检索结果-内蒙古大学图书馆

Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Taehee Ko Xiantao Li Chunhao Wang School of Computational sciences Korea Institute for Advanced Study South Korea Department of Mathematics Pennsylvania State University USA Department of Computer Science and Engineering Pennsylvania State University USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

At the heart of quantum chemistry and materials science lies the critical task of estimating ground-state properties. We present a quantum algorithm for this task by quantizing the density-functional theory (DFT). A key aspect of imple-menting DFT faithfully is the requirement for self-consistent calculations, which involve repeated di-agonalizations of the Hamiltonian. This procedure, however, creates a significant bottleneck, as a classical algorithm generally demands a computational complexity that grows cubically with the number of electrons, restricting the scalability of DFT for tackling large-scale problems that involve complex chemical environments and microstructures. This article presents the first quantum algorithm that has provided substantial speedup for the ground state computation, by improving the complexity to one with a linear scaling with the number of atoms. The algorithm leverages the exponential speedup by the quantum singular value transformation to generate a quantum circuit to encode the density-matrix, followed by an efficient estimation method for the output electron density, which constitutes a simple hybrid approach for achieving self-consistency. Moreover, the algorithm produces the ground state Hamiltonian, from which the ground state energy and band structures can be efficiently computed. The proposed framework is accompanied by a rigorous error analysis that establishes the convergence and quantifies various sources of error and the overall computational complexity. The combination of effi-ciency and precision opens new avenues for exploring large-scale physical systems.

关键词： Quantum algorithm Quantum chemistry Scalability Stationary state Discrete Fourier transforms Estimation Atoms computational complexity Quantum circuit Electrons

来源：评论

学校读者我要写书评

暂无评论

ConMamba: A Convolution-Augmented Mamba Encoder Model for Efficient End-to-End ASR Systems

ConMamba: A Convolution-Augmented Mamba Encoder Model for Ef...

引用

International Symposium on Chinese Spoken Language Processing

作者： Haoxiang Hou Xun Gong Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

End-to-End Automatic Speech Recognition (ASR) models, such as Conformer, excel in accuracy but face limitations in computational complexity and positional awareness, hindering their use in real-time or resource-constrained settings. State Space Models (SSMs), particularly the Mamba model with its time-varying mechanism, offer a more efficient alternative. We propose ConMamba, a Convolution-Augmented Mamba En-coder model, which replaces the Conformer's Multi-Head Self-Attention with Mamba layers and adds convolutional layers for capturing both local and global features. Experiments on the LibriSpeech dataset show ConMamba matches traditional Conformer performance on short speech segments and outperforms them on longer ones, enhancing robustness and efficiency for practical ASR applications.

关键词： Accuracy computational modeling Face recognition Speech enhancement Transformers Robustness Real-time systems computational complexity Automatic speech recognition

来源：评论

学校读者我要写书评

暂无评论

Knowledge Distillation from Discriminative Model to Generative Model with Parallel Architecture for Speech Enhancement

Knowledge Distillation from Discriminative Model to Generati...

引用

International Symposium on Chinese Spoken Language Processing

作者： Tingxiao Zhou Leying Zhang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Al Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Generative speech enhancement methods, especially diffusion methods, have recently gained attention. However, current generative methods face several unresolved issues, such as lagging performance compared to discriminative methods on conventional enhancement metrics and slower inference speeds. In order to address these challenges, we propose a knowledge distillation approach from discriminative to generative models, and we design a parallel architecture with a latent vector fusion module to leverage their respective strengths. We conduct experiments and ablation studies on the LibriMix dataset to validate its effectiveness. The results demonstrate that our approach substantially improves performance over baseline systems in terms of speech quality and inference speed.

关键词： Measurement Speech enhancement Vectors Parallel architectures Faces

来源：评论

学校读者我要写书评

暂无评论

Solar Panel Power Prediction Using Machine Learning Technique

Solar Panel Power Prediction Using Machine Learning Techniqu...

引用

Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： S. Sekar V. Kaviya G. Vivekanandan P. Nithya Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Department of Computational Intelligence SRM Institute of science and Technology Chennai India

One of the most significant renewable energy sources produced by photovoltaic panels (PV) is solar energy. Only during the day is solar energy available, and it is affected by meteorological elements such sun irradiation, cloud coverage, rainfall, and temperature. Due to these variables, the integration of solar power into the power grid is more challenging than it is for conventional energy sources. Changes in the solar power output result in increased electricity demand and higher operating costs. This leads to predict the power produced by solar PV panels accurately. There are numerous methods for estimating the power produced by PV systems, and the data sources used are either just historical data on PV power or historical data on both PV power and weather. In proposed system the data sources used are previous PV power output, weather data and weather forecasts in order to correctly get the effects of solar panel power prediction and actual operating condition data on the efficiency of power generation by using Support vector machine learning algorithm. The model has been trained and validated using previous year data from a input as year, month, date, time, temperature, humidity, direct radiation, diffuse radiation, direct normal irradiance, wind speed and outputs the power generated which gives higher prediction accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Insights from Hyperparameter Scaling of Online Speech Separation

Insights from Hyperparameter Scaling of Online Speech Separa...

引用

International Symposium on Chinese Spoken Language Processing

作者： Xin Zhou Wangyou Zhang Chenda Li Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

With the rapid development of deep learning, a large number of models with excellent performance for speech separation tasks have emerged in the literature. Despite their impressive performance, these models usually cannot be used for online speech separation tasks due to high computational and memory costs. In recent years, some approaches tailored for online speech separation have been developed to achieve state-of-the-art performance while keeping the model lightweight. However, it is often unclear how these models can scale with their architecture-specific hyperparameters as they are under-explored in the literature. To fill this gap, this paper investigates the effect of hyper-parameter scaling on the model performance, number of parameters, and computational cost based on two representative online speech separation models, BSRNN and SkiM. Experimental results on the Libri2Mix dataset reveal the relationship between model performance and composite hyperparameter configurations. We also derive the patterns of the number of parameters and computational costs varying with hyperparameters. Based on the results of the analysis, we give suggestions for scaling the two models and summarize two common trends.

关键词： Degradation Deep learning Analytical models Costs computational modeling Market research computational efficiency Speech processing Guidelines

来源：评论

学校读者我要写书评

暂无评论

Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration

Exploring the Effectiveness of Deep Learning in Audio Compre...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： S. Pushparani K. Sashi Rekha V. M. Sivagami R. Usharani M. Jothi Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case.

关键词： Deep learning Training Filtering Bit rate Noise computer architecture Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

引用

International Conference on computational and Characterization Techniques in engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning Data preprocessing Reinforcement learning Feature extraction Data models Predictive analytics Stress

来源：评论

学校读者我要写书评

暂无评论

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis

arXiv

引用

arXiv 2025年

作者： Bringedal, C. Kiemle, S. van Duijn, C.J. Helmig, R. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Science Bergen Norway Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands

Evaporation from a porous medium partially saturated with saline water, causes the salinity (salt concentration) to increase near the top of the porous medium as water leaves while salt stays behind. As the density of the water increases with increased salt concentration, the evaporation leads to a gravitational unstable setting, where density instabilities can form. Whether density instabilities form, depends on a large range of parameters like the evaporation rate and intrinsic permeability of the porous medium, but also on the water saturation. As water saturation decreases, the storage, convection and diffusion of salt also decrease, which all influence the onset of instabilities. By performing a linear stability analysis on the governing equations, we give criteria for onset of instabilities. Numerical simulations give information about the further development of these instabilities. With this knowledge we can predict whether and when density instabilities occur, and how they will influence the further development of salt concentration in the porous medium. © 2025, CC BY.

关键词： Stability criteria

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：