Volume-12 Issue-9, August 2023 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

I97120812923

IJITEE Journal — Thu, 31 Aug 2023 08:58:18 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for 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Prediction of Strap Joint Design Margin in After Treatment System
Aniket M Pawar¹, Kishor P Deshmukh²
¹Aniket Pawar, Department of Design Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.

²Kishor Deshmukh, Applied Mechanics Engineer-Technical Specialist, Cummins Technical Center India, Pune (Maharashtra), India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The automotive vehicle has an exhaust system, also known as the after-treatment System. It consists of various components, including Diesel Oxidation Catalysts (DOC), Diesel Particulate Filters (DPF), and Selective Catalytic Reduction Systems (SCR), among others. These components are mounted in the chassis with the help of strap joints. The strap joints provide flexible and serviceable connections between ATS bodies and chassis. The strap joint assembly consists of a T-bolt, trunnion, and nut, all of which are secured by a strap. As the nut tightened, the tension induced in the strap clamped the body. If the tension in the strap is insufficient, it may cause the joint to fail. The failure can be characterised by the yielding of material, slipping, and separation of the body. The strap joint should be designed to avoid these kinds of failures. This paper presents work on calculating the design margin using both analytical and FEA methods. The failure modes addressed in this paper are the yielding of the strap joint due to the applied preload on the T-bolt, slipping, and separation of the ATS body from the strap due to the application of dynamic loads, such as acceleration G loads in multiple directions. The acceleration load is calculated from the PSD profile. For calculating design margins using the FEA method, a nonlinear analysis is performed, followed by a PSD analysis to estimate the reaction forces on the bracket. These reaction forces are used to calculate slip and separation margins. The tests were conducted under similar conditions on a shaker table, and the results from both analytical and FEA methods were correlated with test observations to validate the proposed method for calculating the strap join design margin.

Keywords: After Treatment System, Design Margin, Hoop Stress, Strap Joint
Scope of the Article: Design and Diagnosis

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I97130812923

IJITEE Journal — Sat, 26 Aug 2023 09:51:42 +0000

Revolutionizing Solar Energy Conversion: A Neural MPPT-Controlled Photovoltaic Regulator
Ibrahima Gueye¹, Abdoulaye Kebe², Oumar Dia³, Moustapha Diop⁴
¹Ibrahima Gueye, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.

²Abdoulaye Kebe, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.

³Oumar Dia, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.

⁴Mosstapha Diop, ENSETP, Université Cheikh Anta DIOP, Dakar, Sénégal.

Abstract: This article presents the design of an innovative photovoltaic solar regulator equipped with a neural MPPT (Maximum Power Point Tracking) control and an advanced battery charge and discharge management algorithm. The primary objective of this research is to significantly enhance the efficiency of solar energy conversion into electrical energy by optimising the maximum power point and effectively regulating battery charging and discharging. The neural MPPT control represents a significant advancement in the field of solar energy. Unlike conventional algorithms, this approach enables the regulator to adapt to environmental variations, such as fluctuations in sunlight. As a result, the regulator can constantly adjust the maximum power point, ensuring a high efficiency of the solar system. The battery charge and discharge management algorithm is a crucial element in the regulator’s design. Effective battery management is essential for maintaining a balance between the solar energy supply and the electrical equipment’s consumption. Through this algorithm, the battery is kept within optimal charge ranges, thereby avoiding overcharging or excessive discharging, which contributes to prolonging its lifespan. To evaluate the performance of the proposed photovoltaic solar regulator, detailed simulations were conducted using the MATLAB/Simulink software. The obtained results confirmed a significant improvement in solar energy conversion efficiency. The combination of the neural MPPT control and the battery management algorithm allows the system to operate optimally, even under changing environmental conditions. The practical applications of this research are diverse. This enhanced solar regulator could be deployed in remote regions without access to the traditional power grid. It also provides an effective solution for rural or isolated areas where solar energy can be a viable energy source, but intelligent management is required to ensure a stable electrical supply. In conclusion, this study presents a significant advancement in the field of photovoltaic solar energy, combining a novel neural MPPT control with an advanced battery management algorithm. The simulation results demonstrate a substantial improvement in solar energy conversion efficiency and more efficient battery management. This regulator opens up new possibilities for utilising solar energy in various demanding environments, offering a promising solution for powering remote or off-grid areas.

Keywords: Photovoltaic Solar Regulator, MPPT, Neural Control, Battery Charging, Energy Efficiency, Energy Optimization.
Scope of the Article: Renewable Energy Technology

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I97020812923

IJITEE Journal — Sat, 26 Aug 2023 09:45:06 +0000

Assessing the Life Cycle Performance in the Metallurgical Sector: A Case Study in Italy
Vinci Giuliana¹, Barricella Carlo², Ruggeri Marco³
¹Giuliana Vinci, Department of Management, Sapienza University of (Rome), Italy.

²Marco Ruggeri, Department of Management, Sapienza University of (Rome), Italy.

³Carlo Barricella, Department of Management, Sapienza University of (Rome), Italy.

Abstract: This study compared two types of sheets: one made of steel and one made of aluminum, using Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA). The LCA results show that the aluminium sheet had a greater environmental impact than the steel one in 11 out of 18 impact categories, including a 5% higher Global Warming Potential, a 3% higher Stratospheric Ozone Depletion, and a 72% higher Ionising Radiation. The main contributing factors were energy-intensive processes and bauxite processing associated with aluminium production. The LCC results also supported the findings of the LCA, demonstrating that the raw material, electricity, and labour costs for aluminium sheets were higher compared to those for steel, with the aluminium sheets costing €163 and the steel ones costing €19. However, the S-LCA results presented a contrasting perspective. They indicated that steel sheets exhibited poorer social performance compared to aluminium ones, particularly regarding child labour and discrimination, primarily due to the harsh working conditions in Ghana, the primary source of iron used in steel production. Overall, this study highlights the environmental superiority of steel screens over aluminium ones, while also underscoring the social challenges associated with steel production.

Keywords: Aluminum, Life Cycle Thinking, Steel, Sustainability
Scope of the Article: Environmental Engineering

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I97030812923

IJITEE Journal — Sat, 26 Aug 2023 09:39:51 +0000

A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)
Mahesh Enumula¹, M.Giri², V.K. Sharma³
¹Mahesh Enumula, Research Scholar, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India.

²Dr. M. Giri, Professor, Department of Computer Science and Engineering, Siddharth Institute of Engineering and Technology, Puttur (Karnataka), India.

³Dr. V. K. Sharma, Professor, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India.

Abstract: In the market, new, updated editing tools and technologies are available to edit images, and with the help of these tools, images can be easily forged. In this research paper, we propose a new forgery detection technique that estimates the noise on various scales of input images. The effect of noise on input images is also taken into account. Additionally, the frequency of images is altered due to noise. Furthermore, the noise signal is correlated with the original input images, and in compressed images, the quantisation level frequency is also changed due to noise. We partition the input image into M × N blocks. The resized blocks are then processed further, taking into consideration the image colours. The noise value of each block is evaluated at both local and global levels. For each colour channel of the input image, regional and global noise levels are estimated and compared using a binning method. Also, a heat map was measured for each block and each colour channel of the input image, and all these values were stored in bins. Finally, calculate the average mean value of noise from all noise values. With these values, decide whether the input image is a forgery or not. The performance of the proposed method is then compared with that of existing processes.

Keywords: Image Processing, Machine Learning, Retouching, Binning, Forgery, Water Marking.
Scope of the Article: Machine Learning

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I96960812923

IJITEE Journal — Sat, 26 Aug 2023 09:32:16 +0000

A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction
Bhautik Daxini¹, M.K. Shah², Rutvik K. Shukla³, Rohit Thanki⁴, Viral Thakar⁵
¹Bhautik Daxini, Research Scholar, Department of Instrumentation and Control, Gujarat Technological University, Ahmedabad (Gujarat), India.

²Dr. M.K. Shah, Associate Prof. & Head, Department of Instrumentation & Control Engineering, Vishwakarma Government Engineering College, Chandkheda, (Gujarat), India.

³Rutvik K. Shukla, Assistant Prof., Department of Instrumentation & Control Engineering, Government Engineering College, Rajkot (Gujarat), India.

⁴Dr. Rohit Thanki, Data Scientist, KRiAN GmbH, Wolfsburg, Germany.

⁵Viral Thakar, Senior Machine Learning Engineer, Autodesk, Toronto, Ontario, Canada.

Abstract: Deep learning methodologies are now feasible in practically every sphere of modern life due to technological advancements. Due to its high level of accuracy, deep learning can automatically diagnose and classify a wide range of medical conditions in the field of medicine. The coronavirus first appeared in Wuhan, China, in December 2019 and quickly spread worldwide. The COVID-19 pandemic presented significant challenges to the world’s healthcare system. PCR and medical imaging can be used to diagnose COVID-19. It hurts the health of people as well as the global economy, education, and social life. The most significant challenge in containing the rapid spread of the disease is identifying positive COVID-19 patients as promptly as possible. Because there are no automated tool kits, additional diagnostic equipment will be required. According to radiological studies, these images include essential information about the coronavirus. Accurate treatment of this virus and a solution to the problem of a lack of medical professionals in remote areas may be possible with the help of a specialized Artificial Intelligence (AI) system and radiographic pictures. We utilised pre-trained CNN models, including Xception, Inception, ResNet-50, ResNet-50V2, DenseNet-121, and MobileNetV2, to refine the COVID-19 classification analytics. In this paper, we investigate COVID-19 detection methods that utilise chest Xrays. According to the findings of our research, the pre-trained CNN Model that utilises MobileNetV2 performs better than other CNN techniques in terms of both solution size and speed. Our method may be of use to researchers fine-tuning the CNN model for efficient COVID-19 screening.

Keywords: Diabetes, Misinformation, Fake, NLP, Monitoring
Scope of the Article: Deep learning

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