• Korean Journal of Environmental Biology
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• v.41 no.2
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• pp.145-166
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• 2023

Parrots have been threatened by global trade to meet their high demand as pets. Controlling parrot trade is essential because parrots play a vital role in the ecosystem. Accurate species identification is crucial for controlling parrot trade. Parrots have been traded as eggs due to their advantages of lower mortality rates and more accessible transport than live parrots. A molecular method is required to identify parrot eggs because it is difficult to perform identification using morphological features. In this study, DNAs were obtained from 43 unidentified parrot eggs using a non-destructive sampling method. Partial cytochrome b (CYTB) gene was then successfully amplified using polymerase chain reaction (PCR) and sequenced. Sequences newly obtained in the present study were compared to those available in the GenBank by database searching. In addition, phylogenetic analysis was conducted to identify species using available sequences in GenBank along with sequences reported in previous studies. Finally, the 43 parrot eggs were successfully identified as seven species belonging to two families and seven genera. This non-destructive sampling method for obtaining DNA and molecular identification might help control the trade of parrot eggs and prevent their illegal trade.

• Smart Structures and Systems
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• v.10 no.1
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• pp.47-65
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• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• Smart Structures and Systems
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• v.26 no.6
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• pp.735-751
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• 2020

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.17-34
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• 2006

Inverse coefficient identification problems associated with the fourth-order Sturm-Liouville operator in the steady state Euler-Bernoulli beam equation are investigated. Unlike previous studies in which spectral data are used as additional information, in this paper only boundary information is used, hence non-destructive tests can be employed in practical applications.

• Structural Engineering and Mechanics
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• v.6 no.3
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• pp.259-274
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• 1998

The test results from non-destructive and destructive field testing of a three-span deteriorated reinforced concrete slab bridge are used as a vehicle to examine the reliability of available tools for finite-element analysis of in-situ structures. Issues related to geometric modeling of members and connections, material models, and failure criteria are discussed. The results indicate that current material models and failure criteria are adequate, although lack of inelastic out-of-plane shear response in most nonlinear shell elements is a major shortcoming that needs to be resolved. With proper geometric modeling, it is possible to adequately correlate the measured global, regional, and local responses at all limit states. However, modeling of less understood mechanisms, such as slab-abutment connections, may need to be finalized through a system identification technique. In absence of the experimental data necessary for this purpose, upper and lower bounds of only global responses can be computed reliably. The studies reaffirm that success of finite-element models has to be assessed collectively with reference to all responses and not just a few global measurements.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.216-228
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• 2023

The purpose of this study is to apply the handheld Raman spectrometer to identify the coloring materials used in a large Buddhist painting (of Maitreya Buddha) at Janggoksa Temple through cross-validation with HH-XRF. An in situ investigation was performed together with use of a digital microscope and HH-XRF analysis to verify the properties of pigments used in the gwaebul ("large Buddhist painting") via a non-destructive method. However, the identification of coloring materials composed of light elements and mixed or overlaid pigments is difficult using only non-destructive analysis data. Unlike in situ investigation, laboratory analysis often required samples yet the sampling is restricted to a small quantity due to the cultural heritage characteristic. Thus, it is necessary to develop a non-destructive in situ method to supplement the HH-XRF data. The large Buddhist painting at Janggoksa Temple was painted mainly using white, red, yellow, green, and blue colors. The Raman spectroscopy provides molecular information, while XRF spectroscopy provides information about elemental composition of the pigments. Analysis results identified various coloring materials: inorganic pigment, such as lead white, minium, cinnabar, and orpiment, as well as organic pigment such as gamboge and indigo. Therefore, it is possible to obtain more information for the identification of pigments; organic pigment and mixed or overlaid pigments, while at the same time minimizing the collection sample and simplifying the analysis procedure compared to previously used methods. The results of this study will be used as basic data for the analysis of painting cultural heritage through a non-destructive in situ method in the future.

• Structural Engineering and Mechanics
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• v.56 no.3
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• pp.409-422
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• 2015

In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

• Smart Structures and Systems
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• v.4 no.2
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• pp.195-208
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• 2008

This paper summarizes the experimental vibration-based structural health monitoring study on a historical monument in Jordan. In this work, and within the framework of the European Commission funded project "wide-Range Non-Intrusive Devices Toward Conservation of Historical Monuments in the Mediterranean Area", a seven and a half century old minaret located in Ajloun (73 km north of the capital Amman) is studied. Because of their cultural value, touristic importance and the desire to preserve them for the future, only non-destructive tests were allowed for the experimental investigation of such heritage structures. Therefore, after dimensional measurements and determination of the current state of damage in the selected monument, ambient vibration tests are conducted to measure the accelerations at strategic locations of the system. Output-only modal identification technique is applied to extract the modal parameters such as natural frequencies and mode shapes. A Non-linear version of SAP 2000 computer program is used to develop a three-dimensional finite element model of the minaret. The developed numerical model is then updated according to the modal parameters obtained experimentally by the ambient-vibration test-results and the measured characteristics of old stone and deteriorated mortar. Moreover, a parametric identification method using the N4Sid state space model is employed to model the dynamic behavior of the minaret and to build up a robust, immune and noise tolerant model.

• Advanced Composite Materials
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• v.16 no.2
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• pp.115-134
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• 2007

This study proposes two new approaches for identifying damage patterns in a holed CFRP cross-ply laminate using an embedded fiber Bragg grating (FBG) sensor. It was experimentally confirmed that the reflection spectrum from the embedded FBG sensor was significantly deformed as the damage near the hole (i.e. splits, transverse cracks and delamination) extended. The damage patterns were predicted using forward analysis (a damage analysis and an optical analysis) with strain estimation and the proposed damage-identification method as well as the forward analysis only. Forward analysis with strain estimation provided the most accurate damage-pattern estimation and the highest computational efficiency. Furthermore, the proposed damage identification significantly reduced computation time with the equivalent accuracy compared to the conventional identification procedure, by using damage analysis as the initial estimation.