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Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

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Advances in Atmospheric sciences 2024年第8期41卷 1601-1613页

作者： Jiaqi ZHENG Qing LING Jia LI Yerong FENG School of Computer Science and Engineering/Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen UniversityGuangzhou 510006China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction Guangzhou 510641China Shenzhen Institute of Meteorological Innovation Shenzhen 518038China

Due to various technical issues,existing numerical weather prediction(NWP)models often perform poorly at forecasting rainfall in the first several *** correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting,we propose a deep learning-based approach called UNet Mask,which combines NWP forecasts with the output of a convolutional neural network called *** UNet Mask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation *** overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a *** UNet Mask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask,which provides the corrected 6-hour rainfall *** evaluated UNet Mask on a test set and in real-time *** results showed that UNet Mask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI *** tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNet Mask's forecast *** study shows that UNet Mask is a promising approach for improving rainfall forecasting of NWP models.

关键词： deep learning numerical weather prediction(NWP) 6-hour quantitative precipitation forecast

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Hybrid architecture CNN-BLSTM for automatic speech recognition 3

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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SMT-Solving Induction Proofs of Inequalities 7

SMT-Solving Induction Proofs of Inequalities

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7th International Workshop on Satisfiability Checking and Symbolic Computation Square Workshop, SC-Square 2022

作者： Uncu, Ali K. Davenport, James H. England, Matthew Austrian Academy of Science Johann Radon Institute for Computational and Applied Mathematics Linz Austria University of Bath Faculty of Science Department of Computer Science Bath United Kingdom Coventry University Research Centre for Computational Science and Mathematical Modelling Coventry United Kingdom

This paper accompanies a new dataset of non-linear real arithmetic problems for the SMT-LIB benchmark collection. The problems come from an automated proof procedure of Gerhold-Kauers, which is well suited for solution by SMT. The problems of this type have not been tackled by SMT-solvers before. We describe the proof technique and give one new such proof to illustrate it. We then describe the dataset and the results of benchmarking. The benchmarks on the new dataset are quite different to the existing ones. The benchmarking also brings forward some interesting debate on the use/inclusion of rational functions and algebraic numbers in the SMT-LIB. © 2023 CEUR-WS. All rights reserved.

关键词： Rational functions

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PLOD: Point cloud level of detail for polygon mesh 32

PLOD: Point cloud level of detail for polygon mesh

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2024

作者： Konks, Eric Shcherbakov, Alexandr Phystech School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny Oblast Moscow141701 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow119991 Russia

Rendering high-polygonal models from distant perspectives has certain performance issues related to high density of subpixel triangles, which can be solved by levels of detail, a classical optimization method. Since a mesh occupies a small area on the screen, an alternative representation of the geometry in the form of a point cloud can replace the original geometry with little or no change to the image, and allowing for significant performance improvements due to the smaller number of primitives rendered. This paper presents a method for automatically generating a point cloud from a polygonal mesh with nested levels of detail. It also considers a method for rendering cloud with dynamically varying cloud density in real time based on view frustum and distance. © 2024 University of West Bohemia. All rights reserved.

关键词： Mesh generation

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Enhanced Metaheuristics with Deep Learning Model for Blockchain Assisted Cyber Security Solution in Internet of Things Environment 2

Enhanced Metaheuristics with Deep Learning Model for Blockch...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Perumal, Eswaran Arulanthu, Pramila Ramachandran, Raja Singh, Robert Alagappa University Department of Computer Applications Karaikudi India Alagappa University Ramanujan Centre for Higher Mathematics Karaikudi India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350328202

Internet of Things (loT) devices can be extensively utilized by several industries containing smart farming, smart logistics, smart medicine, smart cities, and so on. But, Distributed Denial of Service (DDoS) outbreaks position is a critical attack to IoT safety. Attackers simply employ the vulnerabilities of IoT devices and they control a portion of botnets to introduce DDoS attacks. IoT devices can be limited resources with restricted memory and calculating resources. While a developing technology, Blockchain (BC) has a great to resolving the security problems from the IoT. As a result, it can be essential to analyze distinct BC-based solutions for mitigating DDoS attacks from the IoT. This study develops Enhanced Metaheuristics with Deep Learning Model for BC Assisted Cybersecurity Solution (EMDLM-BCCS) technique in IoT platform. To allow secure data transmission from the IoT networks, BC technology can be applied. The EMDLM-BCCS algorithm follows initial stage of data pre-processing to normalize the input data. For attack detection, the EMDLM-BCCS approach makes use of extreme learning machine (ELM) approach. To enhance the detection results of the ELM model, elite-oppositional grasshopper optimization algorithm (EGOA) can be utilized. The simulated value of the EMDLM-BCCS approach can be validated on BoT-IoT dataset. The obtained values infer the better solution of the EMDLM-BCCS algorithm in terms of different measures. © 2024 IEEE.

关键词： Internet of things

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Absorbing Interface Conditions for the Simulation of Wave Propagation on Non-Uniform Meshes

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Numerical mathematics(Theory,Methods and Applications) 2023年第2期16卷 433-452页

作者： Changshi Li Yuhui Liu Fengru Wang Jerry Zhijian Yang Cheng Yuan Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072P.R.China School of Mathematics and Statistics Wuhan UniversityWuhan 430072P.R.China Hubei Key Laboratory of Computational Science Wuhan UniversityWuhan 430072P.R.China

We proposed absorbing interface conditions for the simulation of linear wave propagation on non-uniform *** on the superposition principle of second-order linear wave equations,we decompose the interface condition problem into two subproblems around the interface:for the first one the conventional artificial absorbing boundary conditions is applied,while for the second one,the local analytic solutions can be *** proposed interface conditions permit a two-way transmission of low-frequency waves across mesh interfaces which can be supported by both coarse and fine meshes,and perform a one-way absorption of high-frequency waves which can only be supported by fine meshes when they travel from fine mesh regions to coarse *** examples are presented to illustrate the efficiency of the proposed absorbing interface conditions.

关键词： Local time step non-uniform mesh absorbing interface condition

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A new class of generalized quasi-variational inequalities with applications to Oseen problems under nonsmooth boundary conditions

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science China mathematics 2024年第2期67卷 315-338页

作者： Shengda Zeng Akhtar A.Khan Stanislaw Migórski Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Faculty of Mathematics and Computer Science Jagiellonian University in Krak´owKrak´ow 30348Poland Center for Applied and Computational Mathematics School of Mathematical SciencesRochester Institute of TechnologyRochesterNY 14623USA College of Sciences Beibu Gulf UniversityQinzhou 535000China

In this paper, we study a generalized quasi-variational inequality (GQVI for short) with twomultivalued operators and two bifunctions in a Banach space setting. A coupling of the Tychonov fixedpoint principle and the Katutani-Ky Fan theorem for multivalued maps is employed to prove a new existencetheorem for the GQVI. We also study a nonlinear optimal control problem driven by the GQVI and givesufficient conditions ensuring the existence of an optimal control. Finally, we illustrate the applicability of thetheoretical results in the study of a complicated Oseen problem for non-Newtonian fluids with a nonmonotone andmultivalued slip boundary condition (i.e., a generalized friction constitutive law), a generalized leak boundarycondition, a unilateral contact condition of Signorini’s type and an implicit obstacle effect, in which themultivalued slip boundary condition is described by the generalized Clarke subgradient, and the leak boundarycondition is formulated by the convex subdifferential operator for a convex superpotential.

关键词： generalized quasi-variational inequality existence theorem optimal control Kakutani-Ky Fan theorem Oseen problem non-Newtonian fluid nonmonotone slip boundary condition

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Batch and match: black-box variational inference with a score-based divergence 24

Batch and match: black-box variational inference with a scor...

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Proceedings of the 41st International Conference on Machine Learning

作者： Diana Cai Chirag Modi Loucas Pillaud-Vivien Charles C. Margossian Robert M. Gower David M. Blei Lawrence K. Saul Center for Computational Mathematics Flatiron Institute Center for Computational Mathematics Flatiron Institute and Center for Computational Astrophysics Flatiron Institute Center for Computational Mathematics Flatiron Institute and CERMICS Laboratory Ecole des Ponts ParisTech Department of Statistics Department of Computer Science Columbia University

Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge slowly due to the high variance of their gradient estimates and their sensitivity to hyperparameters. In this work, we propose batch and match (BaM), an alternative approach to BBVI based on a score-based divergence. Notably, this score-based divergence can be optimized by a closed-form proximal update for Gaussian variational families with full covariance matrices. We analyze the convergence of BaM when the target distribution is Gaussian, and we prove that in the limit of infinite batch size the variational parameter updates converge exponentially quickly to the target mean and covariance. We also evaluate the performance of BaM on Gaussian and non-Gaussian target distributions that arise from posterior inference in hierarchical and deep generative models. In these experiments, we find that BaM typically converges in fewer (and sometimes significantly fewer) gradient evaluations than leading implementations of BBVI based on ELBO maximization.

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