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Sliding window-based LightGBM model for electric load forecasting using anomaly repair

JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 12857-12878页

作者： Park, Sungwoo Jung, Seungmin Jung, Seungwon Rho, Seungmin Hwang, Eenjun Korea Univ Sch Elect Engn Seoul South Korea Chung Ang Univ Dept Ind Secur Seoul South Korea

Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can lead to inefficient operation if the meter data are damaged or tampered with during collection or transmission. Therefore, it is important to identify abnormalities in smart grid data and process them accordingly. Various anomaly detection models have been proposed using statistical methods, but they cannot detect some anomaly patterns accurately, and the models generally did not consider repair strategies for the detected anomalies. Anomaly repair should be included with model training to improve forecasting performance. This paper proposes a robust sliding window-based LightGBM model for short-term load forecasting using anomaly detection and repair. We first show how to detect anomalies using a variational autoencoder and then how they can be repaired using a random forest method. Finally, we verify that the proposed sliding window-based LightGBM achieves superior forecasting performance in combination with anomaly detection and repair.

关键词： Anomaly detection Data repair Electric load forecasting variational autoencoder Random forest LightGBM Sliding window

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Editorial overview: Artificial intelligence (AI) methodologies in structural biology

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CURRENT OPINION IN STRUCTURAL BIOLOGY 2022年第0期74卷 102387页

作者： Cheng, Feixiong Tuncbag, Nurcan Cleveland Clin Genom Med Inst Lerner Res Inst Cleveland OH 44195 USA Case Western Reserve Univ Dept Mol Med Cleveland Clin Lerner Coll Med Cleveland OH 44195 USA Case Western Reserve Univ Case Comprehens Canc Ctr Sch Med Cleveland OH 44106 USA Koc Univ Dept Chem & Biol Engn Coll Engn TR-34450 Istanbul Turkey Koc Univ Sch Med TR-34450 Istanbul Turkey

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A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images

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REMOTE SENSING 2024年第5期16卷 781-781页

作者： Abady, Lydia Dimitri, Giovanna Maria Barni, Mauro Univ Siena Dept Informat Engn & Math I-53100 Siena Italy

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers generally achieve high detection accuracies, they struggle to generalize to image domains and generative architectures different from those encountered during training. In this paper, we propose a one-class classifier based on Vector Quantized variational autoencoder 2 (VQ-VAE 2) features to overcome the limitations of two-class classifiers. We start by highlighting the generalization problem faced by binary classifiers. This was demonstrated by training and testing an EfficientNet-B4 architecture on multiple multi-spectral datasets. We then illustrate that the VQ-VAE 2-based classifier, which was trained exclusively on pristine images, could detect images from different domains and generated by architectures not encountered during training. Finally, we conducted a head-to-head comparison between the two classifiers on the same generated datasets, emphasizing the superior generalization capabilities of the VQ-VAE 2-based detector, wherewe obtained a probability of detection at a 0.05 false alarm rate of 1 for the blue and red channels when using the VQ-VAE 2-based detector, and 0.72 when we used the EfficientNet-B4 classifier.

关键词： generative adversarial networks variational autoencoder EfficientNet detection Sentinel-2 remote sensing

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Small time asymptotics of the entropy of the heat kernel on a Riemannian manifold

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APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2024年 71卷

作者： Menkovski, Vlado Portegies, Jacobus W. Ravelonanosy, Mahefa Ratsisetraina Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands Eindhoven Univ Technol EAISI Eindhoven Netherlands Eindhoven Univ Technol Dept Math & Comp Sci POB 513 NL-5600 MB Eindhoven Netherlands

We give an asymptotic expansion of the relative entropy between the heat kernel q(Z)(t, z, w) of a compact Riemannian manifold Z and the normalized Riemannian volume for small values of t and for a fixed element z is an element of Z. We prove that coefficients in the expansion can be expressed as universal polynomials in the components of the curvature tensor and its covariant derivatives at z, when they are expressed in terms of normal coordinates. We describe a method to compute the coefficients, and we use the method to compute the first three coefficients. The asymptotic expansion is necessary for an unsupervised machine-learning algorithm called the Diffusion variational autoencoder.

关键词： Kullback-Leibler divergence Entropy Heat kernel Parametrix expansion variational autoencoder

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An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Yao, Changqing Nagao, Masahiro Datta-Gupta, Akhil Mishra, Srikanta Texas A&M Univ College Stn TX 77843 USA Battelle Mem Inst 505 King Ave Columbus OH 43201 USA

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation;second, we use variational autoencoderdecoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration. The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatu

关键词： CO 2 plume visualization Time -of -Flight Deep learning variational autoencoder

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Synthesizing affective neurophysiological signals using generative models: A review paper

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JOURNAL OF NEUROSCIENCE METHODS 2024年 406卷 110129页

作者： Nia, Alireza F. Tang, Vanessa Talou, Gonzalo Maso Billinghurst, Mark Bioengn Inst 70 Symonds St Auckland 1010 New Zealand

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end -to -end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near -Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

关键词： Generative Adversarial Network variational autoencoder Augmentation Data scarcity Human-computer interaction Emotion recognition EEG fNIRS

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User preference-aware content caching strategy for video delivery in cache-enabled IoT networks

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COMPUTER NETWORKS 2024年 240卷

作者： Firouzjaee, Mostafa Taghizade Jamshidi, Kamal Moghim, Neda Shetty, Sachin Univ Isfahan Dept Comp Engn Esfahan *** Iran Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to counteract this traffic upswing. Caching not only improves network efficiency but also enhances user service quality. Critical to the development of an optimal caching algorithm is the accurate prediction of future content popularity. This prediction hinges on the ability to anticipate users' content preferences, which is a pivotal method for assessing content popularity. In this study, we introduce a novel caching strategy termed User Preference-aware content Caching Strategy (UPCS) tailored for an IoT platform, where users access multimedia services offered by remote Content Providers (CPs). The UPCS encompasses three key algorithms: a content popularity prediction algorithm that utilizes variational autoencoders (VAE) to forecast users' future content preferences based on their prior requests, an online algorithm for dynamic cached content replacement, and a cooperative caching algorithm to augment caching efficiency. The proposed content caching strategy outperforms alternative methods, exhibiting superior cache hit rates and reduced Content Retrieval Delays (CRD).

关键词： Content caching Cooperative caching IoT Networks Popularity prediction Deep learning variational autoencoder

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Deep variational magnetic resonance image denoising via network conditioning

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Aetesam, Hazique Maji, Suman Kumar Birla Inst Technol Mesra Dept Comp Sci & Engn Patna 800014 India Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801106 India

In this paper, we propose a variational approach towards denoising magnetic resonance images (MRI) corrupted by spatially variant and signal-dependent Rician noise in a deep learning framework. To obtain a mathematically sound inference network, approximate variational posteriors are designed keeping in mind the Rician nature of noise. The proposed work tackles the denoising problem in several different ways. Firstly , the prior assumption on data in the variational posterior is motivated by the heavy-tailed marginal distribution of image gradients in natural images. This is captured by the sparsity promoting hyper-Laplacian prior on MR data. Similarly, median absolute deviation under Gaussian prior helps in the estimation of noise in the variational lower bound of marginal log likelihood term. Secondly , noise estimation from the background regions of the noisy data under the assumption of Rayleigh distribution prevents the addition of extra subnetwork for the estimation of spatially variant noise level parameters. Thirdly , feature-wise transformation of intermediate layers is performed using anatomical planes segmentation maps (APSM) for context-based network conditioning. Here, affine transformation parameters generated from APSM are modulated with the input features for spatial feature transformation. Fourthly , to capture the long-range dependencies lost in deeper convolutional layers, multiscale global feature fusion block (GFFuB) is used. Lastly , experimental results over synthetically corrupted MR data and real data obtained from MR scanners suggest the potential utility of the proposed model in real time.

关键词： Magnetic resonance imaging Noise estimation Rician distribution variational autoencoder Network conditioning

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Two-pronged feature reduction in spectral clustering with optimized landmark selection

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Rouhi, Alireza Bouyer, Asgarali Arasteh, Bahman Liu, Xiaoyang Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China

Spectral clustering is widely employed for clustering data points, particularly for non-linear and non-convex structures in high-dimensional spaces. However, it faces challenges due to the high computational cost of eigen decomposition operations and the performance limitations with high-dimensional data. In this paper, we introduce BVA_LSC, a novel spectral clustering algorithm designed to address these challenges. Firstly, we incorporate an advanced feature reduction stage utilizing Barnes-Hut t-SNE and a deep variational autoencoder (VAE) to efficiently reduce the dimensionality of the data, thereby accelerating eigen decomposition. Secondly, we propose an adaptive landmark selection strategy that combines the Grey Wolf Optimizer (GWO) with a novel objective function and K-harmonic means clustering. This strategy dynamically determines an optimal number of landmarks, enhancing the representativeness of the data and reducing the size of the similarity matrix. We assess the performance of our algorithm on various standard datasets, demonstrating its superiority over state-of-the-art methods in terms of accuracy and efficiency.

关键词： Spectral Clustering variational autoencoder Landmark Grey Wolf Optimization K-harmonic means Unsupervised Learning

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Learning to drive as humans do: Reinforcement learning for autonomous navigation

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第5期21卷

作者： Ge, Lun Zhou, Xiaoguang Wang, Yongcong Beijing Univ Posts & Telecommun Sch Modern Post Sch Automat 10 Xitucheng Rd Beijing 100088 Peoples R China Neolix Technol Co Ltd Beijing Peoples R China

This article introduces an approach aimed at enabling self-driving cars to emulate human-learned driving behavior. We propose a method where the navigation challenge of autonomous vehicles, from starting to ending positions, is framed as a series of decision-making problems encountered in various states negating the requirement for high-precision maps and routing systems. Utilizing high-quality images and sensor-derived state information, we design rewards to guide an agent's movement from the initial to the final destination. The soft actor-critic algorithm is employed to learn the optimal policy from the interaction between the agent and the environment, informed by these states and rewards. In an innovative approach, we apply the variational autoencoder technique to extract latent vectors from high-quality images, reconstructing a new state space with vehicle state vectors. This method reduces hardware requirements and enhances training efficiency and task success rates. Simulation tests conducted in the CARLA simulator demonstrate the superiority of our method over others. It enhances the intelligence of autonomous vehicles without the need for intermediate processes such as target detection, while concurrently reducing the hardware footprint, even though it may not perform as well as the currently available mature techniques.

关键词： Reinforcement learning autonomous driving soft actor-critic variational autoencoder navigation CARLA

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