Active thermography : application of deep learning to defect detection and evaluation

La thermographie à phase pulsée (TPP) a été présentée comme une nouvelle technique robuste de thermographie infrarouge (TIR) pour les essais non destructifs (END). Elle utilise la transformée de Fourier discrète (TFD) sur les images thermiques obtenues après un chauffage flash de la surface avant d'un spécimen pour extraire les informations de délai de phase (ou phase). Les gammes de phase calcules (ou cartes de phase) sont utilises pour la visualisation des défauts dans de nombreux matériaux. Le contraste de température permet de détecter les défauts à partir des données thermographiques. Cependant, les images thermiques comportent généralement un niveau de bruit important et des arrière-plans non uniformes causés par un chauffage inégal et des réflexions environnementales. Par conséquent, il n'est pas facile de reconnaître efficacement les régions défectueuses. Dans ce travail, nous avons appliqué la technique LSTM (Long Short Term Memory) et des réseaux de neurones convolutifs (RNC) basés sur des modèles d'apprentissage profond (AP) à la détection des défauts et à la classification de la profondeur des défauts à partir de données d'images thermographiques. Nos résultats expérimentaux ont montré que l'architecture proposée basée sur l'AP a obtenu des scores de précision de 0.95 et 0.77 pour la classification des pixels sains et défectueux. En outre, les résultats expérimentaux ont montré que les techniques LSTM et RNC ont obtenu des précisions de 0.91 et 0.82 pour la classification de la profondeur des défauts, respectivement. Par conséquent, la technique LSTM a surpassé la technique RNC pour les cas de détection des défauts et de classification de la profondeur des défauts.Pulse Phase Thermography (PPT) has been introduced as a novel robust Non-Destructive Testing (NDT) Infrared Thermography (IRT) technique. It employs Discrete Fourier Transform (DFT) to thermal images obtained following flash heating of the front surface of a specimen to extract the phase delay (or phase) information. The computed phase grams (or phase maps) are used for defect visualization in many materials. The temperature contrast enables defect detection based on thermographic data. However, thermal images usually involve significant measurement noise and non-uniform backgrounds caused by uneven heating and environmental reflections. As a result, it is not easy to recognize the defective regions efficiently. In this work, we applied Long Short-Term Memory (LSTM) and Convolutions Neural Networks works (CNNs) based on deep learning (DL) models to defect detection and defect depth classification from thermographic image data. Our experimental results showed that the proposed DL-based architecture achieved 0.95 and 0.77 accuracy scores for sound and defected pixels classification. Furthermore, the experimental results illustrated that LSTM and CNN techniques achieved 0.91 and 0.82 accuracies for defect-depth classification, respectively. Consequently, the LSTM technique overcame the CNNs technique for defect detection and defect-depth classification cases

A micromechanical Sliding-Damage Model Under Dynamic Compressive Loading

For most rock materials, there exists a strong coupling between plastic flow caused by sliding along micro-crack faces and damage evolution due to nucleation and growth of wing-cracks. The aim of this article is to develop the self-consistent based micromechanical model by taking into account the coupling between frictional sliding and damage process under dynamic compressive loading. The developed model algorithm was programmed in the commercial finite difference software environment for numerical simulation of rock material to investigate the relationship between the mechanical behaviour and microstructure. Eventually while the stress intensity factor at flaw tips exceeds the material fracture toughness, the wing-cracks are sprouted and damage evolution occurs. For frictional closed cracks, an appropriate criterion for the onset of frictional sliding along micro-cracks was proposed in this paper. Also, plastic strain increments were determined by the flow rule, consistency condition and normality rule within the thermodynamic framework. The simulation results demonstrate that the developed micromechanical model can adequately reproduce many features of the rock behaviour such as hardening prior to the peak strength, softening in post-peak region, damage induced by wing-cracks and irreversible deformations caused by frictional sliding along micro-cracks. Furthermore, the softening behaviour of material in post-peak region is affected and the material undergoes higher values of strains and damage up to the residual strength. Therefore, the rock sample simulation with the coupled frictional sliding-damage model could increase plasticity and ductility of the rock in post-peak region because of regarding plastic strains caused by the frictional sliding along micro-cracks

Would the interference phenomenon be applied as an alternative option for prophylaxis against COVID-19?

The coronavirus disease 2019 (COVID-19) is an emerged infectious disease characterized by a severe pneumonia leading to death in some cases. Currently, no licensed vaccines, drugs, or biologics have been confirmed to be absolutely effective in prophylaxis or treatment of this novel infection. Therefore, the treatment of this highly contagious disease remains a global concern and emergency. The viral interference is a competition phenomenon by which a primary virus infecting a cell prohibits the infection of the same cell by another (secondary) virus. The phenomenon has recently been indicated to be exploited for antiviral strategies. This strategy, particularly when there is no efficient drug against a viral infection, is of high importance. Some researchers have studied the application of the phenomenon among different viruses. In this paper, I discussed the possibility of the application of interference phenomenon in prophylaxis of the disease

Utjecaj procesa proizvodnje pulpe i potrošnje energije na svojstva filmova na bazi nanofibrilirane lignoceluloze (NFLC) izolirane iz pšenične slame

The present research has primarily focused on the production of nanofibrillated lignocellulose (NFLC) instead of nanofibrillated cellulose (NFC), which could be produced with less energy and is expected to have similar uses as NFC, especially in the sectors where the transparency is not important. Furthermore, the effect of energy consumption needed for NFLC production and also the influence of pulping methods on the produced NFLC properties has been surveyed. Through mechanical refining and different passes in microfluidizer, the results showed the average diameter of NFLC declined from around 19000 nm to 36 nm. Soda-NFLC films had higher calliper and lower roughness, compared to those of MEA at given energy consumption in refiner and microfluidizer. For both kinds of pulps, the optimum level of energy consumption to reach the best tensile index of NFLC films was 258 kWh/t, with three passes through microfluidizer. More increase in the number of passes and pressure only resulted in increasing of energy consumption without any positive effect on improving the tensile index. The maximum tensile indices of NFLC films obtained from soda and MEA pulping processes were 113.5 and 119.86 N·m/g, respectively. The burst index of 8.5 kP·m2/g and the energy consumption of 458 kWh/t were obtained for five passes through microfluidizer. With the increase of the number of passes of soda and MEA samples through microfluidizer, the opacity decreased but transparency increased.Ovo je istraživanje usmjereno na proizvodnju nanofibrilirane lignoceluloze (NFLC) umjesto nanofibrilirane celuloze (NFC). Ta bi se celuloza (NFLC) mogla proizvesti s manje energije i moglo bi se očekivati da će imati sličnu uporabu kao NFC, osobito u područjima gdje transparentnost nije osobito važna. Ispitan je i učinak potrošnje energije potrebne za proizvodnju NFLC-a, kao i utjecaj metode proizvodnje pulpe na svojstva proizvedene lignoceluloze. Rezultati istraživanja pokazali su da je mehaničkim oplemenjivanjem i uz različit broj prolazaka kroz mikrofluidizator prosječni promjer NFLC-a pao s oko 19 000 nm na 36 nm. NFLC filmovi od natronske pulpe pri određenoj su potrošnji energije u rafinatoru i mikrofluidizatoru imali veću debljinu i manju hrapavost u usporedbi s onima od MEA pulpe. Optimalna razina potrošnje energije za postizanje najboljega vlačnog indeksa NFLC filmova za obje vrste pulpe bila je 258 kWh/t, uz tri prolaska kroz mikrofluidizator. Povećanje broja prolazaka i tlaka rezultiralo je samo povećanjem potrošnje energije bez ikakva pozitivnog učinka na poboljšanje indeksa kidanja. Maksimalni indeksi kidanja NFLC filmova od pulpe dobivene natronskim i MEA postupkom bili su 113,5 odnosno 119,86 N·m/g. Indeks prskanja od 8,5 kP·m2/g i potrošnja energije od 458 kWh/t dobiveni su prolaskom pulpe kroz mikrofluidizator pet puta. S porastom broja prolazaka uzoraka natronske i MEA pulpe kroz mikrofluidizator smanjila se neprozirnost, ali se povećala transparentnost uzoraka

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

3D Numerical Investigation of Ground Settlements Induced by Construction of Istanbul Twin Metro Tunnels with Special Focus on Tunnel Spacing

One of the most important considerations of tunneling in urban areas is controlling the amount of surface settlement that occurs during construction stages. The goal of this paper is to investigate the effect of spacing of Istanbul Twin Metro Tunnels on the surface settlement excavated by NATM method in YENIKAPI-UNKAPANI metro line. For this purpose, the focus has been placed on the effect of longitudinal and transversal spacing between tunnels supported by an umbrella arch protecting method. (FLAC3D) was implemented to simulate the excavation sequence. According to the analysis, the amount of settlement by numerical approach was about 23.5 mm which was in good agreement with the field monitoring results that was 26.5 mm. Moreover, the interaction between twin tunnels by the increase in spacing between twin tunnels in the direction perpendicular to tunnel axis decreases and becomes less effective at the location about 3 times of the tunnel diameter. Similarly, the interaction between twin tunnels in the direction parallel to tunnel axis decreases as the spacing increases. In other words, by increasing the distance between tunnel faces in longitudinal direction at a distance about 3 times of the tunnel diameter, there is still interaction between tunnels and it doesn’t disappear completely. Therefore, it is recommended to keep this distance at about more than 2.5 times of tunnel diameter so that settlement can stay within acceptable range

A new approach for harmonic detection based on eliminating oscillatory coupling effects in microgrids

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs LicenseThe primary goal of grid-connected microgrids is to control the active and reactive power, which is reachable by the inner current control loop in the control structure of power converters. However, when facing unbalanced conditions, the inner current control loop implemented in the dq frame does not function properly. In such conditions, the popular current control loop malfunctions since there is an oscillatory coupling between harmonic components. Therefore, in this study, a new harmonic detector based on decoupled double synchronous reference frame within the current control loop is proposed in which the oscillatory coupling between harmonic components is eliminated, and the overall performance of the power converter control system is significantly improved. The performance of the precisely developed mathematical models is verified by Matlab simulations, and the simulation results confirm the accuracy and proper operation of the proposed strategy

Dynamic Resource Allocation Model for Distribution Operations using SDN

In vehicular ad-hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, a big storage and high computation platform is needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with resource allocation problem assisted by cloud computation. By integration of EC and software defined networking (SDN), this study provides a new software defined networking edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multi-agent RL-based approach using experience reply. The proposed algorithm stores the users’ communication information and the network tracks’ state in real-time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study