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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.
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.
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.