Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Ambiguous Data ◽

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, the Quantization Error (SOM-QE). Shape pairs with perfect geometric 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, longer choice 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.

Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry ◽

10.3390/sym13020299 ◽

2021 ◽

Vol 13 (2) ◽

pp. 299

Author(s):

Birgitta Dresp-Langley ◽

John M. Wandeto

Keyword(s):

Neural Network ◽

Mirror Symmetry ◽

Image Data ◽

Choice Response ◽

Local Contrast ◽

Self Organizing Map ◽

The Neural Network ◽

Ambiguous Data ◽

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.

FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK

International Journal of Neural Systems ◽

10.1142/s0129065708001555 ◽

2008 ◽

Vol 18 (03) ◽

pp. 233-256 ◽

Cited By ~ 10

Author(s):

ALIREZA FATEHI ◽

KENICHI ABE

Keyword(s):

Neural Network ◽

Minimum Spanning Tree ◽

Model Space ◽

Self Organizing Map ◽

Local Models ◽

Tree Graph ◽

Self Organizing Maps ◽

The Neural Network ◽

The Neural Networks ◽

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v4i2.105 ◽

2017 ◽

Vol 4 (2) ◽

pp. 198

Author(s):

Fatma Agus Setyanngsih

Keyword(s):

Neural Network ◽

Time Series ◽

Self Organizing Map ◽

Rainfall Prediction ◽

The Neural Network ◽

Network Time ◽

Neural Network Algorithm ◽

Network Prediction ◽

The City ◽

The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148. Keywords: Rainfall, Neural Network, Time Series, Self Organizing MapPrediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. Kata kunci: Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map

Clustering of Experimental Seismo-Acoustic Events Using Self-Organizing Map (SOM)

Frontiers in Earth Science ◽

10.3389/feart.2020.581742 ◽

2021 ◽

Vol 8 ◽

Author(s):

Flora Giudicepietro ◽

Antonietta M. Esposito ◽

Laura Spina ◽

Andrea Cannata ◽

Daniele Morgavi ◽

...

Keyword(s):

Neural Network ◽

Variable Viscosity ◽

Self Organizing Map ◽

Gas Flux ◽

Volcanic System ◽

Physical Conditions ◽

Variable Surface ◽

Analogue Experiments ◽

The analogue experiments that produce seismo-acoustic events are relevant for understanding the degassing processes of a volcanic system. The aim of this work is to design an unsupervised neural network for clustering experimental seismo-acoustic events in order to investigate the possible cause-effect relationships between the obtained signals and the processes. We focused on two tasks: 1) identify an appropriate strategy for parameterizing experimental seismo-acoustic events recorded during analogue experiments devoted to the study of degassing behavior at basaltic volcanoes; 2) define the set up of the selected neural network, the Self-Organizing Map (SOM), suitable for clustering the features extracted from the experimental events. The seismo-acoustic events were generated using an ad hoc experimental setup under different physical conditions of the analogue magma (variable viscosity), injected gas flux (variable flux velocity) and conduit surface (variable surface roughness). We tested the SOMs ability to group the experimental seismo-acoustic events generated under controlled conditions and conduit geometry of the analogue volcanic system. We used 616 seismo-acoustic events characterized by different analogue magma viscosity (10, 100, 1000 Pa s), gas flux (5, 10, 30, 60, 90, 120, 150, 180 × 10−3 l/s) and conduit roughness (i.e. different fractal dimension corresponding to 2, 2.18, 2.99). We parameterized the seismo-acoustic events in the frequency domain by applying the Linear Predictive Coding to both accelerometric and acoustic signals generated by the dynamics of various degassing regimes, and in the time domain, applying a waveform function. Then we applied the SOM algorithm to cluster the feature vectors extracted from the seismo-acoustic data through the parameterization phase, and identified four main clusters. The results were consistent with the experimental findings on the role of viscosity, flux velocity and conduit roughness on the degassing regime. The neural network is capable to separate events generated under different experimental conditions. This suggests that the SOM is appropriate for clustering natural events such as the seismo-acoustic transients accompanying Strombolian explosions and that the adopted parameterization strategy may be suitable to extract the significant features of the seismo-acoustic (and/or infrasound) signals linked to the physical conditions of the volcanic system.

Line Segment-Based Clustering Approach With Self-Organizing Maps

Journal of Information Technology Research ◽

10.4018/jitr.2021100103 ◽

2021 ◽

Vol 14 (4) ◽

pp. 33-44

Author(s):

G. Chamundeswari ◽

G. P. S. Varma ◽

C. Satyanarayana

Keyword(s):

Neural Network ◽

Line Segment ◽

Feature Vector ◽

Input Image ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Clustering Techniques ◽

Hough Transformation ◽

The Neural Network ◽

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

ERRATUM: "FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK"

International Journal of Neural Systems ◽

10.1142/s0129065708001579 ◽

2008 ◽

Vol 18 (04) ◽

pp. 347-370 ◽

Cited By ~ 5

Author(s):

ALIREZA FATEHI ◽

KENICHI ABE

Keyword(s):

Neural Network ◽

Minimum Spanning Tree ◽

Model Space ◽

Self Organizing Map ◽

Local Models ◽

Tree Graph ◽

Self Organizing Maps ◽

The Neural Network ◽

The Neural Networks ◽

Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.348-349.177 ◽

2007 ◽

Vol 348-349 ◽

pp. 177-180

Author(s):

Guang Lan Liao ◽

Tie Lin Shi ◽

Zi Rong Tang

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Fault Diagnosis ◽

Failure Detection ◽

Operating Conditions ◽

Hierarchical Architecture ◽

Self Organizing Map ◽

Hierarchical Relations ◽

Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of independent growing SOMs. The GHSOM can adapt its architecture during unsupervised training process and provide a global orientation in the individual layers of the hierarchy; hence the original data structure can be described correctly for machine faults diagnosis. Gearbox vibration signals measured under different operating conditions are analyzed using the proposed technique. The results prove that the hierarchical relations in the gearbox failure data can be intuitively represented, and inherent structure can be unfolded. Then gearbox operating conditions including normal, tooth cracked and tooth broken are classified and recognized clearly. The study confirms that GHSOM is very useful and effective for pattern recognition in mechanical fault diagnosis, and provides a good potential for application in practice.

Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2018-0100 ◽

2018 ◽

Vol 4 (1) ◽

pp. 419-422

Author(s):

Redwan Abdo A. Mohammed ◽

Daniel Schäle ◽

Christoph Hornberger ◽

Steffen Emmert

Keyword(s):

Neural Network ◽

Hyperspectral Image ◽

Image Data ◽

Network Models ◽

Least Square Method ◽

Least Square ◽

Self Organizing Map ◽

Neural Network Models ◽

Wound Tissue ◽

AbstractThe purpose of this study is to develop a method to discriminate spectral signatures in wound tissue. We have collected a training set of the intensity of the remitted light for different types of wound tissue from different patients using a TIVITA™ tissue camera. We used a neural network technique (self-organizing map) to group areas with the same spectral properties together. The results of this work indicates that neural network models are capable of finding clusters of closely related hyperspectral signatures in wound tissue, and thus can be used as a powerful tool to reach the anticipated classification. Moreover, we used a least square method to fit literature spectra (i.e. oxygenated haemoglobin (O2Hb), deoxygenated haemoglobin (HHb), water and fat) to the learned spectral classes. This procedure enables us to label each spectral class with the corresponding absorbance properties for the different absorbance of interest (i.e. O2Hb, HHb, water and fat). The calculated parameters of a testing set were consistent with the expected behaviour and show a good agreement with the results of a second algorithm which is used in the TIVITA™ tissue camera.