检索结果-内蒙古大学图书馆

Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

引用

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

引用

International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

GSKTM: efficient of query search for spatial keyword in text mining

引用

International Journal of Information and Communication Technology 2024年第4期25卷 352-379页

作者： Reddy, Ramya Rayacherlu Sambasadasiva Manu, Darshan Naveen Raju, G. Nimbhorkar, Sejal Santosh Rajuk, Venugopal Kuppanna Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University MiamiFL United States INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

In today’s world, geo-positioning technologies, location-based services have attracted many researcher’s due to the increasing amount of spatio textual objects in various applications like social networks, geo location services. Each spatial object consists of spatial locations and a set of query terms. In this paper, an efficient group of query search for spatial keyword in text mining is proposed that retrieves both spatial and textual keyword objects to effectively reduce the search space. The clusters and subclusters are constructed based on the calculated range of the objects location and categories in the dataset. Further, categorylist is constructed that identifies the category of interest (CoI) of users query. Experiments are conducted on two real dataset namely Euro and geographic names. It is observed that GSKTM outperforms inverted linear quad-tree (ILQ) with improved response time and provides groupwise top-k results. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： group keyword query search spatial text feature selection

来源：评论

学校读者我要写书评

暂无评论

Sawtooth-enhanced bend sensor for gesture recognition

引用

science China(Technological sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

来源：评论

学校读者我要写书评

暂无评论

Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

引用

science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

来源：评论

学校读者我要写书评

暂无评论

Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhavani, R. Kathirvelu, Kalaivani Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Multi-Class Classification Models for Obesity Risk Prediction

Comparative Analysis of Multi-Class Classification Models fo...

引用

2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Naik, Ds Bhupal Afrin, Mohammad Salafsha Rao, P Srinivasa Reddy, Bhavanam Amarendra Advanced Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (纸本)9798331533311

Obesity is a global health issue associated with increased risks of various chronic diseases, such as diabetes, cardiovascular disease, and certain cancers. Accurate prediction of obesity risk can enable early interventions, improve patient outcomes, and alleviate healthcare burdens. This study performs a comparative analysis of several multiclass classification models to predict obesity risk levels based on demographic, lifestyle, and health-related features. we aimed to predict obesity risk in individuals using various machine learning algorithms, with a particular focus on the Light Gradient Boosting Machine (LGBM) classifier. Obesity is a growing global health issue linked to numerous serious diseases. We evaluated several algorithms, including LGBM, Random Forest, Decision Tree, Support Vector Classifier (SVC), Logistic Regression, and AdaBoost algorithms to determine their effectiveness in accurately classifying obesity risk. Our findings revealed that the LGBM classifier outperformed the others with an impressive accuracy of over 9 0%. Random Forest followed closely, while Decision Tree, SVC, Logistic Regression, and AdaBoost showed progressively lower accuracies. The LGBM algorithm's superior performance highlights its potential as a valuable tool for early obesity risk detection and intervention, enabling healthcare providers to take proactive steps in managing and preventing obesity. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

引用

Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

来源：评论

学校读者我要写书评

暂无评论

Rotten and Fresh Fruits Classification using Deep Learning 5

Rotten and Fresh Fruits Classification using Deep Learning

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mishra, Prashant Kumar Singh, Jagrati Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350383522

Accurate and timely classification of fresh and decaying fruits is crucial for upholding quality standards in the food industry. This study introduces a deep learning approach that utilizes images to distinguish between fresh and rotten fruits. The proposed solution effectively addresses this classification challenge by control the capabilities of Convolutional Neural Networks. In this research a diverse dataset is used, comprising labeled images of various fruits in varying states of freshness, encompassing both rotten and fresh examples. Key preprocessing steps, including resizing and normalizing the dataset, is implemented to ensure that the model receives a uniform input. The core of the methodology involves initializing a pre-trained CNN architecture. This approach enables the adaptation of the pretrained model to the specific fruit classification task, ultimately providing an efficient and effective solution for quality control in the food sector. Through the utilization of this approach, our model can inherit valuable feature representations from its pretrained counterpart. To evaluate the performance of the trained model, a distinct test dataset is employed and the results encompass key measurements of performance. With an impressive accuracy of 97.5%, the proposed model outperforms the base paper, which achieves 96.05%. This advancement highlights the model's proficiency in distinguishing between fresh and rotten fruits, underscoring its practical effectiveness. © 2024 IEEE.

关键词： Convolutional Neural Networks KNN Rotten and Fresh Fruits Classification VGG16

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：