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Forecasting Karachi's Air Temperature Variation: Leveraging Mobile and Multimedia Dataset for Global Warming Insights

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Journal of Mobile Multimedia 2024年第6期20卷 1289-1320页

作者： Zaffar, Asma Ahmed, Shakil Rafique, Aysha Imran, Muhammad Amir, Muhammad Uddin, Vali Department of Mathematics and Sciences Sir Syed University of Engineering and Technology Karachi Pakistan Department of Computer Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Department of Telecommunication Engineering Sir Syed University of Engineering and Technology Pakistan Department of Civil Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Sir Syed University of Engineering and Technology Professor and Dean Computer and Electrical Engineering Karachi Pakistan Sir Syed University of Engineering and Technology Professor and Vice Chancellor Karachi Pakistan

In Karachi, the transport and building industries have a major impact on carbon dioxide (CO2) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time ... 详细信息

In Karachi, the transport and building industries have a major impact on carbon dioxide (CO₂) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time and forecasts the maximum and minimum temperatures in Karachi. Specifically, it looks at 60 years' worth of mean monthly maximum and minimum air temperatures in Karachi, spanning from (1961 to 2020) dataset classes. The monthly average air temperature in Karachi, both at its minimum and maximum, remains constant. Using dataset from the Pakistan Metrological Department (PMD) and, the ARMA (p, q) model technique was used to assess forecasting and modelling the behavior of the maximum and minimum air temperatures in Karachi. The least values Akaike information criterion (AIC), the Bayesian Schwarz information criterion (SIC), and the Hannan Quinn information criterion (HIC) are used to describe how adequate the model is. Additionally, the Durbin-Watson (DW) test is used. The average monthly maximum and minimum air temperature in Karachi are strongly correlated, as indicated by DWvalues (<2). The lowest and maximum monthly average air temperatures in Karachi are predicted using diagnostic checking techniques such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil's U-Statistics. The results indicate that there is a strong correlation between the air temperature and previously measured values, with Theil's U-Statistics values for each month lying close to zero. This study is a great resource for observing how air temperature affects global warming. This study also highlights the the innovative aspect of utilizing vast datasets, possibly including those from mobile and multimedia sources, to address a critical environmental issue. It also emphasizes the predictive modelling aspect of your study, which is central to understanding and mitigating global warming effects. © 2025 River Publisher

关键词： air temperature ARMA (p kurtosis q) model root mean square error skewness

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Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-20页

作者： Islam, Md Mahamodul Das, Pobithra Rahman, Md Mahbubur Naz, Fasiha Kashem, Abul Nishat, Mosaraf Hosan Tabassum, Nujhat Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Leading University Sylhet Bangladesh Department of Industrial and Systems Engineering Lamar University Beaumont United States Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Civil and Environmental Engineering Shahjalal University of Science & amp Technology Sylhet Bangladesh Department of Civil and Environmental Engineering Islamic University Of Technology -IUT(OIC) Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Forecasting the compressive strength of high-performance concrete (HPC) is crucial for its practical applications. However, conducting experimental tests for this purpose demands significant resources and time. In recent times, data-driven modeling, particularly machine learning, has become prominent in this field. Thus, this study employed machine learning methods, including Gradient Boosting, Random Forest, and CatBoost algorithms, to predict the compressive strength of high-performance concrete (HPC). Furthermore, interpretable algorithms like SHapley Additive exPlanations (SHAP) were utilized in this study to assess the contribution of input variables to predictions. The dataset includes parameters such as cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, with compressive strength as the output variable for models development. The CatBoost model performance was superior to GBM and RF models, with R² values of 0.979 for the training stage and 0.959 for the testing stage. Moreover, it demonstrated minimal mean absolute errors (MAE) of 2.37 MPa and 3 MPa for compressive strength prediction in the training and testing stages, respectively. Furthermore, the SHAP analysis revealed that age, cement, and superplasticizer are the primary influencers in determining compressive strength. The machine learning models presented in this study exhibit promising potential for practical implementation in different engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Compressive strength High-performance concrete Machine learning SHAP analysis

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Advancements in 275 nm UV-LED Technology for Deactivation of Bacteriophages, Phi6 and MS2 20

Advancements in 275 nm UV-LED Technology for Deactivation of...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Bhattarai, Trailokya Ebong, Abasifreke Raja, M. Yasin Akhtar Munir, Mariya Unc Charlotte Department of Electrical & Computer Engineering Charlotte United States Unc Charlotte Department of Electrical & Computer Engineering and Phys & Ose CharlotteNC United States Unc Charlotte Department of Civil and Environmental Engineering Charlotte United States

ISBN: (纸本)9798350331110

This paper presents a comprehensive investigation of the efficacy of a 275 nm UV-LEDs system in deactivating two key bacteriophages, MS2 and Phi6, employed as surrogates for non-enveloped and enveloped viruses. This research includes designing and building the 275 nm UV-disinfection system and testing its effectiveness in deactivating the bacteriophages. Our research centers on meticulous control of UV exposure parameters, including precise adjustments to UV dose, irradiance, and exposure time culminating in substantial microbial reduction. Notably, the Phi6 bacteriophage demonstrated a remarkable 1.26 log reduction factor, corresponding to 94.50% reduction in viability, achieved at a UV dose of 32.81 mJ/cm2, Similarly, the MS2 bacteriophage exhibited a log reduction factor of 1.16 and a corresponding 93% reduction, attained at a UV dose of 26.82 mJ/cm2. These outcomes underscore the considerable potential of the designed 275 nm UV-LED system as a targeted and versatile disinfection technology, bearing substantial implications for real-world applications. Moreover, the prospect of integrating solar cell technology in future research endeavors holds promise for the development of self-sustained and renewable disinfection systems. © 2023 IEEE.

关键词： Bacteriophages

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Rural Road Pavement Panel Detection with Unmanned Aerial Vehicles 15

Rural Road Pavement Panel Detection with Unmanned Aerial Veh...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Maluya, Melody Mae O. Aleluya, Earl Ryan M. Alagon, Francis Jann A. Clar, Steve E. Salaan, Carl John O. Opon, Joel G. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Civil Engineering Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Since they connect major population and economic hubs across borders, roads and transportation play a crucial role in a nation's economy. Crash and accident rates are a direct result of poor and substandard road conditions. Road inspectors can better plan repair and assessment tasks with the help of the Pavement Maintenance System (PMS). Distress detection surveys are still carried out manually in developing nations. However, manual road inspection techniques carried out on the ground are dangerous, time-consuming, and labor-intensive. Unmanned aerial vehicles (UAVs) offer the flexibility and manoeuvrability to swiftly visually inspect the damaged area and gather the required data without endangering human safety. Surveyors would therefore be better able to assess the state of road pavement in rural areas if pavement panel detection using unmanned aerial vehicles (UAVs) was implemented. In this study, the precision, recall, and latency of the YOLOv7 and YOLOv8 instance segmentation models for pavement panel detectors were examined. This paper also introduced a new dataset of UAV images from complex and unstructured road environments within the highway areas in the Philippines. The findings indicate that YOLOv8 performed better, with a latency of 84.65 ms, a precision of 97.36% ±0.002, and a recall of 98.71 %±0.004. Road users and managers may benefit from an accelerated pavement assessment process in rural areas thanks to the suggested pavement panel detection model. © 2023 IEEE.

关键词： Antennas

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Revolutionizing Road Maintenance: A Data Fusion and AI-Based Approach 14

Revolutionizing Road Maintenance: A Data Fusion and AI-Based...

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14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Huang, Yu-Ting Sanka, Nikkhil Vijaya Jahanshahi, Mohammad R. Shen, Fangjia Lyles School of Civil Engineering Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Lyles School of Civil Engineering and Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9781605956930

The deterioration of pavement conditions over time can lead to significant costs for rehabilitation once the pavement conditions fall below a certain level of deterioration. To mitigate these costs, it is possible to extend the service life of pavement through periodic maintenance at a relatively low cost. To increase the frequency of inspections and enhance the effectiveness of maintenance activities, this study proposes a crowdsourcing-based inspection system for autonomous road condition assessment that can be mounted on multiple vehicles. The proposed inspection system can evaluate the pavement condition based on the Pavement Surface and Evaluation Rating (PASER) system. Vehicles equipped with this data acquisition system are driven over the road through the cities twice per week, thus comprehensive RGB-D data of the road surface are collected timely and widely. The system can detect various defects, including transverse cracks, longitudinal cracks, alligator cracking, and 3D defects such as rutting and potholes through a trained deep learning-based classification model. Furthermore, the proposed autonomous road inspection system can monitor the evolutionary changes of defects such as cracks and potholes to enable damage prognosis. © 2023 by DEStech Publi cations, Inc. All rights reserved

关键词： Deterioration

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Probabilistic Microgrid Economic Dispatch via Economic Model Predictive Control Considering Weather and Load Data Uncertainty

Probabilistic Microgrid Economic Dispatch via Economic Model...

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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Shadaei, Maral Moazeni, Faegheh Khazaei, Javad Lehigh University Electrical and Computer Engineering BethlehemPA United States Lehigh University Civil and Environmental Engineering BethlehemPA United States

ISBN: (纸本)9798350347432

The output power of distributed generation (DG), particularly renewable energy sources depend on weather conditions. The inclusion of renewable sources and the uncertainty they induce in generation adds to the complexity of today's power systems and makes the economic dispatch problem more difficult to solve. In this paper, a framework for solving economic dispatch is proposed, which first models the uncertainties in the power system using Fuzzy Monte Carlo simulation. Then, an economic model predictive control (EMPC) algorithm is developed to address the microgrid's resource allocation problem, taking into account the uncertainty in both generation and demand. The goal is to make energy management more cost-effective while tracking a specified reference trajectory. To validate the effectiveness of the proposed approach, a microgrid composed of a photovoltaic (PV) array, a wind turbine, a diesel generator, and an electrical Storage System (ESS) is developed. It is shown that the proposed EMPC method is a viable alternative to conventional economic dispatch in the presence of uncertainty in the input variables. © 2023 IEEE.

关键词： Model predictive control

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An Attention-based Self-Supervised Approach to Monocular Depth Estimation from UAV captured Video Sequences 6

An Attention-based Self-Supervised Approach to Monocular Dep...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Benkhoui, Yasmina El-Korchi, Tahar Ludwig, Reinhold Worcester Polytechnic Institute Electrical and Computer Engineering Department United States Worcester Polytechnic Institute Civil and Environmental Engineering Department United States

ISBN: (纸本)9798350325485

UAV-based inspections represent a promising solution to contactless civil infrastructure condition assessment, especially bridges. In this context, depth estimation is crucial as it helps predict the distance to objects, plan flight trajectories, and avoid obstacles. Recently, learning-based methods made significant progress in monocular depth estimation. However, supervised approaches require a large amount of annotated depth data which remains challenging to collect. As an alternative, researchers have been investigating self-supervised learning, where the depth estimation network is trained by projecting a view to nearby views and minimizing the re-projection photometric loss. However, current methods lack accuracy and robustness since they do not exploit structural information, leading to inaccurate spatial representations and surface discontinuities. In addition, monocular depth estimation suffers from scale ambiguity, and external or prior information is needed for scale recovery. This work sought to overcome these issues by adapting the standard joint network of estimating depth and pose based on a self-learned depth prediction network from monocular videos. The proposed method introduces a soft attention mechanism and a recurrent block. The attention mechanism guides the neural network's attention to the global structures and local attributes and recovers the spatial relationship of visual features, whereas the recurrent block leverages sequence frames to capture temporal information in addition to spatial information. Our experiments and ablation study on AeroBridge dataset demonstrate that these modifications efficiently capture spatio-Temporal information and generate competitive qualitative and quantitative results compared to state-of-The-Art methods confirming the practical applicability of our design in inspection tasks. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Robust High-Precision LiDAR Localization in Construction Environments 41

Robust High-Precision LiDAR Localization in Construction Env...

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41st International Symposium on Automation and Robotics in Construction, ISARC 2024

作者： Yarovoi, Andrew Mo, Pengyu Cho, Yong Kwon Woodruff School of Mechanical Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Civil and Environmental Engineering Georgia Institute of Technology United States

ISBN: (纸本)9780645832211

Accurate localization plays a crucial role in the effective operation of autonomous robotics systems, especially in dynamic environments such as construction sites. Simultaneous Localization and Mapping (SLAM) utilizing LiDAR sensors has emerged as a popular solution due to its ability to function without external infrastructure. However, existing algorithms exhibit significant shortcomings. Despite current methods achieving high accuracy over long trajectories, they struggle with precision and reliability in complex indoor environments. This paper introduces a novel feature-based LiDAR SLAM system designed to address these limitations and enhance short-term precision and overall robustness. The proposed system is evaluated using both existing datasets and a physical robot platform, addressing the limitations of current implementations and showcasing improved performance in challenging real-world scenarios, particularly in construction environments. © 2024 ISARC. All Rights Reserved.

关键词： Mapping

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Measuring Pavement Smoothness from the Perspective of E-scooters

Measuring Pavement Smoothness from the Perspective of E-scoo...

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2023 Annual Intermountain engineering, Technology and Computing, IETC 2023

作者： Apelu, Dylan MacFarlane, Gregory S. Guthrie, W. Spencer Adams, Nicole Mazzeo, Brian A. Brigham Young University Civil and Construction Engineering Provo United States Brigham Young University Electrical and Computer Engineering Provo United States

ISBN: (纸本)9798350335903

As e-scooter rental services have become a popular transportation mode in many cities, pavement quality data are needed for e-scooter paths. The objectives of this research were to develop and demonstrate a device for measuring pavement smoothness along e-scooter paths using commercially available equipment, including an e-scooter and a mobile phone with accelerometer and geolocation features. A method of data processing was developed to produce a pavement smoothness metric from the acceleration measurements. The results of data collection and analysis suggest that the instrumented e-scooter is able to efficiently measure and quantify pavement smoothness for bike lanes and paths, where observed differences in pavement smoothness were clearly correlated with differences in pavement distress. Because e-scooters available through typical shared mobility programs are already equipped with sensors like those utilized in this study, the possibility exists for e-scooter rental services to collect useful pavement quality data from their riders' information and provide it to cities, where the data could inform master plans related to infrastructure improvement. © 2023 IEEE.

关键词： Pavements

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Model Identification of Distributed Energy Resources Using Sparse Regression and Koopman Theory

Model Identification of Distributed Energy Resources Using S...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Khazaei, Javad Moazeni, Faegheh Lehigh University Electrical and Computer Engineering MiddletownPA United States Lehigh University Civil and Environmental Engineering BethlehemPA United States

ISBN: (纸本)9781728170251

A complex physics-based modeling procedure and the uncertainty and confidentiality of internal parameters of distributed energy resources (DERs) motivate system identification tools. With the availability of high-fidelity measurements and historical data, model-free identification of DERs can facilitate the control design without tedious modeling of these nonlinear systems. This paper develops a framework for data-driven nonlinear modeling of DERs using sparse identification of nonlinear dynamics (SINDy). In addition, to avoid the complexities of nonlinear control designs, the identified nonlinear dynamics will be lifted to a linear space utilizing Koopman theory. Compared with existing physics-based designs that heavily rely on knowing the detailed system dynamics or data-driven designs that relay on large historical data and are not interpretable, the proposed model-free DER identification framework can accurately capture the dynamics of the DERs with available measurements and lift them to a linear space that provides guaranteed performance for tracking problems. Time-domain simulations were carried out to validate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Energy resources

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