An optimized classification algorithm by BP neural network based on PLS and HCA

A back propagation neural network model for accurately predicting the removal efficiency of ammonia nitrogen in wastewater treatment plants using different biological processes

Accurately predicting the water quality of treated water from a water treatment plant (WWTP) based on the obtained operating database is of great significance. However, it is difficult for common mechanistic models to work well. In this study, a back propagation artificial neural network (BPANN) model with high accuracy was developed to predict the denitrification efficiency based on a 1-year operating database. Standardized principal component analysis (PCA) methods were used to address the data, and the PCA processed data exhibited the best accuracy. In three WWTPs adopting the anaerobic/anoxic/oxic (A²O) process, the ammonia nitrogen removal efficiency of WWTPs was successfully predicted by using five variables: inlet flow rate, pH value, original ammonia nitrogen concentration, Chemical oxygen demand (COD) concentration, and total phosphorus concentration. Importantly, the obtained BPANN model can be effectively used for other widely used treatment processes, such as oxidation ditch (OD), sequencing batch reactor activated sludge process (SBR), membrane bioreactor (MBR), and cyclic activated sludge technology (CAST), by simply optimizing the training data ratios between 50/50 and 90/10. This is the first trial to set up a universal model for predicting the denitrification efficiency of WWTPs adopting common biological processes. The model could be used to choose the optimum treatment process in the new WWTP design or take action in advance to avoid the risk of excessive emissions when the already built WWTPs are subjected to sudden shocks.

Application of Biomaterials in Tendon Injury Healing and Adhesion in Sports

High-intensity sports make tendon injury of professional athletes occur frequently. However, tendon adhesion in the healing process of tendon injury seriously affects the normal functional training of athletes after rehabilitation. Therefore, based on the theory of tendon injury healing, the MRDM image data of tendon injury healing are obtained by using medical image analysis technology, and the useless image data are screened by using the RANSAC algorithm. Through the analysis of filtered MRDM image data, it is found that the application of biomaterials has a positive effect on promoting the stable healing of tendon. A multilevel model was used to evaluate the actual effect of several commonly used biomaterials in repairing tendon injury and adhesion. The results showed that sodium hyaluronate had the best repair effect on tendon injury.

Raman spectroscopy may allow rapid noninvasive screening of keratitis and conjunctivitis

Keratitis and conjunctivitis are the most common ocular diseases, their symptoms are similar and easy to confuse, however infectious conjunctivitis is highly contagious. If misdiagnosed, it may worsen the disease and pose a threat to public health.This is a preclinical study to propose a method for rapid and accurate screening of keratitis and conjunctivitis by combining tear Raman spectroscopy with deep learning models that may be applied to clinical applications in the future.The tears of 16 cases of keratitis patients, 13 cases of conjunctivitis patients and 46 cases of healthy subjects were collected, and their Raman spectra were compared and analyzed. By adding different decibels of Gaussian white noise to expand the data, the performance of the tear Raman spectra with a large sample size in the deep learning model was discussed. Principal component analysis (PCA), partial least squares (PLS) and maximum correlation minimum redundancy (mRMR) were used for feature extraction. The processed data were imported into convolutional neural network (CNN) and recurrent neural network (RNN) depth models for classification. After the data were enhanced and processed by PLS, the highest classification accuracy of healthy subjects and keratitis patients, healthy subjects and conjunctivitis patients, and keratitis and conjunctivitis patients reached 94.8%, 95.4%, and 92.7%, respectively. The results of this study show that the use of large sample tear Raman spectra data combined with PLS feature extraction and depth learning algorithms may have great potential in clinical screening of keratitis and conjunctivitis.

A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines

A method that employs the back propagation (BP) neural network is used to predict the growth of corrosion defect in pipelines. This method considers more diversified parameters that affect the pipeline’s corrosion rate, including pipe parameters, service life, corrosion type, corrosion location, corrosion direction, and corrosion amount in a three-dimensional direction. The initial corrosion time is also considered, and, on this basis, the uncertainties of the initial corrosion time and the corrosion size are added to the BP neural network model. In this paper, three kinds of pipeline corrosion growth models are constructed: the traditional corrosion model, the corrosion model considering the uncertainties of initial corrosion time and corrosion depth, and corrosion model also considering the uncertainties of corrosion size (length, width, depth). The rationality and effectiveness of the proposed prediction models are verified by three case studies: the uniform model, the exponential model, and the gamma process model. The proposed models can be widely used in the prediction and management of pipeline corrosion.

Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network

Fixed beam structures are widely used in engineering, and a common problem is determining the load conditions of these structures resulting from impact loads. In this study, a method for accurately identifying the location and magnitude of the load causing plastic deformation of a fixed beam using a backpropagation artificial neural network (BP-ANN). First, a load of known location and magnitude is applied to the finite element model of a fixed beam to create plastic deformation, and a polynomial expression is used to fit the resulting deformed shape. A basic data set was established through this method for a series of calculations, and it consists of the location and magnitude of the applied load and polynomial coefficients. Then, a BP-ANN model for expanding the sample data is established and the sample set is expanded to solve the common problem of insufficient samples. Finally, using the extended sample set as training data, the coefficients of the polynomial function describing the plastic deformation of the fixed beam are used as input data, the position and magnitude of the load are used as output data, a BP-ANN prediction model is established. The prediction results are compared with the results of finite element analysis to verify the effectiveness of the method.

Urban cold-chain logistics demand predicting model based on improved neural network model

With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.

Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Feature Input Symmetry Algorithm of Multi-Modal Natural Language Library Based on BP Neural Network

When using traditional knowledge retrieval algorithms to analyze whether the feature input of words in multi-modal natural language library is symmetrical, the symmetry of words cannot be analyzed, resulting in inaccurate analysis results. A feature input symmetric algorithm of multi-modal natural language library based on BP (back propagation) neural network is proposed in this paper. A Chinese abstract generation method based on multi-modal neural network is used to extract Chinese abstracts from images in multi-modal natural language library. The Word Sense Disambiguation (WSD) Model is constructed by the BP neural network. After the word or text disambiguation is performed on the Chinese abstract in the multi-modal natural language library, the feature input symmetry problem in the multi-modal natural language library is analyzed according to the sentence similarity. The experimental results show that the proposed algorithm can effectively analyze the eigenvalue symmetry problem of the multi-modal natural language library. The maximum error rate of the analysis algorithm is 7%, the growth rate of the analysis speed is up to 50%, and the average analysis time is 540.56 s. It has the advantages of small error and high efficiency.

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.

Non-targeted metabolite profiling and specific targeted discrimination strategy for quality evaluation of Cortex Phellodendri from different varieties

Cortex Phellodendri is derived from two species of Phellodendron amurense Rupr. and Phellodendron chinense Schneid. Traditionally, the two species are utilized interchangeably under the name of “huangbo” in the clinic because they are believed to share the same clinical efficacy. However, the chemical analysis in vitro couldn't directly reflect the pharmacological effects. Therefore, whether the constituents could be absorbed into the blood becomes the uppermost problem to account for the clinical efficacy differences of the two species. Therefore, a rapid and sensitive approach to differentiate the two species of Cortex Phellodendri based on non-targeted metabolite profiling and the specific targeted discrimination strategy was first established. Samples from different cultivars were clearly discriminated by principal component analysis and orthogonal partial least squares discriminant analysis. 17 prototype compounds and 22 metabolites contributing to the group separation were identified and tentatively characterized, three of which were found for the first time. Moreover, six of them were screened out as the chemical markers which contribute most to the differences between the two species. Taken together, the application of the non-targeted metabolite profiling and specific targeted discrimination strategy is suitable for the assessment of Cortex Phellodendri.

Non-targeted metabolite profiling and specific targeted discrimination strategy coupled with pattern recognition to differentiate the two varieties in rats.

Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform

The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity).

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.