Advanced Aspects of Engineering Research Vol. 11

By in situ polymerization of aniline in the presence of MWCNT and negatively charged ZnS nanoparticles, polyaniline (PANI)-MWCNT-Zinc sulphide (ZnS) nanocomposites have been developed. HRTEM, FTIR, UV-VIS absorption spectroscopy, and photoluminescence (PL) spectroscopy were all used to classify the nanocomposites. Different nanocomposites' particle diameters were determined to be around 3.21 nm using HRTEM. Absorbance measurements indicates that the incorporation of ZnS nanoparticles in PANI-MWCNT have occurred the red shift of band gap. Holstein model nonadiabatic small polaron model is applicable for these samples.

During working, metals are always in contact with its environment whether air, vapor, water, and other chemicals which emerges chemical interactions between metals and their respective environments that resulted in an insidious localized form of corrosion causing much devastating destruction to structural members such as stainless steel in H₂SO₄ environment. Weight loss experiments, were employed to study the corrosion activity. The present work discusses experimentally the performance of three types of oil-dispersed polymers; low-density polyethylene (LDPE), high-density polyethylene (HDPE) and Polysulphide rubber (PSR) on the abrasive sliding wear of stainless steel. The external voltage increases both friction coefficient and wear scar diameter on which PSR has a lower scar diameter followed by both LDPE and HDPE trends respectively. Negative applied voltage has lower scar diameter compared to the positive applied voltage for all applied polymers. The wear rate trends of HDPE has higher wear rates followed by LDPE and PSR respectively. The metallographic examination showed that the H₂SO₄ solution interacts with the specimen’s surface causes pitting corrosion and makes it weak, damaged and changes their roughness with immersion time.

Breast cancer is one of the most dangerous cancers in women, with developing breast tissue leading to death. Surgery, radiation, chemicals in conjunction with hormone therapy, and biological therapy are some of the current therapies for breast cancer that have made significant progress. The Deep Neural Network (DNN) model is implemented on the AWS machine learning framework in this work, as well as a comparison with other machine learning techniques such as XGBoost and Random Forest on a public dataset.  The plot of model accuracy for the training and validation sets, as well as performance assessment metrics to evaluate the model, show that breast cancer prediction based on DNN model with Hyperparameter tuning has the best results.

Clustering is a technique for grouping data objects based on similarity features that is widely used. Similarity metrics such as Euclidean, cosine, and others are used to generate similarity features. Traditional clustering approaches like k-means and other graph-based strategies are commonly used to find clusters. However, in order to determine the number of clusters, these approaches necessitate user intervention. Traditional clustering algorithms divide data without considering the number of clusters or cluster tendency beforehand. By using either k-means or graph-based clustering methods with an intractable ‘k' value set by the consumer, there is a risk of bad clustering performance. As a result, for prior knowledge of the number of clusters in clustering, it is essential to concentrate on cluster tendency methods. The various visual access tendency (VAT) methods for determining the number of clusters are presented in this paper.

The 8-bit RGB image of a cracked concrete surface, obtained with a high resolution camera based on a close distance photographing and using an optical microscope is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then. a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively for the crack samples used for the experiments. OriginLab tools were used for mathematical processing of image data and statistical calculations of geometric parameters of cracks in concrete.

It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

Some east countries have line voltage fluctuations and voltage drops which can cause cooling problems on refrigerators because compressors cannot start up at these low line voltages. That’s why not only COP[1], but also start up performance at low voltages is critical on single speed compressors. Torque-speed curves of single speed asynchronous motors are so critical for compressor start up performance which takes form by motor design. Depending on compressor mechanical efficiency and shaft power needed, the asynchronous motor winding design should be fine-tuned and torque speed curve should be adjusted for start up properly at low voltage values. Even when the start torque at 0 rpm and breakdown torque values are high enough to provide low voltage compressor starting, instant torque decreases along the curve negatively effects start up performance. Start winding distribution has a remarkable effect on compressor start up performance. This study analyses torque-speed curve characteristics of asynchronous motors and determines stator winding design methods to improve compressor start up performance. The findings are also validated with motor and compressor test results.

[1]COP: Acronym of Coefficient of Performance, which is ratio of compressor cooling capacity to input power at steady state working condition.

The article is focused on the accuracy of quantification for the tribological properties of a frictional carbon-carbon material during braking. Carbon-carbon materials are among the most promising materials for large use in friction units of braking devices for transport facilities. The experiments were performed with the IM-58 frictional testing machine when the rotating shaft with a previously established flyweight was in the braking mode. The tests for frictional heat resistance were performed with 2168 UMT. The research deals with the changes in friction and wear properties under the conditions of sample heating. The paper describes accuracy characteristics for the measured tribological properties. The maximum permissible temperature of frictional heating in terms of wear was established for reliable operation of the brake unit.

Brain-Computer-Interface (BCI) is used to acquire the brain signals, analyse and convert them into the commands which can help humans in controlling the outside environment. BCI is used applications namely security, medicine, games, self- regulation, research etc. BCI establishes a mutual connection with the brain and the external world using various Electroencephalography (EEG) devices. Today there are many people who are paralysed, these EEG devices can help them to communicate with the outside world. This chapter aims at exploring various EEG devices which vary in number of electrodes, their advantages and disadvantages and applications which are useful to the society in the long run.

Gauss-Jacobi combinatorial algorithm is an alternative approach to traditional iterative estimation methods. The combinatorial algorithm is often used for outlier diagnosis in nonlinear models, where the other parameter estimation methods lose their efficiency. The paper describes comparison of both of Gauss-Jacobi and Gauss-Markov models applied on parameter estimation process of levelling network for the reason to find the efficiency of combinatorial algorithm in simply linear model.

Man-Machine-Interface (MMI) is a communication system between the brain and the computer to obtain and investigates the brain signals. The EEG captures the electrical signals produced by the nerve cells. The purpose of this paper is to report the results related to classification of EEG signals and based on the emotion playing an appropriate music to indicate the emotion of different people. The testing has been done on the dataset subjected to 10 subjects associated with hearing music chosen from patriotic, happy, romantic and sad songs along with relaxation activity.

The purpose of this chapter is to develop a virtual simulation environment useful for electric vehicle performance analysis. For user convenience, it is possible to change the electric vehicle's key components or modify their parameters using MATLAB/Simulink. In addition, the developed simulation model can be simulated in real-time, and it is mounted on the driving simulator so that the designer can observe the change in performance due to modification in components or specifications. Using a driving simulator, the actual driver drives in different traffic conditions, resulting in various simulation results. Therefore, designers can change the specifications of the components used or the components themselves to find the impact on the electric vehicle's performance. This process will shorten the development time of electric vehicles by selecting components suitable for electric vehicles' required performance.

Deep-neural networks have made significant progress in speech recognition, computer vision and natural language processing. To address the major issue in synergetic or collaborative -filtering based on the concept of hidden feedback, we focused on neural network techniques in this mission. While deep learning has been used in a few recent studies, it was mainly used to sculpt auxiliary facts, such as textual metaphors of objects and music's acoustic capabilities. When it comes to the most important aspect of synergetic filtering, communication between customer and object capabilities, matrix factorization is still used, and a core product based on secret customer and object capabilities is introduced. We present Artificial Neural Synergetic Filtering (ANSF), a common framework for replacing the core makeup with a neural design that could be very efficient in analysing data with a random function. ANSF is a common and potentially unique framework that popularises matrix-factorization. We suggest using a multi-layer perceptron to investigate the customer–object contact mechanism to improve ANSF modelling with non-linearities. Experimental results on real global databases show that our proposed ANSF improves significantly over current techniques. The application of core layers of artificial neural networks improves overall efficiency, according to research findings. This work enhances the main stream shallow models for synergetic filtering, starting up a brand new road of study possibilities for recommendation based totally on deep learning.

Uninterrupted investigation of Power Quality (PQ) issues in the electrical power distribution network has become a major issue for customers at various levels. Assessment of PQ disturbances is required for maintaining accurate output of electrical and electronic equipment. This paper presents a new technique for detecting and classifying PQ disturbances with minimal computational complexity. The occurrence of a disturbance in a sinusoidal signal is detected with the voltage slope detection system, and the type of disturbance is defined with the Discrete Fourier Transform (DFT). Results proves that the proposed method is optimum and the overall accuracy is high compared to other methods. It is observed that this method correctly detects and classifies the eleven types of PQ disturbances with higher accuracy, in the presence of noise environment also.

To reduce power loss and improve the voltage profile of the power system, proper placement and sizing of distributed generation is needed. Solar photovoltaic (PV) and wind energy are two common distributed generation technologies. The authors present a novel multi-objective pareto-based approach for analysing the optimal positioning and sizing of solar PV and wind generation systems in a radial distribution system in this chapter. The built algorithm will be tested using a 33-bus distribution system. By placing a DG source at each bus and satisfying the objective function, the best location is detected. Here the maximum amount of Solar PV and Wind DG power injections for the distribution system considered have been identified considering the system constraints, beyond which it is technically as well as economically beneficial to pump the power to the grid. For optimum sizing of PV and wind systems, the Jaya algorithm is used. The system has also been analysed by installing the solar PV and wind generation systems separately and then together. The data is tabulated and visualised using three-dimensional plots.