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Arokiyamary Delphina, A., M. Kamarasan, and S. Sathiamoorthy. "Self-Organization Map Based Segmentation of Breast Cancer." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 31–36. http://dx.doi.org/10.51983/ajeat-2018.7.2.1015.
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Breast cancer is second major leading cause of cancer fatality in women. Mammography prevails best method for initial detection of cancers of breast, capable of identifying small pieces up to two years before they grow large enough to be evident on physical testing. X-ray images of breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, Radio-graphic images into regions of similar texture is usually performed during method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and implied transition from one texture to makes segmentation remarkably hard. The task of analyzing different texture areas can be considered form of exploratory report since priori awareness about number of different regions in image is not known. This paper presents a segmentation method by utilizing SOM.
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Tiňo, Peter, Igor Farkaš, and Jort van Mourik. "Dynamics and Topographic Organization of Recursive Self-Organizing Maps." Neural Computation 18, no. 10 (2006): 2529–67. http://dx.doi.org/10.1162/neco.2006.18.10.2529.
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Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizing map (SOM) for processing sequential data, recursive SOM(RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data.
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Khalifa, Khaled Ben, Ahmed Ghazi Blaiech, Mehdi Abadi, and Mohamed Hedi Bedoui. "New Hardware Architecture for Self-Organizing Map Used for Color Vector Quantization." Journal of Circuits, Systems and Computers 29, no. 01 (2019): 2050002. http://dx.doi.org/10.1142/s0218126620500024.
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In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.
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Furukawa, Masashi, Michiko Watanabe, and Yusuke Matsumura. "Local Clustering Organization (LCO) Solving a Large-Scale TSP." Journal of Robotics and Mechatronics 17, no. 5 (2005): 560–67. http://dx.doi.org/10.20965/jrm.2005.p0560.
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The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.
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Wiemer, Jan C. "The Time-Organized Map Algorithm: Extending the Self-Organizing Map to Spatiotemporal Signals." Neural Computation 15, no. 5 (2003): 1143–71. http://dx.doi.org/10.1162/089976603765202695.
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The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).
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Mulier, Filip, and Vladimir Cherkassky. "Self-Organization as an Iterative Kernel Smoothing Process." Neural Computation 7, no. 6 (1995): 1165–77. http://dx.doi.org/10.1162/neco.1995.7.6.1165.
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Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.
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Qu, Na, Jiatong Chen, Jiankai Zuo, and Jinhai Liu. "PSO–SOM Neural Network Algorithm for Series Arc Fault Detection." Advances in Mathematical Physics 2020 (January 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/6721909.
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Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning. It has the strong abilities of self-organization and self-learning. However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network. Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network. PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network. The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.
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Tucci, Mauro, and Marco Raugi. "A Sequential Algorithm for Training the SOM Prototypes Based on Higher-Order Recursive Equations." Advances in Artificial Neural Systems 2010 (January 19, 2010): 1–10. http://dx.doi.org/10.1155/2010/142540.
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A novel training algorithm is proposed for the formation of Self-Organizing Maps (SOM). In the proposed model, the weights are updated incrementally by using a higher-order difference equation, which implements a low-pass digital filter. It is possible to improve selected features of the self-organization process with respect to the basic SOM by suitably designing the filter. Moreover, from this model, new visualization tools can be derived for cluster visualization and for monitoring the quality of the map.
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Yen, Chia-Liang, Ming-Chyuan Lu, and Jau-Liang Chen. "Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting." Mechanical Systems and Signal Processing 34, no. 1-2 (2013): 353–66. http://dx.doi.org/10.1016/j.ymssp.2012.05.001.
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Pal, Chinmoy, Shigeru Hirayama, Sangolla Narahari, Manoharan Jeyabharath, Gopinath Prakash, and Vimalathithan Kulothungan. "An insight of World Health Organization (WHO) accident database by cluster analysis with self-organizing map (SOM)." Traffic Injury Prevention 19, sup1 (2018): S15—S20. http://dx.doi.org/10.1080/15389588.2017.1370089.
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Dresp-Langley, Birgitta, and John M. Wandeto. "Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map." Symmetry 13, no. 2 (2021): 299. http://dx.doi.org/10.3390/sym13020299.
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Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.
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RÍOS, SEBASTIÁN A., and JUAN D. VELÁSQUEZ. "FINDING REPRESENTATIVE WEB PAGES BASED ON A SOM AND A REVERSE CLUSTER ANALYSIS." International Journal on Artificial Intelligence Tools 20, no. 01 (2011): 93–118. http://dx.doi.org/10.1142/s0218213011000048.
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Enhancing the content and structure of a web site is a very important task which can help to maintain people visiting a web site and gain new visits (or customers). Web mining area helps to enhance a web site organization and contents using data mining algorithms. In particular we may perform Web Mining using a Self Organizing Feature Map (SOFM or SOM) it is always needed an analysis phase by experts. To help analysts to perform this phase after SOFMs' training, many post-processing techniques have been developed (component planes, labels, etc.); however, none of these techniques are useful when working in web mining for off-line enhancements of a web site. In this paper an algorithm called Reverse Cluster Analysis (RCA) will be provided. It aims to identify important web pages based on a self organizing feature map (SOFM) when performing web text mining (WTM) and web usage mining (WUM). We successfully applied this technique in a real web site to show its effectiveness. We have extended previous work performing a comparison with another unsupervised technique, administrators survey and an extended survey.
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Abdulrahman, Aysar A. "Mineral Exploation Using Neural Netowrks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 9 (2016): 7110–16. http://dx.doi.org/10.24297/ijct.v15i9.707.
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Establishing a new site for mining operation is required to find and develop a new source of minerals with precise characteristics such as location, depth, quality and thickness. Mineral exploration is a sequential process of informationgathering that assesses the mineral potential of given area. It starts with an idea of geologic model that identifies lands worthy of further exploration, and it’s the one of risky and costly investments for companies. In this paper, a new method for choosing a scientific map of exploration using neural network was introduced rather than an arbitrary map. The goal of this paper is to demonstrate the effectiveness of Self Organization Map (SOM) algorithm in visual exploration of physical geographic data. The SOM is one of the most popular neural network models, which provides a data visualizationtechnique which helps to understand high dimensional data by reducing the dimensions of data to maps. In the present paper, the algorithm is based on unsupervised learning, and Java programming codes is used to simulate the SOM algorithm.
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Yan, Ge, Heqin Cheng, Lizhi Teng, et al. "Analysis of the Use of Geomorphic Elements Mapping to Characterize Subaqueous Bedforms Using Multibeam Bathymetric Data in River System." Applied Sciences 10, no. 21 (2020): 7692. http://dx.doi.org/10.3390/app10217692.
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Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on the rhythmic bed surface morphology and have extracted crest and trough, while flat bed and scour pit have been ignored. In this study, to extend the feature description of riverbeds, geomorphic elements mapping was used by employing three geomorphic element classification methods: Wood’s criteria, a self-organization map (SOM) technique, and geomorphons. The results showed that geomorphic element mapping can be controlled by adjusting the slope tolerance and curvature tolerance of Wood’s criteria, using the map unit number and combination of the SOM technique and the flatness of geomorphons. Relatively flat bed can be presented using “plane”, “flat planar”, and “flat” elements, while scour pit can be presented using a “pit” element. A comparison of the difference between parameter settings for landforms and bedforms showed that SOM using 8 or 10 map units is applicable for land and underwater surface and is thus preferentially recommended for use. Furthermore, the use of geomorphons is recommended as the optimal method for characterizing bedform features because it provides a simple element map in the absence of area loss.
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Pacella, Massimo, Antonio Grieco, and Marzia Blaco. "On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5139574.
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In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.
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Arora, Suman, and Dharminder Kumar. "Hybridization of SOM and PSO for Detecting Fraud in Credit Card." International Journal of Information Systems in the Service Sector 9, no. 3 (2017): 17–36. http://dx.doi.org/10.4018/ijisss.2017070102.
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Fraud Detection is a detection of criminal activity that generally occurs in commercial organization. Detection of such fraud can prevent a great economic loss. Credit card fraud depends upon usage of card, its unusual transactions behavior or any unauthorized activity on a credit card. Clustering process can divide the data into subsets and it can be very helpful in credit card fraud detection where outlier may be more interesting than common cases. Self-organizing Map (SOM) is unsupervised clustering technique which is very efficient and handling large and high dimensional dataset. Particle Swarm Optimization (PSO) is another stochastic optimization technique based on intelligent of swarms. In the present study, we combine these two methods and present a new hybrid approach self-organizing Particle Swarm Optimization (SOPSO) in detection of credit card fraud. In order to apply our method, we demonstrated an example and its results are compared with previous techniques. Some challenges shown in the previous researches such as time and space complexity, false positive rate and supervised techniques. Our approach is efficient as it implements one of the optimization technique and unsupervised approach which results in less time and space complexity and false positive rate is very low. Domain independency is also achieved in our approach.
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ZHANG, WENDONG, and YANPING BAI. "A HYBRID ELASTIC NET METHOD FOR SOLVING THE TRAVELING SALESMAN PROBLEM." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (2005): 447–53. http://dx.doi.org/10.1142/s0218194005002233.
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The purpose of this paper is to present a new hybrid Elastic Net (EN) algorithm, by integrating the ideas of the Self Organization Map (SOM) and the strategy of the gradient ascent into the EN algorithm. The new hybrid algorithm has two phases: an EN phase based on SOM and a gradient ascent phase. We acquired the EN phase based on SOM by analyzing the weight between a city and its converging and non-converging nodes at the limit when the EN algorithm produces a tour. Once the EN phase based on SOM stuck in local minima, the gradient ascent algorithm attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. These two phases are repeated until the EN gets out of local minima and produces the short or better tour through cities. We test the algorithm on a set of TSP. For all instances, the algorithm is showed to be capable of escaping from the EN local minima and producing more meaningful tour than the EN.
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Bouyer, Asgarali, and Abdolreza Hatamlou. "Hybridization of the LEACH Protocol with Penalized Fuzzy C-Means (PFCM) and Self-Organization Map (SOM) Algorithms for Decreasing Energy in Wireless Sensor Networks." International Journal of Business Data Communications and Networking 10, no. 3 (2014): 46–64. http://dx.doi.org/10.4018/ijbdcn.2014070103.
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Wireless Sensor Networks (WSNs) consist of many sensor nodes, which are used for capturing the essential data from the environment and sending it to the Base Station (BS). Most of the research has been focused on energy challenges in WSN. There are many notable studies on minimization of energy consumption during the process of sensing the important data from the environment where nodes are deployed. Clustering-based routing protocols are an energy-efficient protocols that improve the lifetime of a wireless sensor network. The objective of the clustering is to decrease the total transmission power by aggregating into a single path for prolonging the network lifetime. However, the problem of unbalanced energy consumption exists in some cluster nodes in the WSNs. In this paper, a hybrid algorithm is proposed for clustering and cluster head (CH) election. The proposed routing protocol hybridized Penalized Fuzzy C-Means (PFCM) and Self Organization Map (SOM) algorithms with LEACH protocol for the optimum numbers of the CHs and the location of them. Simulation results reveal that the proposed algorithm outperforms other existing protocols in terms of network life, number of dead sensor nodes, energy consumption of the network and convergence rate of the algorithm in comparison to the LEACH algorithm.
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Hayashi, Mitsuharu, and Ken Nagasaka. "Application of Modular Network Self-Organization Map to Temporal and Spatial Projection of Wind Speed with Wind Data at Biased Positions." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 1 (2018): 133–40. http://dx.doi.org/10.20965/jaciii.2018.p0133.
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Wind generation is one of the fastest growing resources among renewable energies worldwide including Japan. As Japan is an island country surrounded by ocean, the on-shore landscape topography suitable for wind generation is limited. Therefore, based on the wind map until the year 2030, it is expected that new off-shore wind generation installations will be more suitable. For this reason, it is very important to determine the wind characteristics of the candidate areas for installing wind generation; however, in most off-shore installation sites, availability of weather condition data is poor and significant time and cost are required to accurately measure pin-point wind/weather conditions data. In this study, our goal is to project the wind speed of an unseen area (where weather condition data are not available) by mapping the seen areas (where weather condition data are available) around the target area using a modularized Artificial Neural Network (ANN) referred to as a Self-Organization Map (SOM). By learning the correlation between modularized ANNs of seen and unseen areas, the result of this temporal and spatial projection is the prediction of wind speed of a target area. We believe that the proposed technique will significantly reduce the amount of time and cost involved in selection of off-shore installation sites. Moreover, it should contribute to accelerated development and implementation of off-shore wind power generation in the future.
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Cabanes, Guénaël, Younès Bennani, and Dominique Fresneau. "Mining RFID Behavior Data using Unsupervised Learning." International Journal of Applied Logistics 1, no. 1 (2010): 28–47. http://dx.doi.org/10.4018/jal.2010090203.
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Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.
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Li, Kexin, Jun Wang, and Dawei Qi. "An Intelligent Warning Method for Diagnosing Underwater Structural Damage." Algorithms 12, no. 9 (2019): 183. http://dx.doi.org/10.3390/a12090183.
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A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations.
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Hosoda, Kenji, Masataka Watanabe, Heiko Wersing, et al. "A Model for Learning Topographically Organized Parts-Based Representations of Objects in Visual Cortex: Topographic Nonnegative Matrix Factorization." Neural Computation 21, no. 9 (2009): 2605–33. http://dx.doi.org/10.1162/neco.2009.03-08-722.
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Object representation in the inferior temporal cortex (IT), an area of visual cortex critical for object recognition in the primate, exhibits two prominent properties: (1) objects are represented by the combined activity of columnar clusters of neurons, with each cluster representing component features or parts of objects, and (2) closely related features are continuously represented along the tangential direction of individual columnar clusters. Here we propose a learning model that reflects these properties of parts-based representation and topographic organization in a unified framework. This model is based on a nonnegative matrix factorization (NMF) basis decomposition method. NMF alone provides a parts-based representation where nonnegative inputs are approximated by additive combinations of nonnegative basis functions. Our proposed model of topographic NMF (TNMF) incorporates neighborhood connections between NMF basis functions arranged on a topographic map and attains the topographic property without losing the parts-based property of the NMF. The TNMF represents an input by multiple activity peaks to describe diverse information, whereas conventional topographic models, such as the self-organizing map (SOM), represent an input by a single activity peak in a topographic map. We demonstrate the parts-based and topographic properties of the TNMF by constructing a hierarchical model for object recognition where the TNMF is at the top tier for learning high-level object features. The TNMF showed better generalization performance over NMF for a data set of continuous view change of an image and more robustly preserving the continuity of the view change in its object representation. Comparison of the outputs of our model with actual neural responses recorded in the IT indicates that the TNMF reconstructs the neuronal responses better than the SOM, giving plausibility to the parts-based learning of the model.
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Soliman, Mona M., Aboul Ella Hassanien, and Hoda M. Onsi. "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods." International Journal of Computer Vision and Image Processing 3, no. 2 (2013): 43–53. http://dx.doi.org/10.4018/ijcvip.2013040104.
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Blind and robust watermarking of 3D mesh aims to embed message into a 3D mesh model such that the mesh is not visually distorted from the original model. An essential condition is that the message should be securely extracted even after the mesh model was processed. This paper explores use of artificial intelligence techniques to build blind and robust 3D-watermarking approach. It is based on clustering 3D vertices into appropriate or inappropriate candidates for watermark insertion using K-means clustering and Self Organization Map (SOM) clustering algorithms. The watermark insertion were performed only on set of selected vertices come out from clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. Through the simulations, the authors prove that the proposed approach is robust against various kinds of geometrical attacks such as mesh smoothing, noise addition and mesh cropping.
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Li, Xiaojian, Zhengxian Liu, and Yujing Lin. "Multipoint and Multiobjective Optimization of a Centrifugal Compressor Impeller Based on Genetic Algorithm." Mathematical Problems in Engineering 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/6263274.
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The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis.
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Wei, C. Z., and T. Blaschke. "IDENTIFYING LOCAL SCALE CLIMATE ZONES OF URBAN HEAT ISLAND FROM HJ-1B SATELLITE DATA USING SELF-ORGANIZING MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (October 14, 2016): 1431–36. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1431-2016.
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With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI) has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border) in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM). The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95%) in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. <b>UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.</b>
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Osman, Ahmed Hamza, Hani Moetque Aljahdali, Sultan Menwer Altarrazi, and Ali Ahmed. "SOM-LWL method for identification of COVID-19 on chest X-rays." PLOS ONE 16, no. 2 (2021): e0247176. http://dx.doi.org/10.1371/journal.pone.0247176.
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The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.
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Luo, Jingjing, Yajing Gao, Wenhai Yang, Yongchun Yang, Zheng Zhao, and Shiyu Tian. "Optimal Operation Modes of Virtual Power Plants Based on Typical Scenarios Considering Output Evaluation Criteria." Energies 11, no. 10 (2018): 2634. http://dx.doi.org/10.3390/en11102634.
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Stimulated by the severe energy crisis and the increasing awareness about the need for environmental protection, the efficient use of renewable energy has become a hot topic. The virtual power plant (VPP) is an effective way of integrating distributed energy systems (DES) by effectively deploying them in power grid dispatching or electricity trading. In this paper, the operating mode of the VPP with penetration of wind power, solar power and energy storage is investigated. Firstly, the grid-connection requirements of VPP according to the current wind and solar photovoltaic (PV) grid-connection requirements, and analyzed its profitability are examined. Secondly, under several typical scenarios grouped by a self-organization map (SOM) clustering algorithm using the VPP’s output data, a profit optimization model is established as a guideline for the VPP’s optimal operation. Based on this model, case studies are performed and the results indicate that this model is both feasible and effective.
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Grossberg, Stephen, and Praveen K. Pilly. "Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1635 (2014): 20120524. http://dx.doi.org/10.1098/rstb.2012.0524.
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A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.
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Puttinaovarat, Supattra, and Paramate Horkaew. "Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 11 (2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.
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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
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Chinelatto, Guilherme Furlan, Michelle Chaves Kuroda, and Alexandre Campane Vidal. "Relação entre biofábrica e porosidade, coquinas da Formação Morro do Chaves (Barremiano/Aptiano), Bacia de Sergipe-Alagoas, NE-Brasil." Geologia USP. Série Científica 18, no. 4 (2018): 57–72. http://dx.doi.org/10.11606/issn.2316-9095.v18-140513.
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As coquinas da Formação Morro do Chaves são consideradas rochas análogas aos reservatórios da fase rifte do pré-sal nas bacias de Campos e Santos, são compostas principalmente por conchas e fragmentos de bivalves revelando uma grande variedade da biofábrica e do sistema poroso que possibilita o uso da tafonomia para relacionar o ambiente deposicional dessas rochas com as características de porosidade. Os principais atributos tafonômicos abordados nesse trabalho são: orientação das valvas; empacotamento das conchas; granulometria e seleção; e grau de fragmentação, abrasão e arredondamento. A interpretação dos dados teve como objetivo agrupar as amostras com relação às diferentes características tafonômicas e a porosidade, tendo como ferramenta auxiliar o método de redes neurais artificias (Self-Organization Map — SOM). A análise dos dados resultou em três agrupamentos: A1 com altos valores de porosidade (entre 11 e 23%), cujas características tafonômicas revelam ambientes de alta energia; A2 com valores intermediários de porosidade (entre 7 e 15%), cujas características tafonômicas indicam um ambiente de transição entre alta e baixa energia; por fim, o grupo A3 com menores valores de porosidade (entre 0 e 7%), cuja tafonomia indica ambientes de baixa energia. Como resultado final, as características tafonômicas revelam a energia do ambiente deposicional e é possível associar ambientes de alta energia com as altas porosidades para as coquinas da Formação Morro do Chaves.
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Tselentis, G.-Akis, Anna Serpetsidaki, Nikolaos Martakis, Efthimios Sokos, Paraskevas Paraskevopoulos, and Sotirios Kapotas. "Local high-resolution passive seismic tomography and Kohonen neural networks — Application at the Rio-Antirio Strait, central Greece." GEOPHYSICS 72, no. 4 (2007): B93—B106. http://dx.doi.org/10.1190/1.2729473.
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A high-resolution passive seismic investigation was performed in a [Formula: see text] area around the Rio-Antirio Strait in central Greece using natural microearthquakes recorded during three months by a dense, temporary seismic network consisting of 70 three-component surface stations. This work was part of the investigation for a planned underwater rail tunnel, and it gives us the opportunity to investigate the potential of this methodology. First, 150 well-located earthquake events were selected to compute a minimum (1D) velocity model for the region. Next, the 1D model served as the initial model for nonlinear inversion for a 3D P- and S- velocity crustal structure by iteratively solving the coupled hypocenter-velocity problem using a least-squares method. The retrieved [Formula: see text] and [Formula: see text] images were used as an input to Kohonen self-organizing maps (SOMs) to identify, systematically and objectively, the prominent lithologies in the region. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. This analysis revealed the existence of five major clusters, one of which may be related to the existence of an evaporite body not shown in the conventional seismic tomography velocity volumes. The survey results provide, for the first time, a 3D model of the subsurface in and around the Rio-Antirio Strait. It is the first time that passive seismic tomography is used together with SOM methodologies at this scale, thus revealing the method’s potential.
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Chen, T., and Y.-C. Wang. "A fuzzy-neural system with error feedback to adjust classification for forecasting wafer lot flow time: A simulation study." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 5 (2007): 807–17. http://dx.doi.org/10.1243/09596518jsce373.
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Estimating lot flow (cycle) time is a critical task for a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying wafer lots before estimating the flow times is beneficial to estimation accuracy. In this aspect, various classification approaches, e.g. k-means (kM), fuzzy c-means (FCM), and self-organization map (SOM), have been applied. After pre-classification, to estimate the flow times for lots belonging to different categories, different approaches (that are in fact the same approaches but with different parameter settings) are applied. However, these applications of classification approaches considered only the data of wafer lots, but ignored whether the classification approaches combined with the subsequent estimation techniques were suitable for the data. To tackle this problem, instead of trying many possible classification and forecasting approaches to find out the most suitable combination, a FCM and back propagation network (BPN) combination is chosen in the current study. In the proposed methodology, the classification results by FCM will be adjusted with forecasting error fed back from the BPN. In this way, if the FCM-BPN combination is not good enough for the data, then a forecasting error will be generated and fed back to the FCM classifier to adjust the classification results. After some replications, the FCM-BPN combination will become more suitable for the data. To evaluate the effectiveness, production simulation is applied in the present study to generate test data. According to experimental results, the forecasting accuracy of the proposed methodology is significantly better than those of many existing approaches. The effects of adjusting classification results with prediction error are also revealed.
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Chi, Dongxiang. "Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map." ISRN Signal Processing 2011 (June 7, 2011): 1–18. http://dx.doi.org/10.5402/2011/393891.
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Natural image segmentation is an important topic in digital image processing, and it could be solved by clustering methods. We present in this paper an SOM-based k-means method (SOM-K) and a further saliency map-enhanced SOM-K method (SOM-KS). In SOM-K, pixel features of intensity and L∗u∗v∗ color space are trained with SOM and followed by a k-means method to cluster the prototype vectors, which are filtered with hits map. A variant of the proposed method, SOM-KS, adds a modified saliency map to improve the segmentation performance. Both SOM-K and SOM-KS segment the image with the guidance of an entropy evaluation index. Compared to SOM-K, SOM-KS makes a more precise segmentation in most cases by segmenting an image into a smaller number of regions. At the same time, the salient object of an image stands out, while other minor parts are restrained. The computational load of the proposed methods of SOM-K and SOM-KS are compared to J-image-based segmentation (JSEG) and k-means. Segmentation evaluations of SOM-K and SOM-KS with the entropy index are compared with JSEG and k-means. It is observed that SOM-K and SOM-KS, being an unsupervised method, can achieve better segmentation results with less computational load and no human intervention.
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Hassan, Azmi, Muhammad Ridwan Andi Purnomo, and Putri Dwi Annisa. "Clustering Using Genetic Algorithm-Based Self-Organising Map." Advanced Materials Research 1115 (July 2015): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.573.
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This paper presents a comparative study of clustering using Artificial Intelligence (AI) techniques. There are 3 methods to be compared, two methods are pure method, called Self Organising Map (SOM) which is branch of Artificial Neural Network (ANN) and Genetic Algorithm (GA), while one method is hybrid between GA and SOM, called GA-based SOM. SOM is one of the most popular method for cluster analysis. SOM will group objects based on the nearest distance between object and updateable cluster centres. However, there are disadvantages of SOM. Solution quality is depend on initial cluster centres that are generated randomly and cluster centres update algorithm is just based on a delta value without considering the searching direction. Basically, clustering case could be modelled as optimisation case. The objective function is to minimise total distance of all data to their cluster centre. Hence, GA has potentiality to be applied for clustering. Advantage of GA is it has multi searching points in finding the solution and stochastic movement from a phase to the next phase. Therefore, possibility of GA to find global optimum solution will be higher. However, there is still some possibility of GA just find near-optimum solution. The advantage of SOM is the smooth iterative procedure to improve existing cluster centres. Hybridisation of GA and SOM believed could provide better solution. In this study, there are 2 data sets used to test the performance of the three techniques. The study shows that when the solution domain is very wide then SOM and GA-based SOM perform better compared to GA while when the solution domain is not very wide then GA performs better.
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Hua, Wenqi, and Lingfei Mo. "Clustering Ensemble Model Based on Self-Organizing Map Network." Computational Intelligence and Neuroscience 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.1155/2020/2971565.
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This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.
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Linan, Maureen Nettie N., Bobby D. Gerardo, and Ruji P. Medina. "Improving self-organizing map with nguyen-widrow initialization lgorithm." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 535. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp535-542.
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<span lang="EN-US">The quality of cluster result and the learning speed of Self-organizing map (SOM) are dependent on the initialization of weights since the initial values for weight vectors affect the performance of SOM training when applied to clustering. In this paper, improvement of SOM was achieved with the application of the Nguyen-Widrow algorithm to initialize weights. Nguyen-Widrow initialization algorithm is a method for initialization of the weights of neural networks to speed up the training process. Performance of the modified SOM was determined in terms of cluster error rate and the number of iterations to achieve convergence using different datasets and results show that the modified SOM algorithm produces better cluster results and improved training speed compared to traditional SOM.</span>
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Hasan, S., and S. M. Shamsuddin. "Multistrategy Self-Organizing Map Learning for Classification Problems." Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/121787.
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Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.
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Aoki, Takaaki, and Toshio Aoyagi. "Self-Organizing Maps with Asymmetric Neighborhood Function." Neural Computation 19, no. 9 (2007): 2515–35. http://dx.doi.org/10.1162/neco.2007.19.9.2515.
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The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N3) to O(N2) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.
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Doan, Quang-Van, Hiroyuki Kusaka, Takuto Sato, and Fei Chen. "S-SOM v1.0: a structural self-organizing map algorithm for weather typing." Geoscientific Model Development 14, no. 4 (2021): 2097–111. http://dx.doi.org/10.5194/gmd-14-2097-2021.
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Abstract. This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.
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Khotimah, Tutik, Abdul Syukur, and M. Arief Soeleman. "CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 8, no. 1 (2017): 15–20. http://dx.doi.org/10.24176/simet.v8i1.808.
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Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.
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Sheridan, Scott C., and Cameron C. Lee. "The self-organizing map in synoptic climatological research." Progress in Physical Geography: Earth and Environment 35, no. 1 (2011): 109–19. http://dx.doi.org/10.1177/0309133310397582.
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Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. This improved visualization has led to SOMs becoming an increasingly popular tool in various research with climatological applications from other disciplines as well.
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Febrita, Ruth Ema, Wayan Firdaus Mahmudy, and Aji Prasetya Wibawa. "High Dimensional Data Clustering using Self-Organized Map." Knowledge Engineering and Data Science 2, no. 1 (2019): 31. http://dx.doi.org/10.17977/um018v2i12019p31-40.
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As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.
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Khalifa, Khaled Ben, and Mohamed Hédi Bedoui. "A Massively Parallel Implementation of a Modular Self-Organizing Map on FPGAs." Journal of Circuits, Systems and Computers 28, no. 03 (2019): 1950054. http://dx.doi.org/10.1142/s0218126619500543.
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This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network’s size and the input data vectors’ dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A [Formula: see text] SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297[Formula: see text]MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.
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Riesenhuber, M., H. U. Bauer, D. Brockmann, and T. Geisel. "Breaking Rotational Symmetry in a Self-Organizing Map Model for Orientation Map Development." Neural Computation 10, no. 3 (1998): 717–30. http://dx.doi.org/10.1162/089976698300017719.
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We analyze the pattern formation behavior of a high-dimensional self-organizing map (SOM) model for the competitive projection of ON-center-type and OFF-center-type inputs to a common map layer. We mathematically show, and numerically confirm, that even isotropic stimuli can drive the development of oriented receptive fields and an orientation map in this model. This result provides an important missing link in the spectrum of pattern formation behaviors observed in SOM models. Extending the model by including further layers for binocular inputs, we also investigate the combined development of orientation and ocular dominance maps. A parameter region for combined patterns exists; corresponding maps show a preference for perpendicular intersection angles between iso-orientation lines and ocularity domain boundaries, consistent with experimental observations.
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Christyawan, Tomi Yahya, Ahmad Afif Supianto, and Wayan Firdaus Mahmudy. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 919. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp919-926.
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<p><span>The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.</span></p>
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Lisangan, Erick Alfons, Aina Musdholifah, and Sri Hartati. "Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 373. http://dx.doi.org/10.11591/tijee.v15i2.1552.
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Recently, clustering algorithms combined conventional methods and artificial intelligence. FSC-SOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster centers are processed twice i.e. searching for data with high density at FSC then updating the cluster centers at SOM. FSC-SOM was tested using 10 datasets that is measured with F-Measure, entropy, Silhouette Index, and Dunn Index. The result showed that FSC-SOM can improve the cluster center of FSC with SOM in order to obtain the better quality of clustering results. The clustering result of FSC-SOM is better than or equal to the clustering result of FSC that proven by the value of external and internal validity measurement.
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Similä, Timo. "Self-Organizing Map Learning Nonlinearly Embedded Manifolds." Information Visualization 4, no. 1 (2005): 22–31. http://dx.doi.org/10.1057/palgrave.ivs.9500088.
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One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.
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FURUKAWA, Tetsuo. "Self-organizing map of a set of self-organizing maps." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 19, no. 6 (2007): 618–26. http://dx.doi.org/10.3156/jsoft.19.6_618.
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Callan, Daniel E., Ray D. Kent, Nelson Roy, and Stephen M. Tasko. "Self-Organizing Map for the Classification of Normal and Disordered Female Voices." Journal of Speech, Language, and Hearing Research 42, no. 2 (1999): 355–66. http://dx.doi.org/10.1044/jslhr.4202.355.
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The goal of this research was to train a self-organizing map (SOM) on various acoustic measures (amplitude perturbation quotient, degree of voice breaks, rahmonic amplitude, soft phonation index, standard deviation of the fundamental frequency, and peak amplitude variation) of the sustained vowel /a/ to enhance visualization of the multidimensional nonlinear regularities inherent in the input data space. The SOM was trained using 30 spasmodic dysphonia exemplars, 30 pretreatment functional dysphonia exemplars, 30 post-treatment functional dysphonia exemplars, and 30 normal voice exemplars. After training, the classification performance of the SOM was evaluated. The results indicated that the SOM had better classification performance than that of a stepwise discriminant analysis over the original data. Analysis of the weight values across the SOM, by means of stepwise discriminant analysis, revealed the relative importance of the acoustic measures in classification of the various groups. The SOM provided both an easy way to visualize multidimensional data, and enhanced statistical predictability at distinguishing between the various groups (over that conducted on the original data set). We regard the results of this study as a promising initial step into the use of SOMs with multiple acoustic measures to assess phonatory function.
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Meenakshi, A. "Knowledge Management in Edaphology Using Self Organizing Map (Som)." International Journal of Database Management Systems 4, no. 5 (2012): 91–102. http://dx.doi.org/10.5121/ijdms.2012.4507.
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