Journal articles: '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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Wang, Yonggang, Deng Li, Xiaoming Lu, Xinyi Cheng, and Liwei Wang. "Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors." IEEE Transactions on Nuclear Science 61, no. 5 (October 2014): 2446–55. http://dx.doi.org/10.1109/tns.2014.2347295.

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Kuo, R. J., C. F. Wang, and Z. Y. Chen. "Integration of growing self-organizing map and continuous genetic algorithm for grading lithium-ion battery cells." Applied Soft Computing 12, no. 8 (August 2012): 2012–22. http://dx.doi.org/10.1016/j.asoc.2012.01.018.

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Andrades, Ignacio Sánchez, Juan J. Castillo Aguilar, Juan M. Velasco García, Juan A. Cabrera Carrillo, and Miguel Sánchez Lozano. "Low-Cost Road-Surface Classification System Based on Self-Organizing Maps." Sensors 20, no. 21 (October 23, 2020): 6009. http://dx.doi.org/10.3390/s20216009.

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Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.

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Joudar, Nour-eddine, Zakariae En-naimani, and Mohamed Ettaouil. "Using continuous Hopfield neural network for solving a new optimization architecture model of probabilistic self organizing map." Neurocomputing 344 (June 2019): 82–91. http://dx.doi.org/10.1016/j.neucom.2018.09.095.

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FOMIN, TIBOR, TAMÁS ROZGONYI, CSABA SZEPESVÁRI, and ANDRÁS LŐRINCZ. "SELF-ORGANIZING MULTI-RESOLUTION GRID FOR MOTION PLANNING AND CONTROL." International Journal of Neural Systems 07, no. 06 (December 1996): 757–76. http://dx.doi.org/10.1142/s0129065796000713.

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A fully self-organizing neural network approach to low-dimensional control problems is described. We consider the problem of learning to control an object and solving the path planning problem at the same time. Control is based on the path planning model that follows the gradient of the stationary solution of a diffusion process working in the state space. Previous works are extended by introducing a self-organizing multigrid-like discretizing structure to represent the external world. Diffusion is simulated within a recurrent neural network built on this multigrid system. The novelty of the approach is that the diffusion on the multigrid is fast. Moreover, the diffusion process on the multigrid fits well the requirements of the path planning: it accelerates the diffusion in large free space regions while still keeps the resolution in small bottleneck-like labyrinths along the path. Control is achieved in the usual way: associative learning identifies the inverse dynamics of the system in a direct fashion. To this end there are introduced interneurons between neighboring discretizing units that detect the strength of the steady-state diffusion and forward control commands to the control neurons via modifiable connections. This architecture forms the Multigrid Position-and-Direction-to-Action (MPDA) map. The architecture integrates reactive path planning and continuous motion control. It is also shown that the scheme leads to population coding for the actual command vector.

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Roushangar, Kiyoumars, Farhad Alizadeh, Jan Adamowski, and Seyed Mehdi Saghebian. "Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps." Journal of Water and Climate Change 11, no. 3 (February 7, 2019): 655–76. http://dx.doi.org/10.2166/wcc.2019.097.

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Abstract This study utilized a spatio-temporal framework to assess the dispersion and uncertainty of precipitation in Iran. Thirty-one rain gauges with data from 1960 to 2010 were selected in order to apply the entropy concept and study spatio-temporal variability of precipitation. The variability of monthly, seasonal and annual precipitation series was studied using the marginal disorder index (MDI). To investigate the intra-annual and decadal distribution of monthly and annual precipitation values, the apportionment disorder index (ADI) and decadal ADI (DADI) were applied to the time series. The continuous wavelet transform was used to decompose the ADI time series into time-frequency domains. The decomposition of the ADI series into different zones helped to identify the dominant modes of variability and the variation of those modes over time. The results revealed the high disorderliness in the amount of precipitation for different temporal scales based on disorder indices. Based on the DI outcome for all rain gauges, a self-organizing map (SOM) was trained to find the optimum number of clusters (seven) of rain gauges. It was observed from the clustering that there was hydrologic similarity in the clusters apart from the geographic neighborhood.

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Low, Kian Hsiang, Wee Kheng Leow, and Marcelo H. Ang. "An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks." Neural Computation 17, no. 6 (June 1, 2005): 1411–45. http://dx.doi.org/10.1162/0899766053630378.

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Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a nonholonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated motion tasks even though the weighted-sum ensemble approach also operates in continuous motor control space.

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Xiang, Zuquan, Tao Tao, Lifei Song, Zaopeng Dong, Yunsheng Mao, Shixin Chu, and Hanfang Wang. "Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092529. http://dx.doi.org/10.1177/1729881420925294.

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The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.

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Parpia, Pasha. "Reappraisal of the Somatosensory Homunculus and Its Discontinuities." Neural Computation 23, no. 12 (December 2011): 3001–15. http://dx.doi.org/10.1162/neco_a_00179.

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Neuroscience folklore has it that somatotopy in human primary somatosensory cortex (SI) has two significant discontinuities: the hands and face map onto adjacent regions in SI, as do the feet and genitalia. It has been proposed that these conjunctions in SI result from coincident sources of stimulation in the fetal position, where the hands frequently touch the face, and the feet the genitalia. Computer modeling using a Hebbian variant of the self-organizing Kohonen net is consistent with this proposal. However, recent work reveals that the genital representation in SI for cutaneous sensations (as opposed to tumescence) is continuous with that of the lower trunk and thigh. This result, in conjunction with reports of separate face innervation and its earlier onset of sensory function, compared to that of the rest of the body, allows a reappraisal of homuncular organization. It is proposed that the somatosensory homunculus comprises two distinct somatotopic regions: the face representation and that of the rest of the body. Principles of self-organization do not account satisfactorily for the overall homuncular map. These results may serve to alert computational modelers that intrinsic developmental factors can override simple rules of plasticity.

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Aliouane, Leila, Sid Ali Ouadfeul, and Amar Boudella. "Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network." Arabian Journal of Geosciences 6, no. 6 (November 29, 2011): 1681–91. http://dx.doi.org/10.1007/s12517-011-0459-4.

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Lee, Do-Hun, Nam Jung, Yong-Hyeok Jang, KyoungEun Lee, Joobaek Lim, Gab-Sue Jang, Jae Woo Lee, and Tae-Soo Chon. "Spatial Movement Patterns and Local Co-Occurrence of Nutria Individuals in Association with Habitats Using Geo-Self-Organizing Map (Geo-SOM)." Biology 10, no. 7 (June 28, 2021): 598. http://dx.doi.org/10.3390/biology10070598.

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Nutrias (Myocastor coypus) were imported to South Korea for farming in 1985; individuals escaped captivity and established wild populations in natural ecosystems in the late 1990s. Numerous studies have focused on their monitoring and management; however, information on the continuous movement of individuals is not available. In this study, telemetry data from field conditions were used to identify the nearest-neighbor distances of individuals in association with environmental factors, including plant type, land cover, and biological parameters. The minimum nearest-neighbor distances for the different sexes were, overall, according to the minimum distances for the same sex. Local co-occurrences of individuals, either of the same or different sex, were seasonal. Tall grasslands, followed by herbaceous vegetation, were associated with the co-occurrence of different sexes. Conversely, floating-leaved hydrophytes, followed by xeric herbaceous vegetation, were correlated with the co-occurrence of the same sex. Local female–male co-occurrences were negatively associated with male–male co-occurrences but not with female–female co-occurrences, suggesting male dominance in group formations. Movement and co-occurrence information extracted using Geo-self-organizing maps furthers our understanding of population dispersal and helps formulate management strategies for nutria populations.

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Camacho-Navarro, Jhonatan, Magda Ruiz, Rodolfo Villamizar, Luis Mujica, and Oscar Pérez. "Evaluation of Piezodiagnostics Approach for Leaks Detection in a Pipe Loop." Key Engineering Materials 713 (September 2016): 107–10. http://dx.doi.org/10.4028/www.scientific.net/kem.713.107.

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Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self-Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self-Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.

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Chen, Yue, Yingming Gao, and Yi Xu. "Computational Modelling of Tone Perception Based on Direct Processing of f0 Contours." Brain Sciences 12, no. 3 (March 2, 2022): 337. http://dx.doi.org/10.3390/brainsci12030337.

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It has been widely assumed that in speech perception it is imperative to first detect a set of distinctive properties or features and then use them to recognize phonetic units like consonants, vowels, and tones. Those features can be auditory cues or articulatory gestures, or a combination of both. There have been no clear demonstrations of how exactly such a two-phase process would work in the perception of continuous speech, however. Here we used computational modelling to explore whether it is possible to recognize phonetic categories from syllable-sized continuous acoustic signals of connected speech without intermediate featural representations. We used Support Vector Machine (SVM) and Self-organizing Map (SOM) to simulate tone perception in Mandarin, by either directly processing f0 trajectories, or extracting various tonal features. The results show that direct tone recognition not only yields better performance than any of the feature extraction schemes, but also requires less computational power. These results suggest that prior extraction of features is unlikely the operational mechanism of speech perception.

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Vassilas, Nikolaos. "Theoretical analysis of the batch variant of the self-organizing feature map algorithm for 1-d networks mapping a continuous 1-d input space." International Journal of Computer Mathematics 67, no. 1-2 (January 1998): 77–103. http://dx.doi.org/10.1080/00207169808804653.

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HSU, WEI-YEN. "APPLICATION OF COMPETITIVE HOPFIELD NEURAL NETWORK TO BRAIN-COMPUTER INTERFACE SYSTEMS." International Journal of Neural Systems 22, no. 01 (February 2012): 51–62. http://dx.doi.org/10.1142/s0129065712002979.

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We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.

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Kumar, Swagat, Premkumar P., Ashish Dutta, and Laxmidhar Behera. "Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network." Robotica 28, no. 6 (September 21, 2009): 795–810. http://dx.doi.org/10.1017/s026357470999049x.

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SUMMARYThis paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.

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Hosoda, Kenji, Masataka Watanabe, Heiko Wersing, Edgar Körner, Hiroshi Tsujino, Hiroshi Tamura, and Ichiro Fujita. "A Model for Learning Topographically Organized Parts-Based Representations of Objects in Visual Cortex: Topographic Nonnegative Matrix Factorization." Neural Computation 21, no. 9 (September 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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Hulskemper, D., K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh. "CHARACTERIZATION OF MORPHOLOGICAL SURFACE ACTIVITIES DERIVED FROM NEAR-CONTINUOUS TERRESTRIAL LIDAR TIME SERIES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W2-2022 (December 8, 2022): 53–60. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w2-2022-53-2022.

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Abstract. The Earth’s landscapes are shaped by processes eroding, transporting and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict (e.g., the landward movement of a shoreline), knowledge is needed on the occurrences and impact of these activities. Near-continuous terrestrial laser scanning enables the acquisition of large datasets of surface morphology, represented as three-dimensional point cloud time series. Exploiting the full potential of this large amount of data, by extracting and characterizing different types of surface activities, is challenging. In this research we use a time series of 2,942 point clouds obtained over a sandy beach in The Netherlands. We investigate automated methods to extract individual surface activities present in this dataset and cluster them into groups to characterize different types of surface activities. We show that, first extracting 2,021 spatiotemporal segments of surface activity using an object detection algorithm, and second, clustering these segments with a Self-organizing Map (SOM) in combination with hierarchical clustering, allows for the unsupervised identification and characterization of different types of surface activities present on a sandy beach. The SOM enables us to find events displaying certain type of surface activity, while it also enables the identification of subtle differences between different events belonging to one specific surface activity. Hierarchical clustering then allows us to find and characterize broader groups of surface activity, even if the same type of activity occurs at different points in space or time.

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Zhang, Jianxin, Junkai Wu, Xudong Hu, and Xinen Zhang. "Multi-color measurement of printed fabric using the hyperspectral imaging system." Textile Research Journal 90, no. 9-10 (November 4, 2019): 1024–37. http://dx.doi.org/10.1177/0040517519883953.

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Printed fabrics usually have multiple colors and intricate patterns, which make it difficult to directly measure the colors of the printed fabrics with a traditional spectrophotometer. However, a hyperspectral imaging system (HIS) can measure multiple colors since it acquires the spectral reflectance of a continuous band at every point of the fabric. For multiple-color printed fabrics, color segmentation is also very important. In this paper, color measurement of printed fabrics using the HIS was implemented; an algorithm which combines the self-organizing map (SOM) algorithm and the density peaks clustering (DPC) algorithm was then proposed to automatically determine the number of colors on the printed fabric and accurately segment the color regions for measurement. Firstly, the SOM algorithm was used to identify the main clusters, the DPC algorithm with Silhouette Index was then used to identify the optimal number of colors and merge the clusters. Experimental results show that this algorithm not only automatically determines the optimal number of colors for printed fabric and achieves accurate color segmentation, but requires less time for execution.

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Ji, Chang Woo, Young-Seuk Park, Yongde Cui, Hongzhu Wang, Ihn-Sil Kwak, and Tae-Soo Chon. "Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map." International Journal of Environmental Research and Public Health 17, no. 8 (April 11, 2020): 2627. http://dx.doi.org/10.3390/ijerph17082627.

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Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.

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CORRADINI, ANDREA, HANS-JOACHIM BOEHME, and HORST-MICHAEL GROSS. "A HYBRID STOCHASTIC-CONNECTIONIST APPROACH TO GESTURE RECOGNITION." International Journal on Artificial Intelligence Tools 09, no. 02 (June 2000): 177–203. http://dx.doi.org/10.1142/s0218213000000148.

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In this paper a person-specific saliency system and subsequently two architectures for the recognition of dynamic gestures are described. The systems implemented are designed to take a sequence of images and to assign it to one of a number of discrete classes where each of them corresponds to a gesture from a predefined small vocabulary. Since we think that for a human-computer interaction the localization of the user is essential for any further step regarding the recognition and the interpretation of gestures, in the first part, we begin with describing our saliency system dedicated to the person localization task in cluttered environments. Successively, the intrinsic gesture recognition process is broken down into an initial preprocessing stage followed by a mapping from the preprocessed input variables to an output variable representing the class label. Subsequently, we utilize two different classifiers for mapping the ordered sequence of feature vectors to one gesture category. The first classifier utilizes a hybrid combination of Kohonen Self-Organizing Map (SOM) and Discrete Hidden Markov Models (DHMM). As second recognizer a system of Continuous Hidden Markov Models (CHMM) is used. Preliminary experiments with our baseline systems are demonstrated.

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Woldaregay, Ashenafi Zebene, Ilkka Kalervo Launonen, David Albers, Jorge Igual, Eirik Årsand, and Gunnar Hartvigsen. "A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism." Journal of Medical Internet Research 22, no. 8 (August 12, 2020): e18912. http://dx.doi.org/10.2196/18912.

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Background Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. Objective This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. Methods Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. Results The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. Conclusions We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.

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Colantonio, Lorenzo, Lucas Equeter, Pierre Dehombreux, and François Ducobu. "A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques." Machines 9, no. 12 (December 10, 2021): 351. http://dx.doi.org/10.3390/machines9120351.

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In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

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Li, Baozhong, Yanming Liu, and Hailin Li. "Position Estimation Based on Grid Cells and Self-Growing Self-Organizing Map." Computational Intelligence and Neuroscience 2019 (February 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/3606397.

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As the basis of animals’ natal homing behavior, path integration can continuously provide current position information relative to the initial position. Some neurons in freely moving animals’ brains can encode current positions and surrounding environments by special firing patterns. Research studies show that neurons such as grid cells (GCs) in the hippocampus of animals’ brains are related to the path integration. They might encode the coordinate of the animal’s current position in the same way as the residue number system (RNS) which is based on the Chinese remainder theorem (CRT). Hence, in order to provide vehicles a bionic position estimation method, we propose a model to decode the GCs’ encoding information based on the improved traditional self-organizing map (SOM), and this model makes full use of GCs’ firing characteristics. The details of the model are discussed in this paper. Besides, the model is realized by computer simulation, and its performance is analyzed under different conditions. Simulation results indicate that the proposed position estimation model is effective and stable.

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Franco-Cuervo, K. J., K. Maldonado-Cañón, L. F. Niño Vásquez, and G. Quintana Lopez. "POS0545 A NEURAL NETWORK BASED CLUSTERING MODEL OF A COLOMBIAN COHORT OF RHEUMATOID ARTHRITIS PATIENTS." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 536.1–536. http://dx.doi.org/10.1136/annrheumdis-2022-eular.2870.

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BackgroundRheumatoid Arthritis (RA) is a chronic disease characterized by inflammation and joint pain. In daily clinical practice, it is usual to have multiple variables of different nature to define the current state of the disease, the patient’s risk profile, and the subsequent optimal treatment.ObjectivesWe aimed to identify the most influential variables from a suitable multivariable clustering and its labeling for an outpatient clinic-based cohort of Colombian RA patients.MethodsWe execute a clustering model (Kohonen’s self-organizing map – SOM), applied to 23 variables (17 continuous and 6 discrete) obtained from 14,811 related follow-up visit records hosted on a previously preprocessed database of a cohort with data prospectively collected between 2013 and 2020. The included variables were the disease activity indexes (DAS28-ESR/CRP, CDAI, and SDAI; as outcome variables), serological status (autoantibodies positivity), and patients’ sociodemographic and clinical characteristics. Clustering method used for generating the groups was SOM with a size of 25 x 25 neurons and 10000 iterations. SOM allows us to generate the groups by the comparison of the Euclidean distance in the hyperspace generated by the dimensions composed by the variables. After clustering, a discrete label built upon the categorization of the disease activity allowed us to identify the behavior of the included variables regarding the aforementioned outcomes, without affecting the clustering process. We evaluated the corresponding weights and their influence on the proposed neural network.ResultsData from a total of 1,277 patients were included in the analysis. When both continuous and discrete variables were integrated, discrete data were transformed using the one-hot encoding method, creating new variables according to the corresponding number of categories. Dissimilarity between groups was very low when considering only the continuous variables, and it increases when adding all the other variables; likewise, regardless of the clinimetric index used for labeling, the clustering organization remains (Figure 1a).Figure 1.Clusters and heatmaps of variables’ weightsIn the construction of the groups, the influence of the RF and ACPA positivity was confirmed; furthermore, the antinuclear antibodies (ANAs) delivered a significant effect, especially those with negative ANAs or positive ANAs with a homogeneous pattern, on disease activity (Figure 1b).ConclusionSOM, as well as other artificial neural networks (ANN) are important methods for clustering and 2D visualization, due to the multivariate nature of the clinical data and its difficult visualization in the generated n-dimensional hyperspace. The utilized labels confirm that the clustering is adequate when considering that there was an identical grouping behavior for those registers with similar characteristics and an equivalent disease activity score. The findings of this research provide insights into a potentially pivotal role of the influence of RF, ACPA, and ANAs and their interaction with the proposed outcome variables in the understanding and development of future classification or prediction models; based on artificial intelligence and big data methods rather than on classical epidemiological approaches.References[1]Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, et al. 2010 Rheumatoid Arthritis Classification Criteria. Arthritis & Rheumatism. 2010;62(9):2569–2581Disclosure of InterestsNone declared.

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Roobaert, Alizée, Laure Resplandy, Goulven G. Laruelle, Enhui Liao, and Pierre Regnier. "A framework to evaluate and elucidate the driving mechanisms of coastal sea surface <i>p</i>CO<sub>2</sub> seasonality using an ocean general circulation model (MOM6-COBALT)." Ocean Science 18, no. 1 (January 10, 2022): 67–88. http://dx.doi.org/10.5194/os-18-67-2022.

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Abstract. The temporal variability of the sea surface partial pressure of CO2 (pCO2) and the underlying processes driving this variability are poorly understood in the coastal ocean. In this study, we tailor an existing method that quantifies the effects of thermal changes, biological activity, ocean circulation and freshwater fluxes to examine seasonal pCO2 changes in highly variable coastal environments. We first use the Modular Ocean Model version 6 (MOM6) and biogeochemical module Carbon Ocean Biogeochemistry And Lower Trophics version 2 (COBALTv2) at a half-degree resolution to simulate coastal CO2 dynamics and evaluate them against pCO2 from the Surface Ocean CO2 Atlas database (SOCAT) and from the continuous coastal pCO2 product generated from SOCAT by a two-step neuronal network interpolation method (coastal Self-Organizing Map Feed-Forward neural Network SOM-FFN, Laruelle et al., 2017). The MOM6-COBALT model reproduces the observed spatiotemporal variability not only in pCO2 but also in sea surface temperature, salinity and nutrients in most coastal environments, except in a few specific regions such as marginal seas. Based on this evaluation, we identify coastal regions of “high” and “medium” agreement between model and coastal SOM-FFN where the drivers of coastal pCO2 seasonal changes can be examined with reasonable confidence. Second, we apply our decomposition method in three contrasted coastal regions: an eastern (US East Coast) and a western (the Californian Current) boundary current and a polar coastal region (the Norwegian Basin). Results show that differences in pCO2 seasonality in the three regions are controlled by the balance between ocean circulation and biological and thermal changes. Circulation controls the pCO2 seasonality in the Californian Current; biological activity controls pCO2 in the Norwegian Basin; and the interplay between biological processes and thermal and circulation changes is key on the US East Coast. The refined approach presented here allows the attribution of pCO2 changes with small residual biases in the coastal ocean, allowing for future work on the mechanisms controlling coastal air–sea CO2 exchanges and how they are likely to be affected by future changes in sea surface temperature, hydrodynamics and biological dynamics.

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Barbariol, Francesco, Francesco Marcello Falcieri, Carlotta Scotton, Alvise Benetazzo, Sandro Carniel, and Mauro Sclavo. "Wave extreme characterization using self-organizing maps." Ocean Science 12, no. 2 (March 10, 2016): 403–15. http://dx.doi.org/10.5194/os-12-403-2016.

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Abstract. The self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it is commonly recognized, and herein confirmed, that a SOM is a good regressor of a sample if the frequency of events is high (e.g., for low/moderate sea states), while a SOM fails if the frequency is low (e.g., for the most severe sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979–2008. Three different strategies derived by SOM have been tested in order to capture the most extreme events. The first contemplates a pre-processing of the input data set aimed at reducing redundancies; the second, based on the post-processing of SOM outputs, consists in a two-step SOM where the first step is applied to the original data set, and the second step is applied on the events exceeding a given threshold. A complete graphical representation of the outcomes of a two-step SOM is proposed. Results suggest that the post-processing strategy is more effective than the pre-processing one in order to represent the wave climate extremes. An application of the proposed two-step approach is also provided, showing that a proper representation of the extreme wave climate leads to enhanced quantification of, for instance, the alongshore component of the wave energy flux in shallow water. Finally, the third strategy focuses on the peaks of the storms.

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Park, Hyun Jun, Kwang Baek Kim, and Eui-Young Cha. "An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5302957.

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Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method.

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Liu, Yuedan, Chunlei Xia, Zhongya Fan, Renren Wu, Xianglin Chen, and Zuoyi Liu. "Implementation of Fractal Dimension and Self-Organizing Map to Detect Toxic Effects of Toluene on Movement Tracks of Daphnia magna." Journal of Toxicology 2018 (February 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/2637209.

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Movement behaviors of an indicator species, Daphnia magna, in response to contaminants have been implemented to monitor environmental disturbances. Complexity in movement tracks of Daphnia magna was characterized by use of fractal dimension and self-organizing map. The individual movement tracks of D. magna were continuously recorded for 24 hours before and after treatments with toluene at the concentration of 10 mg/L, respectively. The general complexity in movement tracks (10 minutes) was characterized by fractal dimension. Results showed that average fractal dimension of movement tracks was decreased from 1.62 to 1.22 after treatments. The instantaneous movement parameters of movement segments in 5 s were input into the self-organizing map to investigate the swimming pattern changes under stresses of toluene. Abnormal behaviors of D. magna are more frequently observed after treatments than before treatments. Computational methods in ecological informatics could be utilized to obtain the useful information in behavioral data of D. magna and would be further applied as an in situ monitoring tool in water environment.

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Sibero, Alexander F. K., Opim Salim Sitompul, and Mahyuddin K. M. Nasution. "Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q." Data Science: Journal of Computing and Applied Informatics 2, no. 2 (August 3, 2018): 62–73. http://dx.doi.org/10.32734/jocai.v2.i2-324.

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Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.

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Barbariol, F., F. M. Falcieri, C. Scotton, A. Benetazzo, S. Carniel, and M. Sclavo. "Self-Organizing Maps approaches to analyze extremes of multivariate wave climate." Ocean Science Discussions 12, no. 4 (August 25, 2015): 1971–2006. http://dx.doi.org/10.5194/osd-12-1971-2015.

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Abstract. In this paper the Self-Organizing Map (SOM) technique to assess the multivariate sea wave climate at a site is analyzed and discussed with the aim of a more complete representation which includes the most severe sea states that otherwise would be missed by the standard SOM. Indeed, it is commonly recognized, and herein confirmed, that SOM is a good regressor of a sample where the density of events is high (e.g. for low/moderate and frequent sea states), while SOM fails where the density is low (e.g. for severe and rare sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period, and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979–2008. Three different strategies derived by the standard SOM have been tested in order to widen the range of applicability to extreme events. The first strategy contemplates a pre-processing of the input dataset with the Maximum Dissimilarity Algorithm; the second and the third strategies focus on the post-processing of SOM outputs, resulting in a two-steps SOM, where the first step is the standard SOM applied to the original dataset, and the second step is an additional SOM on the events exceeding a threshold (either taking all the events over the threshold or only the peaks of storms). Results suggest that post-processing strategies are more effective than the pre-processing one in representing the extreme wave climate, both in the time series and probability density spaces. In addition, a complete graphical representation of the outcomes of two-steps SOM as double-sided maps is proposed.

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Toiviainen, Petri, and Carol L. Krumhansl. "Measuring and Modeling Real-Time Responses to Music: The Dynamics of Tonality Induction." Perception 32, no. 6 (June 2003): 741–66. http://dx.doi.org/10.1068/p3312.

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We examined a variety of real-time responses evoked by a single piece of music, the organ Duetto BWV 805 by J S Bach. The primary data came from a concurrent probe-tone method in which the probe-tone is sounded continuously with the music. Listeners judged how well the probe tone fit with the music at each point in time. The process was repeated for all probe tones of the chromatic scale. A self-organizing map (SOM) [Kohonen 1997 Self-organizing Maps (Berlin: Springer)] was used to represent the developing and changing sense of key reflected in these judgments. The SOM was trained on the probe-tone profiles for 24 major and minor keys (Krumhansl and Kessler 1982 Psychological Review89 334–368). Projecting the concurrent probe-tone data onto the map showed changes both in the perceived keys and in their strengths. Two dynamic models of tonality induction were tested. Model 1 is based on pitch class distributions. Model 2 is based on the tone-transition distributions; it tested the idea that the order of tones might provide additional information about tonality. Both models contained dynamic components for characterizing pitch strength and creating pitch memory representations. Both models produced results closely matching those of the concurrent probe-tone data. Finally realtime judgments of tension were measured. Tension correlated with distance away from the predominant key in the direction of keys built on the dominant and supertonic tones, and also correlated with dissonance.

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Graupe, D., and H. Kordylewski. "A Large Memory Storage and Retrieval Neural Network for Adaptive Retrieval and Diagnosis." International Journal of Software Engineering and Knowledge Engineering 08, no. 01 (March 1998): 115–38. http://dx.doi.org/10.1142/s0218194098000091.

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The neural network discussed in this paper is a self trained network for LArge Memory STorage And Retrieval (LAMSTAR) of information. It employs features such as forgetting, interpolation, extrapolation and filtering, to enhance processing and memory efficiency and to allow zooming in and out of memories. The network is based on modified SOM (Self-Organizing-Map) modules and on arrays of link-weight vectors to channel information vertically and horizontally throughout the network. Direct feedback and up/down counting serve to set these link weights as a higher-hierarchy performance evaluator element which also provides high level interrupts. Pseudo random modulation of the link weights prevents dogmatic network behavior. The input word is a coded vector of several sub-words (sub-vectors). These features facilitate very rapid intelligent retrieval and diagnosis of very large memories, that have properties of a self-adaptive expert system with continuously adjustable weights. The authors have applied the network to a simple medical diagnosis and fault detection problems.

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Licen, Sabina, Alessia Di Gilio, Jolanda Palmisani, Stefania Petraccone, Gianluigi de Gennaro, and Pierluigi Barbieri. "Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site." Sensors 20, no. 7 (March 29, 2020): 1887. http://dx.doi.org/10.3390/s20071887.

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Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.

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Doufesh, Hazem, Fatimah Ibrahim, Noor Azina Ismail, and Wan Azman Wan Ahmad. "APPLICATION OF SELF ORGANIZING MAP FOR CORRELATION HUNTING BETWEEN ALPHA BAND POWER OF EEG SIGNALS AND OTHER PHYSIOLOGICAL PARAMETERS DURING MUSLIM PRAYER (SALAT)." Biomedical Engineering: Applications, Basis and Communications 30, no. 04 (August 2018): 1850029. http://dx.doi.org/10.4015/s1016237218500291.

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This study investigates a new approach to determine the correlations between alpha ([Formula: see text]) electroencephalography (EEG) and other physiological parameters during Muslim prayer (Salat) utilizing the self organizing map (SOM). The powerfulness of SOM in visualizing, understanding, and exploring the complexity in multivariable data can be used to determine the relationships between the input variables. Thirty healthy Muslim male subjects were recruited in the study. Their electroencephalogram (EEG), electrocardiogram (ECG), respiration rate (RSP), and oxygen saturation (SPO2) were continuously recorded using computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Three maps were constructed to determine the correlations in pre-baseline, during Salat, and post-baseline conditions utilizing SOM. The visualized results during Salat indicated that, alpha power (P[Formula: see text]) showed significant positive correlation in the occipital and parietal electrodes with the normalized unit of high-frequency HF (n.u.) power of heart rate variability (HRV) components (as a parasympathetic index), heart rate (HR), and RSP. Significant negative correlation was also observed between P[Formula: see text] with the normalized unit of low-frequency LF (n.u.) power and LF/HF of HRV (as sympathetic indices). SPO2 showed no correlation with P[Formula: see text]. While the results in pre-baseline and post-baseline conditions also did not show any correlation between the variables. The SOM proves that it can be successfully employed as a powerful technique in correlation analysis. The results were presented and compared with a previous study. Thus, it can be applied successfully in various biomedical engineering applications.

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Zhang, Gaosheng, Linlin Chen, Yuedan Liu, TaeSoo Chon, Zongming Ren, Zijian Wang, Jianping Zhao, and Yangyong Zhao. "A new online monitoring and management system for accidental pollution events developed for the regional water basin in Ningbo, China." Water Science and Technology 64, no. 9 (November 1, 2011): 1828–34. http://dx.doi.org/10.2166/wst.2011.750.

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Due to urgency of the accidental pollution events (APE) on one side and the variability in water quality data on the other side, a new online monitoring and management system (OMMS) was developed for the purpose of sustainable water quality management and human health protection as well. The Biological Early Warning System (BEWS) based on the behavioral responses (behavior strength) of medaka (Oryzias latipes) were built in combination with the physico-chemical factor monitoring system (PFMS) in OMMS. OMMS included a monitoring center and six monitoring stations. Communication between the center and the peripheral stations was conducted by the General Packet Radio Service (GPRS) network transmission complemented by a dial-up connection for use when GPRS was unavailable. OMMS could monitor water quality continuously for at least 30 days. Once APEs occurred, OMMS would promptly notify the administrator to make some follow up decisions based on the Emergency Treatment of APE. Meanwhile, complex behavioral data were analyzed by Self-Organizing Map to properly classify behavior response data before and after contamination. By utilizing BEWS, PFMS and the modern data transmission in combination, OMMS was efficient in monitoring the water quality more realistically.

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Thiradathanapattaradecha, Thanapon, Roungsan Chaisricharoen, and Thongchai Yooyativong. "Competitiveness Evaluation Techniques for Cosmeceuticals E-Commerce Platform." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (March 5, 2019): 130–39. http://dx.doi.org/10.37936/ecti-cit.2018122.131873.

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Currently, the popularity of cosmeceuticals e-commerce is continuously increasing. A competition level among entrepreneurs is higher precisely, especially in the e-commerce platform. As a result, this situation causes to strengthen the competitiveness of several businesses. However, an evaluation of business operation planning which directly associates with the competitiveness of entrepreneurs is still the main issue. Therefore, this study proposes the methodology to define the cosmeceuticals e-commerce competitiveness through SWOT analysis, useful clustering methods including Self-Organizing Map (SOM) and K-means clustering, and Normalized Weight of Criteria for competitiveness criteria evaluation. The SWOT analysis is a reliable algorithm, which can adequately be used to evaluate the business performance with valid questionnaires. It provides the grouped attributes as 4 groups including Strengths, Weaknesses, Opportunities, Threats. Moreover, the appropriated factors result from the previous step will be clustered by SOM and K-means clustering for better data interpretation. SOM calculated 203 instances into 3 clusters; Low, High, No Class with 18 sec for execution time and 94.98% for accuracy. Meanwhile, K-means clustered previous dataset into 2 groups; high performance business and low performance business with 91.33% accuracy. In addition, the clustered data will be specified by Normalized Weight of Criteria for most influential criteria of Strengths, Weaknesses, Opportunities, and Threats factors to accomplish the high Competitiveness level of entrepreneurs. The results from this analysis can definitely help cosmeceuticals entrepreneurs to enhance the business strategy and advanced planning.

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Jangid, Hitesh, Subham Jain, Beteley Teka, Rekha Raja, and Ashish Dutta. "Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine." Robotica 38, no. 8 (November 22, 2019): 1415–33. http://dx.doi.org/10.1017/s0263574719001541.

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SUMMARYA mobile manipulator system (MMS) consists of a robotic arm mounted on a mobile platform that is used in rescue and relief, space exploration, warehouse automation, etc. As the total system has 14 Degrees of Freedom (DOF), it does not have a closed-form inverse kinematics (IK) solution. A learning-based method is proposed, which uses the forward kinematics data to learn the IK relation for motion of an MMS on a rough terrain, using a one-class support vector machine (SVM) framework. Once trained, the model estimates the joint probability distribution of the MMS configuration and end-effector position. This distribution is used to find the MMS configuration for a given desired end-effector path. Past research using a Kohonen Self organizing map (KSOM) neural network-based open-loop control method has shown that the MMS deviates from its desired path while moving on an uneven terrain due to unknown disturbances such as wheel slip, slide, and terrain deformation. Therefore, a new sequential two-stage SVM-based end-effector path-tracking control scheme is proposed to control the end-effector path. In this scheme, the error in the end-effector path is continuously tracked with the help of a Microsoft Kinect 2.0 (Microsoft Regional Sales, Singapore 119968) and is sent as a feedback to the controller. Once the error reaches a threshold value, the error correction step of the controller gets activated to correct the error until the desired accuracy is reached. The effectiveness of the proposed approach is proved through extensive simulations and experiments conducted on 3D terrain in which it is shown that the end effector can follow the desired path with an average experimental error of around 2 cm between the desired and final corrected path.

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Deng, Fei, and Shengliang Pu. "Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning." Applied Sciences 8, no. 9 (August 24, 2018): 1448. http://dx.doi.org/10.3390/app8091448.

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Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

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Abdelhedi, Fatma, and Nabil Derbel. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. 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J. 2(3), 492-497 (2017); View Description Design, Fabrication and Testing of a Dual-Range XY Micro-Motion Stage Driven by Voice Coil Actuators Xavier Herpe, Matthew Dunnigan, Xianwen Kong Adv. Sci. Technol. Eng. Syst. J. 2(3), 498-504 (2017); View Description Self-Organizing Map based Feature Learning in Bio-Signal Processing Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily Adv. Sci. Technol. Eng. Syst. J. 2(3), 505-512 (2017); View Description A delay-dependent distributed SMC for stabilization of a networked robotic system exposed to external disturbances." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (June 2016): 513–19. http://dx.doi.org/10.25046/aj020366.

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

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