检索结果-内蒙古大学图书馆

International conference on Open Source Systems and Technologies (ICOSST)

作者： Mehwish Naz Zaeem Anwaar Wasi Haider Butt College of Electrical and Mechanical Engineering National University of Sciences & Technology (NUST) Islamabad Pakistan

Software testing is a significant and complex phase of software development as it ensures the complete functionality of the software. To evaluate the behaviour of the software the testing process acquires generation of test cases as an input for the system being tested. Generation of test cases remains a challenging task usually as it is done based on human intelligence and it has become a much complicated and time-consuming process. Therefore, it can be enhanced by automating the test case generation mechanism to identify and eliminate bugs. Unified modelling Language (UML) is a defacto standard that has been used in academia and industry currently. Test case automation using UML diagrams is more effective and efficient as it is done early in the software development life cycle. In this research, UML sequence diagram and class diagram have been chosen for white box testing. However, in this article ”Unified Non-Sorting Genetic Algorithm” (U-NSGA-III) has been proposed for automated test case generation and optimization. The proposed approach has achieved 95% statement coverage for the System Under Test (SUT). In this paper, a benchmark case study has been used and controlled experimentation has been performed for the optimization of test cases. The comparison of our approach (U-NSGA-III) is done with GA and PSO. From our results, it has been concluded that the proposed U-NSGA-III has performed better in terms of execution time, convergence speed and its percentage.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A hybrid intelligent closed-loop model for exploration of cardiovascular interactions

A hybrid intelligent closed-loop model for exploration of ca...

引用

IEEE EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： E. Elsamahy M. Mahfouf D. Linkens Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Blood is a vital source for delivering oxygen and nutrients to trillions of cells in the body; this makes the function of the cardiovascular system essential to our existence. In the last few years, research interests have been directed to exploring its behaviour as well as its interactions with other systems in the human body under different types of stresses, i.e. mental and physical. In this study, we propose a closed-loop model built for the cardiovascular system, and analyse its interactions under physical-stress conditions. In this study, the signals of interest are: blood pressure, heart rate, temperature and respiration; our proposed model is based on the original Luczak model introduced in 1975. An intelligent system based modelling approach was used to represent the interactions between the cardiovascular system and the other control systems involved; based on the use of three neuro-fuzzy models for generating the blood pressure, heart rate, and temperature signals and a fuzzy-ARX model for generating the respiration signal. The interactions between the control systems that generate these signals are identified, and the estimated signals generated during simulation, for different patients, show good fits when compared with the actual measurements. The description of such complex interactions in patients will allow one to carry out diagnosis and prognosis in terms of the influence of stresses on systemic body functions.

关键词： Cardiology Signal generators Cardiovascular system Biological system modeling Blood pressure Heart rate Control system synthesis Signal processing Cells (biology) humans

来源：评论

学校读者我要写书评

暂无评论

Planning LV grids by predicting residual loads of households via methods of machine learning

Planning LV grids by predicting residual loads of households...

引用

CIRED 2020 Berlin Workshop (CIRED 2020)

作者： Maximilian Rose Lukas Lenz Torsten Sowa Imke Hebbeln Schleswig-Holstein Netz Schleswag-HeinGas-Platz 1 25451 Quickborn Germany

The use of decentralised energy resources (DER) in private households will change the load and feed-in behaviour in the future and thus influence the power flow at the low-voltage level. Therefore, grid planning, especially at a low-voltage level, must be adapted. Hence, new approaches for modelling the residual load of a household, with regard to different portfolios of DER, must be addressed. For this, an existing mathematical optimisation model to optimise both, a household's investment decision in DER and their operating times, is extended using machine learning (ML) approaches. Several methods of ML have been tested, however, the highest solution quality was achieved using neural networks. Following this, the prediction of the investment in DER by classification algorithms with convolutional neural networks shows the highest agreement with the deterministic approach. In the case of the prediction of DER operating times and residual loads based on them, recurrent neural networks with long short-term memory are used. Finally, this study indicates that the resulting predicted time series of residual load can be used for power flow calculations in the low-voltage grid. This allows grid calculations to be performed depending on existing or future political and market conditions.

关键词： investment load flow power grids distributed power generation recurrent neural nets time series learning (artificial intelligence) pattern classification convolutional neural nets power generation planning load management power engineering computing building management systems

来源：评论

学校读者我要写书评

暂无评论

Using Deep Learning for Predictive modelling in Fluid Dynamics

Using Deep Learning for Predictive Modelling in Fluid Dynami...

引用

Artificial Intelligence, human-Computer Interaction and Robotics (AIHCIR), International conference on

作者： Saadaldeen Rashid Ahmed Ari Taha Guron Duaa A. Majeed Mohammed Abdul Malek Al Saadi Wameedh Abduladheem Zainab T. Al-Sharify Al-Lbraheemi Fuqdan Taj-Aldeen Naser Abdali Rafad Imad Kadhim Computer Science Department Bayan University Kurdistan Iraq Artificial Intelligence Engineering Department College of Engineering AI-Ayen University Thi-Qar Iraq Aeronautical Engineering Department Baghdad University Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Al-Ma'moon University College Baghdad Iraq Pharmacy department Al Hikma University College Baghdad Iraq Chemical Engineering Birmingham University UK College of Dentistry University of Al-Ameed Karbala Iraq Mathematics Department College of Basic Education Misan University Misan Iraq Medical Devices Technology Engineering University of hilla Babylon Iraq

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

Predictive modeling of fluid dynamics is vital for many engineering applications, but classical computational fluid dynamics (CFD) methods tend to suffer with high computing costs and low scalability, notably in the complicated turbulent flow regimes. In this work we investigate the use of deep learning models-specifically Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Physics-Informed Neural Networks (PINN s)-to develop predictive models that may supplement fluid dynamics simulations with potentially im-proved accuracy and efficiency. Perform an evaluation and comparison of the forecasting ability for the velocity fields of these models with an emphasis on turbulent flow regimes. The authors apply each model to a real-world fluid dynamics dataset, assessing their success across multiple axes: large-scale feature capture, localized interactions, and physical law adherence. However, the maj or findings demonstrate that PINN s had the best performance with a coefficient of determination (R2) of 0.97 and mean absolute error (MAE) of 0.015 across turbulent regions but relatively weaker CNNs and GNNs with an accuracy figure of 95%. Although CNNs were successful in modeling the large-scale flow features and GNN s were easy to extract localized interactions, PINN s delivered the most accurate results by adding the N avier-Stokes equations into the loss functions [11]. Abstract Smooth and accurate solutions are vital in fluid dynamics, especial-Iy for achieving physical consistency. This work highlights the potential of PINNs for obtaining such predictions, giving a pathway towards efficient simulations in practical applications.

关键词： Deep learning Training Accuracy Fluid dynamics Computational modeling Computational fluid dynamics Predictive models Feature extraction mathematical models Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An effective fraud detection using competitive swarm optimization based deep neural network

引用

Measurement: Sensors 2023年 27卷

作者： Karthikeyan, T. Govindarajan, M. Vijayakumar, V. Department of Computer Science & Engg Annamalai University Annamalai Nagar India Department of Computer Science & Engg. Sri Manakula Vinayagar Engineering College Puducherry India

Frauds have no persistent patterns. They constantly alter their behaviour, necessitating the use of unsupervised learning. Fraudsters gain access to latest technology that enables them to commit scams via internet transactions. Consumers' habitual behaviour is assumed by fraudsters, and fraud trends very quickly. Fraud detection systems must utilize unsupervised learning to identify online payments since some fraudsters initially use online channels before switching towards other methods. The goals of this research are to construct a deep convolutional neural network model to detect anomalies from regular patterns produced by competitive swarm optimization, with a particular emphasis on fraud scenarios that cannot be detected using prior records or supervised learning (CSO). An unsupervised learning method called the suggested CSO-DCNN employs ReLu by making the inputs and outputs equal. The CSO-DCNN is classifies the fraud activity using real-time and other available dataset that is compared with the existing algorithms. The experimental results shows that the suggested CSO-DCNNobtains98.20%, 99.77%, 95.23% of accuracy for credit card, insurance and Mortgage Data set. © 2023

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics

引用

BMC BIOINFORMATICS 2021年第Sup2期22卷 90-90页

作者： Cazzaniga, Paolo Raposo, Maria Besozzi, Daniela Merelli, Ivan Staiano, Antonino Ciaramella, Angelo Rizzo, Riccardo Manzoni, Luca Univ Bergamo Dept Human & Social Sci Piazzale St Agostino 2 I-24129 Bergamo Italy Univ Nova Lisboa Fac Sci & Technol Dept Phys Campus Caparica P-2829516 Caparica Portugal Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336 I-20126 Milan Italy CNR Inst Biomed Technol Via Fratelli Cervi 93 I-20090 Segrate Italy Univ Naples Parthenope Dept Sci & Technol Ctr Direz C4 Isl I-80143 Naples Italy Natl Res Council Italy CNR ICAR Via Ugo La Malfa 153 I-90146 Palermo Italy Univ Trieste Dept Math & Geosci Via Valerio 12-1 I-34127 Trieste Italy

CIBB is a venue that embraces researchers with different backgrounds, ranging from mathematics to computer science, from materials science to medicine, and from engineering to biology, all interested in the investigation and application of computational intelligence methods to open problems in bioinformatics, biostatistics, systems biology, synthetic biology, and medical informatics. The program of this edition was organized with contributions on the main conference scientific area with heterogeneous open problems at the forefront of current research, and in special sessions on specific themes as Computational Methods for Neuroimaging Analysis, Machine Learning in Health Informatics and Biological Systems, Soft Computing Methods for characterizing Diseases from Omics Data, engineering Bio-Interfaces and Rudimentary Cells as a way to Develop Synthetic Biology, modelling and Simulation Methods for System Biology and System Medicine, Fast and Efficient Solutions for Computational Intelligence Methods in Bioinformatics, Systems, and Computational Biology, Networking Biostatistics and Bioinformatics, Machine Explanation—Interpretation of Machine Learning Models for Medicine and Bioinformatics. The organization of this edition of CIBB was supported by the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy, and by the Institute of Biomedical Technologies of the National Research Council, Italy. Besides the papers focused on computational intelligence methods applied to open problems of bioinformatics and biostatistics, the works submitted to CIBB 2019 dealt with algebraic and computational methods to study RNA behaviour, intelligence methods for molecular characterization and dynamics in translational medicine, modeling and simulation methods for computational biology and systems medicine, and machine learning in healthcare informatics and medical biology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a digital twin of a tablet that mimics a real solid dosage form: Differences in the dissolution profile in conventional mini-USP II and a biorelevant colon model

引用

EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES 2022年 179卷 106310页

作者： Schuett, M. Stamatopoulos, K. Batchelor, H. K. Simmons, M. J. H. Alexiadis, A. Univ Birmingham Sch Chem Engn Edgbaston Birmingham B15 2TT Warwickshire England David Jack Ctr RD Platform Technol & Sci Biopharmaceut Pharmaceut Dev Pk Rd Ware SG12 0DP Hertfordshire England Univ Strathclyde Strathclyde Inst Pharm & Biomed Sci 161 Cathedral St Glasgow City G4 0RE Scotland

The performance of colon-targeted solid dosage forms is commonly assessed using standardised pharmacopeial dissolution apparatuses like the USP II or the miniaturised replica, the mini-USP II. However, these fail to replicate the hydrodynamics and shear stresses in the colonic environment, which is crucial for the tablet's drug release process. In this work, computer simulations are used to create a digital twin of a dissolution apparatus and to develop a method to create a digital twin of a tablet that behaves realistically. These models are used to investigate the drug release profiles and shear rates acting on a tablet at different paddle speeds in the mini-USP II and biorelevant colon models to understand how the mini-USP II can be operated to achieve more realistic (i.e., in vivo) hydrodynamic *** behaviour of the tablet and the motility patterns used in the simulations are derived from experimental and in vivo data, respectively, to obtain profound insights into the tablet's disintegration/drug release processes. We recommend an "on-off" operating mode in the mini-USP II to generate shear rate peaks, which would better reflect the in vivo conditions of the human colon instead of constant paddle speed.

关键词： mathematical modelling Smoothed Particle Hydrodynamics (SPH) Colon mini-USP II Dissolution profile Digital twin

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

Task Switching Model for Acceleration Control of Multi-DOF Manipulator Using Behavior Trees

Task Switching Model for Acceleration Control of Multi-DOF M...

引用

Annual conference of Industrial Electronics Society

作者： Yuki Tanaka Seiichiro Katsura Dept. of System Design Engineering Keio University Yokohama Japan

In recent years, there has been a growing interest in developing robots capable of replacing human labor, driven by factors such as the decline in the working-age population. One crucial aspect of developing such robots is the ability to decompose complex tasks into manageable subtasks and establish models that govern the switching between these subtasks. Finite state machines (FSMs) and behavior trees (BTs) are two commonly used models for task-switching in robotics. FSMs are mathematical models that describe the behavior of a system with a finite number of states. They have been extensively employed in various robotic applications, including gait pattern, trajectory, and motion generation of robots. However, these transition models primarily focus on the relationships at the higher level. Additionally, FSMs are rarely utilized in acceleration control systems, which offer precise position and velocity control, as well as flexible force control and hybrid control capabilities. BTs, on the other hand, are graphical models that represent an agent's behavior as a graph composed of nodes and edges. BTs also enable modelling of switching between multiple tasks and have been explored for automatically generating behavior trees through machine learning techniques. This approach is well-suited for motion control using acceleration control. In this research, by combining BT-based task-switching with acceleration control architecture, we enable autonomous switching between target object approaching and obstacle avoidance. The proposed method is validated through simulations and experiments using a 6-DOF manipulator.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Computational Analysis of the Action of Chloroquine on Short QT Syndrome Variant 1 and Variant 3 in human Ventricles

Computational Analysis of the Action of Chloroquine on Short...

引用

Annual International conference of the IEEE engineering in Medicine and Biology Society

作者： Cunjin Luo Kuanquan Wang Tong Liu Henggui Zhang Laboratory of Medical Electrophysiology Ministry of Education Collaborative Innovation Centre for Prevention and Treatment of Cardiovascular Disease/Institute of Cardiovascular Research Southwest Medical University Luzhou China Harbin Institute of Technology Harbin Heilongjiang CN Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin China The University of Manchester Manchester Manchester GB

Aims: The short QT syndrome (SQTS) is a rare genetic disorder associated with arrhythmias and sudden cardiac death (SCD). The SQT1 and SQT3, SQTS variants, result from gain-of-function mutations (N588K and D172N, respectively) in the KCNH2-encoded and KCNJ2-encoded potassium channels, in which treatment with potassium channel blocking agents has demonstrated some efficacy. This study used in silico modelling to gain mechanistic insights into the actions of anti-malarial drug chloroquine (CQ) in the setting of SQT1 and SQT3. Methods and Results: The ten Tusscher et al. human ventricle model was modified to a Markov chain formulation of I_(Kr) and a Hodgkin-Huxley formulation of I_(K1) describing SQT1 and SQT3 mutant conditions, respectively. Cell models were incorporated into heterogeneous one-dimensional (1D) transmural ventricular strand model to assess prolongation of the QT intervals. The blocking effects of CQ on I_(K1) and I_(Kr) were modelled by using Hill coefficient and IC_(50) from literatures. At the single cells, CQ prolonged the AP duration (APD) under both the SQT1 and SQT3 conditions;at the multi-cell strand level, CQ prolonged the QT intervals and declined the T-wave amplitude under both conditions. Conclusions: This computational study provides novel insights into the efficacy of CQ in the setting of SQT1 and SQT3 variants, and indicates that CQ is a useful drug in the treatment of SQTS.

关键词： Biological system modeling mathematical model Potassium Computational modeling Electronic mail Drugs Avalanche photodiodes

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：