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Analysis of the Brownian Motion Approach for Ballistic Resistance Evaluation Using the Maximum Likelihood Inference
Full text linkArmor technologists’ improvement of protection systems led to the design of complex systems. Given the risk factor on human life, increasing requirements on the ballistic resistance evaluation are imposed. Consequently, an increased effort is dedicated to estimating the perforation probability curve as a function of the bullet impact velocity. The main limitation of methods that fits a normal law to perforation velocities is their purely statistical character. A Brownian-based approach that couples the system response variability and physics was proposed using the Chi-square and Kolmogorov-Smirnov criterion function for model parameters estimation. One major limitation of this inference approach is the large experimental database required for its execution. The contribution of this paper is the introduction of the maximum likelihood inference for parameters estimation of the Brownian-based approach. The agreement between the obtained results and the experimental ones confirms the appropriateness of the likelihood inference to solve the studied problem. Moreover, the estimations uncertainty was analyzed and compared to the existing method ones. It was observed that the proposed model reduces the confidence intervals on key velocity estimations. Accordingly, the present work encourages the adoption of this proposed methodology in a laboratory context with a restrained sample size
Evaluation of Boron Combustion for Ducted Rocket Applications Using Condensed Product Analysis
Full text linkBoron, a metalloid, produces high energy upon combustion. It is recommended as an ingredient for fuel or propellant in rocket propulsion, despite the challenge of extracting its full thermal energy. So far no one has claimed the complete energy conversion of boron upon combustion. On the other hand, the current propulsion system of the Meteor missile uses boron-loaded propellant. The boron-loaded propellant provides an approximately three-fold increase in specific impulse compared to conventional propellants. The present study focuses on boron-HTPB-based solid fuels impregnated with early ignited particles as additives, aiming to assess the combustion performance of boron particles. These additives are magnesium (Mg), titanium (Ti), and activated charcoal (C), and their effects are evaluated based on the residual active boron content in the condensed combustion products (burned residues). An economical tool commonly called stagnation flow or opposed flow burner (OFB) is used to deflagrate the fuel sample by means of pressurized oxygen gas. The condensed combustion products are examined using a field emission scanning electron microscope (FESEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential thermal analysis (DTA). Among the fuel combinations investigated here, magnesium has been found to be a good burning enhancer of boron, leaving the lowest active boron content (30%) in the burned-out residue. The current research aims to develop an efficient boron-containing solid fuel for hybrid propellant ducted rocket engine applications
Machine learning Based Bearing Fault Classification Using Higher Order Spectral Analysis
Full text linkIn the defense sector, where mission success often hinges on the reliability of complex mechanical systems, the health of bearings within aircraft, naval vessels, ground vehicles, missile systems, drones, and robotic platforms is paramount. Different signal processing techniques along with Higher Order Spectral Analysis (HOSA) have been used in literature for the fault diagnosis of bearings. Bispectral analysis offers a valuable means of finding higher-order statistical associations within signals, thus proving to detect the nonlinearities among Gaussian and non-Gaussian data. Their resilience to noise and capacity to unveil concealed information render them advantageous across a range of applications. Therefore, this research proposesa novel approach of utilizing the features extracted directly from the Bispectrum for classifying the bearing faults, departing from the common practice in other literature where the Bispectrum is treated as an image for fault classification. In this work vibration signalsare used to detect the bearing faults. The features from the non-redundant region and diagonal slice of the Bispectrum are used to capture the statistical and higher-order spectral characteristics of the vibration signal. A set of sixteen machine learning models, viz., Decision Trees, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine, is employed to classify the bearing faults. The evaluation process involves a robust 10-fold cross-validation technique. The results reveal that the Decision Tree algorithm outperformed all others, achieving a remarkable accuracy rate of 100 %. The naive Bayes algorithm also demonstrated the least performance, with an accuracy score of 99.68 %. The results obtained from these algorithms have been compared with those achieved using Convolutional Neural Network (CNN), revealing that the training time of these algorithms is significantly shorter in comparison to CNN
Radiation Behavior of Synthesized LiTi Ferrite Based Microstrip Antenna in X Band
Full text linkThe paper comprehensively explores the development, characteristics, and antenna applications of lithium titanium (LiTi) ferrite. Utilising a solid-state reaction technique, the study examines the substrate’s electrical, magnetic, and structural properties in detail. Additionally, it assesses the far-field radiation patterns of a magnetically-biased LiTi ferrite-based antenna. Key findings point to a noticeable reduction in mutual coupling and radiated power, along with an isotropic redistribution of minor side lobes. Comparative analysis with RT-duroid substrates in X band frequency spectrum highlights the LiTi ferrite-based antenna’s remarkable 62.85 % miniaturization and consistent directivity, along with a superior quality factor. These findings emphasise LiTi ferrite’s potential for compact, high-performance applications in demanding environments. LiTi ferrite’s advantages in miniaturisation and stability position it for specific applications, despite trade-offs in bandwidth, gain, and impedance compared to RT-duroid substrates
An Efficient Visual Tracking System Based on Extreme Learning Machine in the Defence and Military Sector
Full text linkVisual tracking is the capacity to estimate or forecast a target object’s location in each frame of a video after specifying its starting position. Visual tracking is of essential relevance in defence and military operations. The military can use it to improve situational awareness, improve precise targeting, acquire intelligence in real-time, and efficiently respond to a variety of threats and circumstances. In the past, object tracking systems have relied mostly on algorithms based on deep learning techniques and these tracking algorithms are lacking in both accuracy and speed. In this research, an Extreme Learning Machine-based visual tracking system has been proposed that incorporates properties like high accuracy, low training time, and less network computing complexity as compared to existing deep learning-based tracking algorithms. The Haar wavelet transform is utilized in the recommended technique for feature learning, while the extreme learning machine is utilised for classification and recognition. A benchmark dataset object tracking benchmark-2013 has been used to carry out the experiments. The experiment values indicated that the proposed technique has accomplished enhanced performance over another tracking model. Additionally, we tested the proposed method’s accuracy and robustness regarding certain visual characteristics: Illumination variation, occlusion, deformation, out-of-plane rotation, background clutters, and in-plane rotation. The findings of the simulation revealed that the objects in videos have been 84% accurately tracked by the suggested method
Fractal Inspired Hybrid Microstrip Patch Antenna for Surveillance Drone Applications
Full text link Drone based surveillance and communication systems are playing a vital role in modern days for both military and civilian applications. Antennas with low profile, smaller size, and light weight are essential for the realization of these systems. A novel fractal-inspired hybrid microstrip patch antenna realized with Sierpinski gasket fractal slots cut in the radiating patch along with corresponding ground structure simultaneously defected with Sierpinski carpet fractal slots is reported for the first time. The antenna resonates at 2.4 GHz with a measured return loss of 17 dB, gain of 7 dB and measured 3 dB beamwidth is of the order of 70 deg. The current approach leads to the size reduction of the antenna to the extent of 55% in comparison to standard radiating patch antenna resonating at the same frequency. The fractal-inspired hybrid microstrip patch antenna is a promising candidate for drone-based surveillance and communication applications owing to its miniaturization and good directional radiation properties
Unscented Kalman Filters Integrated with Deep Learning Approaches for Active Sonar Based 2D Underwater Target Tracking
Full text linkThis manuscript proposes a new approach to track 2D targets using a combination of machine learning algorithms and the Unscented Kalman filter (UKF). The approach makes use of active sonar sensors to measure range and bearing, which are used to predict the target’s course and speed. So far in the literature of target tracking, researchers assumed covariance matrix of the noise in sonar measurements. In this manuscript, it is tried to estimate the same using deep learning algorithms. The Machine Learning algorithms, such as multilayer perceptron, convolutional neural network, long-short term memory, and gated recurrent unit, are employed to approximate the covariance of the noise in the input measurements. Simultaneously, the Unscented Kalman Filter (UKF) is utilised to mitigate the noise in the measurements and to estimate the position and speed of the target. The results are quantified through Monte Carlo simulations in a simulated underwater environment. The measurements are assumed to conform to a normal Gaussian distribution with a mean of zero. The findings indicate that LSTM has superior performance compared to the other models. Nevertheless, it is important to note that the results are constrained in their applicability due to the restricted set of variables employed for training the machine learning models
Understanding of MILD Combustion Characteristics of NH3 Air Flames in N2 And H2O Steam Diluted Environment at Atmospheric Pressure
Full text linkAmmonia is becoming increasingly popular as a carbon-neutral fuel with zero carbon dioxide emissions. However, a significant hurdle lies in its combustion, which leads to substantial emissions of NOx. The current research involves conducting a chemical kinetic investigation to examine the characteristics of Intense Low oxygen Dilution (MILD) or Moderate combustion in ammonia (NH3)/air flames. This study is carried out under specific conditions, such as oxygen concentrations ranging from 11to 23%, premixed reactant temperatures between 1300 and 1700 K, and a pressure of one atmosphere. The study focuses on investigating the combustion characteristics of MILD using dilution with H2O and N2.With the rise in the inlet temperature of the premixed reactant, the peak temperature of the flame also rises. Moreover, flames diluted with H2O exhibit lower peak temperatures compared to flames diluted with N2.Flames diluted with H2O result in lower NOx emissions compared to flames diluted with N2. Additionally, for N2diluted flames, the exit NOx emissions rise as the oxygen concentration increases.Despite this, NOx emissions from H2Odiluted flames demonstrate non-monotonic behaviour.This means that the exit NOx increases initially as the oxygen concentration reaches 21%, but then begins to decrease. In contrast to N2and H2Odiluted flames exhibits a wider regime of no-ignition.Moreover, the rise in peak temperature in H2Odiluted flames is less apparent than in N2diluted flames, corresponding to broader ranges MILD combustion ranges.Furthermore, to attain MILD combustion in H2O diluted flames at a specific O2 concentration, the temperature of reactant needs to be higher than that required for N2diluted flames.
 
RAZOR A Lightweight Block Cipher for Security in IoT
Full text linkRapid technological developments prompted a need to do everything from anywhere and that is growing due to modern lifestyle. The Internet of Things (IoT) technology is helping to provide the solutions by inter-connecting the smart devices. Lightweight block ciphers are deployed to enable the security in such devices. In this paper, a new lightweight block cipher RAZOR is proposed that is based on a hybrid design technique. The round function of RAZOR is designed by mixing the Feistel and substitution permutation network techniques. The rotation and XOR based diffusion function is applied on 32-bit input with 8 branches and branch number 7 to optimize the security. The strength of RAZOR is proved against differential, linear, and impossible differential attacks. The number of active S-boxes in any 5-round differential characteristic of RAZOR is 21 in comparison to the 10, 6, 4, 7, and 6 for PRESENT, Rectangle, LBlock, GIFT, and SCENERY respectively. RAZOR provides better security than the existing lightweight designs. The average throughput of 1.47 mega bytes per second to encrypt the large files makes it a better choice for software oriented IoT applications
Micro Tool Fabrications through Electrochemical Spark Machining Process
Full text link This article investigates the feasibility of producing an in-situ micro tool rod using micro-electrochemical spark machining (µ-ECSM) technology. The study included the examination of both electrical factors (such as voltage and duty factor) and non-electrical factors (such as electrolyte concentration and spindle speed) as the input parameters for the machining process. The responses measured in the study were the reduction in tool diameter and the surface roughness of the micro tool produced. The potassium hydroxide solution is used as a working fluid. The results indicate that voltage is the most crucial factor that influences micro tool fabrication. The utmost reduction in tool diameter, measuring 279.5 µm, occurred when utilizing machining parameters of 35V, 30%, 4 wt.%, and 600 rpm. Meanwhile, the lowest surface roughness for the micro tool was 3.42 µm, achieved with machining parameters of 35V, 10%, 4 wt.%, and 600 rpm. Additionally, the impact of machining settings on the micro tool electrode is covered