Fast self-organizing feature map algorithm

Su, Mu-Chun; Chang, Hsiao-Te

doi:10.1109/72.846743

Cited by 87 publications

(6 citation statements)

References 33 publications

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“…SOFM is a neural network based on unsupervised learning that can be utilized for the image cluster analysis [44]. As depicted in figure 6, the basic structure of SOFM network is divided into input layers and output layers.…”

Section: Image Post-processingmentioning

confidence: 99%

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Sun,

Hou,

Fan

et al. 2023

Smart Mater. Struct.

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Concrete piers are the key load-bearing components of wading bridges. Most detection equipment and methods focus on the surface defects of underwater piers, whereas the detection methods of internal defects are very deficient. This study proposes an ultrasonic computed tomography method combining Linear Travel time Interpolation and Simultaneous Iterative Reconstruction Techniques for detecting internal defects in underwater concrete piers. The two-dimensional Median Filtering and Self-Organizing Feature Map are utilized to post-process tomographic images and the confidence approach is employed to detect defects. The efficacy of the proposed method is validated through numerical simulation and underwater experiments, respectively. The COMSOL is utilized to simulate the two-dimensional random aggregate model of the concrete and internal defects are added to validate the method's effectiveness from a mesoscopic perspective. Then actual scale piers containing embedded defects are cast and detected. The results demonstrate that the method can identify and locate internal defects in numerical simulations and practical tests. The numerical simulation experiments results are superior to those of scale concrete specimens. In addition, underwater concrete piers with larger internal defects, square holes, and no steel reinforcements tend to have better detection results.

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Section: Image Post-processingmentioning

confidence: 99%

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Sun,

Hou,

Fan

et al. 2023

Smart Mater. Struct.

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“…,

and

), initial values of weight vectors, and the number of iterations are predetermined. In our experiments discussed in Section 4 , the initialization method proposed in [ 35 ] was utilized to initialize the weight vectors to quickly construct a good initial map. As for the values of the main parameters ( i.e.…”

Section: Brief Review Of the Som Algorithmmentioning

confidence: 99%

Depth-Sensor-Based Monitoring of Therapeutic Exercises

Jhang

Hsieh

et al. 2015

Sensors

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In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.

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“…SOMs are classified as machine learning algorithms. They were introduced by Kohonen et al [21] and since then they have been applied for chemical space navigation, discrimination of libraries for various targets based on a variety of structural elements or properties [27][28][29][30][31][32][33][34] It is a widely known method that is easily interpretable and visualizable. To the best of our knowledge, however, SOMs have not been applied for the analysis and comparison of various supplier catalogs and thereby for the exploration and exploitation of currently unpopulated parts of the purchasable and druglike chemical space using physicochemical properties as descriptors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the uncharted, druglike property space by self-organizing maps

Takács

Sándor²,

Szalai³

et al. 2021

Mol Divers

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Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets. Graphic abstract

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Fast self-organizing feature map algorithm

Cited by 87 publications

References 33 publications

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Depth-Sensor-Based Monitoring of Therapeutic Exercises

Analysis of the uncharted, druglike property space by self-organizing maps

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