检索结果-内蒙古大学图书馆

Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Gede Indra Raditya Martha Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute Technology Bandung Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Research related to digital assets & ownership is currently rising along with the escalation utilization of Blockchain through various use cases and its derevatives, for example in facilitating Digital Assets in the form of virtual property (virtual houses / buildings and virtual land) in the Metaverse. These digital assets often have utilities, features, and even limited copies of quantities so that these digital objects have more value for specific users on a platform and even general people who are interested in and value them. To be able to preserve the value of digital assets in the Metaverse such as virtual property previously mentioned especially for transactions, a set of regulations/governance, as well as sustainable development in the future in the Metaverse is really needed so that the use of Decentralized Autonomous Organization (DAO) in the metaverse is one way to maintain the sustainability of the ecosystem & market and its potential of virtual property. After conducting research for DAO development for virtual property transactions, starting with determining goals & purposes, breaking down the DAO focus object (virtual property), clustering relevant attributes, matching socio-economic impacts, and finally designing smart contracts & governance consensus, we obtained 4 main layers namely Consensus Governance, Smart Contract, Virtual Property & Socio-Economic.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Asymmetric Modulated Predictive Current Control for Dual Three-Phase PMSM With Improved Performance 10

Asymmetric Modulated Predictive Current Control for Dual Thr...

引用

10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Li, Ze Zheng, Xingyu Xia, Jinhui Liu, Hangan Gao, Xiaonan Rodriguez, Jose Wang, Liling Zhang, Zhaoyan Liu, Zhiheng Hebei University College of Electronic and Information Engineering China Southeast University School of Automation China Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Sweden Universidad San Sebastian Faculty of Engineering Chile

ISBN: (纸本)9798350351330

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme. © 2024 IEEE.

关键词： Synchronous motors

来源：评论

学校读者我要写书评

暂无评论

Acquiring Automation and Control Data in The Manufacturing Industry: A Systematic Review

引用

Procedia computer science 2023年 227卷 214-222页

作者： Andi Sama Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Faculty of Computer Science Universitas Indonesia Depok Indonesia 16424

Industry 4.0 has driven the need for information technology (IT) & Operational technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure.

关键词： Industry 4.0 Operational technology PLC SCADA MES Data Acquisition IT/OT convergence Manufacturing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

引用

computers, Materials & Continua 2022年第2期70卷 2953-2969页

作者： Omar M.El-Habbak Abdelrahman M.Abdelalim Nour H.Mohamed Habiba M.Abd-Elaty Mostafa A.Hammouda Yasmeen Y.Mohamed Mohanad A.Taifor Ali W.Mohamed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative *** use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical *** solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice *** of these processed speech signals were obtained and experimented on by random forest and logistic regression *** were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression ***,a deep neural network was implemented to investigate if such variation in method could add to the *** proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’*** research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

关键词： Early diagnosis logistic regression neural network Parkinson’s disease random forest speech signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

IoT-based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor 8

IoT-based tire pressure monitoring system for air and temper...

引用

8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Pangestu, Agung Sodikin, Iing Yusro, Muhammad Sapundani, Rum Al Hakim, Rosyid Ridlo Wilyanti, Sinka Jakarta Global University Faculty of Engineering and Computer Science Electrical Engineering Department Depok Indonesia State University of Jakarta Faculty of Engineering Department of Electronic Engineering Jakarta Indonesia University of Muhammadiyah Purwokerto Electromedical Engineering Technology Faculty of Health Science Purwokerto Indonesia Jakarta Global University Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Depok Indonesia

ISBN: (纸本)9781665453899

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1-the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2-the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-Time via a smartphone using the IoT-based. © 2022 IEEE.

关键词： Thermometers

来源：评论

学校读者我要写书评

暂无评论

Recent Progress in Reinforcement Learning and Adaptive Dynamic programming for Advanced Control Applications

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

来源：评论

学校读者我要写书评

暂无评论

Learning Bezier-Durrmeyer Type Descriptors for Classifying Curves – Preliminary Studies 1

引用

22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science Concordia University 1455 De Maisonneuve West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Comunication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

We aim to propose a new approach for generating descriptors for functional data that are represented by curves that may not be functions but rather parametrically described curves in 2D, 3D, etc. The idea of generating these descriptors is based on the Bezier curves, but instead of using classical control points, we propose to generalize the Durrmeyer approach for parametrically defined vector curves. The Durrmeyer-type descriptors are then estimated from noisy samples of the underlying curve and serve as input vectors to a selected classifier that is learned to recognize from which class of curves noisy observations come. The Bezier curves are not rapidly convergent. However, our aim is not to reconstruct functions but to recognize them, maintaining the shape-preserving and variation-diminishing properties of these curves that increase the classification accuracy in the presence of noise without invoking pre-filtering procedures. For more complicated curves, one can directly apply our approach to their parts in a similar way as polynomial splines are used. The proposed algorithm for learning descriptors of the Bezier-Durrmeyer type and training classifiers on them was tested on synthetic, but interesting per se, data from two families of the Lissajous curves observed with high amplitude noise. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Jing, Jinfeng Yang, Yaozong Zhou, Xiaokang Huang, Jiwei Qi, Lianyong Chen, Ying Beijing Information Science and Technology University School of Computer Science Beijing100101 China Kansai University Faculty of Business and Data Science Japan China University of Petroleum Beijing Key Laboratory of Petroleum Data Mining Beijing102249 China College of Computer Science and Technology China

The development of the Internet of Things (IoT) has made the traditional Consumer Electronics (CE) evolve into a next-generation CE with many intelligent application which generates a large number of computing-intensive tasks and is sensitive to latency. The combination of blockchain technology and multi-UAV-assisted edge computing is considered a promising technology that can process tasks near CE devices instead of sending data to remote data centers, and it has advantages in flexible deployment, differentiated services, and security assurance. This paper considers a Blockchain-enabled MEC scenario where CE devices can offload tasks to multiple-UAV-edge layers for decentralized cooperative computing, and utilize the reputation mechanism of blockchain to identify abnormal nodes in edge offloading. To solve the problem of computation offloading and resource allocation in this scenario, we jointly optimize the CPU cycle frequencies and offloading resources of UAVs and CE devices, and the decision of UAVs. Our goal is to minimize system energy consumption while ensuring the quality of service. The stochastic optimization technique is employed to transform the original stochastic optimization problem into a deterministic optimization problem, decomposing it into multiple sub-problems which can be solved in parallel. Additionally, we design a dynamic priority scoring strategy to facilitate cooperative processing among multiple UAVs and propose a Blockchain-Based Multi-UAV Cooperative Task Offloading (BMCTO) algorithm. Through theoretical analysis and simulation experiments, we demonstrate that the BMCTO algorithm can effectively reduce system energy consumption while maintaining system performance. © 1975-2011 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

A Generalized Integer programming Model for Summer Resort Beneficiaries Selection 6

A Generalized Integer Programming Model for Summer Resort Be...

引用

6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Soufyane, Majdoub Awatif, Karim Adil, Bouhouch Chakir, Loqman University Sidi Mohamed Ben Abdellah LISAC Laboratory Faculty of Sciences Dhar El Mahraz Computer Science Department Fez Morocco Graduate School of Technology Moulay Ismail University IMAGE Laboratory SCIAM Team Meknes Morocco

ISBN: (纸本)9798350351200

This paper presents a generalized integer program-ming model for managing booking stays at a foundation's summer resorts. The model aims to optimize beneficiary selection for the camping service, considering various constraints such as booking rules and member preferences. By adopting a seasonal approach, the model effectively manages the booking process throughout the year. The proposed model was successfully tested on real data from a Moroccan foundation. Comparative analysis between the generalized model and a traditional approach demonstrates its superior performance in satisfying member preferences and achieving optimal booking solutions. © 2024 IEEE.

关键词： Integer linear programming

来源：评论

学校读者我要写书评

暂无评论

A Multi-module 3D U-Net Learning Architecture for Brain Tumor Segmentation 7th

A Multi-module 3D U-Net Learning Architecture for Brain Tum...

引用

7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Ali, Saqib Li, Jianqiang Pei, Yan Rehman, Khalil Ur Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science Division University of Aizu Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9789811989902

Segmentation of gliomas is a crucial step in brain tumor surgical planning, and it serves as the foundation for further diagnosis of brain tumors. Tumor borders are usually unclear, and a significant amount of heterogeneity in the structure, causing brain tumor segmentation a tough task. However, for tumor segmentation, approaches based on deep learning have shown promising results. This study develops a multi-module U-Net system that utilizes multiple U-Net modules to collect spatial detail at varying resolutions. We use various up-inception and down-inception modules to extract and exploit enough features. Experimental results show that the dice scores of 0.95, 0.90, 0.84, and 0.91, 0.84, 0.77 were achieved for the whole tumor, core tumor, and enhancing tumor, using the BraTS 2018 and local private dataset, respectively. When compared to cutting-edge methods, this study achieves competitive segmentation results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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：