Relevant bibliographies by topics / Self-Organizing Continuous Map

Academic literature on the topic 'Self-Organizing Continuous Map'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Self-Organizing Continuous Map"

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Benabdeslem, Khalid, and Kais Allab. "Bi-clustering continuous data with self-organizing map." Neural Computing and Applications 22, no. 7-8 (July 13, 2012): 1551–62. http://dx.doi.org/10.1007/s00521-012-1047-6.

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Torres, H. M., J. A. Gurlekian, H. L. Rufiner, and M. E. Torres. "Self-organizing map clustering based on continuous multiresolution entropy." Physica A: Statistical Mechanics and its Applications 361, no. 1 (February 2006): 337–54. http://dx.doi.org/10.1016/j.physa.2005.05.073.

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Getmanets, O., and M. Pelikhatyi. "SELF ORGANIZING NEURAL MAPS IN THE PROBLEMS OF ECOLOGICAL MONITORING." Visnyk of Taras Shevchenko National University of Kyiv. Geology, no. 2 (93) (2021): 112–17. http://dx.doi.org/10.17721/1728-2713.93.13.

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There is a certain problem in ecological monitoring of the environment state according to the measured values of a certain abiotic factor. Namely, how to build a continuous map of environmental pollution throughout the controlled area, based on the results of measurements carried out at a finite number of points inside the controlled territory. The aim of the work is to study the possibility of using the method of self organizing neural maps (SOM) for the problems of the ecological monitoring of the environment, and specifically for building an accurate continuous map of environmental pollution on the ground. The materials and methods of researches are the results of measurements the ambient equivalent of the continuous X-ray and gamma radiation dose rate on a territory of the historical center of Kharkiv has been used as research materials; processing of the obtained data by SOM's methods using MatLab 8.1 and STATISTICA 10 computer programs has been done. Results: in the process of 1000 self-learning cycles of a neural network of 100 initial active neurons randomly located on the controlled area map, 25 neural clusters have been obtained, the coordinates of the centers of which practically coincided with the 25 control points coordinates. A continuous map of the background radiation on the controlled area has been built. The accuracy of this map was no worse than 0.25 μR/hour. Conclusions: the possibility of using the SOM methods to build a continuous map of the level of environmental pollution on the ground based on the results of measuring the values of a certain abiotic factor in a finite number of points has been proven. It has been proven that this method is more accurate compared to the methods of regression mapping and cluster analysis, from which it is essentially different. The possibilities for a significant improvement in the accuracy of the method lie in increasing the number of initial neurons on the terrain map and the number of iterations during their training.
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Montazeri, Hesam, Sajjad Moradi, and Reza Safabakhsh. "Continuous state/action reinforcement learning: A growing self-organizing map approach." Neurocomputing 74, no. 7 (March 2011): 1069–82. http://dx.doi.org/10.1016/j.neucom.2010.11.012.

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Rumbell, Timothy, Susan L. Denham, and Thomas Wennekers. "A Spiking Self-Organizing Map Combining STDP, Oscillations, and Continuous Learning." IEEE Transactions on Neural Networks and Learning Systems 25, no. 5 (May 2014): 894–907. http://dx.doi.org/10.1109/tnnls.2013.2283140.

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Schwardt, Martin, and Jan Dethloff. "Solving a continuous location‐routing problem by use of a self‐organizing map." International Journal of Physical Distribution & Logistics Management 35, no. 6 (July 2005): 390–408. http://dx.doi.org/10.1108/09600030510611639.

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Wang, Yonggang, Liwei Wang, Deng Li, Xinyi Cheng, and Yong Xiao. "Self-Organizing Map Neural Network-Based Depth-of-Interaction Determination for Continuous Crystal PET Detectors." IEEE Transactions on Nuclear Science 62, no. 3 (June 2015): 766–72. http://dx.doi.org/10.1109/tns.2015.2421290.

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Costa, Emanoel L. R., Taiane Braga, Leonardo A. Dias, Édler L. de Albuquerque, and Marcelo A. C. Fernandes. "Analysis of Atmospheric Pollutant Data Using Self-Organizing Maps." Sustainability 14, no. 16 (August 20, 2022): 10369. http://dx.doi.org/10.3390/su141610369.

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Atmospheric pollution is a critical issue in our society due to the continuous development of countries. Therefore, studies concerning atmospheric pollutants using multivariate statistical methods are widely available in the literature. Furthermore, machine learning has proved a good alternative, providing techniques capable of dealing with problems of great complexity, such as pollution. Therefore, this work used the Self-Organizing Map (SOM) algorithm to explore and analyze atmospheric pollutants data from four air quality monitoring stations in Salvador-Bahia. The maps generated by the SOM allow identifying patterns between the air quality pollutants (CO, NO, NO2, SO2, PM10 and O3) and meteorological parameters (environment temperature, relative humidity, wind velocity and standard deviation of wind direction) and also observing the correlations among them. For example, the clusters obtained with the SOM pointed to characteristics of the monitoring stations’ data samples, such as the quantity and distribution of pollution concentration. Therefore, by analyzing the correlations presented by the SOM, it was possible to estimate the effect of the pollutants and their possible emission sources.
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Li, Jia, Zheming Shi, Guangcai Wang, and Fei Liu. "Evaluating Spatiotemporal Variations of Groundwater Quality in Northeast Beijing by Self-Organizing Map." Water 12, no. 5 (May 13, 2020): 1382. http://dx.doi.org/10.3390/w12051382.

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As one of the globally largest cities suffering from severe water shortage, Beijing is highly dependent on groundwater supply. Located northeast of Beijing, the Pinggu district is an important emergency-groundwater-supply source. This area developed rapidly under the strategy of the integrated development of the Beijing–Tianjin–Hebei region in recent years. It is now important to evaluate the spatiotemporal variations in groundwater quality. This study analyzed groundwater-chemical-monitoring data from the periods 2014 and 2017. Hydrogeochemical analysis showed that groundwater is affected by calcite, dolomite, and silicate weathering. Self-organizing map (SOM) was used to cluster sample sites and identify possible sources of groundwater contamination. Sample sites were grouped into four clusters that explained the different pollution sources: sources of industrial and agricultural activities (Cluster I), landfill sources (Cluster II), domestic-sewage-discharge sources (Cluster III), and groundwater in Cluster IV was less affected by anthropogenic activities. Compared to 2014, concentrations of pollution indicators such as Cl−, SO42−, NO3−, and NH4+ increased, and the area of groundwater affected by domestic sewage discharge increased in 2017. Therefore, action should be taken in order to prevent the continuous deterioration of groundwater quality.
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Zhong, Chaoliang, Shirong Liu, Qiang Lu, and Botao Zhang. "Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network." International Journal of Advanced Robotic Systems 14, no. 6 (November 2017): 172988141774361. http://dx.doi.org/10.1177/1729881417743612.

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Dissertations / Theses on the topic "Self-Organizing Continuous Map"

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(14042402), Paul K. Hannah. "Data-fusion, self-organizing continuous maps, and eigenflames applied to modeling, control and visualization." Thesis, 2005. https://figshare.com/articles/thesis/Data-fusion_self-organizing_continuous_maps_and_eigenflames_applied_to_modeling_control_and_visualization/21454035.

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In this thesis a new Data-Fusion framework is presented. An object-oriented approach to neural network programming is considered, and the approach is used to develop the Self-Organizing Continuous Map, a family of neural networks based upon the self-organizing feature map. eigenflame and tomographic techniques are used for gas-turbine flame analysis.

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Book chapters on the topic "Self-Organizing Continuous Map"

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Smith, Toby, and Damminda Alahakoon. "Growing Self-Organizing Map for Online Continuous Clustering." In Studies in Computational Intelligence, 49–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01088-0_3.

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Joudar, Nour-Eddine, En-naimani Zakariae, and Mohamed Ettaouil. "Using Continuous Hopfield Neural Network for Choice Architecture of Probabilistic Self-Organizing Map." In Advances in Intelligent Systems and Computing, 123–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91337-7_12.

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Jose Saucedo-Dorantes, Juan, David Alejandro Elvira-Ortiz, Arturo Yosimar Jaen-Cuéllar, and Manuel Toledano-Ayala. "Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96145.

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The inclusion of intelligent systems in the modern industry is demanding the development of the automatic monitoring and continuous analysis of the data related to entire processes, this is a challenge of the industry 4.0 for the energy management. In this regard, this chapter proposes a novelty detection methodology based on Self-Organizing Maps (SOM) for Power Quality Monitoring. The contribution and originality of this proposed method consider the characterization of synthetic electric power signals by estimating a meaningful set of statistical time-domain based features. Subsequently, the modeling of the data distribution through a collaborative SOM’s neuron grid models facilitates the detection of novel events related to the occurrence of power disturbances. The performance of the proposed method is validated by analyzing and assessing four different conditions such as normal, sag, swell, and fluctuations. The obtained results make the proposed method suitable for being implemented in embedded systems for online monitoring.
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Luo, Yifan, Matteo Toso, Bailu Si, Federico Stella, and Alessandro Treves. "Grid Cells Lose Coherence in Realistic Environments." In Hippocampus - Cytoarchitecture and Diseases. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100310.

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Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an incoherent motley of unit clusters, flexible or outright unstable.
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MONTERO, JOSE. "MACHINE LEARNING APPLICATIONS FOR FORMATION EVALUATION." In Resumos do I Encontro Brasileiro de Petrofísica de Campos Maduros. Editora Realize, 2022. http://dx.doi.org/10.46943/i.ebpcm.2022.01.008.

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METHODS FOR AUTOMATED WELL INTERPRETATION IS A DEVELOPMENT GOAL FOR MOST OF THE COMPANIES IN THE OIL AND GAS INDUSTRY. DATABASES INCLUDE HUNDREDS OF THOUSANDS OF UNINTERPRETED WELLS EXIST GLOBALLY THAT WOULD TAKE HUNDREDS OF PERSON-YEARS TO INTERPRET MANUALLY. TO ACCOMPLISH THIS GOAL A CRITICAL STEP IS TO AUTOMATE THE INTERPRETATION OF WELLS FROM WIRELINE LOG DATA. CURRENT PETROTECHNICAL SUITE INCLUDES MACHINE LEARNING (SUPERVISED AND UNSUPERVISED) TOOLS RELATED TO THE INTERPRETATION OF CONTINUOUS LOGS AND DISCRETE CATEGORIES (FACIES, ROCK TYPING, FLAGS, ETC). THESE ARE THE NEURAL NETWORK MODULE (SUPERVISED ML METHOD) AND THE SELF ORGANIZING MAPS MODULE (UNSUPERVISED ML METHOD). FURTHEREMORE, MACHINE LEARNING ALGORITHMS REQUIRE A COMPETENT AND A CONSISTENT TRAINING DATASET TO ASSOCIATE THE DIFFERENT PETROPHYSICAL SIGNALS WITH LITHOLOGY/CATEGORIES. WHICH INCLUDES A CUSTOMIZABLE TRAINING DATA SET BUILDER MODULE WHERE THE USERS CAN CREATE THEIR OWN MODELS TO APPLY THE CORRESPONDING MACHINE LEARNING TYPES. IN SUMMARY, THE MACHINE LEARNING APPLICATIONS HELPS OPERATORS TO SPEED UP THE INTERPRETATION PROCESS IN AN AGILE MANNER CLOSING THE GAP BETWEEN DIFFERENT PETROPHYSICS AND CRITICAL GEOSCIENCE AND ENGINEERING DISCIPLINES WITH BEST-IN-CLASS SOLUTIONS FROM EXPLORATION TO PRODUCTION. IT GIVES ACCESS TO USERS FOR A WIDE RANGE OF PETROTECHNCIAL TOOLS THAT APPLY USER-FRIENDLY WORKFLOWS THAT HELP TO INTERPRET WELL DATA IN A RAPID AND EFFICIENT WAY WITH THE BEST-IN-CLASS SOLUTIONS ON ONE SINGLE OPEN PLATFORM.
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Conference papers on the topic "Self-Organizing Continuous Map"

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Hu, Renjie, Venous Roshdibenam, Hans J. Johnson, Emil Eirola, Anton Akusok, Yoan Miche, Kaj-Mikael Bjork, and Amaury Lendasse. "ELM-SOM: A Continuous Self-Organizing Map for Visualization." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489268.

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Hikawa, Hiroomi, Hidetaka Ito, and Yutaka Maeda. "A New Self-Organizing Map with Continuous Learning Capability." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628891.

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"Self-organizing map neural network based positioning scheme for continuous crystal PET detectors." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829419.

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Ouadfeul, S. "Reservoir Characterization Using the Continuous Wavelet Transform (CWT) Combined with the Self Organizing Map (SOM)." In 11th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2008. http://dx.doi.org/10.3997/2214-4609.20146449.

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Kohonen, Teuvo. "Self-organized feature maps." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tum4.

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The biological brain is able to form various feature maps and abstractions of sensory signals. For a characteristic sensory stimulus, a response is obtained from such a map at a location which corresponds to some quantitative feature value of the stimulus. This paper offers an explanation for this ability, using an idealized model of a self-organizing collective system. The model consists of a 2-D array of identical processing elements which receive a set of sensory signals in parallel and change their sensitivity or tuning to these signals, controlled by the input signals and also by the reactions from the neighboring elements. As a result, the various elements in the array become automatically adjusted in a continuous 2-D order. The coordinates on the array then correspond to some feature dimensions that are present in the input signals. For example, such a map may represent optical features, colors, spatial frequencies, acoustical frequencies, phonemes, taxonomic classifications, depending on the signal detectors used and stimuli presented at the input. This paper describes in what conditions a collective system starts to organize itself in this way and also gives several examples of maps already produced in computer simulations.
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Dallag, Mohammed, Mustafa Bawazir, and Ali Al-Ali. "Digital Solution to Extend the Life of Wells with Continuous Corrosion Monitoring Based on Machine Learning Algorithms." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22472-ms.

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Abstract Well integrity in the oilfield is one of the challenges that petroleum engineers face, as they seek to monitor well corrosion in the field to optimize well performance. Most of these fields can be categorized as brownfields, with some of the wells considered aged and have expected integrity issues. To achieve sustainable production targets with cost-effective and safe operations from these fields requires a close monitoring of the integrity of all elements involved in the production chain. Addressing these challenges requires the engineers to coordinate and analyze several data elements, including casedhole, openhole, reservoir, well, and production data from multiple sources. Another challenge is to create and automate a corrosion workflow that saves the engineers’ time and improves efficiency. In this paper, we introduce an innovative workflow that uses the historical corrosion data while integrating the multiple production and reservoir variables. The innovative approach uses machine learning (ML) algorithms to provide a powerful tool for workover (W/O) candidate selection and for optimizing the corrosion evaluation frequency, which are required in different areas of the fields. Different ML methods (random forest classification and neural net) were applied on training data. Different models were created, and the best model will be used. This offered key insights on the rate of corrosion and corrosion patterns. Further, the developed workflow was designed to be self-sustaining and acting as a surveillance tool for monitoring the integrity of the wells. The first step of the workflow was to start with organizing and auditing the available corrosion data, followed by a review and analysis of existing openhole, casedhole, production, and reservoir engineering data. This approach led us to understand the extent and severity of corrosion in terms of the corrosion rate and the corrosion index. The corrosion logs were digitally interpreted depth-wise in order to explore the maximum metal loss for each interval. New animated conformance corrosion maps were created. The successful diagnosis through data analytics in a modern integrated software platform will assist in corrosion monitoring and decision-making. The multiple corrosion maps can be animated to visualize the current corrosion profile and predict the corrosion over time, in addition to ranking the wells for W/O candidate selection.
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