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Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures
No full textCracks are one of the forms of damage to concrete structures that debase the strength and durability of the building material and may pose a danger to the living being associated with it. Proper and regular diagnosis of concrete cracks is therefore necessary. Nowadays, for the more accurate identification and classification of cracks, various automated crack detection techniques are employed over a manual human inspection. Convolution Neural
Network (CNN) has shown excellent performance in image processing. Thus, it is becoming the mainstream choice to replace the manual crack classification techniques,
but this technique requires huge labeled data for training. Transfer learning is a strategy that tackles this issue by using pre-trained models. This work first time strives to classify concrete surface cracks by re-training of six pre-trained deep CNN models such as VGG-16, DenseNet-121, Inception-v3, ResNet-50, Xception, and InceptionResNet-v2 using transfer learning and comparing them with different metrics, such as Accuracy, Precision, Recall, F1-Score, Cohen Kappa, ROC AUC, and Error Rate in order to find the model with the best suitability. A dataset from two separate sources is considered for the retraining of pre-trained models, for the classification of cracks on concrete surfaces. Initially, the selective crack and non-crack images of the Mendeley dataset are considered, and later, a new dataset is used. As a result, the re-trained classifier of CNN models provides a consistent performance with an accuracy range of 0.95 to 0.99 on the first dataset and 0.85 to 0.98 on the new dataset. The results show that these CNN variants can produce the best outcome when finding cracks in the real situation and have strong generalization capabilities
A novel methodology to update table in air data system of a high performance fighter aircraft
No full textAccurate air data parameters and measurements are critical for the successful flight missions of any modern fighters. Externally mounted vanes and probes measure the local flow angles, pressure, and temperature. These local measurements are to be corrected through a calibration process to obtain the free stream flow angles, pressure, and ambient temperature, etc. The preflight calibration of these sensors are carried out using wind tunnel tests/CFD computations. This paper presents a novel methodology to update air data table of these sensors, post-flight. The Air Data Computer (ADC) of the aircraft discussed in this paper hosts air data tables that are pre-calibrated using the Maximum Likelihood Estimation (MLE) method. However, challenges have been experienced in extending MLE methods for unsteady flights, when dynamic effects prevail. Unsteady conditions during wind up turns are inevitable to perform calibration at High Angles of Attack (AoA) regimes. Hence, an extended Kalman filter based methodology is proposed for calibration and table update. A complete process is tested for an entire flight envelope having 200 and odd flights. The results demonstrate the strength of the technique for air data calibration and table update in ADC
A Novel Design Approach for Low-Speed Recovery of High-Performance Fighter Aircrafts
No full textIn this paper, a novel design approach for low-speed recovery of a high-performance fighter aircraft is presented. It is shown that the phugoid mode has an important bearing on the problem of low-speed departure. Based on the analysis of the phugoid mode trajectories, a novel low-speed protection algorithm is presented in this paper. The proposed low speed recovery is achieved in three phases. The first phase consists of detecting the incipient departure followed in the second phase by the application of suitable recovery controls and finally the third phase ends with the transfer of controls to the pilot. The design of the first and the third phase consist of choosing the correct trigger conditions which ensures safe recovery of the aircraft in all conditions. The proposed automatic low speed recovery is triggered when the aircraft trajectory crosses a fixed boundary in the region spanned by the dynamic pressure and its rate of decrease. It is observed that this boundary is approximately a straight line, implying that it is equivalent to a forward prediction in time to indicate when the aircraft will reach the lowest controllable airspeed. This Automatic Low Speed Recovery with Forward Prediction (ALSR-FP) algorithm is found to be simpler than other existing design methods and effective in preventing low speed departure for a variety of pilot inputs that result in the aircraft losing airspeed leading to stall. In the second phase control inputs are chosen to align the velocity vector to the direction of local gravity. The recovery phase is considered complete after the aircraft reaches the dynamic pressure which is approximately 10 % higher than the minimum dynamic pressure for control. Performance of the ALSR-FP is demonstrated using the high-performance fighter aircraft Aero-Data Model In a Research Environment (ADMIRE) model which has a delta wing configuration, canards and multiple redundant controls. It is also shown that the proposed algorithm can be easily implemented on board for any other fighter and civil aircraft
Nonlinear damping model for supersonic air-intake buzz
No full textAir-intake buzz was initiated at Mach number of 3.0 on a two-dimensional isolated air-intake model in a wind tunnel by throttling the exit area. Schlieren images of shock oscillation around the intake entry during buzz were recorded using a high-speed camera. The recordings show a strong coupling between the bow-shock at the cowl lip and the oblique shock from the ramp. Image analyses were carried out, considering each image as a matrix of pixels and the change of intensity of light analyzed. A dominant frequency of 103.8 Hz (Strouhal no = 0.008 based on throat height) associated with the shock oscillations and harmonics of the shock oscillations are indicated. Phase-plots of the intensity and the rate of change of intensity show nearly perfect ellipse after filtering at the dominant frequency. Thus, the buzz phenomenon is associated with a limit-cycle oscillation as in a nonlinear Van-Der Pol oscillator. The computed damping factor synchronizes with the amplitude at all times, establishing that buzz is a stable and self-sustained oscillation with equilibrium between inertial and damping forces. It is proposed that a control scheme based on feed-forward control system using a suitable forcing function could be mathematically developed to suppress supersonic air-intake buzz
On the thermal stability and performance evaluation of Si doped transition metal nitride/oxide nanolayered multilayer-based spectrally selective absorber for high-temperature photothermal applications
No full textDeveloping an absorber coating with high absorptance (α) in the solar spectrum region and low thermal emittance (ε) in the infrared region for concentrated solar power (CSP) applications, operating at temperatures >400 °C, is still a great challenge. Herein, we describe a multilayer solar selective coating on stainless steel 304 (SS 304) substrates with α of 0.954 and ε of 0.07. The multilayer solar selective coating consists of: (1) tungsten (W) infrared reflector layer, (2) titanium aluminum nitride (TiAlN) absorber layer, (3) titanium aluminum silicon nitride (TiAlSiN) absorber layer, (4) titanium aluminum silicon oxy-nitride (TiAlSiON) semi-absorber layer and (5) titanium aluminum silicon oxide (TiAlSiO) anti-reflection layer. The compositions of the individual layers have been selected in such a way that they easily form protective layers of Al2O3, TiO2 and SiO2 on the coating surface when exposed to high temperature in air. Further, addition of Si in different layers not only improves the thermal stability but also helps in densifying the microstructure of the layers. Moreover, the presence of multilayer structure hinders the formation of pinholes and pores along with columnar microstructure, a typical characteristic of the sputter deposited transition metal nitrides and oxides. This unique coating design, thus, leads to high spectral selectivity (α/ε) of 13.6 on SS 304 substrate along with thermal stability up to 600 °C for 1000 h in vacuum under cyclic heating conditions. These properties of the developed solar absorber coating demonstrate its suitability for evacuated receiver tubes in CSP plants
Physiochemical characterization and thermal behaviour of transparent wood composite
No full textThis paper describes the fabrication of large size transparent wood composite (TWC) from longitudinal veneers of three hardwood wood species viz., Populus deltoides (Poplar), Grevillea robusta (Silver oak) and Melia dubia (Melia) based on two-step lignin modification bleaching process and polymer (epoxy resin) infiltration. High optical transmittance of ~83.5%, ~76.7% and ~72.8% was obtained at 550 nm wavelength for TWC prepared using 2 mm thick veneers of Poplar, Melia and Silver oak, respectively. Fourier transform infrared spectroscopy and scanning electron microscopy validate preservation of substantial lignin and successful inclusion of polymer in the wood micro-structure. X-Ray diffraction technique was used for analyzing crystallinity in wood and TWC material. Thermal behavior of wood and TWC was studied using thermogravimetric analysis and differential scanning calorimetry. TWC exhibited low density, good mechanical properties, low thermal conductivity (0.36 W/mK) and good thermal stability
Transition metal nitride/oxide-based multilayer PVD coating with sol–gel derived ormosil passivation layer as an efficient solar absorber: Studies on high temperature stability and performance evaluation
No full textWe describe a hybrid multilayer solar absorber coating consisting of physical vapor deposited (PVD) Ti interlayer, aluminum titanium nitride (AlTiN) main absorber layer, aluminum titanium oxynitride (AlTiON) semi-absorber layer, aluminum titanium oxide (AlTiO) anti-reflection layer and sol–gel deposited ormosil passivation layer. The ormosil layer, which is essentially organically modified silica, also acts like an anti-reflection coating, thus, increasing the absorptance (α) of the multilayer stack to ∼ 0.950 (c.f., α = 0.930 for the PVD coating on stainless steel substrate) along with an emittance (ε) of 0.17@82 °C. Generally, pure inorganic sol–gel coatings like silica contain defects, which allow gases and corrosive substances to penetrate the coating/substrate interface, thereby, deteriorating the performance of the coating under extreme environments. However, the presence of an organic moiety in the inorganic network results in a dense microstructure with improved mechanical flexibility and hardness. We discuss in detail the effect of ormosil layer on the performance of the PVD deposited solar absorber coating. We report that the unique and stable microstructure of the ormosil layer helps in increasing the thermal stability of the underneath PVD coated solar absorber coating, particularly suitable for applications in concentrating solar collectors useful for solar steam generation
Flutter Reliability Analysis of an Aircraft Wing: A Comparative Study
No full textIn this paper, a comparative flutter reliability study is presented for various types of limit state functions using first-order second moment (FOSM) method and the same is compared with Monte Carlo simulation (MCS). For the reliability study, a straight cantilever wing is considered in low subsonic flow, where aerodynamic modeling is based on Theodorsen’s aerodynamic-based strip theory, and for structure, finite element method (FEM) is used. Various parameters such as dimensionless static unbalance, mass moment of inertia, bending stiffness, and torsional stiffness are considered as independent Gaussian random variables. Results show that the probability density functions (PDFs) of various types of limit state function change with parameters, and also for some parameters, the distributions are not unique. The cumulative distribution function (CDF) of flutter velocity among different forms of limit state function obtained from FOSM method is best represented by flutter margin-based limit state function. Among various parameters considered, the most sensitive parameter is torsional stiffness and the least is bending stiffness
Energy Efficient Hardware Implementation of 2-D Convolution for Convolutional Neural Network
No full textOver the last year, Deep neural networks (DNN) have been significantly accepted for computer vision applications because of high classification accuracy and versatility. Convolutional Neural Network (CNN) is one of the most popular architectures of DNN which is widely adopted for image, speech and video recognition. Extensive computation and large memory requirement of CNN s poses the bottleneck on its application. Field Programmable Gate Arrays (FPGAs) are considered to be suitable hardware platforms for deployment of CNNs with low power requirements. This paper focus on the design and implementation of hardware accelerator to perform the convolution product (matrix-matrix multiplication. We have used two optimization techniques to achieve energy efficiency. First, dataflow of the convolution phase is rescheduled to reduce the undesired on-chip memory accesses. Further, efficiency is enhanced by reducing the internal parallelism of structure as much as possible. Our architecture is implemented on the Xilinx ZCU104 evaluation board. The implemented design attains 98.1 GOPS/Joule and 32.77 GOPS/Joule for 8-bit and 16-bit data width respectively
Multi-Class Classification of Lamb-Wave Based Sensor De-bond Identification Using Artificial Neural Network For Structural Health Monitoring
No full textThe ultrasonic Lamb wave-based Structural Health Monitoring is the current trend in diagnosing engineering structures. The dynamic response of sensors assesses the structural health; hence the interfacing of the sensor and structure should be integrated appropriately. Any fault in the sensor leads to misinterpretation of structural damages. Therefore, the present work aims to model sensor de-bond assessment by implementing the Artificial Neural Network (ANN) algorithm. The ANN model is trained by the statistical indicator extracted from raw dynamic ultrasonic Lamb waves to classify different types of sensor de-bond. The Lamb waves experiment was conducted on an isotropic planner plate structure, which represents the aircraft fuselage panel by surface bonding the piezoelectric emitter at the centre of the isotropic planner plate, and a circular PZT sensor network is mounted around the emitter. The experimental work was carried out using the National Instrument (NI) DAQ system, consisting of a NI signal generation card to generate Lamb waves and a NI digitizer card to capture the propagating Lamb wave signals. The captured raw signals were processed in the signal processing workstation, and statistical indicator was extracted. These statistical indicators serve as the features to the ANN model. The obtained results exhibited that, the proposed ANN architecture for sensor interface assessment has high classification accuracy in classifying different types of sensor de-bond