Feature extraction using coordinate logic filters and Artificial Neural Networks

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

The Sixth International Symposium on Neural Networks (ISNN 2009)

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

In this paper we present a method for developing a fully automated computer aided diagnosis (CAD) system to help radiologist in detecting and diagnosing micro-calcifications (MCCs) in digital format mammograms. One aim of the CAD system is to increase the effectiveness and efficiency of screening procedures by using computer. Another aim of the CAD is to extract and analyze the characteristics of vary lesions in an objective manner, then can improve the diagnostic accuracy and reduce the numbers of false-positive diagnoses of malignancies. Automatic segmentation, features extraction, suspicious area detection, classification and a back propagation neural network (BPNN) techniques were used in the development of this system. And the BPNN was used to classifying the marked regions into benign and malignant as the most important stage.

An automatic digital classification system for lung cancer detection of Computed Tomography Images using Artificial Neural Network (ANN) and Self Organizing Map (SOM) method is presented. The image samples used in this study are CT Thorax images showing lungs that are healthy and those infected with cancer stage I and II. Before feature extraction, the images are subjected to segmentation by thresholding to obtain the lung and cancer areas. This is followed by morphological operations such as erosion and dilation. Three features extracted are area, perimeter, and shape and they are fed into the ANN classifier. SOM training showed 87% accuracy, where 29 out of 31 images that were used had been successfully identified. Results of a program validation test obtained by data testing showed accuracy levels as high as 100% for healthy lung, 80% for stage I lung cancer, and 100% for stage II lung cancer. Based on these results, a system designed by using a Self-Organizing Map (SOM) can identify lung cancer stages. This prediction system is useful for the doctors to take an appropriate decision based on patient’s condition.

The main objective of this research work was to use Artificial Neural Network (ANN) based method for solving Power Flow Problem for a power system in Nigeria. This was achieved using the Backpropagation (multilayered feed-forward) Neural Network model. Two Backpropagation neural networks were designed and trained; one for computing voltage magnitudes on all buses and the other for computing voltage phase angles on all PV and PQ buses for different load and generation conditions for a 7-bus 132 kV power system in South-West Nigeria (Ayede). Due to unavailability of historical field records, data representing different scenarios of loading and/or generation conditions had to be generated using Newton-Raphson non-linear iterative method. A total of 250 scenarios were generated out of which 50% were used to train the ANNs, 25% were used for validation and the remaining 25% were used as test data for the ANNs. The test data results showed very high accuracy for the ANN used for computing voltage magnitudes for all test data with a Mean Square Error (MSE) of less than 10-6. Also, the ANN used for computing voltage phase angles showed very high accuracy in about 80% of the test data and acceptable results in about 97% of the test data. The MSE for all the test data results for the ANN computing voltage phase angles was less than 10-2.

An accurate estimation of porosity and permeability are extremely essential for designing an ideal and efficient program of an oil and gas field development. Numerous methods have been developed to determine the porosity and permeability including laboratory measurements and log derived models. Artificial neural network (ANN) provides an efficient technique that successfully addressed several engineering and geological challenges. In the present study ANN is applied to help in predicting porosity and permeability in carbonate reservoirs using back propagation neural network (BPNN) with high accuracy on well log data from numerous fields worldwide. ANN has the ability to understand a highly non – linear relationship and to perform simulation studies in a rapid manner. The BPNN model of porosity and permeability is developed using a set of well logging data as input layers and core porosity and core permeability as output layers. Two scenarios are considered to develop by ANN. The first scenario considers using all available logging data directly as input for ANN. In the second scenario an additional input, diagenesis parameter, is added as input to ANN which is essentially calculated from logging data. In each scenario two models are developed; the first for porosity prediction and the second predicts permeability in carbonate reservoirs. The optimal learning rate and momentum constant used in the BPNN model are achieved after serial combinative trials. The available data was assigned 80% for training and 20% for verification. The results of the developed porosity and permeability models are well compared to core data in verification. In the first scenario, cross-plot of the actual porosity versus ANN predicted Porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.80. In the second scenario, cross-plot of the actual porosity versus ANN predicted porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.98. Such data indicate that the developed models are successful in predicting the porosity and permeability for carbonate reservoirs.

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.

In this paper we introduce a novel feature extraction scheme as a preprocessor for artificial neural network (ANN) classification. We have shown that the feature extraction scheme implemented via a non-stationary Gaussian Markov random field based on a multiresolution wavelet framework can provide effective features for both the ANN and fuzzy c-means (FCM) classification. In our experiment with natural textures and real world digital mammography, each region of the tested images is assumed to be a different class. A label field with each region or class being represented by the same gray-scale was then found by the backpropagation neural network and FCM clustering algorithm using the extracted discriminatory features. Further enhancement of the segmented result was achieved via Bayesian learning. The formulation of this maximum a posteriori (MAP) estimator was based on the Gibbs prior assumption which is especially appropriate for modeling real world mammograms. Although being estimated by constrained optimization, the MAP estimator can also be found from neural networks such as the Boltzmann and the mean-field-theory machines.

This paper is aimed at investigating two kinds of Artificial Neural Network (ANN) applied to quantitative fault diagnosis of turbofan engine gas path components. Among them, one is Back Propagation neural Network (BPN) and the other is Adaptive Probabilistic Neural Network (APNN). Using BPN in order to achieve quantitative fault diagnosis, number of training samples will increase greatly which may lead to the difficulty of iteration convergence. A new learning rule named hybrid rule is introduced to avoid the algorithm falling into static areas and expedite convergence. Recently, a new method to improve the adaptability of multi-layer feed-forward neural network has been developed by the application of Radial Basis Function (RBF). In this paper, the APNN is put forward based on the theory of radial basis function, Bayesian estimation and normal distribution hypothesis of information. It is proposed that the adaptability of APNN can be obtained by applying maximum-likelihood estimation of the output of test case based on a posteriori probability of its input. The investigation shows that BPN and APNN have their own advantages and disadvantages. BPN has faster diagnostic speed and fits the requirement of quantitative diagnosis for single fault. APNN is more adaptive and fit better to quantitative diagnosis for multiple faults.

In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand for electricity, and voltage stability is therefore a worth consideration. The growing demand penetration of solar photovoltaic (PV) systems has some potential stability challenges due to their intermittent characteristics. Existing research evaluates the test systems of voltage stability and does not optimize using Intelligent models, which have variable power generation from Solar PV and Wind, as well as uncertainties in load demand and the initial step considering the evaluation of the model have to cover either inputs. A feed–forward back propagation artificial neural network (ANN) for voltage stability assessment to classify the operating scenario of a power system into one of two states, i.e. safe or unsafe. Neural network input data is generated from load flow analysis using Newton-Raphson (NR), and stability indices are computed over different scenarios. Line Stability Index (Lmn), counts the stability of a transmission line in power systems and provides an indication of its potential voltage collapse. The Fast Voltage Stability Index (FVSI), which also estimates line stability through reactive power and impedance characteristics. Voltage Collapse Proximity Index (VCPI) assesses system proximity to voltage collapse and identifies weak transmission lines. Values approaching 1 suggest voltage collapse conditions. The FVSI, VCPI, and Lmn are compared to a baseline NR stability index. Moreover, self-organizing map (SOM) neural network is used for stability results classification. Multi-objective grasshopper optimization algorithm (MOGOA) has been employed as an optimization network on the dual axis solar PV and wind turbine-based hybrid power generation system to analyze technical, economic, as well as environmental relations of energy conversion. The findings reveal that the adapted grid-tied solar-wind composite power system substantially mitigates the power deficiencies, reduces the real-power outages, diminishes the motion voltage small amendments, maximizes voltage prosperity, and improves power system resilience. The main findings show a significant reduction in real power loss, which went from 289,391.30 kW to only 5.43 kW, and a remarkable decrease in voltage deviation, which reduced from 0.56 p.u to 0.0493 p.u. Ensures Improved Service Quality and Enhanced Equipment Protection While gas generation costs increased by a small amount, it is important to note that this is an acceptable trade-off for a more robust and sustainable system, with costs per kWh of $0.142 vs $0.2881 per kWh, respectively. In addition, stability index was substantially decreased from 0.7666 to 0.0768, indicating better grid and resilience to disturbances. Findings emphasize the contribution of embedding MOGOA in conjunction with machine learning models including ANN and Self Organizing Maps (SOM) to fulfill the primal requisites of stability, efficiency and sustainability in modernized power systems, illustrating a coupled advancement in the realm of power systems engineering and optimization.

Spiking Self-organizing Maps for Classification Problem

Speech can be represented phonetically by a finite set of symbols called the phonemes of the language. Identification of these phonemes is the most important part of any speech recognition system. A work is described in this paper where an Artificial Neural Network (ANN) based approach is carried out to recognize consonant and vowel phonemes of two alphabet Assamese words. A Self Organizing Map (SOM) based segmentation algorithm segments the incoming speech signal into its constituent phonemes and from the SOM segmented phonemes the three constituent phonemes are identified by some sort of recognition algorithm based on two different ANN structures Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ). Formant frequency of Assamese phonemes is used very effectively as a priori knowledge in the proposed algorithm. The SOM based segmentation algorithm shows distinct advantage in terms of recognition success rate in comparison to the conventional speech segmentation methods like windowing or Discrete Wavelet Transform (DWT).

Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.