Journal of Engineering Technology and Applied Physics

Effects of Infill Density and Printing Speed on The Tensile Behaviour of Fused Deposition Modelling 3D Printed PLA Specimens

Muhammad Farhan Muzli — 2024-09-15

The mechanical properties such as tensile behavior of a 3D printed object can be influenced by various printing parameters, including printing temperature, orientation, infill density, and printing speed. This study focuses on investigating the effects of infill density and printing speed. Thirty dog-bone specimens were 3D printed using Fused Deposition Modelling (FDM) technique with Polylactic Acid (PLA) filament. Three different infill density settings (40%, 60%, and 80%) and three printing speed settings (30 mm/s, 60 mm/s, and 90 mm/s) were used. Tensile tests were performed on each specimen using a Universal Testing Machine. The experimental results indicate a clear trend of tensile behaviour with infill density. Increasing the infill density leads to improved tensile behaviour in the specimen. The highest Young’s Modulus and ultimate tensile strength (UTS) were achieved at 541.67 MPa and 24.3 MPa, respectively, with an infill density of 80%. On the other hand, printing speed showed an inverse relationship with tensile behaviour. As the printing speed increased, the Young’s Modulus and UTS decreased. However, the effect of printing speed on the mechanical properties was not as significant as that of infill density. When increasing the printing speed from 30 mm/s to 90 mm/s, the UTS only decreased by 5.61%. In contrast, increasing the infill density from 40% to 80% resulted in a UTS increase of 35.23%.

Mind Care Solution Through Human Facial Expression

Ali Asghar — 2024-09-15

Using proposed system psychologists can use technology to make decisions which can provide ease for both patients and psychologists. Psychologists can check the progress of patients by analysing emotions reports of patient over time. Using historical data and emotion detection technology psychologists can make more accurate decisions. Using proposed system patient and psychologists don’t have to go to anywhere they only need a device and internet. Based on the characteristics of patient emotion psychologist only need report generated by system and prescribe medicine in emergency situation. Proposed system improves consultancy method by using machine learning emotion detection algorithm. Proposed system detects facial emotion of patient by using CNN with HAAR cascade classifier. We use FER 2013 dataset to train our model. We use VGG 19 architecture to train our model for optimization function to enhance the accuracy of model. We use RELU. We use DJANGO framework for integration with frontend. Result of our model on dataset 82.3 % after find tuning the accuracy goes to 82.3 % to 92 %. We use recall and F1 method to check the performance of model. We trained model on the testing dataset which have gray scale images and 48*48 pixel images to achieve his performance. To achieve our accuracy goal, we split dataset into trainee validation and testing dataset. We use CNN and achieve 93 % accuracy in our system which help patient to get feedback only selected question and psychologist. Patients select psychologist to answer questions of psychologist system stores emotions of patient against every question to generate emotion report. Psychologist can analyze emotion report to provide better prescription to patient.

Binary Particle Swarm Optimization for Fair User Association in Network Slicing-Enabled Heterogeneous O-RANs

Jing Ren Sue — 2024-09-15

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks.

Experimental Characterization of Process Pressure Variations on The Accuracy and Performance of Liquid Ultrasonic Flow Meters

Paul Ogheneochuko Ohwofadjeke — 2024-09-15

This paper investigated the influence of process pressure variations on the accuracy and performance of ultrasonic flow meters. Process measurement technology provides a tool for optimizing production processes and dosing operations. Accurate measurement is key and primary to profitability in the business of supply and purchase of liquids like petroleum, gas and chemical products. Three 6” size ultrasonic flow meters were mounted on a skid and used to carry out the experiment parallel in connections each other to take flows from a common header, measure and discharge their individual flows into a common discharge header. The three meters were designate 1, 2 and 3 respectively. Meters 1 and 2 being service meters while Meter 3 is the calibrated master meter. The experiment was carried ten times to increase reliability of results. Experimental data were collected and analyzed using computational formulae technique. Results showed that; Meter 1 had an optimum process pressure of 12.38 and 9.43 bar with respect to flow rate and meter factor respectively as performance indicator. While Meter 2 had an optimum process pressure of 12.4 and 12.41 bar with respect to flow rate and meter factor respectively as performance indicator. Findings indicated significant relationship between process pressure, flow rate and meter factor using ultrasonic flow meter. The outcome of this study will be a useful guide to users of ultrasonic flow meters to maintain optimum process pressures of each meter during fluid supply.

Sine Cosine Algorithm for Enhancing Convergence Rates of Artificial Neural Network: A Comparative Study

Maria Ali — 2024-09-15

Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30 %, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Muliady Ang — 2024-09-15

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Performance Analysis of Internet-based DGPS and Commercial Satellite-based Augmentation System: A Case Study in Peninsular Malaysia

Ooi Wei Han — 2024-09-15

Global Navigation Satellite Systems (GNSS) has become an essential component in modern times for positioning, navigation, and timing. A fast-growing economic region in Malaysia required a GNSS-based augmentation positioning and navigation service. To improve navigation solution, several augmentation techniques exist, such as a differential Global Positioning System (DGPS) assisted by reference station, or augmentation service provided by commercial communication satellite. The DGPS correction is applied through a real-time communication medium and can be received at the user side by several communication methods including internet-based, and this method is favourable for land and near-coast areas. This case study aims to investigate the reliability of internet-based DGPS and SBAS along the Peninsular Malaysia. A test was conducted and data were collected over 15 hours at a rate of 1 Hz from the available GNSS satellite using a geodetic-grade receiver mounted on a moving vehicle. The obtained results showed that DGPS and SBAS perform better than the navigation solution with an accuracy of 1.536 m and 0.955 m respectively, compared to the navigation solution with an accuracy of 3.159 m. The limitations of both augmentation techniques were also analysed and discussed in this study.

Microwave Sensor for Sodium Chloride Density Measurement in Aqueous Solutions

Kim Ho Yeap — 2024-09-15

Accurate determination of sodium chloride (NaCl) density in water is vital for assessing environmental impact, preventing soil salinization in agriculture, ensuring quality and consistency in industrial processes, facilitating medical treatments, and maintaining taste and preservation standards in the food and beverage industry. This paper introduces a novel microwave sensor design specifically tailored to accurately assess NaCl density in aqueous solutions. Starting with a standard solution of 10 g of salt dissolved in 100 ml of water, resulting in a molarity of approximately 1.71 M, five distinct samples are meticulously prepared. These samples cover a range of NaCl concentrations, with different ratios of salt solution and drinking water, including pure water, 10 ml of salt solution with 90 ml of water, 20 ml of salt solution with 80 ml of water, 30 ml of salt solution with 70 ml of water, and 40 ml of salt solution with 60 ml of water. Each sample undergoes analysis using the developed microwave sensor to determine its transmission coefficient. The magnitude of the transmission coefficient is closely tied to the density of the salt solution based on molarity. Through a detailed regression analysis, a strong quantitative relationship between the transmission coefficient and salt solution density is revealed. This correlation can be accurately represented by a third-order polynomial equation. This research is significant as it advances microwave sensor technology, allowing for accurate and efficient measurement of NaCl density in water.

Evolution of Requirements Engineering in Agile Methodology – Literature Review

Ayesha Anees Zaveri — 2024-09-15

Requirements Agile approaches have transformed engineering. This paper shows how RE in Agile software development has evolved from documentation-heavy to collaborative, adaptable, and customer-focused. Agile was born in the mid-1990s when the industry realized it needed to respond to changing client needs and market volatility. This evolution includes iterative development, client interaction, and emphasizing communication above documentation, as discussed in the paper. By comparing conventional and Agile RE approaches, we demonstrate the benefits of adapting to change, working with customers, and delivering functional software faster. This analysis provides a persuasive description of Agile RE implementation methodologies and resources through a detailed literature review and real-world experiences. User stories and backlog refinement are notable techniques. The research finishes by exploring how these techniques affect team dynamics, project success, and customer satisfaction. RE's Agile difficulties and opportunities are also examined. The findings illuminate RE methods' successful adaptation to Agile projects' dynamic character. Software development is more responsive and effective due to this adaptation.

CRATSM: An Effective Hybridization of Deep Neural Models for Customer Retention Prediction in the Telecom Industry

Johnson Olanrewaju Victor — 2024-09-15

In the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.

Optical and Structural Properties of V2O5 Electrochromic Thin Films

Ming Yue Tan — 2024-09-15

The increase in global temperature has led to a significant surge in energy consumption within the air conditioning industry, resulting in environmental deterioration. Electrochromic (EC) windows have emerged as a promising solution to address these challenges. Vanadium pentoxide (V₂O₅) stands out among all metal oxide materials due to its remarkable EC properties, including substantial Li⁺ ion insertion capacity and multicolor capabilities. Despite the potential of V₂O_5, there remains a lack of comprehensive research on the structural and optical properties of V₂O₅ films with varying thicknesses. Therefore, this study aims to investigate the structural and optical properties of V₂O₅ thin films with thicknesses ranging from 46 to 344 nm. By employing the sol-gel spin coating method, V₂O₅thin films were fabricated and analyzed using X-ray diffraction (XRD) spectroscopy and ultraviolet-visible (UV-Vis) spectrophotometry. The fabricated V₂O₅ thin films with thicknesses of 46-274 nm demonstrated an average film transparency of 83 %. XRD analysis further revealed that the V₂O₅ thin films reached their peak crystallinity at a thickness of 344 nm. Moreover, CV analysis revealed that the V₂O₅ device, with a thickness of 274 nm, exhibited a cathodic peak current of -1.63 mA, indicating its excellent ability to facilitate Li⁺ ion diffusion. Additionally, CA measurements displayed a high optical modulation of 37.78 %. Ultimately, this research contributes to the development of energy-efficient solutions for sustainable environmental practices.

Temporal Convolutional Recurrent Neural Network for Elderly Activity Recognition

Jia Hui Ng — 2024-09-15

Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3 %.

Study on Cation-Exchange of Removal Metallic Ions at PPy/PVS Film

Lay Lian Teo — 2024-09-15

Due to the presence of heavy metals in wastewater treatment and manufacturing water purification processes in industries, investigations into potential methods have been widely conducted. In this paper, we report the incorporation of cation-exchange for removing heavy metals from aqueous solutions. We further investigated the properties cation-exchange using potential step method on PPy/PVS film at titanium electrode. Satisfactory results have been obtained with the small scale of experimental. It was found that the PPy/PVS film is able to remove heavy metal ions such as copper, nickel, and cobalt at very low concentrations, less than or equal to 10 mg/L, reducing them to approximately 1 mg/L. The Leica Q500MC Image Processing and Analysis system displayed the uneven deposition of heavy metal ions on the surface of PPy/PVS. Furthermore, Electron Spectroscopy for Chemical Analysis (ESCA) demonstrated the deposition of heavy metals on the PPy/PVS film.