Improved cloud phase retrieval approaches for China's FY-3A/VIRR multi-channel data using Artificial Neural Networks

A backpropagation neural network (BPNN) approach is proposed for the forecasting and verification of optical turbulence profiles in the offshore atmospheric boundary layer. To better evaluate the performance of the BPNN approach, the Holloman Spring 1999 thermosonde campaigns (HMNSP99) model for outer scale, and the Hufnagel/Andrew/Phillips (HAP) model for a single parameter are selected here to estimate profiles. The results have shown that the agreement between the BPNN approach and the measurement is very close. Additionally, statistical operators are used to quantify the performance of the BPNN approach, and the statistical results also show that the BPNN approach and measured profiles are consistent. Furthermore, we focus our attention on the ability of the BPNN approach to rebuild integrated parameters, and calculations show that the BPNN approach is reliable. Therefore, the BPNN approach is reasonable and remarkable for reconstructing the strength of optical turbulence of the offshore atmospheric boundary layer.

Generalized part family formation through fuzzy self-organizing feature map neural network

A method for automatic detection of weld defects of short-circuit gas metal arc welding is presented. It is based on the extraction of arc signal features as well as classification of the obtained features using self-organizing feature map (SOM) neural networks in order to get the weld quality information, for example, to determine if there is a defect in the product. This is important for the online monitoring of weld quality especially in robotic welding and lays the foundation for further real-time control of weld quality.

Decomposition of interacting features using a Kohonen self-organizing feature map neural network

This work addressed the intercalibration of the infrared window channels of InfraRed Atmospheric Sounder (IRAS), Visible and InfraRed Radiometer (VIRR) and MEdium Resolution Spectral Imager (MERSI) aboard Chinese second generation polar-orbiting meteorological satellite FengYun 3A (FY-3A) with high spectral resolution data acquired by the Atmospheric InfraRed Sounder (AIRS) aboard Aqua. A North Pole study area was selected according to the instruments' viewing geometry. The FY-3A L1 data and AIRS/Aqua 1B Infrared geolocated and calibrated radiances (AIRIBRAD) in July of 2008 or 2009 were used in this work. A sub-pixel registration method was developed and applied to the satellite images to improve the intercalibration accuracy. The co-located measurement pairs were picked out with absolute Viewing Zenith Angle differences less than 5°(|VZA|<;5°), absolute Viewing Azimuth Angle differences less than 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> (|VAA|<;90°), and absolute time differences less than 15 min (|ΔT|<;15'). The results show that the FY-3A measurements were linearly related to the convolved AIRS/Aqua measurements with high correlation coefficients and calibration discrepancies exist between FY-3A and AIRS channels, especially for MERSI channel 5.

China’s vast rural landscape exhibits pronounced regional disparities in both foundational resources and development potential. In the context of nationwide rural revitalization efforts, the emergent divergence in village development pathways underscores a pressing need for context-specific, classified interventions. To furnish a scientifically grounded typology of villages and inform differentiated development planning, this investigation focuses on Hubei Province as an illustrative case. Synthesizing survey data from 32,457 villages, we developed a multidimensional evaluation framework encompassing four pivotal domains: economic vitality, social service provision, ecological integrity, and cultural value. Leveraging the Self-Organizing Feature Map (SOFM) neural network—an unsupervised machine learning algorithm—we performed a cluster analysis on multi-source, heterogeneous datasets. This technique enabled the objective delineation of spatial typological patterns among Hubei’s villages, elucidated their underlying classification architecture shaped by multifaceted drivers, and demonstrated the methodological robustness and applicability of this approach for large-scale village categorization. Grounded in the derived typologies and informed by strategic directives from higher-tier planning instruments, we conducted a nuanced examination of the distinctive attributes characterizing each village type. The findings provide scientific evidence and decision-making support for village classification and rural revitalization planning in Hubei Province, with valuable implications for other regions with similar development foundations in China.

Traditional sensor fault diagnosis is mainly based on statistical classification methods. The discriminant functions in these methods are extremely complex, and typical samples of reference modes are not easy to get, therefore it is difficult to meet the actual requirements of a project. In view of the deficiencies of conventional sensor fault diagnosis technologies, a fault diagnosis method based on self-organizing feature map (SOFM) neural network is presented in this paper. And it is applied to the fault diagnosis of pipeline flow sensor in a dynamic system. The simulation results show that the fault diagnosis method based on SOFM neural network has a fast speed, high accuracy and strong generalization ability, which verifies the practicality and effectiveness of the proposed method.

The image edge detection is rather difficult due to its natures of diversity and complexity. For retaining correlations between the three color components of color medical images and avoiding human intervention such as setting threshold of image edge judgment, a novel edge detection method for color medical images has been proposed in this paper. The approach proposed here combines the self-organizing feature map (SOFM) neural network and quaternion. Based on the rotation characteristic of quaternion in 3D vector space and reprocessing results by calculating the Euclidean distance, an edge characteristic vector has been constructed. The SOFM neural network is trained by the vector and subsequently used for edge detection. The experimental results demonstrate the method has stronger retention capacity of the details. The noise can be removed based on the method when 30 or more neurons network is adopted.

Since the reform and opening up, China's regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, technologies, and policies, the level of economic development in different regions is uneven. Clustering analysis is a data mining method that clusters or classifies entities according to their characteristics and then discovers the whole spatial distribution law of datasets and typical patterns. It is of great significance to classify, compare, and study the economic development level of different regions in order to formulate the regional economic development strategy. In this paper, a self-organizing feature map (SOM) neural network with the hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated, and the empirical results are good. From this, some meaningful conclusions can be drawn, which can provide reference for the decision-making of coordinating regional economic development.

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.

At present, study on the non-asbestos gasket materials is the hotspot research in static sealing field. The non-asbestos sealing gaskets research and development has made great strides into the practical phase. Formula is an important factor of material, which determines performance of material. In order to obtain well performance, it is needed to optimization formula to get optimal formula that not only improve performance of non-asbestos gasket, but also reduce development time accordingly reduce cost of non-asbestos gasket. Classification of raw materials can be transformed into a mathematical clustering problem. It means that according to some algorithm, there will be some sort of input values of similar links together. Many neural networks were widely used in the classification of different materials. A method of classification by using neural network to the known 15 kinds of the non-asbestos gaskets of formula data was proposed in this paper. By using the PNN (probabilistic neural network), LVQ(Learning Vector Quantization) neural network and SOM (Self-Organizing Feature Map) neural network respectively to classify the non-asbestos gaskets to find a suitable method in the classification of non-asbestos gaskets formula. The results indicated that PNN neural network and LVQ neural network method based on the data that provided in the paper both can effectively classify, while SOM neural network can not classify them ideally, thus it provides a new theoretical basis for the classification of the non-asbestos gaskets.

SummaryIn order to suppress the interference in local area networks, this paper presents a Wireless Local Area Networks (WLAN) interference self‐optimization method based on a Self‐Organizing Feature Map (SOM) neural network model. This method trains the model by using original data sets as the initial vector set and using the whole Signal to Interference plus Noise Ratio (SINR) vector generated by the change of one Wireless Access Point (AP) channel as the basic feature. After the training, the SOM neural network can quickly locate the fault AP and optimize the network according to the changes of the network environment. Simulation results reveal that the proposed scheme can efficiently locate the AP where interference happens and optimize the interference with an improved user experience. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Forty-six samples of Chinese spirits, whose bouquets were determined by sensory evaluations, and 17 compounds characteristic of the flavors determined by gas chromatography/gas chromatography-mass spectrometry (GC/GC-MS), were subjected to neural network analysis and their corresponding factor scores developed. To make the bouquet recognition more efficient, an improved artificial back-propagation neural network (BPNN) was applied. In each kind of data, the BPNN was trained repeatedly until the error rate was less than the predetermined threshold error; then the trained network was applied to the test set that was not involved in the training process to establish the validity of the network, and a correct prediction rate of 100% was obtained. The BPNN provided a correlation between the data offered from sensory evaluations and the data of chemical compositions determined by instrumental analysis. The BPNN approach is feasible regardless of whether the crude data or the factor scores are used; however, recognition results were better with the latter than with the former. In a comparison of all the results obtained by BPNN, cluster analysis, and discriminant analysis, the method of artificial neural network analysis appeared to be the optimal technique for recognizing the bouquet of Chinese spirits.

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