Differential cryptanalysis of substitution permutation networks and Rijndael-like ciphers

A block cipher, in general, consist of several repetitions of a round transformation. A round transformation is a weak block cipher which consists of a nonlinear substitution transformation, a linear diffusion transformation and a key mixing. Differential cryptanalysis is a well known chosen plaintext attack on block ciphers. In this project, differential cryptanalysis is performed on two kinds of block ciphers: Substitution Permutation Networks(SPN) and Rijndael-like Ciphers. In order to strengthen a block cipher against differential attack, care should be taken in the design of both substitution and diffusion components and in the choice of number of rounds. In this context, most of the researches has been focused on the design of substitution component. In this project, differential cryptanalysis is carried out on several SPNs to find the role of permutation. Differential analysis on Rijndael-like ciphers is done to find the strength of the cipher as a whole. Tools are developed to configure and to perform differential analysis on these ciphers. In the context of SPN, the importance of permutation, the effect of bad permutation, no permutation and sequentially chosen plaintext pairs are discussed. The diffusion strength of SPN and Rijndael-like ciphers are discussed and compared

A comprehensive approach for detecting brake pad defects using histogram and wavelet features with nested dichotomy family classifiers

The brake system requires careful attention for continuous monitoring as a vital module. This study specifically focuses on monitoring the hydraulic brake system using vibration signals through experimentation. Vibration signals from the brake pad assembly of commercial vehicles were captured under both good and defective conditions. Relevant histograms and wavelet features were extracted from these signals. The selected features were then categorized using Nested dichotomy family classifiers. The accuracy of all the algorithms during categorization was evaluated. Among the algorithms tested, the class-balanced nested dichotomy algorithm with a wavelet filter achieved a maximum accuracy of 99.45%. This indicates a highly effective method for accurately categorizing the brake system based on vibration signals. By implementing such a monitoring system, the reliability of the hydraulic brake system can be ensured, which is crucial for the safe and efficient operation of commercial vehicles in the market

Experimental and computational vibration analysis for diagnosing the defects in high performance composite structures using machine learning approach

Delamination in laminated structures is a concern in high-performance structural applications, which challenges the latest non-destructive testing techniques. This study assesses the delamination damage in the glass fiber-reinforced laminated composite structures using structural health monitoring techniques. Glass fiber-reinforced rectangular laminate composite plates with and without delamination were considered to obtain the forced vibration response using an in-house developed finite element model. The damage was diagnosed in the laminated composite using machine learning algorithms through statistical information extracted from the forced vibration response. Using an attribute evaluator, the features that made the greatest contribution were identified from the extracted features. The selected features were further classified using machine learning algorithms, such as decision tree, random forest, naive Bayes, and Bayes net algorithms, to diagnose the damage in the laminated structure. The decision tree method was found to be a computationally effective model in diagnosing the delamination of the composite structure. The effectiveness of the finite element model was further validated with the experimental results, obtained from modal analysis using fabricated laminated and delaminated composite plates. Our proposed model showed 98.5% accuracy in diagnosing the damage in the fabricated composite structure. Hence, this research work motivates the development of online prognostic and health monitoring modules for detecting early damage to prevent catastrophic failures of structures

Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning

A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data

Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations

This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability

Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape during Sustainable Turning of Bearing Steel

In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system

Brake fault diagnosis using histogram features and artificial immune recognition system (AIRS)

Brakes are one of the most important components in automobiles because they allow the vehicle to stop or slow down. It requires extra caution in terms of safety and dependability. As a result, it is critical to monitor the brake system’s condition in order to assure safety. Vibration signals play an important function in detecting brake system faults. A machine learning approach was employed in this work to identify brake defects under various scenarios. A piezoelectric type transducer and data collecting system were used to collect vibration signals. The vibration signals were used to obtain the relevant histogram features. The feature selection and feature classification were done using the vibration signals obtained from the transducer. An artificial immune recognition system was used to classify the extracted features (AIRS). The classification accuracy as well as the classifier’s performance level have been reported

Powering the Future: Progress and Hurdles in Developing Proton Exchange Membrane Fuel Cell Components to Achieve Department of Energy Goals—A Systematic Review

This comprehensive review explores recent developments in Proton Exchange Membrane Fuel Cells (PEMFCs) and evaluates their alignment with the ambitious targets established by the U.S. Department of Energy (DOE). Notable advancements have been made in developing catalysts, membrane technology advancements, gas diffusion layers (GDLs), and enhancements in bipolar plates. Notable findings include using carbon nanotubes and graphene oxide in membranes, leading to substantial performance enhancements. Innovative coatings and materials for bipolar plates have demonstrated improved corrosion resistance and reduced interfacial contact resistance, approaching DOE targets. Nevertheless, the persistent trade-off between durability and cost remains a formidable challenge. Extending fuel cell lifetimes to DOE standards often necessitates higher catalyst loadings, conflicting with cost reduction objectives. Despite substantial advancements, the ultimate DOE goals of USD 30/kW for fuel cell electric vehicles (FCEVs) and USD 600,000 for fuel cell electric buses (FCEBs) remain elusive. This review underscores the necessity for continuous research and innovation, emphasizing the importance of collaborative efforts among academia, industry, and government agencies to overcome the remaining technical barriers

Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals

Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers