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF), other rhythm, and too noisy to classify. APPROACH The proposed algorithm consisted of three steps: (1) signal pre-processing based on the wavelet method; (2) feature extraction, the extracted features including one power feature, two spectrum features, two entropy features, 17 RR interval-related features, and 11 P wave features; and (3) classification using the SVM classifier. MAIN RESULTS The algorithm was trained by 8528 single-lead ECG recordings lasting from 9 s to just over 60 s and then tested on a hidden test set consisting of 3658 recordings of similar lengths, which were all provided by the PhysioNet/Computing in Cardiology Challenge 2017. The scoring for this challenge used an F 1 measure, and the final F 1 score was defined as the average of F 1n (the F 1 score of normal rhythm), F 1a (the F 1 score of AF rhythm), and F 1o (the F 1 score of other rhythm). The results confirmed the high accuracy of our proposed method, which obtained 90.27%, 86.37%, and 75.08% for F 1n , F 1a , and F 1n and the final F 1 score of 84% on the training set. In the final test to assess the performance of all of the hidden data, the obtained F 1n , F 1a , F 1o and the average F 1 were 90.82%, 78.56%, 71.77% and 80%, respectively. SIGNIFICANCE The proposed algorithm targets a large number of raw, short single ECG data rather than a small number of carefully selected, often clean ECG records, which have been studied in most of the previous literature. It breaks through the limitation in applicability and provides reliable AF detection from a short single-lead ECG.

Intelligent control of a grain drying system using a GA-SVM-IMPC controller

ABSTRACT The grain drying is difficult to control because of its characteristics of long delay, strong nonlinearity, and uncertainty parameters. The aim of the paper is to design a suitable controller for a newly designed grain dryer. First, a nonlinear math model of the wheat mixed flow drying was established and analyzed based on the fundamental laws of simultaneous heat and mass transfer. The simulations for the batch circulating drying process and the continuous grain drying process have been made and the simulation results show that it fits well with the actual drying process. Second, an internal model proportional integral derivative (PID) controller (IMPC) based on the support vector machine (SVM) algorithm and the genetic algorithm (GA-SVM-IMPC) was proposed from the view of the energy loss and the dried grain quality. The structure of the GA-SVM-IMPC controller consists of a SVM prediction model, a SVM inverse model controller, a PID controller and a genetic optimization algorithm. Finally, the effectiveness of this controller was demonstrated by computer simulations, and the comparative study with the other controllers further confirmed the superiority of the proposed dryer controller.