International Journal of Innovations in Science & Technology
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Optimizing UAV Wing Performance: A Computational Analysis with Computer-Based Algorithms for Composite Material Integration
Get PDFIntroduction/Importance of Study: The aircraft wing, a vital component, demands intricate design to balance lift generation, drag reduction, and weight minimization. In advanced UAVs (Unmanned Aerial Vehicles), prioritizing stealth and low weight, a pioneering solution involves replacing traditional metallic wing components with composite materials, offering superior lightweight properties, strength, durability, and flexibility.
Novelty Statement: Since most of the studies focus on fuselage, wing ribs, and skin, this research emphasizes spars which are a primary component of the wing.
Material and Method: Composite material T800S/3900-2 is a widely used carbon fiber material in the aerospace industry, which is proposed to be utilized in wing spars. The finite element method is used to carry out the investigation and verification of this transition of materials from metals to composite materials.
Result and Discussion: By varying ply orientations and thicknesses of composite materials to match the stiffness and strength of metal spars, our findings demonstrate that composite wing spars exhibit equivalent stiffness, greater strength, and reduced weight compared to traditional metallic counterparts.
Concluding Remarks: The shift to composite materials in UAV wing design offers a transformative solution. This research shows that for optimal structural performance and achieving lower weight objective composite materials, composite materials are the most suitable materials for UAV wing spars
An Advanced 2-Output DNN Model for Impulse Noise Mitigation in NOMA-Enabled Smart Energy Meters
Get PDFThe next-generation power grid enables information exchange between consumers and suppliers through advanced metering infrastructure. However, the performance of the smart meter degrades due to impulse noise present in the power system. Besides conventional thresholding techniques, deep learning has been proposed in the literature for detecting noise in NOMA-enabled smart energy meters. This research introduces a novel Deep Neural Network (DNN) capable of simultaneously detecting and classifying impulse noise as either high or low impulse. Combining the analysis of detected noise and its class has proven to be more effective in mitigating noise compared to previously proposed methods. The input feature vector to DNN is chosen based on its characteristics to detect impulse noise and its level in the data and includes ROAD characteristics, median differences, and probability of impulse arrival. The performance evaluation shows that the Bit Error Rate (BER) of the proposed DNN is lower than the BER of single output DNN which is proposed in the literature for mitigation only. It is also shown that besides simultaneous detection and mitigation, the second output of the proposed DNN i.e. classification of IN validates the first output which is IN identification
Overview of Immersive Data Visualization: Enhancing Insights and Engagement Through Virtual Reality
Get PDFIn recent years, the explosion of data has been immense, especially in terms of volume and velocity which poses a new challenge in the visualization of data and extracting patterns from it efficiently. Visualization is one the most critical aspects of data analysis as it also helps in the selection of an appropriate model for machine learning. However, this changes when we are dealing with complex data or hyper-dimensional datasets. 2D visualization of this complex or hyper-dimensional dataset can be hard to visualize owing to the inherent loss of information due to spatial constraints which consequently hinders the extraction of meaningful patterns for the development of machine learning models. In recent years, there has been substantial advancement in immersive technologies like Virtual Reality, Augmented Reality, Mixed Reality and adoption in various sectors especially in gaming, entertainment, and training. However, when it comes to data analysis and data visualization, immersive technology is at an emerging stage but has promising potential. This review research paper, through a series of application domains, aims to uncover this promising potential of virtual reality by shedding light on its capabilities and its limitations in representing complex and hyper-dimensional data to uncover new insights, pattern recognition, and decision-making processes
Assessing Eight Years of Monsoon Rainfall Patterns in Karachi, Pakistan: Study of the Intense Rainfall Events
Get PDFRainfall plays a pivotal role in regulating water levels in reservoirs, which can lead to overflow or drought, depending on the unpredictability of rainfall patterns. In 2020 and 2022, Sindh experienced seven episodes of normal to heavy rains, causing flooding and disrupting major highways such as Gwadar-Karachi. This study evaluates daily and cumulative rainfall data in Karachi for the months of June, July, and August from 2016 to 2023. The rainfall data is divided, focusing on the monsoon rains over the eight-year period. Among these years, the highest recorded rainfall of 93.099mm occurred on August 11, 2019, while the lowest rainfall of 0.001mm was noted on July 26, 2016, and June 18, 2017. The yearly (2016-2023) cumulative rainfall for the study period was 114.6mm, 187.0mm, 34.9mm, 310.5mm, 347.9mm, 285.3mm, 761.4mm, and 167.6mm respectively. Notably, the cumulative rainfall and the frequency of rain events were highest in August 2020 and 2022. The monthly data revealed that Karachi experienced exceptionally heavy rainfall in August 2020 and 2022, resulting in significant disruption and chaos in the city. Moreover, when considering the data across the years, it becomes evident that Karachi faced unprecedented rainfall in 2020 compared to the preceding years. This research represents the first comprehensive analysis of the intense rainfall events in August 2020 and 2022 in Karachi. It identifies trends in rainfall patterns that led to flood-like conditions in the city. This study provides a detailed and quantitative understanding of rainfall occurrences during the monsoon season. Such insights are invaluable for assessing flood risks in Karachi, Pakistan
Detection of Holes in Point Clouds Using Statistical Technique
Get PDFA point cloud is a dynamic, three-dimensional geometric representation of data that has different qualities for every point, including geometry, normal vectors, and color. However, holes that often occur during the 3D point cloud collection process provide an immense obstruction to the analysis and reconstruction of point clouds. Thus, detecting these holes is a crucial initial step toward obtaining precise and comprehensive representations of the real surfaces. Although there are several methods available for hole detection and filling, the problem is exacerbated by their shortcomings, which include high computation complexity or limited effectiveness. Our method is based on a sequence of basic but efficient statistical techniques. Our method is based on a sequence of basic but efficient statistical techniques. First, we find the mean distances between each point using the K Nearest Neighbors (KNN) technique. Next, we can categorize normal points and points that belong to holes and borders by using this mean as a threshold. Our method\u27s simplicity and low computational resource needs offer significant advantages over other approaches
Deep Learning-based Skin Lesion Segmentation and Classification
Get PDFBy using deep learning to automate skin lesion segmentation, this work aims to improve the classification of melanoma. By properly segmenting lesions and utilizing the U-Net algorithm\u27s preprocessing capabilities, our research aims to improve the accuracy of skin cancer diagnosis. During preprocessing, raw dermoscopic pictures from the HAM10000 dataset are enhanced and normalized early. Next, the U-Net model is used to accurately segment lesions. Advanced deep learning approaches are applied after segmentation segmented images are subjected to classification, such as Convolutional Neural Networks (CNN) and Vision Transformer (VIT) models. The VIT model demonstrated a high training accuracy of 0.94, indicating its effectiveness in learning from the training data. However, its validation and testing accuracies were at 0.73. The CNN model showed a training accuracy of 0.95, implying its ability to learn the training data effectively. However, its validation and testing accuracies were at 0.73. This all-encompassing method not only improves dermatological image analysis\u27s dependability and effectiveness, but it also shows promise for enhancing clinical outcomes in the diagnosis and management of different forms of skin cancer. Our work is a significant step toward the creation of more reliable techniques in this important area, opening the door for improvements in patient care and healthcare diagnostics
Capturing CO2 and Recovering NH3 by Producing Ammonium Bicarbonate Through Stripping Batch Process at Lab Scale
Get PDFA sustainable Environment is a crucial need for today’s world. To save energy, reduce pollution, and save the economy, researchers are coming up with various sustainable waste management practices to reduce air pollution and water pollution. The production of cement, chemical processing, and power plants are among the industries that release the most CO2. These emissions can be greatly decreased via ammonia-based absorption, which helps to make industrial processes cleaner. One of the methods among all the technologies and solutions is the stripping process where CO2 can be captured by removing ammonia from the water. Not only this but also the chemical is produced NH4HCO3 which can be used in industries or as fertilizer. In this study, A lab-scale stripping process is studied for the recovery of ammonia and capture of CO2 at different experimental conditions which were not studied by other researchers in previous studies. Apart from that, the precipitated product is studied by various characterization techniques including SEM-EDS and XRD. Results show that varying absorption times and flow rates of CO2 and concentrations of solutions affect product quantity. The research concludes the optimum conditions to achieve maximum product i.e., NH4HCO3 was 110 min, 0.5 CO2 gas flow rate, and 15 % NH4OH solution
Efficiency Assessment for Crop Classification Using Multi-Sensor Data in Google Earth Engine
Get PDFAccurate mapping of agricultural lands and crop distribution is crucial for food security, sustainable development, and informed policymaking. This research classified agricultural crops in the Rahim Yar Khan district of Pakistan using multi-sensor images from Sentinel-1 and Sentinel-2 satellites. The study employed the cloud computing platform Google Earth Engine (GEE) and compared the performance of the Random Forest (RF) algorithm using Sentinel-1 (VV, HV, and HV+VV), Sentinel-2, and integrated datasets. Ground truth information obtained from field surveys and high-resolution images served as reference samples for training and validation. The fusion of Sentinel-1 and Sentinel-2 data enhanced feature extraction, leading to improved crop type classification. Post-processing procedures ensured that the maps were visually clear and free of noise, allowing for accurate crop mapping and land cover categorization. The classification results indicated high accuracy for crops such as sugarcane, cotton, rice, and water bodies. The RF classifier using fused data achieved the highest accuracy (overall accuracy of 93% and Kappa coefficient of 90%), followed by Sentinel-2 (89%), Sentinel-1 VV+VH (72%), Sentinel-1 VH (66%), and Sentinel-1 VV (62%). The study underscores the value of data integration in improving the classification accuracy of major crops (sugarcane, cotton, and rice) in the region. While some classes showed exceptional accuracy, others, such as Orchard, require further refinement in categorization methods. Overall, the study provides valuable insights into using multi-sensor remote sensing data for agricultural monitoring and decision-making
Performance Evaluation of Fuzzy Logic Based RPL Objective Functions
Get PDFIntroduction: This paper is based on the evaluation of different fuzzy logic based approaches, implemented by Routing Protocol for Low-power Lossy networks (RPL), carried out using different topologies.
Importance: This study is carried out to find out the strengths and weaknesses of fuzzy logic based approaches in RPL for different topologies. Fuzzy logic based RPL uses multi-metric approach, i.e., a technique which uses more than one metrics for route optimization.
Methodology: Two fuzzy logic based approaches implemented by RPL are selected, and compared with the single metric techniques, for two different topologies. This comparison is carried out in a network simulator called Cooja. Four performance evaluation metrics, i.e., end to end delay, packet delivery ratio (PDR), power consumption and number of parent switches, are used for comparison.
Novelty statement: As per author’s knowledge, Evaluation of the fuzzy logic based RPL techniques for different topologies and impact of node’s relative location on its results is not carried out.
Results and Discussions: It has been shown that using fuzzy logic in RPL, increases the packet delivery ratio and decreases end-to-end delay and power consumption in some cases. However, at the same time, it increases the number of parents switched. Results also reflected that, in case, if there are small number of nodes i.e., no congestion and node is closer to the root, instead of using a complicated and time consuming fuzzy logic based approach, the originally proposed less-complex methods should be preferred, as they consume less power and also add less processing delay. Fuzzy logic shows better results when the nodes are far away from root and there is congestion; in this case, a single metric cannot decide the best route for forwarding data.
Concluding Remarks: In future work, while using fuzzy logic in RPL, a dynamic approach may improve the results by selecting an objective function according to the traffic load, number of nodes and node’s location with respect to the root
Assessing Food Availability Potential in the Drylands of South Punjab
Get PDFIntroduction/Importance of Study: In South Punjab, Pakistan, unpredictable weather patterns and a heavy reliance on rain-fed agriculture pose significant challenges to food security. This study investigates how climatic variability affects food security in the region.
Objective: To evaluate the impact of climate variability on the per capita availability of wheat and rice in the districts of Bahawalpur, Rahim Yar Khan, and Rajanpur in South Punjab, Pakistan, from 1991 to 2021.
Novelty Statement: This study provides a unique analysis of the effects of climatic factors on food security in this under-researched region, offering a novel quantification of per capita wheat and rice availability over a three-decade period.
Material and Method: Temperature and precipitation data were sourced from the CHIRPS and APHRODITE datasets. Data on rice and wheat production were obtained from the Crop Reporting Service. The study assessed per capita availability of wheat and rice and explored correlations between climate data and farmer experiences.
Result and Discussion: From 1991 to 2021, per capita availability of wheat and rice fluctuated across Bahawalpur, Rahim Yar Khan, and Rajanpur districts. Key factors influencing these variations included population growth, water scarcity, extreme weather events, and climate variability. Surveys of farmers revealed the challenges they face in adapting to changing climatic conditions.
Concluding Remarks: Climate variability poses a significant threat to food security in South Punjab. Ensuring long-term food security in the region will require advancements in climate-smart agriculture, improved water management, and the implementation of early warning systems