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Allocation of flood drainage rights in the middle and lower reaches of the Yellow River based on deep learning and flood resilience

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JOURNAL OF HYDROLOGY 2022年 615卷

作者： Zhang, Kaize Dong, Zengchuan Guo, Li Boyer, Elizabeth W. Mello, Carlos R. Shen, Juqin Lan, Ping Wang, Jianlin Fan, Bihang Hohai Univ Coll Hydrol & Water Resources Nanjing 211100 Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Penn State Univ Dept Ecosyst Sci & Management University Pk PA 16802 USA Univ Fed Lavras Water Resources Dept CP 3037 BR-37200900 Lavras MG Brazil Hohai Univ Coll Agr Engn Nanjing 210098 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China

Climate change has increased the intensity and frequency of storms in many world regions, calling for new flood planning and management strategies. The concept of flood drainage rights (FDR), or the legal rights of regions to drain floodwaters into river reaches, is used in watershed planning in China. Quantifying the allocation of FDR remains challenging, where some previous methods have resulted in unreasonable or impractical allocation plans due to incomplete consideration of driving factors or the use of unscientific allocation methods. This study ex-plores the allocation plan of FDR in the middle and lower reaches of the Yellow River Watershed in China. Climatic variability and change have caused frequent flooding in portions of the basin, with significant societal and economic implications. First, we comprehensively analyzed factors driving FDR for regions in the watershed. Following the conceptual flood resilience strategy currently being advocated for the region, we considered natural, socioeconomic, governance, resilience, and resistance factors that influence the complex allocation of FDR and established a qualitative indicator system to reflect the complexity of these driving factors. Second, we quantified FDR values for flood-prone regions in the middle and lower river reaches of this major river basin. We introduced a specific deep learning method, called the variational autoencoder (VAE) model, to quantify FDR allocation, providing a robust solution to the challenge of the multi-objective, high-dimensional, nonlinear, and non-normal distribution of factors driving FDR allocation. Next, using data from 2005 to 2019, this model was applied to the study area. The allocation of FDR (summing to 100%) across five flood-prone provinces of the watershed includes Inner Mongolia (9.36%), Shaanxi (10.00%), Shanxi (10.95%), Henan (32.58%), and Shan-dong (37.12%). Using the harmony evaluation method based on harmony theory, we compared the new VAE allocation method

关键词： Flood drainage rights Flood resilience strategy Deep learning variational autoencoder Harmonious evaluation Yellow River

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ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution

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JOURNAL OF NEURAL ENGINEERING 2021年第4期18卷

作者： Rokai, Janos Racz, Melinda Fiath, Richard Ulbert, Istvan Marton, Gergely Res Ctr Nat Sci Inst Cognit Neurosci & Psychol Magyar Tudosok Korutja 2 H-1117 Budapest Hungary Semmelweis Univ Sch PhD Studies Ulloi Ut 26 H-1085 Budapest Hungary Pazmany Peter Catholic Univ Fac Informat Technol & Bion Prater Utca 50-A H-1083 Budapest Hungary

Objective. The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting. Approach. In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion. Main results. The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F-1 scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points). Significance. ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.

关键词： spike sorting deep learning variational autoencoder

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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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Multimodal Remote Sensing Data Classification Based on Gaussian Mixture variational Dynamic Fusion Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-1页

作者： Wang, Haoyu Liu, Xiaomin Qiao, Zhenzhuang Wang, Guoqing Chen, Haotian China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Space Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the development of sensor technology, the rational use of multimodal data has become a research hotspot in the field of remote sensing. The multimodal fusion method can effectively improve the accuracy of remote sensing data classification by using the complementary information of different modalities. However, the existing multimodal fusion methods face many challenges, including difficulties in suppressing spectral noise, fully mining contextual information, and learning the strong adaptive fusion pattern. To address the above challenges, a Gaussian mixture variational dynamic fusion network (GM-VDFN) is proposed. First, a multimodal multiscale spatial graph is constructed, and the graph convolution is used to learn the multiscale features. In this process, a spatial topology constraint based on GM (STC-GM) is proposed, which suppresses spectral noise by constraining the topological consistency of the two modalities. Second, a multiscale dynamic graph aggregation module (MDGAM) is constructed, which can capture the shareable class identification information from multiscale features and mine personalized fusion patterns suitable for each sample. Finally, the evidence lower bound for the multimodal joint distribution is derived, and a multimodal variational autoencoder (M-VAE) is designed. Optimizing the evidence lower bound to model multimodal joint distributions, thereby learning the strong adaptive fusion pattern between modalities. Experimental results on four fusion datasets (Houston 2013, Trento, MUUFL, and Houston 2018) show that GM-VDFN achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

关键词： Laser radar Noise Remote sensing Topology Data mining Data integration Interference Classification feature fusion hyperspectral image (HSI) light detection and ranging (LiDAR) variational autoencoder

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Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

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SENSORS 2023年第18期23卷 7802页

作者： Tseng, Yu-Hsuan Wen, Chih-Yu Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Smart Sustainable New Agr Res Ctr SMARTer Taichung 40227 Taiwan Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr IDCSA Taichung 40227 Taiwan

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional variational autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

关键词： human activity recognition variational autoencoder generative adversarial networks

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Multimodal representation models for prediction and control from partial information

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ROBOTICS AND AUTONOMOUS SYSTEMS 2020年 123卷

作者： Zambelli, Martina Cully, Antoine Demiris, Yiannis Imperial Coll London Dept Elect & Elect Engn Personal Robot Lab London England Imperial Coll London Personal Robot Lab London England DeepMind London London England Imperial Coll London Dept Comp London England

Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from different sensor modalities is difficult, because the learning model must be able to handle diverse types of signals, and learn a coherent representation even when parts of the sensor inputs are missing. In this paper, a multimodal variational autoencoder is proposed to enable an iCub humanoid robot to learn representations of its sensorimotor capabilities from different sensor modalities. The proposed model is able to (1) reconstruct missing sensory modalities, (2) predict the sensorimotor state of self and the visual trajectories of other agents actions, and (3) control the agent to imitate an observed visual trajectory. Also, the proposed multimodal variational autoencoder can capture the kinematic redundancy of the robot motion through the learned probability distribution. Training multimodal models is not trivial due to the combinatorial complexity given by the possibility of missing modalities. We propose a strategy to train multimodal models, which successfully achieves improved performance of different reconstruction models. Finally, extensive experiments have been carried out using an iCub humanoid robot, showing high performance in multiple reconstruction, prediction and imitation tasks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multimodal learning Autonomous learning variational autoencoder

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Multi source deep learning method for drug-protein interaction prediction using k-mers and chaos game representation

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 246卷

作者： Mesrabadi, Hengame Abbasi Faez, Karim Pirgazi, Jamshid Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Univ Sci & Technol Mazandaran Dept Comp Engn Behshahr Iran

Identification of drug -protein interactions plays an important role in drug discovery. Development of new calculation methods, which have high accuracy solve the problems related to the previous methods, which were expensive and time-consuming. In this article, a new model for drug -protein interactions, and a new mapping approach to represent drug -protein sequences are proposed. The proposed model consists of four parts: drug and protein descriptor section, Drug CNN and Protein CNN sections, Encoder section and classification section. In this method, first the data is prepared. At this stage, the totals are equal to each other. Then in the next step using the k-mers method and Chaos Game, the sequence of drug and protein becomes an image. In the next step, the image is used to train CNN models. These images serve as the input of independent networks for the drug and are considered as a protein. These networks are used to extract feature from drug and protein. In the last layer of these networks, features extracted from drug and protein sequences combine with each other. After concatenating, the number of features will raise. To reduce the number of features and to extract more efficient features, a variational autoencoder is used. In the last step, this combined feature vector is used to train machine learning models. The proposed method has been tested and evaluated on 6 standard data sets. The results of the experiments show that the proposed method has an acceptable performance compared to other methods in this data set.

关键词： Drug target interaction Multi source K-mers Chaos game Deep learning variational autoencoder

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Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities

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SENSORS 2021年第12期21卷 4133页

作者： Heidarivincheh, Farnoosh McConville, Ryan Morgan, Catherine McNaney, Roisin Masullo, Alessandro Mirmehdi, Majid Whone, Alan L. Craddock, Ian Univ Bristol Sch Comp Sci Elect & Elect Engn & Engn Maths Bristol BS8 1UB Avon England Univ Bristol Med Sch Translat Hlth Sci Bristol BS8 1UD Avon England North Bristol NHS Trust Bristol Brain Ctr Bristol BS10 5PN Avon England Monash Univ Dept Human Centred Comp Melbourne Vic 3000 Australia

Parkinson's disease (PD) is a chronic neurodegenerative condition that affects a patient's everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.

关键词： Parkinson's disease deep learning multimodal data missing modality accelerometer computer vision variational autoencoder

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Reconstruction and generation of 3D realistic soil particles with metaball descriptor

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COMPUTERS AND GEOTECHNICS 2023年 161卷

作者： Zhao, Yifeng Gao, Xiangbo Zhang, Pei Lei, Liang Li, Stan Z. Galindo-Torres, S. A. Zhejiang Univ Coll Environm & Resources Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Westlake Univ Sch Engn Key Lab Coastal Environm & Resources Zhejiang Prov 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

The morphology of soil particles is crucial in determining its granular characteristics and assembly responses. However, how to introduce accurate and various morphologies of realistic particles in modeling can be challenging, as it often requires time-consuming and costly X-ray Computed Tomography (XRCT). This has led to two prevalent problems in modeling: morphological reconstruction and generation. For reconstruction, we develop a geometric-based Metaball-Imaging algorithm. This algorithm is capable of accurately reconstructing the complex morphologies of realistic particles, including those with concave voids, which cannot be easily represented using other shape descriptors such as the spherical harmonic function. It employs a two-step approach, capturing the main contour of the particles using a series of non-overlapping spheres and then refining surface-texture details through gradient search. Four types of soil particles, hundreds of samples, are applied for evaluations. The result shows good matches on key morphological indicators (i.e., volume, surface area, sphericity, circularity, corey-shape factor, nominal diameter and surface-equivalent-sphere diameter), confirming its reconstruction precision. For generation, we propose the Metaball variational autoencoder. Assisted by deep neural networks, this method can generate new 3D particles in Metaball form, while retaining coessential morphological features with parental particles. Additionally, this method allows for control over the generated shapes through an arithmetic pattern, enabling the generation of particles with specific shapes. Two sets of XRCT images different in sample number and geometric features are chosen as parental data. On each training set, one thousand particles are generated for validations. The generation fidelity is demonstrated through comparisons of morphologies and shape-feature distributions between generated and parental particles. Examples are also provided to demonstrate contr

关键词： The metaball function Particle morphology Particle reconstruction Optimization method Particle generation variational autoencoder

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Cyberattack Detection Using Deep Generative Models with variational Inference

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2019年第2期145卷

作者： Chandy, Sarin E. Rasekh, Amin Barker, Zachary A. Shafiee, M. Ehsan Xylem Inc Adv Infrastruct Analyt 817 West Peachtree St Atlanta GA 30308 USA Xylem Inc Adv Infrastruct Analyt 639 Davis Dr Morrisville NC 27560 USA

Recent years have witnessed a rise in the frequency and intensity of cyberattacks targeted at critical infrastructure systems. This study designs a versatile, data-driven cyberattack detection platform for infrastructure systems cybersecurity, with a special demonstration in the water sector. A deep generative model with variational inference autonomously learns normal system behavior and detects attacks as they occur. The model can process the natural data in its raw form and automatically discover and learn its representations, hence augmenting system knowledge discovery and reducing the need for laborious human engineering and domain expertise. The proposed model is applied to a simulated cyberattack detection problem involving a drinking water distribution system subject to programmable logic controller hacks, malicious actuator activation, and deception attacks. The model is only provided with observations of the system, such as pump pressure and tank water level reads, and is blind to the internal structures and workings of the water distribution system. The simulated attacks are manifested in the model's generated reproduction probability plot, indicating its ability to discern the attacks. There is, however, need for improvements in reducing false alarms, especially by optimizing detection thresholds. Altogether, the results indicate ability of the model in distinguishing attacks and their repercussions from normal system operation in water distribution systems, and the promise it holds for cyberattack detection in other domains.

关键词： Cybersecurity Water distribution system variational autoencoder Deep learning Anomaly detection

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