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Towards a self-contained data-driven global weather forecasting framework 24

Towards a self-contained data-driven global weather forecast...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yi Xiao Lei Bai Wei Xue Hao Chen Kun Chen Kang Chen Tao Han Wanli Ouyang Department of Computer Science and Technology Tsinghua University Beijing China and Shanghai Artificial Intelligence Laboratory Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China and Qinghai University and Intelligent Computing and Application Laboratory of Qinghai Province China Shanghai Artificial Intelligence Laboratory Shanghai China and School of Information Science and Technology Fudan University Shanghai China

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers; it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy; (2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error; and (3) an autodifferentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields; remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency.

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A Dual-View Fusion Network for Automatic Spinal Keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dandan Zhou Lijun Guo Rong Zhang Xiuchao He Qiang Wang Jianhua Wang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

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mobile Wireless Sensor Networks And Hierarchical Routing Protocols: A Review 1

Mobile Wireless Sensor Networks And Hierarchical Routing Pro...

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1st International Conference on Computing, Communication and Green Engineering, CCGE 2021

作者： Sharma, Abha Das, Prasenjit Patel, R B Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India School of Computer Application Chitkara University Himachal Pradesh India Chandigarh College of Engineering and Technology Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781665415095

A lot of progression has been observed in mobile Wireless Sensor Network (MWSN) in modern era due to its applications in vicinity. Recent trends show how it is very challenging to retain stability in the network in terms of delay, packet delivery ratio and stability. Nodes mobilization is vital in stabilizing the network, and various routing protocols are used to maintain connectivity, throughput, coverage, minimal energy cost. As a matter of fact it is really demanding that a single routing protocol will be able to cover up for numerous circumstances altogether. Several routing protocols have been worked upon for a variety of network scenarios. The categorization of routing protocols is countered on the basis of type of network structure, information status, network efficiency and mobility. In this paper Hierarchal routing protocols classification is presented which can help enhance network life and save energy consumption. © 2021 IEEE.

关键词： Routing protocols

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Zhang Lijun Guo Lixin Ni Xiuchao He Jianhua Wang Rong Zhang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications.

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Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield

Merging UAV-Derived Metrics with Crop Physiology to Estimate...

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Frontiers of Information technology (FIT)

作者： Usama Athar Muhammad Ali Zuhair Zafar Haris Khurshid Karsten Berns Muhammad Moazam Fraz School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Oilseeds Research Program National Agricultural Research Center (NARC) Islamabad Pakistan Department of Computer Science Rheinland-Pfälzische Technische Universität Kaiserslautern Germany

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined approach that integrates 12 remotely sensed vegetation indices (VIs) with 3 key physiological traits - fresh weight, dry weight, and moisture factor - to capture the complex dynamics affecting sunflower crop. Predictive performance of VIs alone and in combination with physiological traits is evaluated using Root Mean Squared Error (RMSE). Our results show that incorporating these two sets of features, along with their temporal, spectral, and statistical characteristics, leads to consistent improvements in sunflower seed yield estimation that can reduce the root mean squared error to as low as 0.4075 ( $\text{kg} / \text{plot}$ ).

关键词： Vegetation mapping Moisture Autonomous aerial vehicles Physiology Seeds (agriculture) Sensors Yield estimation Reliability Vehicle dynamics Remote sensing

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Enhancing Cloud Data Security with Machine Learning Through the Analysis of Random Forest, Deep Neural Network, and Q-Learning Approaches

Enhancing Cloud Data Security with Machine Learning Through ...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Sreedevi Kadiyala G Sujatha Afreen Fatima Mohammed Lucky Gupta Praveen Kumar Harishchander Anandaram Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Hyderabad India Department of CSE-DS CVR College of Engineering Hyderabad India Department of Management Studies Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science & Application Patna Womens College Patna India Department of Artificial Intelligence Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This research outlines the cloud data security when using machine learning techniques - Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest - 95% of overall accuracy as opposed to 92 %. It is particularly important to consider AVC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization - features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.

关键词： Training Q-learning Accuracy Data security Artificial neural networks Resists Data transfer Data models Servers Random forests

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AEP mobile application Evaluation of Usability, Performance, and Shneiderman's Recommendations

AEP Mobile Application Evaluation of Usability, Performance,...

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International Conference on ICT For Smart Society (ICISS)

作者： Sasmoko Yasinta Indrianti Reinhart Previano Koentjoro Sonya Rapinta Manalu Davy Ronald Hermanus Primary Teacher Education Department Faculty of Humanities Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Universitas Agung Podomoro Jakarta Indonesia Mobile Application & Technology School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bandung Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia

This study emphasizes the mobile app design, usability, and performance evaluation of AEP mobile, a self-assessment mobile application to measure users' entrepreneurial abilities and factors as part of ASEAN Entrepreneurship Profiling (AEP). As the mobile app was designed following the Shneiderman's Eight Golden Rules of Interface Design and (Five) Human Factors of Interactive Software, we would like to evaluate whether potential users would consider the mobile app to be user-friendly according to Shneiderman's recommendations. We evaluated multiple mobile app evaluation metrics and methods and ended up adopting MARS and the original Golden Rules to evaluate our app to Indonesian public as part of the beta testing process. Results collected from Indonesian participants show user's high satisfaction in the user interface, despite having issues especially in app error-handling.

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Improved Speaker-Emotion Voice Conversion with Limited Corpus based on Large Language Model and Pre-trained Model

Improved Speaker-Emotion Voice Conversion with Limited Corpu...

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mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Fanchang Tang YaLing Amit Yadav Abhishek Pratap Sah Asif Khan Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Existing research on speaker and emotional voice conversion often focuses on separate tasks, neglecting their joint exploration. Furthermore, the limited availability of emotional corpora for target speakers poses a significant challenge for training robustness and generalized models. This paper proposes an improved scheme for speaker-emotion voice conversion with limited target speaker's emotional corpus, integrating a large language model and a pre-trained emotional speech synthesis model. It introduces several enhancements to enhance the quality of converted speech in terms of speaker similarity and emotional expressiveness. First, emotionally tagged text is generated using a large language model and emotional speech is synthesized from this text using a fine-tuned pre-trained emotional speech synthesis model. Then, a speaker-emotion voice conversion model is co-trained with both synthesized and real target emotional speech. Finally, the model is fine-tuned with the real target emotional speech to further boost the speaker and emotional similarity.

关键词： Training Large language models Computational modeling Speech enhancement Robustness Libraries Speech synthesis Informatics Optimization mobile computing

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SpDiff: A Speech Sensing System with Diffusion Model Based on mm Wave Radar

SpDiff: A Speech Sensing System with Diffusion Model Based o...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Jin, Can Zhang, Yulong Li, Xuanheng Sun, Yi Wang, Jie Fang, Yuguang School of Information and Communication Engineering Dalian University of Technology Dalian China Southeast University National Mobile Communications Research Laboratory Nanjing China School of Information Science and Technology Dalian Maritime University Dalian China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350368369

Voice control has become an indispensable interaction method in smart devices. Compared to traditional microphones, mm Wave radar offers a promising solution for speech sensing in noisy environments. However, most current research relies on single-view information, such as vocal cord vibrations or lip movements, to classify speech, which overlooks important details like timbre, speech rate, and intonation, limiting the application of speech sensing. To address these issues, we develop a high-quality speech sensing method based on mm Wave radar, named SpDiff. This method accurately localizes the vocalizing target and, based on the human vocal mechanism, extracts multi-view speech features according to the movement characteristics of the vocal cords, lips, and face. Additionally, to generate high-quality speech signals, we design a conditional latent diffusion model (CLDM), which uses multi-view radar information as conditional guidance, accurately capturing the complex mapping relationships between radar and speech signal distributions. To evaluate the SpDiff method, we build a mmWave system using IWR1443Boost and recruit 14 volunteers to construct a dataset. Experimental results show that SpDiff achieves high standards in speech sensing, with the generated speech directly input into existing recognition models, achieving an average character and word error rate (CER/WER) of only 2.33% and 3.05%. © 2025 IEEE.

关键词： Speech analysis

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Recent Advancements on Intelligent Multi-focus Image Fusion

Recent Advancements on Intelligent Multi-focus Image Fusion

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Cognitive Computing in Engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Prabhishek Singh Aayushi Rana Aditya Joshi Mohd Shuaib Manoj Diwakar Amit Kumar Mishra School of Computer Science Engineering & Technology Bennett University Greater Naida India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India Dehradun India Department of CSE Graphic Era Hill University Dehradun India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Recently deep learning approaches are more popular for multi-focus image fusion. In this review article, non-conventional multi focus image fusion techniques recently published were analyzed in detail and current advances as well as impending trends were discussed. The push for ever increasing image quality in multi focus imaging has raised the desire for non-conventional techniques as possible substitutes to conventional techniques. In this review, we systematically examine state of the art research in deep learning models, hybrid methodologies combining multiple transform domains, and adaptive thresholding strategies. We conducted a comparison evaluation of visual data against different strategies utilizing the complementary fusion capabilities of each. In addition, quantitative metrics of these values were used to objectively measure the performance of each strategy. In the paper, important ongoing developments, such as integration of deep learning techniques with transformer methods and adaptive fusion filters to enhance image details while avoiding artefacts are stressed. The implications of this review for academics include a comparison of the progress in the field to date and the potential of non classical techniques to realize higher accuracy and reliability in image fusion. The results suggest that further investigation of more sophisticated hybrid and adaptive techniques will yield improved diagnostic quality in a clinical setting.

关键词： Deep learning Image quality Visualization Adaptation models Filters Accuracy Reviews Transforms Market research Image fusion

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