检索结果-内蒙古大学图书馆

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale

Mathematical Biosciences and engineering 2024年第3期21卷 3838-3859页

作者： Lu, Yu Liu, Tongqiang Sheng, Quan Zhang, Yutao Shi, Haifeng Jiao, Zhuqing School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Department of Nephrology The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University Changzhou213003 China Department of Radiology The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University Changzhou213003 China School of Computer Science and Artificial Intelligence Changzhou University Changzhou213164 China

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients. © 2024 the Author(s).

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

computer Vision Approaches for Automated Bee Counting Application

引用

IFAC-PapersOnLine 2024年第9期58卷 43-48页

作者： Simon Bilik Ilona Janakova Adam Ligocki Dominik Ficek Karel Horak Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic and Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.

关键词： Signal Processing Biomedical systems bee counting bee traffic monitoring computer vision deep classification object detection

来源：评论

学校读者我要写书评

暂无评论

Predicting job execution time on a high-performance Computing cluster using a hierarchical data-driven methodology

Predicting job execution time on a high-performance Computin...

引用

2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022

作者： Bethaz, Paolo Vacchetti, Bartolomeo Capitelli, Enrica Nosenzo, Vladi Chiosso, Luca Cerquitelli, Tania Department of Control and Computer Engineering Politecnico di Torino Italy Iveco Group Torino Italy NPO Torino Srl Torino Italy

Nowadays, evaluating the performance of a vehicle before the production phase is challenging and important. In the automotive industry, many virtual simulations are needed to model the vehicle behavior in the best possible way. However, these simulations require a lot of time without the user knowing their runtime in advance. Knowing the required time in advance would allow the user to manage the simulations more effectively and choose the best strategy to use the available computational resources. For this reason, we present an innovative data-driven method to estimate in advance the execution time of simulations. Our approach integrates unsupervised techniques, such as constrained k-means clustering, with classification and regression algorithms based on tree structures. In this paper, we present an innovative and hierarchical data-driven method for estimating the execution time of jobs. Numerous experiments were conducted on a real dataset to verify the effectiveness of the proposed approach. The experimental results show that the proposed method is promising. © 2022 Copyright for this paper by its authors.

关键词： Hierarchical systems

来源：评论

学校读者我要写书评

暂无评论

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

引用

Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

来源：评论

学校读者我要写书评

暂无评论

BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Classification of Images Based on a System of Hierarchical Features

引用

computers, Materials & Continua 2022年第7期72卷 1785-1797页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Mujahed Al-Dhaifallah Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl-Kharj11991Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Control and Instrumentation Engineering Department and Interdisciplinary Research Center(IRC)for Renewable Energy and Power SystemsKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The results of the development of the new fast-speed method of classification images using a structural approach are *** method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image *** article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification *** have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are *** introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of *** noise immunity of the method to additive noise has been experimentally *** to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit *** costs increase proportionally with decreasing bit *** method can be used for application tasks where object identification time is critical.

关键词： Bitwise distribution computer vision descriptor hierarchical representation image classification keypoint noise immunity processing speed

来源：评论

学校读者我要写书评

暂无评论

FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care

FMCW Radar-Based Human Activity Recognition: A Machine Learn...

引用

IEEE Conference on Wireless Communications and Networking

作者： Mohammadreza Mashhadigholamali Ali Samimi Fard Samaneh Zolfaghari Hajar Abedi Mainak Chakraborty Luigi Borzí Masoud Daneshtalab George Shaker Department of Control and Computer Engineering Politecnico di Torino Turin Italy School of Innovation Design and Engineering Mädlardalen University Västerås Sweden Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demon-strates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained ac-tivities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living.

关键词： Support vector machines Deep learning Radar imaging Millimeter wave radar Feature extraction Vectors Human activity recognition Older adults Millimeter wave communication Long short term memory

来源：评论

学校读者我要写书评

暂无评论

RISC-V Extension for Optimized PWM control

RISC-V Extension for Optimized PWM Control

引用

International Conference on System Theory, control, and Computing (ICSTCC)

作者： Cosmin-Andrei Popovici Andrei Stan Vasile-Ion Manta Department of Computer Engineering Faculty of Automatic Control and Computer Engineering Technical University “Gheorghe Asachi” Iași România

This paper proposes a RISC-V extension, named SigWavy, meant to optimize the PWM control for general purpose or application specific designs. The RISC-V extension named above is a PWM control Unit with a dedicated ISA extension set designed for configuring and driving up to 32 PWM signals. The extension is integrated into RiscPwm, an updated version of our previous work, the RisCanFd SoC, for taking advantage of CAN-FD, a massively used protocol in the areas of automation and mobility. Being configured with the dedicated ISA extension or with parameters directly extracted from CAN-FD commands, the proposed solution manages to configure/reconfigure PWM channels between 4.79x and 9.18x times faster than an ARM Cortex-M7 processor, although our SoC operates with a 6x lower frequency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Complete Characterization of Gorbunov and Pinsker Nonanticipatory Epsilon Entropy of Multivariate Gaussian Sources: Structural Properties

Complete Characterization of Gorbunov and Pinsker Nonanticip...

引用

作者： Charalambous, Charalambos D. Charalambous, Themistoklis Kourtellaris, Christos Van Schuppen, Jan H. Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Van Schuppen Control Research Amsterdam1103 KB Netherlands

This paper derives the optimal test channel distribution and the complete characterization of the classical Gorbunov and Pinsker (1973), Gorbunov and Pinsker (1974) nonanticipatory epsilon entropy of multivariate Gaussian Markov sources with square-error fidelity, which remained an open problem since 1974. The paper also formulates a state dependent nonanticipatory epsilon entropy, in which past reproductions are available to the decoder and not to the encoder, the test channel is specified with respect to an auxiliary (state) random process, and the reproduction process is a causal function of past reproduction and the auxiliary random process. This variation is analogous to the Wyner and Ziv (1976) and Wyner (1978) rate distortion function (RDF), of memoryless sources. It is shown that the operational rate of zero-delay codes, with past reproductions available to the decoder but not to the encoder is bounded below by the state dependent nonanticipatory epsilon entropy rate. For the case of multivariate Gaussian Markov sources with square-error fidelity, the optimal test channel distribution and the complete characterization of the state dependent of nonanticipatory epsilon entropy are derived, and also shown that that the two nonanticipatory epsilon entropies coincide. The derivations are new;they are based on structural properties of the stochastic realizations of the reproduction process that induce the optimal test channel distributions. They are derived using, achievable lower bounds on information theoretic measures, properties of mean-square estimation theory, Hadamard's inequality, and canonical correlation coefficients of a tuple of multivariate jointly Gaussian random processes. Applications of the nonanticipatory epsilon entropy and its state dependent variation are discussed to the areas of control of unstable Gaussian systems over limited memory channels, design of causal estimators for Gaussian Markov sources with a fidelity criterion, computation of

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

A control Theoretic Study on Omnidirectional MAVs with Minimum Number of Actuators and No Internal Forces at Any Orientation

A Control Theoretic Study on Omnidirectional MAVs with Minim...

引用

IEEE Conference on Decision and control

作者： Ahmed Ali Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF’s, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations.

关键词： Three-dimensional displays Propellers Dynamics controllability Regulation Steady-state Feedback linearization control theory Closed loop systems Vehicle dynamics

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：