Study of Material Parameters’ Effect on Polymer Scratch Using SOM Method

2012 ◽  
Vol 452-453 ◽  
pp. 1420-1423
Author(s):  
Jian Wei Zhang ◽  
Han Jiang
Keyword(s):  
Coupling Effect ◽  
Polymeric Materials ◽  
Scratch Resistance ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Material Parameters ◽  
Neural Network Algorithm ◽  
Statistical Relationships ◽  
Artificial Neural Network Algorithm

Due to its inherited complexity, the polymer material parameters’ effect on the scratch resistance is difficult to detect. Using the scratch experimental results of a set of polypropylene (PP), the Self-Organizing Map (SOM) method, an artificial neural network algorithm, was adopted to study the effect of various material parameters on polymer scratch. Especially suitable for the analysis of high-dimensional data with nonlinear statistical relationships, SOM method helps to find out the influence of different material parameters on scratch behavior. This information can be used to estimate the possible performance of polymeric materials to certain extent without extra scratch experimental work. It also helps researchers to decide which group of properties should be paid more attention when studying the coupling effect of material parameters on polymer scratch resistance.

P1DSOM — A FAST SEARCH ALGORITHM FOR HIGH-DIMENSIONAL FEATURE SPACE PROBLEMS

2014 ◽  
Vol 28 (02) ◽  
pp. 1459005 ◽  
Author(s):  
ALAA SAGHEER ◽  
NAYOUKI TSURUTA ◽  
RIN-ICHIRO TANIGUCHI
Keyword(s):  
Search Algorithm ◽  
Computational Cost ◽  
Feature Space ◽  
The Self ◽  
Data Exploration ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Fast Search Algorithm ◽  
Neural Network Algorithm ◽  
Hierarchical Som

The self-organizing map (SOM) is a traditional neural network algorithm used to achieve feature extraction, clustering, visualization and data exploration. However, it is known that the computational cost of the traditional SOM, used to search for the winner neuron, is expensive especially in case of treating high-dimensional data. In this paper, we propose a novel hierarchical SOM search algorithm which significantly reduces the expensive computational cost associated with traditional SOM. It is shown here that the computational cost of the proposed approach, compared to traditional SOM, to search for the winner neuron is reduced into O(D1 + D2 + ⋯ + DN) instead of O(D1 × D2 × ⋯ × DN), where Dj is the number of neurons through a dimension dj of the feature map. At the same time, the new algorithm maintains all merits and qualities of the traditional SOM. Experimental results show that the proposed algorithm is a good alternate to traditional SOM, especially, in high-dimensional feature space problems.

PSS Business Case Map: Supporting Idea Generation in PSS Design

2012 ◽  
Author(s):  
Fumiya Akasaka ◽  
Kazuki Fujita ◽  
Yoshiki Shimomura
Keyword(s):  
Idea Generation ◽  
Business Case ◽  
Literature Survey ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Service Type ◽  
Business Cases ◽  
Low Dimensional

This paper proposes the PSS Business Case Map as a tool to support designers’ idea generation in PSS design. The map visualizes the similarities among PSS business cases in a two-dimensional diagram. To make the map, PSS business cases are first collected by conducting, for example, a literature survey. The collected business cases are then classified from multiple aspects that characterize each case such as its product type, service type, target customer, and so on. Based on the results of this classification, the similarities among the cases are calculated and visualized by using the Self-Organizing Map (SOM) technique. A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) view from high-dimensional data. The visualization result is offered to designers in a form of a two-dimensional map, which is called the PSS Business Case Map. By using the map, designers can figure out the position of their current business and can acquire ideas for the servitization of their business.

scholarly journals Does the use of various population parameters affect fish assemblage structures?

2018 ◽  
Vol 54 ◽  
pp. 10
Author(s):  
Tadeusz Penczak
Keyword(s):  
Fish Assemblages ◽  
Relative Number ◽  
Calorific Value ◽  
Self Organizing Map ◽  
Population Parameters ◽  
Reliable Parameter ◽  
Neural Network Algorithm ◽  
Wet Weight ◽  
Future Work ◽  
Artificial Neural Network Algorithm

While studying the fish populations in small streamlets and their responses to climate change and anthropogenic stress, the following parameters are used: present/absent species, relative number, and relative biomass recently. Although the image/structure of the population differ from these parameters, this problem has not been investigated by researchers in this topic. It is now known that the potential energy accumulated in animal tissues is the best indicator of his strength and importance in nature, but I have not encountered work assessing the image of population structure according to these population parameters. Consequently, most reliable parameter − the relative calorific value of biomass (in the wet weight), was used. Relative biomass is the parameter of the population, which was found to be the closest to the calorific value of the biomass, as demonstrated by the SOM (self-organizing map) artificial neural network algorithm used in this study. For this reason, attempts have been made to convince authors of future work that relative biomass studies are used in the studies of fish assemblages, as research has already been undertaken, and this paper provides evidence that this choice is justified.

scholarly journals Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map

2020 ◽  
Author(s):  
Timothy Kunz ◽  
Lila Rieber ◽  
Shaun Mahony
Keyword(s):  
Genome Organization ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Interaction Space ◽  
The Self ◽  
Chromatin Interaction ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Chromatin Interactions

ABSTRACTFew existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using “heatmap” matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space.We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess the relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.

Training Algorithms

2014 ◽  
pp. 68-84
Keyword(s):  
Back Propagation ◽  
Network Models ◽  
High Dimensional ◽  
Training Algorithms ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
Supervised Training ◽  
Supervised And Unsupervised Learning ◽  
Unsupervised Training ◽  
Statistical Relationships

The process of assigning the weight to each connection is called training. A network can be subject to supervised or unsupervised training. In this chapter, supervised and unsupervised learning are explained and then various training algorithms such as multilayer perceptron (MLP) and Back Propagation (BP) as supervised training algorithms are introduced. The unsupervised training algorithm, namely Kohonen's self-organizing map (SOM), is introduced as one of most popular neural network models. SOMs convert high-dimensional, non-linear statistical relationships into simple geometric relationships in an n-dimensional array.

Dysphonia Detected by Pattern Recognition of Spectral Composition

1992 ◽  
Vol 35 (2) ◽  
pp. 287-295 ◽  
Author(s):  
Lea Leinonen ◽  
Jari Kangas ◽  
Kari Torkkola ◽  
Anja Juvas
Keyword(s):  
Test Word ◽  
Spectral Composition ◽  
Power Spectra ◽  
Self Organizing Map ◽  
Self Organized ◽  
On Line ◽  
Neural Network Algorithm ◽  
The Stability ◽  
Short Time ◽  
Artificial Neural Network Algorithm

The vowel [a:] in a test word, judged normal or dysphonic, was examined with the Self-Organizing Map, the artificial neural network algorithm of Kohonen. The algorithm produces two-dimensional representations (maps) of speech. Input to the acoustic maps consisted of 15-component spectral vectors calculated at 9.83-msec intervals from short-time power spectra. The male and female maps were first calculated from the speech of healthy subjects and then the [a:] samples (15 successive spectral vectors) were examined on the maps. The dysphonic voices deviated from the norm both in the composition of the short-time power spectra (characterized by the dislocation of the trajectory pattern on the map) and in the stability of the spectrum during the performance (characterized by the pattern of the trajectory on the map). Rough voices were distinguished from breathy ones by their patterns on the map. With the limited speech material, an index for the degree of pathology could not be determined. A self-organized acoustic map provides an on-line visual representation of voice and speech in an easily understandable form. The method is thus suitable not only for diagnostic but also for educational and therapeutic purposes.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

Evaluation of Abrasive Wear of Polymeric Materials Using Single-Pass Pendulum Scratching

2002 ◽  
Author(s):  
Marcelo Torres Piza Paes ◽  
Antonio Marcos Rego Motta ◽  
Lauro Lemos Lontra Filho ◽  
Juliano Ose´ias de Morais ◽  
Sine´sio Domingues Franco
Keyword(s):  
Deep Water ◽  
Wear Mechanisms ◽  
Degradation Mechanism ◽  
Single Point ◽  
Polymeric Materials ◽  
Scratch Resistance ◽  
Wear Behaviour ◽  
Sediment Layer ◽  
Materials Used ◽  
Energy Dissipated

Scratching abrasion due to rubbing against the sediment layer is an important degradation mechanism of flexible cable in deep water oil and natural gas exploitation. The present study was initiated to gain relevant data on the wear behaviour of some commercial materials used to externally protect these cables. So, Comparison tests were carried out using the single-point scratching technique, which consists of a sharp point mounted at the extremity of a pendulum. The energy dissipated during the scratching is used to evaluate the relative scratch resistance. The results showed, that the contact geometry strongly affects the specific scratching energy. Using SEM imaging, it was found, that these changes were related to the operating wear mechanisms. The observed wear mechanisms are also compared with those observed on some cables in deep water operations.

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