Improved Algorithm of Pattern Classification and Recognition Applied in a Coal Dust Sensor

To resolve the conflicting requirements of measurement precision and real-time performance speed,an im-proved algorithm for pattern classification and recognition was developed. The angular distribution of diffracted light varies with particle size. These patterns could be classified into groups with an innovative classification based upon ref-erence dust samples. After such classification patterns could be recognized easily and rapidly by minimizing the vari-ance between the reference pattern and dust sample eigenvectors. Simulation showed that the maximum recognition speed improves 20 fold. This enables the use of a single-chip,real-time inversion algorithm. An increased number of reference patterns reduced the errors in total and respiring coal dust measurements. Experiments in coal mine testify that the accuracy of sensor achieves 95%. Results indicate the improved algorithm enhances the precision and real-time ca-pability of the coal dust sensor effectively.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Research on Automatic Diagnostic Technology of Soybean Leaf Diseases Based on Improved Transfer Learning

Soybean diseases and insect pests are important factors that affect the output and quality of the soybean,thus,it is necessary to do correct inspection and diagnosis on them.For this reason,based on improved transfer learning,a classification method of the soybean leaf diseases was proposed in this paper.In detail,this method first removed the complicated background in images and cut apart leaves from the entire image;second,the data-augmented method was applied to amplify the separated leaf disease image dataset to reduce overfitting;at last,the automatically fine-tuning convolutional neural network(AutoTun)was adopted to classify the soybean leaf diseases.The proposed method respectively reached 94.23%,93.51%and 94.91%of validation accuracy rates on VGG-16,ResNet-34 and DenseNet-121,and it was compared with the traditional fine-tuning method of transfer learning.The results indicated that the proposed method had superior to the traditional transfer learning method.

A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

Recognition results classification and post-processing methods for painted characters on billet surface

Automatic identification of characters marked on billets is very important for steelworks to achieve manu- facturing and logistics informatization management. Due to the presence of adhesions, fractures, blurs, and other problems in characters painted on billets, character recognition accuracy with machine vision is relatively low, and hardly meets practical application requirements. To make the character recognition results more reliable and accu- rate, an identification results classification and post-pro- cessing method has been proposed in this paper. By analyzing issues in the image segmentation and recognition stage, the recognition result classification model, based on character encoding rules and recognition confidence, is built, and the character recognition results can be classified as correct, suspect, or wrong. In the post-processing stage, a human-machine-cooperation mechanism with a post- processing interface is designed to eliminate error infor- mation in suspect and wrong types. The system was developed and experiments conducted with images acquired in an iron and steel factory. The results show the character recognition accuracy to be approximately 89% using the character recognizer. However, this result cannot be directly applied in information management systems. With the proposed post-processing method, a human worker will query the suspect and wrong results classified by the system, determine whether the result is correct or wrong, and then, correct the wrong result through the post-processing interface. Using this method, the character recognition accuracy ultimately improves to 99.4%. Thus, the results will be more reliable applied in a practical system.

A PATTERN RECOGNITION APPROACH FOR TONE CLASSIFICATION OF ISOLATED SYLLABLE IN PUTONGHUA

In this paper ,a new approach of pattern recognition for tone classification of Putonghua Which is important for speech recognition of Putonghua is discribed . In this method , four parameters of the fundamental frequency trajectory are selected based on a large number of statistical experiments . It is assumed that the four parameters satisfy multidimensional Gaussion distribution and a non-Euclidean distance function for each tone class is derived according to the rule of minimum probability of calssification error . the optimal decision results are obtained in a sense of statistics . It is proved that this method provides very satisfactory results by the experiments for speaker-independent tone classification of Putonghua .

Detection of oil spills in a complex scene of SAR imagery

We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.