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SSRN 2024年

作者： Liao, Zihao Li, Shaobo Li, Qian Li, Shengping State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Guizhou lnstitute of Technology Guizhou Guiyang550003 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Design and the Built Environment Curtin University Perth Australia

A precise and durable health management strategy for battery power systems is crucial to maintaining the reliable operation of Unmanned Aerial Vehicles(UAVs).This paper introduces an adaptive Decay Regularized Stochastic Configuration Networks(DRSCN) with Multi-level data processing for UAVs Battery Remaining Useful Life(RUL) Prediction. Firstly, a Multi source Signal Enhancement Analysis Framework (MSEAF) is effectively presented to criticalbattery health indicators from complex signals. Moreover, a significant innovation is enhancing the SCN model’s output layer, which introduces Decay Regularization to sparsify the layer’s weights, significantly reducing the risk of overfitting in later prediction stages. To further re fine and optimize DRSCN, a Convex Lens and Dual-Mechanism Enhanced Sand Cat Swarm Optimization (CLDM-SCSO) algorithm is employed for precise hyperparameter tuning, fur ther improving prediction accuracy. Finally, extensive control and ablation experiments on the NASA HIRF battery dataset validate the framework’s superior prediction accuracy and relia bility over existing methods, offering an efficient and reliable UAV battery health monitoring solution. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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MOCSC: a multi-omics data based framework for cancer subtype classification

MOCSC: a multi-omics data based framework for cancer subtype...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuanling Ma Jinting Guan Biosensor National Special Laboratory Key Laboratory of Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Department of Automation Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Using multi-omics data to achieve the classification of cancer subtypes from the perspective of machine learning can facilitate the understanding of cancer mechanism from the biomolecular aspect, which is of guidance to achieve personalized precision medicine in clinical medicine. To address the problem of cancer subtype classification based on multi-omics data, we propose a framework named MOCSC. Considering the high-dimensional complexity of multi-omics data and the heterogeneity among them, we use the idea of late integration model framework. Specifically, after dividing the training and test sets, for each omics data, the training set is first learned using a stacked sparse denoising autoencoder to extract its feature, and then the extracted feature is supplied to a single-layer neural network to obtain an initial prediction. Then, the initial results of all omics-specific classifiers are integrated and put into a view correlation discovery network for training to obtain the final prediction. Finally, the entire trained model is applied to the test set. The designed model is used to explore the best combination of multi-omics data and then tested on several different datasets. We compare our framework with the existing classification tools, showing its effectiveness in cancer subtype classification problem and also other classification problems based on multi-omics data.

关键词： Training Correlation Precision medicine Noise reduction Neural networks Machine learning Feature extraction

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SS-GEN: A Social Story Generation Framework with Large Language Models 39

SS-GEN: A Social Story Generation Framework with Large Langu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Yi Song, Mingyang Wang, Jiaqi Chen, Zhuang Bi, Guanqun Huang, Minlie Jing, Liping Yu, Jian Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing Jiaotong University China Tencent Hunyuan Beijing China Jarvis Research Center Tencent YouTu Lab China School of Computer Science and Engineering Central South University China CoAI Group DCST IAI BNRIST Tsinghua University China

ISBN: (纸本)157735897X

Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories™ are traditionally crafted by psychology experts under strict constraints to address these challenges but are costly and limited in diversity. As Large Language Models (LLMs) advance, there's an opportunity to develop more automated, affordable, and accessible methods to generate Social Stories in real-time with broad coverage. However, adapting LLMs to meet the unique and strict constraints of Social Stories is a challenging issue. To this end, we propose SS-GEN, a Social Story GENeration framework with LLMs. Firstly, we develop a constraint-driven sophisticated strategy named STARSOW to hierarchically prompt LLMs to generate Social Stories at scale, followed by rigorous human filtering to build a high-quality dataset. Additionally, we introduce quality assessment criteria to evaluate the effectiveness of these generated stories. Considering that powerful closed-source large models require very complex instructions and expensive API fees, we finally fine-tune smaller language models with our curated high-quality dataset, achieving comparable results at lower costs and with simpler instruction and deployment. This work marks a significant step in leveraging AI to personalize Social Stories cost-effectively for autistic children at scale, which we hope can encourage future research on special groups. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and Informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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Enhancing Event Tagger with Automatic Speech Recognizer for Audio Multi-task Scenarios by Distillation with Pre-Trained Large Models

Enhancing Event Tagger with Automatic Speech Recognizer for ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jianfeng Cheng Ye Liu Jian Yin Liangdao Wang Yan Pan Artificial Intelligence Department Lizhi Inc. Beijing & Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the continuous expansion of robotics and digital humans in practical applications, the demand for the auditory system is becoming deeper, usually requiring more efficient speech recognition framework capabilities to handle multiple tasks and use fewer resources. In previous audio processing frameworks, each type of audio processing task typically requires constructing a standalone deep network model for training, which results in more training data and higher training time when constructing models for multi-task audio scenarios simultaneously. The recent improvement of audio models based on transformers have brought about methods that can handle multiple audio tasks concurrently. However, recent related methods still require retraining multi-task targets with an amount of data, and achieve the general effect after training for multi-task scenarios than the simple combination of standalone methods processed separately. In order to better build a model that can handle multiple audio tasks, we propose a novel framework of distillation through pre-trained large models for enhancing event tagger with automatic speech recognizer. Through multiple rounds of experiments on several audio datasets, it has been verified that the proposed framework can achieve better results than the baseline for multitasking, and comparative results with less parameters compared to the baselines for single-task scenarios.

关键词： Training Adaptation models Analytical models Training data Speech recognition Speech enhancement Multitasking Transformers data models Robots

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Microscopic study of deformation and orientation effects in heavy-ion reactions above Coulomb barrier using the Boltzmann-Uehling-Uhlenbeck model

arXiv

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arXiv 2024年

作者： Feng, Yujie Liu, Huizi Huang, Yingge Gu, Fuchang Xiao, Erxi Lei, Xin Wang, Hui Huang, Jiali Zhu, Long Su, Jun Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai 519082 China China Nuclear Data Center China Institute of Atomic Energy Beijing 102413 China and Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal University Guilin 541004 China

Background: The understanding of the impact of initial deformation and collision orientation on quasi-fission and fusion-fission reactions remains incomplete. Purpose: This article aims to explore how the orientation of deformed nuclei influences quasi-fission and fusion-fission around 1.2 VB, employing a microdynamical method in systems with diverse shapes, namely 24Mg + 178Hf, 34S + 168Er, and 48Ti + 154Sm. Method: Utilizing the Boltzmann-Uehling-Uhlenbeck model, this study investigates quasi-fission and fusion-fission reactions. The model elucidates micro-dynamic processes and microscopic observables through the definition of the window and event-by-event simulations. Results: The findings reveal that the orientation of deformed nuclei significantly influences the nucleus-nucleus interaction potential, thereby impacting the competition between quasi-fission and fusion-fission. Particularly, the orientation of the deformed target nucleus emerges as the primary factor affecting this competition. Notably, a higher proportion of fusion-fission events is observed when the target nucleus is in the belly orientation compared to the tip. The study also observes that the configuration of the dinuclear system contributes to fluctuations and dissipation. Collisions with different orientations result in distinct dinuclear system configurations, with belly-oriented collisions leading to larger fluctuations between events, while tip-oriented collisions exhibit smaller fluctuations. Conclusions: Considering diverse orientations of nuclei with distinct initial deformations, this study concludes that the orientation of the target nucleus is the key factor influencing quasi-fission and fusion-fission reactions around 1.2 VB. © 2024, CC0.

关键词： Heavy ions

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Effect of Injection Pressure and Ambient Pressure on the Spray Characteristics of Fuels with Different Cetane Numbers

SSRN

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SSRN 2022年

作者： Sun, Bohua Zhao, Shuai Liang, Hongyu Liu, Qiang Wu, Guanghua Wu, Heng State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130025 China Automotive Data Center CATARC Tianjin300000 China Jilin Engineering Normal University School of Mechanical and Vehicular Engineering Jilin Changchun130052 China

The effects of physical and chemical properties of diesel fuel on the spray characteristics of different cetane numbers (CNs) fuels were investigated on a constant volume chamber (CVC) test rig using a high-speed camera method, and the effects of different injection pressures and ambient pressures on the spray characteristics were examined. The test results show that the spray tip penetration of different CNs results from the combined effect of the fuel properties. The spray cone angle of the five fuels increased with the increase of injection and ambient pressure, and the impact of ambient pressure on the spray cone angle was more prominent. Spray tip penetration and spray projection area increase with increase injection pressure and decrease with increase ambient pressure;compared with spray tip penetration, spray cone angle has more influence on spray projection area, especially near-field spray cone angle is the primary influence factor. The spray cone angle and spray projected area of high CN fuel are slightly larger than those of low CN fuel under the same operating conditions, and the atomization quality is better. © 2022, The Authors. All rights reserved.

关键词： Fuels

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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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Effects of incompressibility on the neutron-proton equilibration in 70Zn + 70Zn collisions at 35 MeV/nucleon

arXiv

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arXiv 2024年

作者： Xiao, Erxi Yang, Yu Huang, Yingge Zhang, Zhen Zhu, Long Su, Jun Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519082 China China Nuclear Data Center China Institute of Atomic Energy Beijing102413 China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal University Guilin541001 China

Background: The primary goal of studying isospin dynamics via heavy-ion reactions is to explore the isospin dependence of effective interactions within the nuclear equation of state (EOS). Purpose: This work aims to investigate the effects of nuclear incompressibility (K0) on neutronproton equilibration in projectile-like fragments (PLFs). Method: We simulate 70Zn + 70Zn collisions at 35 MeV/nucleon using the isospin-dependent quantum molecular dynamics (IQMD) model, coupled with the statistical decay code GEMINI. Results: The IQMD simulations not only reproduce experimental data patterns but also reveal the dynamic mechanisms underlying the binary breakup of PLFs. The rotation of PLFs is influenced by the transformation of angular momentum, which is connected to the isoscalar component of the EOS. This connection explains why shifts in K0 affect the description of neutron-proton equilibration as measured by PLF rotation. The simulations demonstrate that a model with a smaller K0 paired with a softer symmetry energy, or a larger K0 with a slightly stiffer symmetry energy, both offer better indications of neutron-proton equilibration. Conclusion: Considering the uncertainty in K0, the slope of the symmetry energy is constrained within the range of L = 20 - 40 MeV, providing valuable insights into the nuclear equation of state. Copyright © 2024, The Authors. All rights reserved.

关键词： Heavy ions

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