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공중 인간 행동 인식을 위한 다양한 관점과 배경 벤치마크
Muhammad Munsif,Haseeb Ali Khan,Minje Kim,Fatema Rahimi,Sana Parez,Mi Young Lee,Soo-Mi Choi,Jong Weon Lee 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06
The aerial view diverse action recognition (AR) benchmark provides a valuable resource for researchers and developers in computer vision (CV) for human actions recognition (HAR) from an aerial perspective. With the increasing use of unmanned aerial vehicles (UAVs) for surveillance, delivery, search, and rescue, a robust understanding of human actions from an aerial view is crucial. Existing datasets lack representation of common outdoor actions and are unsuitable for intelligent UAVs. This article proposes a dataset that captured various actions from diverse viewpoints and in different environments. The dataset includes three viewpoints (Top, left, and right) allowing angle-invariant algorithm development. State-of-the-art algorithms (3D, and 2D convolutions with sequential learning) are evaluated on the dataset. The proposed model demonstrates exceptional performance with high accuracy (87.5%), precision (86.3%), and recall (87.2%) rates. The robustness of the model is showcased through real-time testing, indicating that the proposed dataset and model contribute to advancing research from drone view AR and have the potential to enhance surveillance and other UAV applications.
Efficient Battery’s State of Charge Estimation in Energy Storage Systems
Muhammad Munsif,Noman Khan,Tanveer Hussain,Muhammad Sajjad,Mi Young Lee 한국차세대컴퓨팅학회 2021 한국차세대컴퓨팅학회 학술대회 Vol.2021 No.11
Renewable energies use clean sources for energy generation and have the potential to balance the supply and demand of power. One of the best ways to save energy for high-demand time is to preserve it in a battery energy storage system (BESS). Various methods are presented in the last two decades for battery state of charge (SOC) estimation, however, most of them are focused only on a single battery pack and use data without accurate preprocessing and feature selection strategy. Therefore, in this paper, we conduct a comparative analysis of machine learning (ML) models with a specific preprocessing strategy and suggest a high performer model for battery rack SOC estimation. First, we preprocess the data by cleaning, normalizing, selecting important attributes, and then split it into training and testing sets. Next, four ML models are trained using the training data for SOC estimation, and finally, for better evaluation, each model is evaluated on the testing data using various error metrics. After comprehensive experiments, we suggest multilayer perceptron (MLP) due to high performance for batteries rack SOC estimation.
PV-ANet: Attention-Based Network for Short-term Photovoltaic Power Forecasting
Muhammad Munsif,Habib Khan,Zulfiqar Ahmad Khan,Altaf Hussain,Fath U Min Ullah,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.
Immersive Learning: A Virtual Reality Approach to Combat Light Pollution
Younghoon Kim,Muhammad Munsif,Altaf Hussain 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
Rapid technological advancement and industrial growth pervade modern society. Light pollution has emerged as a significant environmental issue, resulting from excessive artificial nighttime lighting. It obscures celestial views and negatively impacts human health, contributing to sleep disorders and mood imbalances. This article introduces an immersive virtual reality (VR) based learning approach to reduce light pollution and raise awareness about the issue of light pollution. Utilized the META QUEST 2 Head-Mounted Display in a virtual environment, enable users to actively participate in learning scenarios. A variety of terrain textures, city models, furniture, and lighting effects are used to interact with miniatures in a virtual room, influencing the city's lighting and unveiling a starlit sky. The impact of the proposed work is evaluated through experiments and an object recognition module, showing a significant increase in awareness and understanding of light pollution and its effects. By providing an experiential learning opportunity, this tool has the potential to encourage proactive engagement, contribute to efforts to reduce light pollution and promote environmental well-being.
Christopher Alan Muir,Ashish Munsif,Kenrick Blaker,Yvonne Feng,Mario D’Souza,Shailja Tewari 대한갑상선학회 2020 International Journal of Thyroidology Vol.13 No.1
Background and Objectives: Subclinical hypothyroidism in pregnancy has been inconsistently associated with an increased risk of developing gestational diabetes mellitus (GDM). Materials and Methods: We retrospectively examined whether an antenatal thyroid stimulating hormone (TSH) level ≥2.5 mIU/L was associated with increased risk of GDM in 1147 pregnant women residing in a multi-ethnic suburban area of Sydney, Australia. Results: Despite a high prevalence of GDM and hypothyroidism in our study, women with antenatal TSH concentrations ≥2.5 mIU/L were not at increased risk for development of gestational diabetes. Traditional risk factors for GDM, such as maternal body mass index, ethnicity, previous GDM pregnancy and family history of type 2 diabetes were significant predictors of incident GDM on multivariable analyses. Conclusion: Mild elevations in antenatal TSH concentration did not significantly increase risk of incident GDM compared to healthy euthyroid women.
Survey of AI‑Empowered Methods for Detecting Electricity Theft in Smart Grids
Waseem Ullah,Altaf Hussain,Muhammad Munsif,Habib Khan,Min Je Kim,Su Min Lee,Myoung Ho Seong,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.
Evaluation of Humic Acid Application Methods for Yield and Yield Components of Mungbean
Waqas, Muhammad,Ahmad, Bashir,Arif, Muhammad,Munsif, Fazal,Latif Khan, Abdul,Amin, Muhammad,Kang, Sang-Mo,Kim, Yoon-Ha,Lee, In-Jung 'Scientific Research Publishing, Inc.' 2014 American journal of plant sciences Vol.5 No.15
어텐션 매커니즘 기반 심층 컨볼루션 뉴럴 네트워크를 사용한 산업용 불량 칩 검사
Min Je Kim,Altaf Hussain,Muhammad Munsif,Sangil Yoon,Mi Young Lee,Sung Wook Baik 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.06
The identification of anomalies in industrial settings poses a significant challenge, especially when there is a lack of negative samples and when the anomalous regions are small. Although existing computer vision methods have automated this task to some extent, these approaches struggle to extract salient features for inspecting defective chips. To tackle this problem, a deep learning-based framework is proposed for detecting anomalies in industrial settings. The framework utilizes a fine-tuned backbone convolutional neural network model and incorporates an enhanced attention mechanism. The attention module generates discriminative feature maps along two dimensions: channel and spatial. This is achieved by processing intermediate features obtained from the backbone model. These attention maps are then multiplied with the input feature map to dynamically enhance the relevant features. Extensive experiments demonstrate the effectiveness of our proposed method in maintaining a high level of detection accuracy for industrial product inspections. Consequently, our results conclude a suitable solution for optical chip inspection systems in industrial settings.