scholarly journals THE SYSTEM RECOGNIZES SURFACE DEFECTS OF MARBLE SLABS BASED ON SEGMENTATION METHODS

2020 ◽  
Author(s):  
E. Sipko ◽  
O. Kravchenko ◽  
A. Karapetyan ◽  
Zh. Plakasova ◽  
M. Gladka
Keyword(s):  
Pattern Recognition ◽  
Building Materials ◽  
Surface Defects ◽  
Recognition System ◽  
Complex Method ◽  
Recognition Method ◽  
Industrial Enterprises ◽  
Segmentation Methods ◽  
Wide Range ◽  
Gaussian Blurring

A system for recognizing surface defects in marble slabs is proposed. The pattern recognition method based on segmentation methods was further developed. The algorithm of the recognition system. The article describes methods for determining damage from digital images on various hard surfaces. Research in this field is relevant for a wide range of industrial enterprises that specialize in the production of various kinds of materials: parts, marble slabs, building materials, etc. To solve this problem, it is proposed to use the k-means clustering method. It has been experimentally established that Gaussian blurring algorithms, the Hough transform, and the Kenny algorithm are best suited for recognizing defects on the surface of a marble slab. The developed complex method based on the theory of pattern recognition allows you to quickly identify defects and damage on the surfaces of marble slabs. On the basis of the method, a system for understanding defects is implemented in software. The main stages of the system are described in the article. The results of the analysis of the image of the surface of the marble slab on a specific example are presented. The developed complex method based on the theory of pattern recognition allows you to quickly identify defects and damage on the surfaces of marble slabs. On the basis of the method, a system for understanding defects is implemented in software. The main stages of the system are described in the article. The results of the analysis of the image of the surface of the marble slab on a specific example are presented.

Discrimination of structurally similar odorous molecules with various concentrations by using a nanomechanical sensor

2018 ◽  
Vol 10 (30) ◽  
pp. 3720-3726 ◽  
Author(s):  
Kosuke Minami ◽  
Kota Shiba ◽  
Genki Yoshikawa
Keyword(s):  
Pattern Recognition ◽  
Chemical Properties ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Wide Range

Structurally similar odorous molecules can be discriminated based on their chemical properties with reduced influence of their concentrations in a wide range from ppm to ppb levels by a pattern recognition method using a nanomechanical sensor.

Discrimination of Plant Pathogenic Bacteria Using an Electronic Nose

2004 ◽  
Vol 5 (1) ◽  
pp. 21 ◽  
Author(s):  
M. T. Momol ◽  
M. O. Balaban ◽  
F. Korel ◽  
A. Odabasi ◽  
E. A. Momol ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Electronic Nose ◽  
Chemical Sensors ◽  
Pathogenic Bacteria ◽  
Recognition System ◽  
Pattern Recognition System ◽  
Plant Pathogenic Bacteria ◽  
System A ◽  
Wide Range

An electronic nose (EN) is an array of chemical sensors, where each sensor has only fractional specificity to a wide range of odor molecules, connected with a suitable pattern recognition system. A rapid, sensitive, specific, nondestructive, and easy-to-use technique such as the EN could be used to detect and identify plant pathogenic bacteria in diagnostic clinics and quarantine labs. In this study, an EN discriminated between plant pathogenic bacteria belonging to up to seven species from seven different genera. Accepted for publication 31 March 2004. Published 5 April 2004.

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

Recycling and Reuse in the Roman Economy

2020 ◽  
Keyword(s):  
Building Materials ◽  
Computational Modelling ◽  
Late Antique ◽  
Roman Economy ◽  
Wide Range ◽  
The Status ◽  
Recent Developments ◽  
Methodological Approaches ◽  
First Time ◽  
Site Types

The recycling and reuse of materials and objects were extensive in the past, but have rarely been embedded into models of the economy; even more rarely has any attempt been made to assess the scale of these practices. Recent developments, including the use of large datasets, computational modelling, and high-resolution analytical chemistry, are increasingly offering the means to reconstruct recycling and reuse, and even to approach the thorny matter of quantification. Growing scholarly interest in the topic has also led to an increasing recognition of these practices from those employing more traditional methodological approaches, which are sometimes coupled with innovative archaeological theory. Thanks to these efforts, it has been possible for the first time in this volume to draw together archaeological case studies on the recycling and reuse of a wide range of materials, from papyri and textiles, to amphorae, metals and glass, building materials and statuary. Recycling and reuse occur at a range of site types, and often in contexts which cross-cut material categories, or move from one object category to another. The volume focuses principally on the Roman Imperial and late antique world, over a broad geographical span ranging from Britain to North Africa and the East Mediterranean. Last, but not least, the volume is unique in focusing upon these activities as a part of the status quo, and not just as a response to crisis.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

scholarly journals Characterization of a Novel CaCO3-Forming Alkali-Tolerant Rhodococcus erythreus S26 as a Filling Agent for Repairing Concrete Cracks

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2967
Author(s):  
Seunghoon Choi ◽  
Sungjin Park ◽  
Minjoo Park ◽  
Yerin Kim ◽  
Kwang Min Lee ◽  
...  
Keyword(s):  
Building Materials ◽  
Culture Fluid ◽  
Natural Phenomenon ◽  
Environment Friendly ◽  
Wide Range ◽  
Filling Agent ◽  
Calcium Source ◽  
Alkali Tolerance ◽  
High Alkali

Biomineralization, a well-known natural phenomenon associated with various microbial species, is being studied to protect and strengthen building materials such as concrete. We characterized Rhodococcus erythreus S26, a novel urease-producing bacterium exhibiting CaCO3-forming activity, and investigated its ability in repairing concrete cracks for the development of environment-friendly sealants. Strain S26 grown in solid medium formed spherical and polygonal CaCO3 crystals. The S26 cells grown in a urea-containing liquid medium caused culture fluid alkalinization and increased CaCO3 levels, indicating that ureolysis was responsible for CaCO3 formation. Urease activity and CaCO3 formation increased with incubation time, reaching a maximum of 2054 U/min/mL and 3.83 g/L, respectively, at day four. The maximum CaCO3 formation was achieved when calcium lactate was used as the calcium source, followed by calcium gluconate. Although cell growth was observed after the induction period at pH 10.5, strain S26 could grow at a wide range of pH 4–10.5, showing its high alkali tolerance. FESEM showed rhombohedral crystals of 20–60 µm in size. EDX analysis indicated the presence of calcium, carbon, and oxygen in the crystals. XRD confirmed these crystals as CaCO3 containing calcite and vaterite. Furthermore, R. erythreus S26 successfully repaired the artificially induced large cracks of 0.4–0.6 mm width.

scholarly journals 6-Axis Stress Tensor Sensor Using Multifaceted Silicon Piezoresistors

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 279
Author(s):  
Kentaro Noda ◽  
Jian Sun ◽  
Isao Shimoyama
Keyword(s):  
Stress Tensor ◽  
Elastic Body ◽  
Building Materials ◽  
Sensor Response ◽  
Stress Change ◽  
Sensor Chip ◽  
Naked Eye ◽  
Highly Sensitive ◽  
Wide Range

A tensor sensor can be used to measure deformations in an object that are not visible to the naked eye by detecting the stress change inside the object. Such sensors have a wide range of application. For example, a tensor sensor can be used to predict fatigue in building materials by detecting the stress change inside the materials, thereby preventing accidents. In this case, a sensor of small size that can measure all nine components of the tensor is required. In this study, a tensor sensor consisting of highly sensitive piezoresistive beams and a cantilever to measure all of the tensor components was developed using MEMS processes. The designed sensor had dimensions of 2.0 mm by 2.0 mm by 0.3 mm (length by width by thickness). The sensor chip was embedded in a 15 mm3 cubic polydimethylsiloxane (PDMS) (polydimethylsiloxane) elastic body and then calibrated to verify the sensor response to the stress tensor. We demonstrated that 6-axis normal and shear Cauchy stresses with 5 kPa in magnitudes can be measured by using the fabricated sensor.

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