A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

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

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.

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.

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.

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 & Sons, Ltd.

Sustainable urban drainage systems (SUDS), or urban green infrastructure for stormwater control, emerged for more sustainable management of runoff in cities and provide other benefits such as urban mitigation and adaptation to climate change. Research in Spain began a little over twenty years ago, which was later than in other European countries, and it began in a heterogeneous way, both in the SUDS typology and spatially within the peninsular geography. The main objective of this work has been to know through bibliographic review the state of the art of scientific research of these systems and their relationship with the different types of climates in the country. These structures have a complex and sensitive dependence on the climate, which in the Iberian Peninsula is mostly type B and C (according to the Köppen classification). This means little water availability for the vegetation of some SUDS, which can affect the performance of the technique. To date, for this work, research has focused mainly on green roofs, their capabilities as a sustainable construction tool, and the performance of different plant species used in these systems in arid climates. The next technique with the most real cases analyzed is permeable pavements in temperate climates, proving to be effective in reducing flows and runoff volumes. Other specific investigations have focused on the economic feasibility of installing rainwater harvesting systems for the laundry and the hydraulic performance of retention systems located specifically in the northeast of the Iberian Peninsula. On the contrary, few scientific articles have appeared that describe other SUDS with vegetation such as bioretention systems or green ditches, which are characteristic of sustainable cities, on which the weather can be a very limiting factor for their development.

Ecological function zoning is an essential means of scientific management of ecosystems. According to the characteristics of ecological function zoning, implementing zoning control is conducive to the governance and protection of the ecological environment and the maintenance of ecological sustainability. This study was conducted with the Nansi Lake Basin as the cross-section for 2018. The Integrated Valuation of Ecosystem Services and Trade-offs model was adopted to assess and measure five ecosystem services, including water yield, crop production, soil conservation, carbon storage and carbon sequestration, and habitat quality. The Self-Organizing Feature Maps neural network was applied to obtain the ecosystem service bundles, and then, the ecological function zones were divided. The results indicated that the overall spatial pattern of ecosystem services in the study zone showed a decreasing schema from east to west; There was a trade-off between supply services and support services and a synergy between supply services and regulatory services; according to the bundling results, the Nansi Lake Basin was divided into four ecological functional zones: the eastern ecological surplus zone, the central crop supply zone, the western ecological balance zone, and the lake habitat protection zone. The results showed that (1) the spatial distribution of various ecosystem services in the Nansi Lake Basin showed spatial heterogeneity and specific regional laws, showing a decreasing pattern from the east to the west as a whole, especially in soil conservation, carbon sequestration, and habitat quality. (2) According to the supply and spatial distribution of each ecosystem service, the Nansi Lake Basin was divided into four ecological functional zones: the eastern ecological surplus zone, the central crop supply zone, the western ecological balance zone, and the lake habitat protection zone. (3) For zone I, provisioning services and regulation services were in synergy. For zone II and zone III, the provisioning service had a trade-off relationship with the regulation service and the supporting service. For zone IV, supporting services were trade-offs not only with provisioning services but also with regulating services. In general, the trade-offs between ecosystem service in the Nansi Lake Basin were stronger than the synergies, and the overall benefits of ecosystem services were smaller. Relying on the perspective of the ecosystem service bundles, at the county level, this study provided an analysis of the trade-offs and synergies among ecosystem services in the Nansi Lake Basin, which helped formulate the management plan for the corresponding region and provided the appropriate recommendations for regional habitat conservation and restoration.

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

In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.

Visual Analysis of SOM Network in Fault Diagnosis

With the continuous complexity of China's market economic environment and the increasing role of social audits, there are an increasing number of audit risk problems, and research on this topic is increasingly important. Based on the above background, a social audit risk statistical method based on machine learning under the background of big data is proposed. Under the guidance of the index principle, this method selects the social audit risk statistical index and constructs the index system. Using the analytic hierarchy process, calculate the weight of social audit risk statistical indicators. The self-organizing feature mapping neural network in machine learning is used to construct the risk statistical model and complete the risk quantitative analysis. The experimental results show that under the application of the research method, the error between the social audit risk statistical results and the actual result is very small, which shows that the statistical accuracy of the research method is high and has wide application prospects.

Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semisupervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In this method, training of theMSOFMis performed iteratively using unlabeled patterns along with a few labeled patterns. A method has been suggested to select unlabeled patterns for training. To check the effectiveness of the proposed methodology, experiments are carried out on two multitemporal remotely sensed images. Results are found to be encouraging.