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A product that might improve voltage sag and swell change and integration of energy storage is the dynamic voltage restorer (DVR). Ultra capacitors, or UCAPs, are conducive in relieve voltage swells and sags which compose a challenge to high power for concise periods of time. In the end, both high power density and low energy density are consummate. One vital outcome of the ongoing study is that the DVR form accommodate rechargeable UCAP-based energy storage. When this system is assimilate, it will banish commitment for the grid to monitor for foible on the grid as long as the UCAPDVR system wield as an active power source and self-adjust to indemnify for unforeseen voltage swells and sags. An engrossing way to lessen sag is to utilize a dual-way DC-DC converter connected to an ultra-capacitor or dynamic voltage restorer to provide an accurate dc-link output voltage for the DVR. As a result of the integrated UCAP- DVR's advancement, speedy harmonic distortion is mitigated by reducing transient voltage swells and sags simultaneously. The PI controller will be leaned over other controllers in this specimen. A suggested framework is confirmed by virtue of, simulations using MATLAB/Simulink software.

In this project, the creation and application of computer-based fault detection methods for transmission lines with multiple terminals are discussed.These methods are part of a fault-finding system that precisely locates the fault site by using voltage and current signal measurements from smart electronic devices installed on transmission-line terminals. The transformer loading, connection type, and electrical characteristics are all data that the algorithms have access to. The power system components used by the fault-location techniques are likewise presented in this project’s phase component models.

In recent years, Biometric system are the one, which is widely used method for the recognition and identification of an individual that are highly demanded approach for its absolute security and accuracy which plays a vital roles in banking, commercials, business and other fields. Moreover this research is based on the multimodal biometrics which is recommended for its high recognition performances and it overcome the demerits of unimodal biometric approach. This research concentrate two multi-modal biometric traits such as face and iris, and propose Gradient Neural Network (Gen-NN) method to improve the biometric authentication by using the VISA face and iris multi-modal biometric database also used ResNet-101 and WaveNet for the feature extraction where the input of face and iris can be extracted.

Stainless steel, an alloy comprising chromium, iron and occasionally nickel and other metals, demonstrates exceptional corrosion resistance. The transformation of metal into thin, flat components is achieved through the industrial process known as sheet metal fabrication. The utilization of metal sheets is widespread, contributing to the creation of numerous everyday items. This study aims to investigate the formability of 310 austenitic stainless steel under varying temperatures (623K, 723K, and 823K). The evaluation was executed utilizing the Nakazima test method within the context of the stretch forming procedure. Prior to delving into the assessment of formability, an exhaustive examination of the mechanical properties of the high-strength stainless steel AISI 310 was conducted. This involved subjecting the material to tensile tests at varying temperatures - specifically, 623K, 723K, and 823K - each performed at a consistent strain rate of 0.1/s. The resultant data encompassed an array of failure modes and stress-strain curves for the individual test specimens, all of which were meticulously obtained and subjected to thorough analysis.Forming limit diagrams were subsequently constructed based on the gleaned results, affording a visual representation of the material’s formability under the specific conditions studied. Moreover, these experiments were replicated through simulations employing the LS-DYNA software, with a subsequent comparative examination conducted against the tangible outcomes derived from practical experimentation.

Cloud computing is a cost-effective approach to provide on demand computing and data storage solutions. Data storage is one of the key services provided by the cloud. Cloud offers improved efficiency, flexibility, and scalability, but all these advantages can be overturned if security is not taken into consideration. It is the cloud vender’s responsibility to keep the data safe with highly secure cloud services, which helps them earn the trust of the customers. Today, cloud security is critical since most organizations are already using cloud computing in alternate forms. There is always a concern that the highly sensitive business information and intellectual property may be exposed or misused due to increasingly sophisticated cyber threats. This research paper provides a distributed architecture for cloud data security which is independent of the underlying platforms. A cloud security architecture provides written and visual model to define how to configure and secure operations within the cloud. This paper compares the performance of RC4 against AES-256. The performance of the proposed encryption algorithms is evaluated on a widely used database MySQL. This paper provides a better solution to ensure the security of cloud databases by using two encryption algorithms.

In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2.

Aerated Concrete (AC) is flexible light weight cement and they are commonly utilized as squares. The investigation of AAC supplanting regular sand by fly debris is examined. Structure AC blend having blend extent 1:3 with water concrete proportion of 0.6 in these examples utilizing with lime, the sand is continually utilized in the examples. The aluminum powder is utilized at the range of 0.25, 0.5, 0.75, and 1gms of absolute load of cement. The properties of the mortar, like thickness, water assimilation and pressure quality for 24 hours stream restoring were resolved. The development material AC contains 60% to 85% air by volume.

Aerated concrete blocks (AC) are widely used as masonry in building construction because of their light weight and low density. Try to determine the strength, bonding, cracking and thermal behavior of aerated concrete blocks at different temperatures, mortar ratios and thickness. The results show that at temperatures above 100°C, the strength will decrease and cracks will appear, and the bonding behavior will vary with the thickness and proportion of the mortar. Compared with the solid concrete block model, Thermal Comfort Research shows better thermal comfort.

The conventional optimization process in Injection Moulding includes actual shop floor trials in which melt temperature, mould temperature, injection time, injection pressure, pattern, feeder size, shape and location cores, mould layout, gating etc. are changed in each iteration which involves high machining cost, tooling cost, modification cost, melting cost, and transportation cost as well as, materials, energy, time are wasted in each trial until and unless the required results are obtained. Water meter component (DN20 Vent) is designed in CREO 5.0, and then components are 3D printed to cross check the dimensions and also to confirm whether all the other components can be accommodated or not. Then the mould flow analysis will be performed on a water meter components using different materials and changing the processing parameters. The input processing parameters considered are melt temperature, mould temperature and injection time, whereas the responses are warpage, volumetric shrinkage, cycle time and quality prediction. Grey relational analysis is carried out to determine the optimum injection moulding processing parameters.. The effort has been made to minimize the warpage, volumetric shrinkage, cycle time and maximize the quality prediction mould cavity and core for the components are designed in CREO 5.0 and manufactured using P20 tool steel. Then the water meter components are manufactured by inputting the optimal processing parameters in injection moulding machine to achieve high productivity and quality.