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共 285 条记录，以下是151-160 订阅

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Investigating the Practicality of the systems engineering Process Approach as an Alternative to SDLC in Developing Health Information systems 13

Investigating the Practicality of the Systems Engineering Pr...

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13th international conference on Information Science and Technology, ICIST 2023

作者： Husin, Noor Hassanah Naim, Nur Faraha Mohd Ismail, Hadzariah Nasirin, Syed Lada, Suddin Yussof, Soffri Miskon, Suraya Tahir, Abdullah M. Kadir, Abdul Faculty of International Finance Universiti Malaysia Sabah Labuan Malaysia Faculty of Computing & Informatics Universiti Malaysia Sabah Labuan Malaysia Decision Support Systems Research Group Universiti Malaysia Sabah Labuan Malaysia Faculty of Management Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Engineering Universiti Malayasia Sabah Kota Kinabalu Malaysia Universitas Sari Mulia Banjarmasin Indonesia

ISBN: (纸本)9798350313925

Many hospitals in developing regions like East Malaysia are increasingly adopting health information systems (HIS) to enhance operational efficiency. the systems development life-cycle (SDLC) has been the traditional approach to developing HIS. Still, the systems engineering process (SEP) approach is gaining popularity as an option. this paper investigates the practicality of the SEP approach as an alternative to the SDLC in developing HIS. the study utilized semi-structured interviews with HIS development experts. this case study discusses the effectiveness of utilizing the SEP methodology in developing HIS for government hospitals. the study highlights the inherent complexity of the healthcare system and the involvement of various stakeholders, which justify the use of SEP. the iterative approach of SEP, coupled with extensive collaboration, ensures that the HIS is tailored to meet the specific goals of the hospital. the SEP approach's testing and integration phase enable seamless integration with existing systems, enhancing interoperability. © 2023 ieee.

关键词： Hospitals

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Consensus control and Disturbance Rejection for Leader-Following Multi-Agent systems Based on Reduced-Order Observers 13

Consensus Control and Disturbance Rejection for Leader-Follo...

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13th ieee international conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Pang, Guochen Xiao, Xiong Chen, Xiangyong Lu, Yang Qiu, Jianlong Liu, Yang School of Automation and Electrical Engineering Linyi University Linyi China College of Mathematics and Computer Science Zhejiang Normal University Jinhua China School of Computer Science and Engineering Linyi University Linyi China

ISBN: (纸本)9798350315196

the focus of this paper is on the issue of disturbance rejection in leader-follower multi-agent systems based on a reduced-order observer. By combining with the practical, unknown perturbations are considered to be generated by the external system by designing a reduced-order estimation observer to obtain an estimate of the disturbances in the followers. Second, a control protocol is designed for each follower based on the obtained estimates. the design method of the reduced-order observer used in this paper simplifies the state equations of the reduced-order observer by reducing the order compared to the full-order observer, which is simpler to compute and can also effectively suppress the effects of disturbances. Finally, the feasibility of this scheme is verified by numerical examples. © 2023 ieee.

关键词： Disturbance rejection

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A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria 13

A Framework for Automated Cardiovascular Magnetic Resonance ...

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13th international conference on Computer and Knowledge engineering, ICCKE 2023

作者： Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran The University of Manchester Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences Manchester United Kingdom

ISBN: (纸本)9798350330151

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. this paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. the quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. the scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. the Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts' quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. the results show that the quality scoring by the proposed framework is highly correlated with the experts' opinions. the proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank. © 2023 ieee.

关键词： Quality control

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Image Reconstruction of IoT based on Parallel CNN 13

Image Reconstruction of IoT based on Parallel CNN

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2020 ieee Congress on Cybermatics: 13th ieee international conferences on Internet of things, ithings 2020, 16th ieee international conference on Green computing and Communications, GreenCom 2020, 13th ieee international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th ieee international conference on Smart Data, SmartData 2020

作者： Zeng, Chunyan Wang, Zhenghui Wang, Zhifeng Hubei Key Laboratory for High-efficiency Utilization of Solar Energy Operation Control of Energy Storage System Hubei University of Technology Wuhan China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan China Hubei Research Center for Educational Informationization Central ChinaNormal University Wuhan China

ISBN: (纸本)9781728176475

As an effective signal acquisition and reconstruction scheme, compressed sensing (CS) is widely used in measurement and reconstruction of Internet of things (IoT). CS can recover images from fewer measurements compared with traditional signal acquisition and reconstruction methods. Recently, many deep learning based CS methods are proposed for image reconstruction and achieve better performance compared with traditional CS reconstruction methods. However these methods usually divide the image into blocks and utilize random measurement matrix for block measurement, which ignores the correlation between blocks. Furthermore, some existing reconstruction methods based on deep learning only adopt simple channel convolutional neural networks (CNN) to complete image reconstruction, which does not make full use of CNN's presentation ability. In order to solve the above problems, we proposes a novel image measurement and reconstruction framework to achieve high quaulity reconstruction. In measurement part, we use a convolutional layer instead of random measurement matrix to directly acquire all measurements which provides more construction information for subsequent image reconstruction and removes block effect. In reconstruction part, this paper firstly uses a deconvolution layer to obtain an initial reconstructed image which has same dimension as the input image. then we employ multiple parallel CNN to obtain multiple feature information. the multiple parallel CNN include dilated convolution kernels of different receptive field to increase the network's receptive field, which can obtain more image structural information for reconstruction. the results show that the performance of image reconstruction is greatly ameliorated compared with the existing the state-of-the-art methods. © 2020 ieee.

关键词： Image reconstruction

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Improvement and Simulation of PID Model Predictive control Algorithm Based on Time Domain 13th

Improvement and Simulation of PID Model Predictive Control A...

引用

13th international conference on Frontier computing, FC 2023

作者： Zheng, Lan The School of Civil Engineering Harbin University Heilongjiang Harbin150086 China

ISBN: (纸本)9789819994151

In the model predictive control scheme, the PID cascade control structure is introduced. By using conventional PID control at the bottom, the interference into the system is suppressed, and MPC at the top, excellent tracking and robustness are obtained. PID controller is a feedback control system, which uses proportional integral derivative (PID) model to estimate the process variables and their derivatives at each time. then, the output of the PID controller is used as an input of another control loop that controls the process variable in response to the change of the error signal. In this way, it can be said that there are two cycles: one is used to estimate process variables, and the other is used to control process variables. this type of control scheme has been widely used in industrial applications, such as power plants and chemical plants. It has also been successfully applied in many other engineering fields. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Model predictive control

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm 13

Trajectory Optimization of Manipulator for Loading and Unloa...

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13th ieee international conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Zhao, Dongliang Fang, Yiming Yang, Chen Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering Hebei Province Qinhuangdao China Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Province Qinhuangdao China Hebei Iron and Steel Group Hebei Province Tangshan China

ISBN: (纸本)9798350315196

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. the improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. the algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. the DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter. © 2023 ieee.

关键词： Trajectories

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Resilient Observer-based Security control for Cyber-Physical systems under Multiple Attacks 13

Resilient Observer-based Security Control for Cyber-Physical...

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13th international conference on Information Science and Technology, ICIST 2023

作者： Yang, Ximing Li, Tieshan Yang, Hanqing Gao, Xiaoyang Liu, Ran Long, Yue Chen, C.L. Philip School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350313925

the issue of security control for cyber-physical systems (CPSs) is now receiving a lot of attention. Among them, the network layer is more seriously affected by cyber-attacks than the physical layer. In this paper, we investigate the denial-of-service (DoS) attacks and false data injection (FDI) attack on data transmission. Firstly, the FDI attack that exists in the actuator is estimated by proposing the observer. then, due to the presence of DoS attacks in the sensor channels, the estimation effect of the former observer is affected. To solve this problem, a new resilient observer is proposed by introducing the switching mechanism. the proposed resilient observer can effectively reduce the adverse effects of DoS attacks on data transmission. Next, a false data estimation-based security controller is proposed, which can ensure the stability of the closed-loop system. Finally, a simulation is given to verify the effectiveness of the proposed method. © 2023 ieee.

关键词： Cyber Physical system

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Secondary Frequency control of Decoupled Trigonometric Saturated Droop controller for Islanded Low-Voltage Mesh Microgrid with Linear Load 13

Secondary Frequency Control of Decoupled Trigonometric Satur...

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13th international conference on Power, Energy and Electrical engineering, CPEEE 2023

作者： Alghobari, Mohammed Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Abusorrah, Abdullah King Abdulaziz University K. A. Care Energy Research and Innovation Center Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9781665490146

this paper presents a robust secondary frequency control integrated with decoupled trigonometric saturated droop controller (DTSDC). the primary frequency controller may induce an error in frequency regulation, which affects the load sharing accuracy among microgrid distributed generators (DGs). therefore, a centralized secondary controller is designed to converge the frequency to its nominal value for grid-forming inverters. Using an advanced communication channel, the proposed primary and secondary controller can primarily restore the frequency during disturbances, such as DG on/off switching and loading system variation. the practicality and performance of the proposed controller have been demonstrated with MATLAB simulation environment on a mesh microgrid system consisting of four parallel DGs-based inverters. Simulation results show the proposed controller's robustness in the microgrid frequency stability. Hence, it achieves accurate active power sharing with a linear load. Moreover, the impact of resistive and inductive line impedance is discussed in this study. A comparison between the microgrid centralized controller (MGCC) and the decoupled trigonometric saturated droop controller (DTSDC) only is conducted to show the robustness of the presented controller performance. © 2023 ieee.

关键词： Distributed power generation

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A Data Mining Approach to Finding Face Mask from Bangladeshi News Channel 13

A Data Mining Approach to Finding Face Mask from Bangladeshi...

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13th international conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Arman, Md Shohel Alam, Md. Rittique Jahan, Hasnur Islam, Lameya Sammak, Musabbir Hasan Biplob, Khalid Been Md. Badruzzaman Daffodil International University DIU Data Science Lab Department of Software Engineering Dhaka Bangladesh

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Face Mask Detection is currently a hot topic that has piqued the interest of researchers all over the world. Today, the entire world is dealing with the COVID-19 pandemic. To control the spread of the Coronavirus the most important task people need to do is use a mask. there is still a lot of research and study being done on COVID-19. Several studies have also shown that wearing a face mask significantly reduces the problem of viral transmission. In addition, a person wearing a face mask perceives a sense of protection. When we are at home, we take care of everything, but when we are in public places such as offices, malls, and colleges, it becomes more difficult to keep people safe. Machine Learning and Data Mining are a collection of technologies that provide effective solutions to complex problems in a variety of fields. We attempted to develop a face mask recognition system using machine learning in order to prevent the spread of the Coronavirus. this is a good system for detecting a face mask in news channel images and videos. It can recognize both Mask and No Mask faces. With the advancement of this system, it will be possible to detect whether or not a person is wearing a face mask. If the person is not wearing a face mask, it will display a message such as "No Mask,"otherwise it will display "Mask Detected." © 2022 ieee.

关键词： Data mining

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BCI based Robotic Arm control using MI-EEG and Spiking Neural Network 13

BCI based Robotic Arm Control using MI-EEG and Spiking Neura...

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13th international conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Alfred, Joshua Harshavardhan, S. Alex, John Sahaya Rani Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology Centre for Healthcare Advancement Innovation and Research Chennai India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Brain Computer Interface is a technology that helps to interact with human nervous system to analyze and stimulate neural circuits. this research work focuses on the Motor Imagery - Electroencephalography signal for robotic hand movements capable of lifting and dropping artificial limbs. the signals are acquired from subjects of age group between 35 to 60 upon lifting and dropping their hand. the raw signals were pre-processed with signal processing techniques for noise and artifact removal. Further, 2D-spectrogram features were extracted for training the 2D-Convolutional Neural Network (2D-CNN) and a Spiking Neural network (SNN). SNN algorithm is a third-generation neural network closely resembling the complex neuron structure of the brain, being energy-efficient with lesser iterations and transmits information as discrete spike trains. the best average accuracy achieved through 2D-CNN is 99.06% with raw signal images and through SNN is 97.38% when trained upon spectrogram images. the trained model is later interfaced with Arduino to move the robotic arm. © 2022 ieee.

关键词： Electroencephalography

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