SR Engineering College

Microprocessor technique empowers us to apply diverse control strategies in the present control systems, including control of electric drives. In the drive controller, the flag processors with inbuilt simple digital converters, clocks, beat width modulators and different gadgets, which streamline drive control, are regularly used.The different control methodologies are utilized to control the drives yet the improvement in semiconductor technology helped the utilization of power gadgets in the drives. The demand for reduced, solid and mechanized drives existed and the advancement of drive has occurred the correct way in assortment of utilizations like power supply, pulling forces, vehicles, and so forth which has no limits for the improvement. PC made the control techniques simple yet past that the microprocessor based single stage drives are demanded in the remote regions. Demand of control of power existed for a long time which drove early advancement of drives. Power handling capacities and exchanging speed of power drives has been expanded with improvement in semiconductor technology.The ongoing advancements in electrical drive technology are spurred by the expanding necessities of mechanical applications for higher execution, better unwavering quality, and lower cost. They are because of the advances in a few zones specifically power electronics, control theory, and microprocessor technology.

The present investigation was carried out to study a)the technology of bacterial and microbial filters through membrane and other means specifically used in the masks and b) to conduct research to find the problems on usage of masks and device mechanism to control humidity. It has been observed that several filtration devices are being used by humans for various applications like, protection from dust, allergens, bacteria, viruses, etc. Masks are made of materials like silk, plastic fibers, cotton and composite materials. N95 mask is found to be effective to control the entry of viruses into the human body through nasal canals. During the literature collection work for this study and structured survey conducted, it was found that no mechanism is available for controlling the moisture liberated from the lungs during exhalation. An innovative process is developed to use desiccant materials incorporated into the mask, which can absorb the moisture content within the mask. The mask is designed to handle 5 to 0.5 grams of moisture absorption within 6 minutes. This makes the need of having a standby mask. The mask can be re-charged with dryness within one hour at room temperature. However the mask can be dried for re-use by keeping the used mask in an oven at 65 °C for 5 minutes. Future work can be taken up to study in details about efficacy and human safety of humidity controlling mask.

Graphene oxide (GO) is a desirable nanoparticle for strengthening composite material owing to its outstanding mechanical characteristics. This research examines the influence of graphene oxide (GO) on the mechanical characteristics of Engineered Cementitious Composites (ECCs), including their mechanical performance under tension and compression conditions. In this study, GO was utilized with different percentages like 0 %. 0.02 %, 0.04 %, 0.05 %, 0.06 %, 0.08 % and 0.10 % by the weight of cement for determining the compressive strength, direct tensile strength and modulus of elasticity of ECC mixture. However, the dry density is improving as the content of GO increases while the water absorption is getting reduced blended with GO as nanomaterials rises in the ECC mixture after 28 days correspondingly. It has been observed that the use of GO as nanomaterial up to 0.08 % in ECC mixture is providing optimum compressive strength, tensile strength and modulus of elasticity after 28 days respectively. The increased composite strength as well as the chemical connection formed by the introduction of GO between the polyvinyl alcohol (PVA) fiber and the cement matrix are both responsible for the excellent mechanical characteristics of the GO-reinforced ECC mixture.

The aim of the effort is to estimate the effect of jute and bamboo fibers with silica fume (SF) of different proportions on mechanical properties of concrete. Cube, cylinder and prism specimens are tested to determine the compressive, split tensile and flexural strength at 14 and 28 days of curing. To verify the experimental findings, further Artificial Neural Network (ANN) analysis is conducted. The study employs neural network (NN), such as the Neural Network-Leven Berg-Marquardt and the Neural Network Gradient Descent. In this investigation, feed-ahead lower back promulgation neural networks were employed. The NN predicted values are validated with actual values and the variation is found to be within 10% only. The predicted ANN results are compared with existing Response Surface Methodology model. Under compressive, split tensile and flexural load, the broken surface is examined at a smaller-scale level with a scanning electron microscope (SEM). The experimental results show that concrete with 0.5% bamboo fibers and 0.5% jute fibers with 10% SF had higher influence on the mechanical properties of concrete. When comparing ANN results, the suggested ANN model showed high level of accuracy in estimating the mechanical properties of natural-fiber-reinforced concrete. SEM examination displayed the failure pattern of concrete and fibers.

Breast cancer is the deadliest and most common cancer in the world. Early treatment of this cancer can help to nip it in the bud. In present medical setting, this cancer is identified by manual clinical procedures, which can lead to human errors and further delay the treatment procedure. So, we propose a Convolutional Neural Network (CNN) model employed with transfer learning approach with RESNET50, VGG19 and InceptionV3 algorithms. The histopathological image dataset is used to detect cancer cells in the tissues of the breast. We examine the performance of different models based on their accuracy, by varying different optimizers (Adam, SGDM and RMSProp) for each transfer learning model. The results show that the Inception-V3 model with Adam optimizer outperforms VGG19 and RESNET-50 in terms of accuracy.