检索结果-内蒙古大学图书馆

Exact Instability Margin Analysis and Minimum-Norm Strong Stabilization - Phase Change Rate Maximization

IEEE Transactions on Automatic Control 2024年第4期69卷 2084-2099页

作者： Hara, Shinji Kao, Chung-Yao Khong, Sei Zhen Iwasaki, Tetsuya Hori, Yutaka Tokyo Institute of Technology Global Scientific Information and Computing Center Tokyo152-8550 Japan National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung80421 Taiwan University of California at Los Angeles Department of Mechanical and Aerospace Engineering Los AngelesCA90095 United States Keio University Applied Physics and Physico Informatics Yokohama223-8522 Japan Monaco

This article is concerned with a new optimization problem named 'phase change rate maximization' for single-input-single-output linear time-invariant systems. The problem relates to two control problems, namely robust instability analysis against stable perturbations and minimum-norm strong stabilization. We define an index of the instability margin called 'robust instability radius (RIR)' as the smallest H∞-norm of a stable perturbation that stabilizes a given unstable system. This article has two main contributions. It is first shown that the problem of finding the exact RIR via the small-gain condition can be transformed into the problem of maximizing the phase change rate at the peak frequency with a phase constraint. Then, we show that the maximum is attained by a constant or a first-order all-pass function and derive conditions, under which the RIR can be exactly characterized, in terms of the phase change rate. Two practical applications are provided to illustrate the utility of our results. © 1963-2012 IEEE.

关键词： Robust control

来源：评论

学校读者我要写书评

暂无评论

Fall Detection in Elderly People Using Compact Wearable Sensors with AI 13

Fall Detection in Elderly People Using Compact Wearable Sens...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Venkateswaran, R. Babu, B.N.V. Madhu Parameswari, M. Vamsilala, Purushothapatnapu Naga Venkata Ambigaipriya, S. Keerthana, N.V. College of Computing and Information Sciences University of Technology and Applied Sciences Cyber and Information Security Salalah Oman Teegala Krishna Reddy Engineering College Department of Cse Telangana Hyderabad India Sona College of Technology Department of Information Technology Tamilnadu India Vignan's Lara Institute of Technology and Science Andra Pradesh Vadlamudi Guntur India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350380583

The most crucial public concern regarding health is elderly people's falls which tend to undesirable injuries leading to an increase in healthcare expenses for them. This research paper describes a method that uses AI technology with a barometer sensor to detect falls in elderly people. In addition to this, the AI model is trained on a diverse dataset comprising various activities of daily living, enabling it to distinguish between normal movements and those that may lead to falls. This method probably gives some notifications or missed calls to the caretakers as well as to the hospitals. When compared to many more sensors such as gyroscopes and some fitness trackers, the Apple Watch 4, and Samsung Gear S3 barometer sensor perform in a better better way. Key performance metrics such as absolute and altitude are calculated and compared based on a natural language processing (NLP) algorithm. © 2024 IEEE.

关键词： Barometers

来源：评论

学校读者我要写书评

暂无评论

LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather Forecasting 16

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather ...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Sivaraman, Arun Kumar Nithiyanantham, Janakiraman Ravindran, Priya Narayanan, S T Sarvesh University of Technology and Applied Sciences Department of Computing and Information Sciences Suhar Oman School of Cse Vellore Institute of Technology Chennai India Digital Engineering Solution Centre Photon Interactive Inc. ON Canada K.L.N. College of Engineering Department of ECE Tamilnadu India School of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9798331505264

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models 2

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Aldweesh, Amjad Alauthman, Mohammad Elgamal, Zenab Almomani, Ammar AlAhliyya Amman University Faculty Of Engineering Communications And Computer Engineering Department Amman19328 Jordan Faculty Of Information Technology Cybersecurity Department Amman11831 Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan

ISBN: (纸本)9798350394962

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers

arXiv

引用

arXiv 2024年

作者： Mesman, Koen J. Tang, Yinglu Möller, Matthias Chen, Boyang Feld, Sebastian QAIMS Delft University of Technology Delft Netherlands Applied Mathematics Delft University of Technology Netherlands Quantum Computing and Engineering Delft University of Technology Netherlands Aerospace Engineering Delft University of Technology Netherlands

A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such as battery materials and high entropy alloys (HAE). While simple models allow for simulations of the required scale, these methods often fail to capture the complex dynamics that determine the characteristics. A long-theorized approach is to use quantum computers for this purpose, which allows for a more efficient encoding of quantum mechanical systems. In recent years, the field of quantum computing has become significantly more mature. Furthermore, the rise in integration of machine learning with quantum computing further pushes to a near-term advantage. In this work we aim to improve the well-established quantum computing method for calculating the inter-atomic potential, the variational quantum eigensolver, by presenting an autoencoded VQE with neural-network predictions: NN-AE-VQE. We apply a quantum autoencoder for a compressed quantum state representation of the atomic system, to which a naive circuit ansatz is applied. This reduces the number of circuit parameters to optimize, while still minimal reduction in accuracy. Additionally, we train a classical neural network to predict the circuit parameters to avoid computationally expensive parameter optimization. We demonstrate these methods on a H2 molecule, achieving chemical accuracy. We believe this method shows promise of efficiently capturing highly accurate systems while omitting current bottlenecks of variational quantum algorithms. Finally, we explore options for exploiting the algorithm structure and further algorithm improvements. © 2024, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Finite Difference Approximation with ADI Scheme for Two-dimensional Keller-Segel Equations

arXiv

引用

arXiv 2023年

作者： Lu, Yubin Chen, Chi-An Li, Xiaofan Liu, Chun Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

Keller-Segel systems are a set of nonlinear partial differential equations used to model chemotaxis in biology. In this paper, we propose two alternating direction implicit (ADI) schemes to solve the 2D Keller-Segel systems directly with minimal computational cost, while preserving positivity, energy dissipation law and mass conservation. One scheme unconditionally preserves positivity, while the other does so conditionally. Both schemes achieve second-order accuracy in space, with the former being first-order accuracy in time and the latter second-order accuracy in time. Besides, the former scheme preserves the energy dissipation law asymptotically. We validate these results through numerical experiments, and also compare the efficiency of our schemes with the standard five-point scheme, demonstrating that our approaches effectively reduce computational costs. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy dissipation

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Based Approach for Janya Raga Classification in Carnatic Music 6

A Deep Learning Based Approach for Janya Raga Classification...

引用

6th SLAAI International Conference on Artificial Intelligence, SLAAI-ICAI-2022

作者： Kavitha, P. Charles, J. Lekamge, L.S. Sabaragamuwa University of Sri Lanka Faculty of Applied Sciences Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Applied Sciences Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9781665476072

Janya Ragas are Carnatic music ragas derived from the fundamental ragas called Melakarta Raga, by the permutation and combination of various ascending and descending swaras. Raga recognition is an important task, essential to research in Music Information Retrieval in Indian Classical Music. Even though the automatic raga recognition methods have been widely adopted in Carnatic music, most of the existing methods are restricted only to Melakarta Ragas. It is an important task to identify the Melakarta Raga of a Janya Raga, especially for improved music recommendation performance. In this study, we used deep learning-based approach to address this problem. Music files were collected for 65 Janya Ragas belonging to seven Melakarta Ragas. Classification-based supervised deep learning models: 1D CNN, LSTM and 1D CNN-LSTM were used in the study. The models were implemented to learn similarities from 1-20 mean values of Mel-Frequency Cepstral Coefficients (MFCCs) features of audio samples, by using TensorFlow and Keras APIs. The results revealed that the 1D CNN-LSTM model outperformed the other models with an accuracy of 82.0%. In the future, 1D CNN-LSTM based Siamese neural networks can be introduced to reduce the dependence on large amounts of labeled audio data. © 2022 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Legendre spectral method for the space-time fractional diffusion-wave equation

Legendre spectral method for the space-time fractional diffu...

引用

第35届中国控制与决策会议

作者： Huilin Feng Wenping Chen Ying Lu School of Mathematics and Computing Science Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi(GUET)

In this paper,we solve the space-time fractional diffusion-wave equation in a bounded domain by the Legendre spectral *** time fractional derivative is in the Caputo sense with the order γ∈(1,2) and the space fractional derivative is in Riesz *** fully discrete scheme based on the L1 approximation coupled with the Crank-Nicolson technique in temporal and Legendre spectral approximations in spatial discretization are proposed,and the stability and convergence of the fully discrete schemes are *** example is carried out to verify the theoretical results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

引用

4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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：