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XAI based Photovoltaic Energy Management Framework for Smart Cities

IEEE Access 2025年 13卷 98349-98361页

作者： Alagarsamy, Malini Rajasekaran, Umamaheswari Ganesan, Sriram Palanivel, Ramyavarshini Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Tamil Nadu Madurai India

The demand for explainable energy forecasting models has been emphasized in recent studies to enhance decision-making transparency. The efficient conversion of solar energy is stochastic and depends on environmental conditions. To address this, we propose a model-chain framework named XPEMM (XAI-based Photovoltaic Energy Management Model chain) for Solar Energy Production and Management in Smart Cities, ensuring explainability using LIME. Our model chain consists of three stages: Long-term forecasting of Global Horizontal Irradiance (GHI) as the first stage, Regression Modeling of GHI with power as the second stage, and Model-Agnostic explanation generation as the third stage. A recursive forecasting strategy was employed, leveraging Recurrent Neural Networks (RNN) to reduce error accumulation over a 5-year forecast horizon. The output of the proposed model chain assists grid cell operators in real-time monitoring, resulting in optimized performance by predicting the causes of high and low performance in advance, allowing for necessary adjustments. It also aids in site selection for high-budget PV-plant installations. We used two different datasets for training and benchmarking the Recursive Multistep GHI Forecasting Model (RMGFM). The proposed recursive forecasting strategy has been evaluated using RMSE and MAE error metrics which are 91.40 and 47.32 respectively, which are 2.9% 29.39%, and 7.9% less than the previously established approach using LSTM, PCR, and SVR based on RMSE measure and 15.9%, 49.9%, 34.8% less based on MAE metric. This study concludes that a recursive approach is best for long-term forecasting of GHI in regions with cyclic climatic patterns based on RMSE and MAE values. The interpretation of the LIME output establishes the fitness of model agnosticity in the Photovoltaic model chain. © 2013 IEEE.

关键词： Recurrent neural networks

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Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification 39

Queryable Prototype Multiple Instance Learning with Vision-L...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gou, Jiaxiang Ji, Luping Liu, Pei Ye, Mao School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static WSI datasets. One of the most obvious weaknesses of these methods is that they cannot efficiently preserve and utilize previously learned knowledge. With any new data arriving, classification models are required to be re-trained on both previous and current new data. To overcome this shortcoming and break through traditional vision modality, this paper proposes the first Vision-Language-based framework with Queryable Prototype Multiple Instance Learning (QPMIL-VL) specially designed for incremental WSI classification. This framework mainly consists of two information processing branches: one is for generating bag-level features by prototype-guided aggregation of instance features, while the other is for enhancing class features through a combination of class ensemble, tunable vector and class similarity loss. The experiments on four public WSI datasets demonstrate that our QPMIL-VL framework is effective for incremental WSI classification and often significantly outperforms other compared methods, achieving state-of-the-art (SOTA) performance. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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AR assistance for efficient dynamic target search

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Computational Visual Media 2023年第1期9卷 177-194页

作者： Zixiang Zhao Jian Wu Lili Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina

When searching for a dynamic target in an unknown real world scene,search efficiency is greatly reduced if users lack information about the spatial structure of the *** target search studies,especially in robotics,focus on determining either the shortest path when the target’s position is known,or a strategy to find the target as quickly as possible when the target’s position is ***,the target’s position is often known intermittently in the real world,e.g.,in the case of using surveillance *** goal is to help user find a dynamic target efficiently in the real world when the target’s position is intermittently *** order to achieve this purpose,we have designed an AR guidance assistance system to provide optimal current directional guidance to users,based on searching a prediction *** assume that a certain number of depth cameras are fixed in a real scene to obtain dynamic target’s *** system automatically analyzes all possible meetings between the user and the target,and generates optimal directional guidance to help the user catch up with the target.A user study was used to evaluate our method,and its results showed that compared to free search and a top-view method,our method significantly improves target search efficiency.

关键词： augmented reality(AR) search guidance

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Privacy-Preserving Publishing Mechanism for Real-Time Vehicle Trajectory in Vehicle-to-Grid Networks 5

Privacy-Preserving Publishing Mechanism for Real-Time Vehicl...

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Wang, Yulong Xu, Jianbo Gong, Bo School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331533694

The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirectional energy exchange with the grid system. As distributed energy storage units within the V2G network, MESVs help meet the grid's load-balancing demands. To accurately schedule grid loads, V2G networks rely on MESVs' real-time trajectory data. After collecting this data, the central authority (CA) publishes it to the grid enterprise (GM) for load-balancing decisions. However, before publication, the CA must process the data to protect the location privacy of MESVs and prevent GM from extracting sensitive information. To address this privacy protection challenge during data publication, we propose a differential privacy-based mechanism. Our mechanism uses the unscented Kalman filter to predict and correct the published data at the sampling point and intelligently adjusts the sampling interval. Additionally, we introduce a privacy budget allocation method based on regional access frequency and travel time, enhancing privacy protection of trajectory in high-access areas and peak hours. Through security analysis and experimental validation, our mechanism effectively preserves the privacy of real-time vehicle trajectory data while ensuring data validity. © 2025 IEEE.

关键词： Vehicle-to-grid

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Cross-modal Gaussian Localization Distillation for Optical Information guided SAR Object Detection

Cross-modal Gaussian Localization Distillation for Optical I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Chao Luo, Lei Fang, Wenxuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350368741

Synthetic Aperture Radar (SAR) images contain a dense clutter of objects that can be better characterized using bounding boxes with angles. However, accurately detecting the angles of objects remains challenging due to the imaging mechanism of SAR. To address this issue, we propose a novel knowledge distillation method called cross-modal Gaussian Localization Distillation (GaLD). It aims to improve SAR object detection performance by utilizing the angle information from optical images. Specifically, we convert the oriented bounding box into a Gaussian distribution and design a Gaussian Angle Distillation (GAD) loss function to align the angle information between optical and SAR images. In addition, to mitigate the negative impact of low-quality angle information on the network, we design an Adaptive Weighting Strategy (AWS) to guide the student network to prioritize high-quality angle information. The lack of high-quality oriented labels and objects in the OGSOD-1.0 dataset has hindered the progress in related fields. Therefore, we have added high-quality oriented labels and images to the OGSOD-1.0 dataset to construct a new dataset. Extensive experiments demonstrate the effectiveness and superiority of our proposed GaLD over existing methods. The dataset and code are available at: https://***/wchao0601/GaLD. © 2025 IEEE.

关键词： knowledge distillation object detection Remote sensing synthetic aperture radar (SAR)

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Hierarchical Federated ADMM

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 11-15页

作者： Azimi-Abarghouyi, Seyed Mohammad Bastianello, Nicola Johansson, Karl H. Fodor, Viktoria KTH Royal Institute of Technology School of Electrical Engineering and Computer Science and Digital Futures Stockholm10044 Sweden

In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM), leveraging a network architecture consisting of a single cloud server and multiple edge servers, where each edge server is dedicated to a specific client set. Within this framework, we propose two novel FL algorithms, which both use ADMM in the top layer: one that employs ADMM in the lower layer and another that uses the conventional gradient descent-based approach. The proposed framework enhances privacy, and experiments demonstrate the superiority of the proposed algorithms compared to the conventional algorithms in terms of learning convergence and accuracy. Additionally, gradient descent on the lower layer performs well even if the number of local steps is very limited, while ADMM on both layers lead to better performance otherwise. © 2019 IEEE.

关键词： Federated learning

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Integrating Personalized Spatio-Temporal Clustering for Next POI Recommendation 39

Integrating Personalized Spatio-Temporal Clustering for Next...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Song, Chao Ren, Zheng Lu, Li School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

Location-Based Social Networks (LBSNs) offer a rich dataset of user activity at Points-of-Interest (POIs), making next POI recommendation a key task. Traditional algorithms face challenges due to broad searching scopes, affecting recommendation accuracy. Users tend to visit nearby POIs and show temporal concentration in their activities, reflecting personalized spatio-temporal clustering. However, individual user data may be insufficient to capture these clustering effects for personalized recommendations. In this paper, we propose an integrated Personalized Spatio-Temporal Clustering Model (iPCM) for next POI recommendation. The model learns this kind of personalized spatio-temporal clustering effect by using global historical trajectory data in conjunction with user feature embeddings. It integrates the features of personalized spatio-temporal clustering with the user's trajectory, and completes the user's POI recommendation through a Transformer encoding and MLP decoding. To enhance the accuracy of predictions, we add a module of probability adjustment. The experimental results on multiple datasets show that with the help of personalized spatio-temporal clustering, the proposed iPCM is superior to existing methods in various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Wavelet-based Diffusion Model for Low-light Image Enhancement in Raw Space 16

A Wavelet-based Diffusion Model for Low-light Image Enhancem...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhang, Xin-Yu Xu, Yang Wei, Zhi-Hui Wu, Ze-Bin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image information and thus have more space for image processing. Existing methods often struggle to approximate real images in terms of image detail enhancement and color restoration. Drawing inspiration from the latest advancements in denoising diffusion models, we introduce an innovative framework that leverages wavelet transform and diffusion models to tackle the low light image enhancement challenge in Raw space. In our method, the input low-light image is first decomposed into different frequency sub-bands by discrete wavelet transform (DWT) from both image and feature levels. Then a redesigned diffusion model is trained to adaptively denoise and enhance the different frequency sub-bands. In order to achieve better results in human eye perception, we also improved the loss function during training process. Finally, the enhanced result of the low-light image is finally reconstructed through inverse wavelet transform (IWT) and subsequent processing, transitioning from Raw space to sRGB space. Extensive experiments on SID and ELD datasets show that our refined method outperforms several existing methods, delivering superior performance both quantitatively and qualitatively. © 2025 SPIE.

关键词： Discrete wavelet transforms

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Faster split-based feedback network for image super-resolution

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High technology Letters 2024年第2期30卷 117-127页

作者：田澍 ZHOU Hongyang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083P.R.China

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep *** work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation ***,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback ***,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution ***,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer *** experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art ***,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

关键词： super-resolution(SR) split-based feedback contrastive learning

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Adversarial Geometric Attacks for 3D Point Cloud Object Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3144-3157页

作者： Yao, Rui Zhang, Anqi Zhou, Yong Zhao, Jiaqi Liu, Bing El Saddik, Abdulmotaleb China University of Mining and Technology School of Computer Science and Technology Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou221116 China University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking models. However, existing adversarial attack methods for 3D PCOT seldom leverage the geometric structure of point clouds and often overlook the transferability of attack strategies. To address these limitations, this paper proposes an adversarial geometric attack method tailored for 3D PCOT, which includes a point perturbation attack module (non-isometric transformation) and a rotation attack module (isometric transformation). First, we introduce a curvature-aware point perturbation attack module that enhances local transformations by applying normal perturbations to critical points identified through geometric features such as curvature and entropy. Second, we design a Thompson sampling-based rotation attack module that applies subtle global rotations to the point cloud, introducing tracking errors while maintaining imperceptibility. Additionally, we design a fused loss function to iteratively optimize the point cloud within the search region, generating adversarially perturbed samples. The proposed method is evaluated on multiple 3D PCOT models and validated through black-box tracking experiments on benchmarks. For P2B, white-box attacks on KITTI reduce the success rate from 53.3% to 29.6% and precision from 68.4% to 37.1%. On NuScenes, the success rate drops from 39.0% to 27.6%, and precision from 39.9 to 26.8%. Black-box attacks show a transferability, with BAT showing a maximum 47.0% drop in success rate and 47.2% in precision on KITTI, and a maximum 22.5% and 27.0% on NuScenes. © 1999-2012 IEEE.

关键词： Iterative methods

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