Recycling of ethylene propylene diene monomer (EPDM) waste. III. Processability of EPDM rubber compound containing ground EPDM vulcanizates

The rheological behavior of ethylene propylene diene monomer (EPDM) compounds containing ground EPDM waste (W‐EPDM) of known composition was studied by using a Monsanto processability tester in a temperature range of 90–110°C and a shear rate range of 306.7–1533.24 s^?1. It is found that the shear viscosity decreases slightly with increasing W‐EPDM loading because of wall slip that results from the migration of lubricants from the W‐EPDM. The addition of W‐EPDM to raw EPDM results in a decreased die swell at all temperatures and shear rates. SEM photomicrographs of the EPDM extrudate surface show improved surface smoothness and reduced extrudate distortion when EPDM is blended with W‐EPDM. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 87: 2204–2215, 2003     

Recycling of silicone rubber waste: Effect of ground silicone rubber vulcanizate powder on the properties of silicone rubber

The silicone rubber vulcanizate powder (SVP) obtained from silicone rubber by mechanical grinding exists in a highly aggregated state. The particle size distribution of SVP is broad, ranging from 2 µm to 110 µm with an average particle size of 33 µm. X‐ray Photoelectron Spectroscopy (XPS) and Infrared (IR) Spectroscopy studies show that there is no chemical change on the rubber surface following mechanical grinding of the heat‐aged (200°C/10 days) silicone rubber vulcanizate. Addition of SVP in silicone rubber increases the Mooney viscosity, Mooney scorch time, shear viscosity and activation energy for viscous flow. Measurement of curing characteristics reveals that incorporation of SVP into the virgin silicone rubber causes an increase in minimum torque, but marginal decrease in maximum torque and rate constant of curing. However, the activation energy of curing shows an increasing trend with increasing loading of SVP. Expectedly, incorporation of SVP does not alter the glass‐rubber transition and cold crystallization temperatures of silicone rubber, as observed in the dynamic mechanical spectra. It is further observed that on incorporation of even a high loading of SVP (i.e., 60 phr), the tensile and tear strength of the silicone rubber are decreased by only about 20%, and modulus dropped by 15%, while the hardness, tension set and hysteresis loss undergo marginal changes and compression stress‐relaxation is not significantly changed. Atomic Force Microscopy studies reveal that incorporation of SVP into silicone rubber does not cause significant changes in the surface morphology.     

A smart application to detect pupil for small dataset with low illumination

Biometric applications are very sensitive to the process because of its complexity in presenting unstructured input to the processing. The existing applications of image processing are based on the implementation of different programing segments such as image acquisition, segmentation, extraction, and final output. The proposed model is designed with 2 convolution layers and 3 dense layers. We examined the module with 5 datasets including 3 benchmark datasets, namely CASIA, UBIRIS, MMU, random dataset, and the live video. We calculated the FPR, FNR, Precision, Recall, and accuracy of each dataset. The calculated accuracy of CASIA using the proposed system is 82.8%, for UBIRIS is 86%, MMU is 84%, and the random dataset is 84%. On live video with low resolution, calculated accuracy is 72.4%. The proposed system achieved better accuracy compared to existing state-of-the-art systems.

     

Effect of poling on piezocatalytic removal of muti-pollutants using BaTiO3

The BaTiO₃ powder was prepared via a solid-state reaction route. It was studied for the degradation of bacterial cells, dye, and pharmaceuticals waste using ultrasonically driven piezocatalytic effect. The bacterial catalytic behavior of poled BaTiO₃ was remarkably increased during ultrasonication (10% E coli survival in 60 minutes). The structural damages were illustrated using scanning electron micrographs of bacterial cells which demonstrated morphological manifestations under different conditions. Methylene blue (MB dye), ciprofloxacin and diclofenac were also cleaned using the piezocatalytic effect associated with the poled BaTiO₃ powder. Around 92, 85, and 78% of degradations were observed within 150 minutes duration for methylene blue, ciprofloxacin, and diclofenac, respectively.     

Microalloyed Steel Bars and Forgings

This paper examines microalloyed steels—steels which develop their properties in the as-received condition without requiring further heat treatment, such as quenching and tempering. Microalloyed steel bars and forgings offer a clear cut potential for cost reduction and energy savings. The metallurgy for producing high strength microalloyed bars and forgings is in place. However, significant improvement in the notch toughness of these materials is necessary, and the metallurgy required to achieve this toughness improvement exists. With application of the necessary metallurgical techniques in rolling mills and forge shops, the utilization of high strength microalloyed steel bars and forgings will increase greatly.     

A piezoelectric model based multi-objective optimization of robot gripper design

The field of robotics is evolving at a very high pace and with its increasing applicability in varied fields, the need to incorporate optimization analysis in robot system design is becoming more prominent. The present work deals with the optimization of the design of a 7-link gripper. As actuators play a crucial role in functioning of the gripper, the actuation system (piezoelectric (PZ), in this case) is also taken into consideration while performing the optimization study. A minimalistic model of PZ actuator, consisting different series and parallel assembly arrangements for both mechanical and electrical parts of the PZ actuators, is proposed. To include the effects of connector spring, the relationship of force with actuator displacement is replaced by the relation between force and the displacement of point of actuation at the physical system. The design optimization problem of the gripper is a non-linear, multi modal optimization problem, which was originally formulated by Osyczka (2002). In the original work, however, the actuator was a ‘constant output-force actuator model’ providing a constant output without describing the internal structure. Thus, the actuator model was not integrated in the optimization study. Four different cases of the PZ modelling have been solved using multi-objective evolutionary algorithm (MOEA). Relationship between force and actuator displacement is obtained using each set of non-dominated solutions. These relationships can provide a better insight to the end user to select the appropriate voltage and gripper design for specific application.     

Creep and shrinkage prediction model for analysis and design of concrete structures— model B3

RILEM Draft Recommendation107-GCS Guidelines for the Formulation of Creep and Shrinkage Prediction Models

Creep and shrinkage prediction model for analysis and design of concrete structures— model B₃     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.