ITEGAM-JETIA

A philosophical analysis of AISI 316/AISI 410 stainless steel joint by means of mechanical and metallurgical properties using TIG welding method

2024-07-04T12:11:44-03:00

This study employed Taguchi's Grey Relational Analysis (GRA) to assess the optimization of process boundaries. Improved metallurgical and mechanical qualities of Martensite (AISI 410) and Austenitic (AISI 316) stainless steel joints were the aim of the investigation. In order to do this, TIG welding experiments were carried out with different current flows, welding speeds, and shielding gas flow rates. Responses like tensile strength and microhardness were taken into consideration to determine the ideal procedure settings. Analysis of variance had been used to determine their impacts. The best set of parameters was found to be 10 lit/min shielding gas flow, 2 m/min welding speed, and 120-amp welding current based on the GRA. The distribution of alloys, the microhardness of different weld zones, and the characterisation of microstructural and mechanical properties were also investigated.

Investigation of leachate migration using electrical resistivity imaging: a case study from an active dumpsite, ilokun, ado-ekiti, Southwest Nigeria

2024-07-02T17:44:46-03:00

Geophysical study involving electrical resistivity imaging was conducted at Ilokun municipal solid waste disposal site in Ado-Ekiti, Southwest Nigeria. The study was designed to map the leachate accumulation and extent of migration of leachate plumes in the subsurface. Leachate generated from dumpsite has been recognized as a major concern to public health. Three 2D resistivity profiles were established, one within and two outside the dumpsite. A traverse length of 140 m was kept for the survey. The apparent electrical resistivity data were subsequently inverted into the true electrical resistivity distributions using RES2DINV software to obtain the subsurface image. Electrical Resistivity Imaging (ERI) revealed leachate accumulation (low resistivity zones of 4.98 – 13.30 Ωm) within the shallow subsurface of the dumpsite up to a depth of 10 m. A fractured zone was delineated within the subsurface. This zone of enhanced porosity and permeability spanning a distance of about 20 m offers the main conduit / seepage path for the leachate to percolate through the subsurface formation. Landfill leachates are potentially harmful to the environment and to human health. Proper mitigation measures are necessary to protect the shallow groundwater system, soil and the environment.

Weapon Detection and Classification Using Deep Learning

2024-07-02T17:44:52-03:00

High gun-related crime rate poses a great threat to society in the present world. There is a serious need for systems to deal with such gun-related crimes. As CCTVs are installed in almost every part of the city, using the CCTV footage to detect the weapon is the simplest and efficient way to deal with such crimes. Unconcealed weapon detection, in images and videos, can help reduce the number of homicides due to the gun-related violence. In this work, we focus on developing a robust and automatic weapon detection system with ability to classify the detected weapon into different categories. This work provides and extensive survey on already existing weapon detection systems, weapon detection datasets, challenges in weapon detection and deep learning based object detection technologies. We have developed a new image dataset for weapon detection and classification task. The experimental analysis shows the superiority of the Faster-RCNN models over SSD models for weapon detection systems. The detection results shows how the final developed system deals with different challenges related to weapon detection and classification in real world scenarios.

Relevance and reliability of NO2 and NO monitoring in low-income countries using low-cost sensors

2024-07-02T17:44:51-03:00

Assessing air quality's impact on human health involves monitoring pollutant concentrations such as NO₂, O₃, CO, SO₂, and particulate matter. While high-income countries rely on expensive reference instruments, low-income nations face technological limitations. This study explores the potential of low-cost scientific devices as a viable solution for these regions. The research focuses on evaluating the reliability of low-cost NO₂ sensors and consistency across five identical sensors. Calibration tests in controlled settings reveal a linear model with high coefficients of determination, contrasting with lower coefficients observed during field tests. Variability in intercepts and slopes is evident across time and campaign contexts. Time series analysis using low-cost NO₂ sensors showed that many of the tall peaks atop a fluctuating baseline correlates with peaks identified by reference instruments. Additionally, NO gas sensors are also able to identify pollution peaks in monitoring campaigns. Therefore, such affordable sensors provide valuable insights into pollutant concentration trends, offering indicative magnitude information. However, improving calibration and reliability of these sensors necessitates further research.

A systematic review of movie recommender systems

2024-07-02T17:44:51-03:00

Recommender systems are vital to everyone's information selection. Managing massive amounts of data is common with recommendation system technology. Annual film releases are rising, and currently films are released within months. With movie releases, apps like Netflix, Viu, Amazon Prime Video, Disney+, etc. have emerged. Thus, Movie Recommender Systems (MRS) are essential to simplify and improve user experience. This research gives a systematic literature review (SLR) of MRS's current condition. Our comprehensive review addresses recommendation algorithms, data processing, and evaluation approaches. In SLR MRS, content-based filtering, collaborative filtering, knowledge-based recommender systems, and hybrid approaches are employed. To achieve this, 66 high-quality studies were selected from 27,187 2019-2023 studies using strict quality criteria. The study found that most MRSs use content-based filtering and machine learning to deliver non-personalized movie suggestions in various domains. The review helps researchers choose MRS development strategies. This study can assist MRS development catch up to other recommendation systems by improving efficiency. The MRS investigation found accuracy, sparsity, scalability, cold start, and operating time issues. Future study will examine how temporal and demographic data affect movie recommendation system relevancy and customization.

CSA implementation strategies unraveling success and challenges

2024-07-02T17:44:51-03:00

This study investigated Corporate Sociopolitical Activism (CSA) and its multilayered dimensions within organizational contexts. The study aimed at examining the likelihood of organizations employing a comprehensive set of CSA strategies to achieve positive outcomes, the impact of CSA strategies on stakeholder engagement and the association between successfully navigating challenges in CSA implementation and adaptive organizational approaches.
By employing a quantitative survey research design, data were gathered through a meticulously designed structured questionnaire administered to a sample of 120 respondents. The study revealed that organizations adopting a comprehensive set of CSA strategies indeed have the potential to achieve positive outcomes, challenging the notion that such endeavours might not be fruitful. Additionally, the effectiveness of CSA strategies displayed a significant impact on stakeholder engagement, emphasizing the pivotal role of sociopolitical initiatives in fostering robust relationships with stakeholders.
The implications of these findings underscore the importance of proactive engagement with sociopolitical issues, not only for organizational success but also for building and maintaining stakeholder support. Recommendations arising from the study encourage organizations to carefully plan and implement CSA strategies to harness their positive impacts fully.

Awareness and adoption readiness of machine learning technology in the construction industry of a developing country: a case of Nigeria

2024-07-04T15:46:56-03:00

This study investigated the awareness of the Nigerian construction organisations on some identified ML application areas, and the readiness of the organisations to adopt ML learning in the identified application areas. A comprehensive Literature review was undertaken to identify the application areas of ML, then, a well-structured questionnaire was developed and used to gather relevant data from construction professionals using the snowball sampling method via electronic means. 143 valid responses were obtained, and the gathered data were analysed using arrays of descriptive and inferential analytical tools. The study revealed that the critical applications areas of ML with higher awareness level and adoption readiness in Nigeria are (1) Health and Safety prediction and management, (2) Waste management, (3) Prediction of and management of construction costs, (4) Risk Management, (5) Structural Health Monitoring and Prediction, and (6) Building Life-Cycle assessment and management. Further, a significant statistical difference was observed between the opinions of the participants regarding the awareness and adoption readiness of the various ML application areas. This study identified critical application areas of ML where the awareness and adoption readiness are very high, thus, signalling the preparedness of the Nigerian construction industry (NCI) to embrace ML to drive sustainable construction.

Colour based identification and sorting of industrial items using Labview

2024-07-02T17:44:50-03:00

This paper focuses on Colour based separation of Industry based products or raw materials using LabVIEW Software for creation of process accurate to Colour identification using Colour Spectrum tool (an add-on of LabVIEW 2023 QI software), various devices used in the project are real-time sensing and actuating devices. Colour Sensing and separation is a very useful application in Food processing, Plastic product based Industries, Marble Industry etc. In this model we use Arduino UNO, USB Webcam, Solenoid Pusher, 12V Electric motor and other Framework modules. This module works based on live action and response type of Industrial process where time is of the essence. LabVIEW software being an integral part of creation of the process and automation, has a number of tools to offer and be used to interface with external devices through wired connection.

Enhancing iot network security through advanced data preprocessing and hybrid firefly-salp swarm optimized deep CNN-based intrusion detection

2024-07-08T10:05:17-03:00

This concept addresses the imperative need for robust Intrusion Detection system (IDs) in Internet of Things (IoT) networks by presenting a comprehensive approach that integrates advanced data preprocessing techniques and Deep Convolutional Neural Network (DCNN) based IDS. The process commences with raw and inherently noisy data generated by IoT sensors. To fortify the detection capabilities, a sequence of preprocessing steps is applied, including data cleaning, one-hot encoding and normalization, ensuring the prepared data is resilient to outliers and irrelevant information while being conducive to Deep Learning (DL) models. The core of the proposed system is a DCNN, adept at capturing sequential patterns within diverse and dynamic IoT data. To further optimize the performance of the DCNN, a hybrid firefly-salp swarm optimization algorithm is employed. This hybrid approach leverages the strengths of both Firefly and salp swarm optimization techniques (FFA-SSA), enhancing the model's ability to identify potential security threats effectively. The synergy of advanced data preprocessing and nature-inspired optimization methods not only strengthens the security posture of IoT networks but also contributes to the resilience and adaptability of intrusion detection systems. The presented concept signifies a crucial step towards ensuring more secure and resilient IoT deployments, acknowledging the pivotal role played by innovative techniques in preparing data and optimizing deep learning models for enhanced cybersecurity.

Dynamics assessment of an inverter fed induction motor drive by an improved predictive controller leveraging finite control set mechanism

2024-07-02T17:44:49-03:00

Accenting the importance of Model Predictive Control (MPC) accross used optimization tools in current engineering applications, the proposed scheme establishes the predictive skills by well defined mathematical model in terms of present state variables. This paper projected a predictive current control(PCC) approach scheduled by fininite control set(FCS) inverter switching mechanism executed by a current modulated objective function.. The anatomy of this controller deals with selecting the control signal from a finite set of signals which satisfies minimum value of the predefined objective function, which is formulated by calculating the square error, i.e. the reference current against the stator measured current of the designed induction motor(IM). The proposed work further enriched with an improved predictive aspect named as integral finite control set(IFCS) action synchronized with a cascade feedback structure with appropriate controller gain to obtain an optimal set of control variables. With the direction in minimization of principle, these methods provide the control of the switching states for inversion, to the inverter and inverter generates actuating voltage signals to the induction motor. IFCS-MPC has the inherent capabilities of compensating steady state errors and slewrates which potrayed this as the preferred forecasted controller as compared to FCS-MPC. This work is also advanced with a comparative demonstration of torque, load currents and speed characteristics of IM, obtained from each of the implemented control techniques to identify the most flexible and dynamic predictive strategy. All these control methods have been investigated using MATLAB/Simulink environment.

Flood prediction and management

2024-07-02T17:44:49-03:00

Floods are one of the most devastating forces of nature, that destroy thousands of lives every year. Not only does the economy of a country suffer because of such a disaster, but the loss of agriculture and people is physically and mentally exhausting for a country. Especially, in a country like India where floods are frequent, and the prevention department lacks, it becomes crucial to early detect these floods, and inform the local authorities to safeguard the lives of thousands of people.

Through this paper, we aim to work on a comprehensive flood prediction system that utilizes machine learning algorithms to enhance the efficiency and accuracy of a flood prediction and management system. In this study, we have used the dataset with 142193 entries and to work with such large data we have used multiple algorithms. These machine learning algorithms have made it easy to analyze or to work with large datasets. We used multiple algorithms that have worked well with this dataset but some of them have performed better than others. Out of all algorithms, XGBoost has performed best. Along with XGBoost algorithms like CatBoost and Random Forest have also performed well as they all have accuracies of more than 90%.

Our target is accurate and early prediction of floods in an area, and then to inform the required local authorities about the forecast. So, that necessary action can be taken, and the flood-prone area can be evacuated in an organized manner.

Development of an ai-enabled video capturing device for bullet trajectory analysis and ballistic research

2024-07-02T17:44:48-03:00

A ballistic experts’ discipline is the ability to compare the characteristic marks found on the surface of different fired bullets to determine whether they were fired from the same gun. These tool marks become a “ballistic fingerprint” that examiners can use to identify specific characteristics of the firearm that discharged the bullet. One such tool mark is the striation marks left on the bullet, identical to scratch marks. Manually done, a comparison microscope is used in this process, where the testing bullet is rotated until a well-defined land or groove comes into view. The sample bullet is then rotated in search of a matching region. But in this process opinions are given through only the manual experimental process and not through an automated system. The proposed solution was to develop a cost-effective automated system that captures the video of the bullet in one go. Also, the focus was to develop a lighting arrangement independent of the environment, so that the device can be efficiently used in any environment.

Design and analysis of novel topology for PV-FED EV charging system

2024-07-02T17:44:45-03:00

This paper presents a novel design for a photovoltaic (PV) powered electric vehicle (EV) charging system. The core of the system is a modified single ended primary inductance converter, chosen for its high efficiency, reduced switch voltage stress, and ample operating range for maximum power point tracking (MPPT). This study details the redesigned SEPIC converter architecture, including with and without the MPPT algorithm. Additionally, it presents an optimized parameter selection, design methodology, and simulation technique for analysing the converter's performance in EV charging applications.

Two MPPT approaches, Perturb and Observe (P&O) and Incremental Conductance (IC), are investigated and compared based on their impact on the converter's switching time under standard solar PV panel testing conditions. To comprehensively evaluate the system's performance, a MATLAB/Simulink model is developed, simulating the charging of a 48V, 200Ah battery using a 2kW solar PV input through the modified SEPIC converter and variations in the battery state of charge (SoC), battery voltage, and charging current are monitored. The simulation results demonstrate that under identical simulated conditions (10 seconds), the battery SoC increases from 50% to 50.034% without MPPT and to 50.042% with MPPT, highlighting the effectiveness of the MPPT algorithms in maximizing harvested solar energy.

RPG based educational game on basic arithmetic using the MDLC method

2024-07-02T17:44:48-03:00

This study aims to build an educational game on basic arithmetic operations (addition, subtraction, multiplication and division) for elementary school students. This educational game application was created using the Multimedia Development Life Cycle (MDLC) development method developed by Luther-Sutopo, with 6 development phases including concept, design, material collection, assembly, testing and distribution. The use of RPG Maker MV to make an educational game called Hero of Mathematics is more interactive and interesting. The results of the material and multimedia test related to the feasibility of the game got a score of 4,025 and 4.4 which are included in the strong interpretation. Meanwhile, in the attractiveness test, the game got a score of 3.8 which is included in the very interesting criteria. Thus, this Pahlawan Matematika (Mathematics Hero) educational game can be said to be feasible to be used as a medium for learning mathematics, especially basic arithmetic operations material for 1st grade elementary school students.

Geophysical investigation of groundwater contamination in urban areas: a case study of active dumpsites in oyo town, Nigeria

2024-07-02T17:44:45-03:00

nvironmental safety is a pressing global concern, particularly in Africa, where poor sanitation and contaminated water sources contribute to rising health issues. Active dumpsites are recognized as a source of groundwater pollution, yet their specific impact on water quality remains insufficiently documented. Therefore, this study aimed to evaluate potential groundwater contamination resulting from active dumpsites in the Ilaka area of Oyo town, Nigeria. Electrical Resistivity Tomography was established on two traverses at the study area with a total length of 150 m and 5 m electrode spacing. Eight Vertical Electrical Sounding points were sounded at the established profiles. The geoelectric sections revealed three distinct layers: the topsoil, the weathered layer, and the fresh basement. In Traverse One, the recorded layer resistivity ranges from 29 to 938 Ωm, while in Traverse Two, it ranges from 30 to 1243 Ωm. The corresponding layer thickness varies from 0.8 to 21 m in Traverse One and from 2.5 to 24 m in Traverse Two, indicating a shallow overburden thickness. The 2D image of ERT revealed extremely low resistivity values in all the traverses across the dumpsites which suggests the presence of clay formation underlying the topsoil which may have been trapped in the contaminant plumes. Groundwater from the study area was found to be free from contaminants.

Unveiling the nexus between fuel consumption, vehicle registration, population and GDP of Nepal

2024-07-02T17:44:45-03:00

Vehicle numbers soar with the increase in travel demand, thus increasing petroleum consumption, one of the extensive non-renewable resources. The increased demand for travel is also linked to Gross Domestic Product (GDP). However, due to the rise in fuel standards and higher fuel efficiency vehicles, the fuel consumption per vehicle is following the decreasing patterns. Thus, this study is about the relationship between petroleum consumption, vehicle registration and GDP of Nepal using regression analysis. Data for analysis were between 1994 and 2022 for registered vehicles, petroleum consumption and GDP whereas population data were collected from 1930 to 2021. The linear regression model came to be statistically significant between variables, (a) vehicles registered and petroleum consumption (diesel and petrol sales); (b) operating vehicles and petroleum consumption; and (c) operating light vehicles and petrol consumption. Similarly, significant exponential regression models were observed between (a) GDP and operating vehicles; and (b) GDP and petroleum consumption. Additionally, the study presented a logistic population growth model and vehicle growth model as significant models to put forth predicted population and vehicles in 2030 and 2040. These models were used to estimate the possible petroleum consumption in 2030 and 2040. Alongside, a situation, where high electric vehicle penetration might be observed, was also taken to predict the possible petroleum consumption. Rising petroleum consumption can be curbed to a certain limit with proper policy interventions and research and development in electric vehicles.

Simulation and Analysis of Lightning Strikes in Electrical Systems by MATLAB/SIMULINK and ATP/EMTP

2024-07-02T17:44:48-03:00

Lightning is a major disruptive phenomenon in the operation of all electrical installations. Lightning has always been a cause of disruption in the use of electricity. However, it is important to note the fairly recent and growing requirement for the quality of electrical systems (reliability, availability, continuity of service, etc.) as well as the constant concern to minimize the costs of using and producing electricity. This leads us to see that lightning becomes a hard point in improving all these factors. In this article, we present the ATP software that we will use in the simulation. After that, we discussed the description of lightning strikes and how to protect facilities from them. The next important step, which represents the novelty of this work, was to create a lightning strike model using the MATLAB/SIMULINK program, based on the mathematical equation of a lightning strike.

After that, by using the model created, we simulated a lightning strike that attacked two different nodes of an electrical network, generators, and nine nodes, using the two-simulation software's ATP/EMTP and MATLAB/SIMULINK to study the effect of the lightning strike on the voltages and currents of our network system. At the end of the work, we compared the results obtained by the two-simulation software, ATP/EMTP and MATLAB/SIMULINK, and we discussed and explained the results obtained by the two software.

Performance Evaluation of Novel EV charging Topology for Standalone PV Systems with charge controller

2024-07-05T11:09:04-03:00

This study explores the novel battery charging topology for standalone solar photovoltaic (PV) systems incorporating charge controller. The research leverages MATLAB/Simulink for modeling and simulation. In standalone PV systems, directly connecting batteries to PV modules can lead to detrimental overcharging or over-discharging, ultimately reducing battery lifespan. To mitigate this challenge, charge controllers are implemented. These intelligent devices regulate the output voltage and current from the solar panels, ensuring safe and efficient battery operation by preventing both overcharging and over-discharging.

The analysis focuses on a PV-powered Single Ended Primary Inductor Converter topology alongside a modified version. These topologies integrate Perturb and Observe (P&O) and Incremental Conductance MPPT algorithms for optimal power extraction. The battery charge controller employs a three-stage strategy i.e., constant current, constant voltage absorption, and float charging to effectively manage the battery charging process. Performance evaluation considers MPPT tracking efficiency (achieving up to 98% under standard test conditions), battery charging effectiveness, and overall charge controller efficiency. The study aims to validate these results by benchmarking against a commercially available MPPT controller. The research concludes that the modified SEPIC topology with the implemented charge controller demonstrates superior suitability for EV charging applications compared to the standard SEPIC topology.

Digital Twin of a photovoltaic system

2024-07-02T17:44:47-03:00

The creation of a Digital Twin, for the simulation of the photovoltaic park installed at the UCLV, which establishes a bidirectional data exchange in real time with the aim of providing a precise virtual copy, in order to monitor it and analyze its reaction to certain stimuli. or circumstances to improve its performance and extend its useful life. A virtual model is built using two tools: Simulink, and the PV_LIB Toolbox in Matlab. The implementation is carried out on a Raspberry Pi model B+. The connection between Matlab and the Raspberry Pi is achieved using appropriate support packages, allowing the Digital Twin to run constantly. The input data comes from the UCLV photovoltaic park, obtained through the ThingSpeak platform, while the outputs include current, voltage and power. The main focus of this work is to achieve the connection between Matlab and the Raspberry Pi, find a virtual model that fits the real plant, its implementation in code and validate the power generation of the Digital Twin in relation to the Physical Twin through root mean square error (RMSE).

Exploring the importance of tutoring in distance education (de): a case study at the fictitious solares university

2024-07-02T17:44:47-03:00

Distance education (DE) has undergone significant evolution in recent decades, largely propelled by advancements in information and communication technologies (ICT). Playing a vital role in DE is e-tutoring, serving as a mediator between teaching and learning, fostering autonomy and collaboration. This article aims to investigate the origins of DE tutoring, tracing its evolution and examining its current importance in educational contexts. In the 19th century, correspondence courses marked the early stages of DE, with tutors and students exchanging letters for interaction. In the early 20th century, institutions like the University of London and the Open University in the United Kingdom pioneered formalized DE programs supported by tutorial systems. The period between the 1980s and 1990s witnessed the emergence of digital DE platforms, driven by the rise of computers and the internet, enabling more immediate interaction between tutors and students.