SPATIAL HABITUATING SELF ORGANIZING MAP

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Muhammad Fahmi Miskon ◽  
Nur Maisarah Mohd Sobran ◽  
Fariz Ali ◽  
Ahmad Zaki Hj Shukor
Keyword(s):  
Mobile Robot ◽  
Spatial Information ◽  
Inspection System ◽  
Self Organizing Map ◽  
Changing Environment ◽  
Inspection Robot ◽  
Space And Time ◽  
Environment Simulation ◽  
Simulation Results ◽  
Self Organizing

This paper presents the development of Spatial Habituating Self Organizing Map (SHSOM) network. This project is inspired by the challenges in underwater wall/pipe or cable inspection application using inspection robot. When exposed to the underwater natural elements, robot’s sensor readings are varied over space and time. Hence, the AUV need to be able to continuously adapt to its environment while performing inspection. For this reason, a new inspection system based on spatial Habituating Self Organizing Map (SHSOM) network is proposed. SHSOM allows the robot to continuously learn and adapt to new changes in its environment by using habituation principle which considers spatial information. WEBOT simulator is used to simulate an inspection scenario involving a mobile robot a changing environment. Simulation results show that the robot successfully learn and detect novel events during inspection.

Modeling and Production of Robot Trajectories Using the Temporal Parametrized Self Organizing Maps

2003 ◽  
Vol 13 (02) ◽  
pp. 119-127 ◽  
Author(s):  
Antonio Carlos Padoan ◽  
Guilherme de A. Barreto ◽  
Aluizio F. R. Araújo
Keyword(s):  
Inverse Kinematics ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Neural Architecture ◽  
New States ◽  
Simulation Results ◽  
Self Organizing

In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.

Memory Association Machine

2011 ◽  
pp. 213-232
Author(s):  
Benjamin David Robert Bogart
Keyword(s):  
Cognitive Processes ◽  
Free Association ◽  
Video Camera ◽  
Sensor Data ◽  
Self Organizing Map ◽  
Site Specific ◽  
Space And Time ◽  
Self Organizing

“Memory Association Machine” (also known as “Self-Other Organizing Structure #1”) is the first prototype in a series of site-specific responsive installations inspired by cognitive processes. The artist provides a mechanism that allows the structure of the artwork to change in response to continuous stimulus from its context. Context is defined as those parameters of the environment that are perceivable by the system and make its place in space and time unique. “Memory Association Machine” relates itself to its context using three primary processes: perception, the integration of sensor data into a field of experience, and the free-association through that field. “Memory Association Machine” perceives through a video camera, integrates using a Kohonen Self-Organizing Map, and free-associates through an implementation of Liane M. Gabora’s model of memory and creativity.

Hardware Feedback Self-Organizing Map and its Application to Mobile Robot Location Identification

2007 ◽  
Vol 11 (8) ◽  
pp. 937-945 ◽  
Author(s):  
Hiroomi Hikawa ◽  
◽  
Kazutoshi Harada ◽  
Takenori Hirabayashi ◽  
Keyword(s):  
Mobile Robot ◽  
Weight Vector ◽  
Hardware Architecture ◽  
The Self ◽  
Sensor Data ◽  
Parallel Structure ◽  
Self Organizing Map ◽  
Cluster Data ◽  
Self Learning ◽  
Self Organizing

We propose new hardware architecture for the self-organizing map (SOM) and feedback SOM (FSOM). Due to the parallel structure in the SOM and FSOM algorithm, customized hardware considerably speeds-up processing. Proposed hardware FSOM identifies the location of a mobile robot from a sequence of direction data. The FSOM is self-trained to cluster data to identify where the robot is. The proposed FSOM design is described in C and VHDL, and its performance is tested by simulation using actual sensor data from an experimental mobile robot. Results show that the hardware FSOM succeeds in self-learning to find the robot’s location. The hardware FSOM is estimated to process 6,992 million weight-vector elements per second.

A DEFECT DETECTION SCHEME FOR WEB SURFACE INSPECTION

2000 ◽  
Vol 14 (06) ◽  
pp. 735-755 ◽  
Author(s):  
JUKKA IIVARINEN ◽  
KATRIINA HEIKKINEN ◽  
JUHANI RAUHAMAA ◽  
PETRI VUORIMAA ◽  
ARI VISA
Keyword(s):  
Defect Detection ◽  
High Speed ◽  
Texture Features ◽  
Inspection System ◽  
Surface Inspection ◽  
Self Organizing Map ◽  
Detection Scheme ◽  
Web Inspection ◽  
Inspection Systems ◽  
Self Organizing

The goal of this work was to develop an improved defect detection scheme for high-speed real-time web surface inspection. This goal was realized by splitting the task into two independent parts: feature extraction and segmentation. Both parts were implemented using efficient algorithms which were implemented in hardware that is suitable and fast enough to be included in a working web inspection system. The proposed scheme is based on some derived texture features and a new self-organizing map variant, the statistical self-organizing map. These techniques offer several improvements over the gray-level thresholding techniques that have been traditionally used in commercial web inspection systems.

scholarly journals eSPRESSO: a spatial self-organizing-map clustering method for single-cell transcriptomes of various tissue structures using graph-based networks

2021 ◽  
Author(s):  
Tomoya Mori ◽  
Toshiro Takase ◽  
Kuan-Chun Lan ◽  
Junko Yamane ◽  
Cantas Alev ◽  
...  
Keyword(s):  
Spatial Information ◽  
Developmental Process ◽  
Heart Diseases ◽  
3D Structure ◽  
Reconstruction Method ◽  
Human Pancreas ◽  
Self Organizing Map ◽  
Reconstruction Methods ◽  
Tissue Reconstruction ◽  
Self Organizing

ABSTRACTAnimal cells are spatially organized as tissues and cellular gene expression data contain such information that governs body structure and morphogenesis during developmental process. Although several computational tissue reconstruction methods using transcriptomic data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we propose eSPRESSO, a powerful in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, together with optimization of gene set by Markov chain Monte Carlo (MCMC) framework, to estimate the spatial domain structure of cells in any topology of tissues or organs from only their transcriptome profiles. We confirmed the performance of eSPRESSO by mouse embryo, embryonic heart, adult cortical layers, and human pancreas organoid with high reproducibility (success rate = 72.5–100%), while discovering morphologically important spatial discriminator genes (SDGs). Furthermore, we applied eSPRESSO to analysis of human adult heart diseases by virtual gene knockouts, and revealed candidate mechanisms of deformation of heart structure. The eSPRESSO may provide novel methods to analyze the mechanisms of 3D structure formation and morphology-based disease mechanisms.

A visual-inspection system using a self-organizing map

2009 ◽  
Vol 14 (4) ◽  
pp. 506-510 ◽  
Author(s):  
Keiko Ikeda ◽  
Moritoshi Yasunaga ◽  
Yoshiki Yamaguchi ◽  
Yorihisa Yamamoto ◽  
Ikuo Yoshihara
Keyword(s):  
Visual Inspection ◽  
Inspection System ◽  
Self Organizing Map ◽  
Self Organizing

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing
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