检索结果-内蒙古大学图书馆

2023 IEEE International conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Ezzeddine, Kawther Chibani, Radhia Hamouda, Mahmoud Kanaan, Hadi Y. Al-Haddad, Kamal Université de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Écolecole Nationale d'Ingénieurs de Sousse Sousse4023 Tunisia 1008 Tunisia Saint Joseph University Beirut Lebanon MontréalQC Canada

ISBN: (纸本)9798350325539

Conventional proportional integral (PI) controllers are the most, commonly, known and used techniques in grid-connected photovoltaic (PV) applications. Indeed, PI-based controllers are, practically, easy to design and implement. Moreover, they provide good performance, i.e., high-quality output signals, in steady-state conditions. Nevertheless, these controllers are, mainly, designed for the control of linear systems. Therefore, when implemented with non-linear systems, they suffer from slow dynamic response. Accordingly, in this paper, a non-linear and advanced PI-based controller is proposed. The proposed controller consists of an auto-tuned PI-based controller using artificial neural network (ANN). The main objective is to enhance the dynamic response of the DClink inductor current in grid-connected PV current source inverter (CSI) and to ensure that the injected grid current is equal to that generated by the DC-bus, which implies that the entire power provided by PV generator is injected into the grid. A numerical simulation model of the grid-connected PV system, i.e., CSI, PI-controller, and MPPT algorithm, is realized using Simulink and PLECS environments to validate the accuracy of the proposed solutions. The numerical results prove that the ANN auto-tuned PI-based controller leads to superior dynamic response of the DC-link inductor current and better-quality of the injected grid current compared to the conventional PI-based controller. © 2023 IEEE.

关键词： Power quality

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results 18

Cybersecurity Threat Detection in the Behavior of IoT Device...

引用

18th conference on Computer Science and Intelligence systems, FedCSIS 2023

作者： Czerwinski, Michal Michalak, Marcin Biczyk, Piotr Adamczyk, Blazej Iwanicki, Daniel Kostorz, Iwona Brzeczek, Maksym Janusz, Andrzej Hermansa, Marek Wawrowski, Lukasz Kozlowski, Artur Qed Software Mazowiecka 11/49 Warsaw0-052 Poland Ukasiewicz Research Network - Institute of Innovative Technologies Emag Leopolda 31 Katowice40-189 Poland Efigo Sp. Z O.o. M.Kopernika 8/6 Katowice40-064 Poland Faculty of Mathematics University of Warsaw Banacha 2 Warsaw02-097 Poland Department of Computer Networks and Systems Faculty of Automatic Control University of Technology Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems. © 2023 Polish Information Processing Society.

关键词： quality control

来源：评论

学校读者我要写书评

暂无评论

An Improved Support Vector Machine Attack Detection Algorithm for Industry controls System 3

An Improved Support Vector Machine Attack Detection Algorith...

引用

3rd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Cui, Shangqing Feng, Dongqin Zhejiang University College of Control Science and Engineering Hangzhou China

ISBN: (纸本)9781665472784

With the application of internet technologies in the industrial field, industrial control systems (ICS) have the possibility of suffering malicious network attacks. Because ICS is widely used in critical infrastructure, designing attack detection algorithms for it can effectively reduce losses. Many papers have built physical models for cyber-attacks against ICS and the corresponding detection algorithms have been proposed. However, the detection performance can be further improved. This paper establishes the simulation attack on the Tennessee Eastman (TE) process and proposes an improved support vector machine (SVM) method to detect integrity attacks. This algorithm uses contribution plots and recursive feature elimination to select features. Compared with the principal component analysis (PCA) feature extraction, the detection accuracy is improved. After adding system dynamics to the algorithm, the time to detect attacks is advanced. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Industrial quality control with Computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

引用

2024 International conference on Distributed systems, Computer networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

performance Analysis of network Coverage Area in WSN Using Optimization Techniques 2nd

Performance Analysis of Network Coverage Area in WSN Using O...

引用

2nd International conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prabhu, R. Nagarajan, R. Kalis, K.R.N. Kannadhasan, S. Department of Electronics and Communication Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electrical and Electronics Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamil Nadu Trichy India Department of Electronics and Communication Engineering Study World College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995233

Wireless sensor networks (WSNs) are created by deploying various sensor units in harsh environments for target monitoring, as well as by deploying various tiny sensors collectively. The sensor units monitor the data in their vicinity and send it to the sink for processing. However, due to various parameters such as coverage area, in-built sensing and processing units, and so on, the sensor nodes may consume more power when forwarding information to the sink. We concentrate on the existing issues in the coverage control of various sensor models for quantitatively and qualitatively solving the sensor network coverage area. Various optimization techniques such as spotted hyena optimization (SHO), particle swarm optimization (PSO), and moth flame optimization (MFO) are studied and discussed. The trade-off between coverage rate, total number of sensor nodes, and energy consumption was observed, and the techniques’ significance was compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

来源：评论

学校读者我要写书评

暂无评论

Wireless Communications for internet of Farming: An Early 5G Measurement Study

引用

IEEE ACCESS 2022年 10卷 105263-105277页

作者： Damsgaard, Sebastian Bro Marcano, Nestor J. Hernandez Norremark, Michael Jacobsen, Rune Hylsberg Rodriguez, Ignacio Mogensen, Preben Aalborg Univ Dept Elect Syst Wireless Commun Networks Sect DK-9220 Aalborgost Denmark Aarhus Univ Dept Elect & Comp Engn Commun Control & Automat DK-8200 Aarhus Denmark Univ Oviedo Dept Elect Engn Area Signal Theory & Commun Gijon 33203 Spain

Data-driven agriculture and internet of Farming (IoF) require reliable communication systems. Nowadays, only some of the key use cases demanded by the agricultural industry verticals get support from multiple state of the art wireless technologies such as 4G, Wi-Fi, or Low Power Wide Area network (LPWAN) technologies, combined with satellite and cloud access. However, the ones demanding very high data rates or very low latency are still not feasible. With 5G, designed for flexible support of Extreme Mobile Broadband (xMBB), Massive Machine-Type Communications (mMTC) and Ultra-reliable Machine-Type Communications (uMTC), more agricultural use cases will be possible. This paper provides a reference list of data-driven agriculture scenarios and use cases with their associated communication requirements, and whose feasibility is evaluated in a live 5G trial performed in a representative rural area scenario in the south of Denmark. The paper details a reference methodology for assessing 5G quality of Service (QoS), including multi-connectivity schemes and reports the empirical 5G performance results, which are put in perspective of the requirements for the different IoF reference scenarios. The empirical results indicate that early 5G deployments are already capable of reliably serving data-driven agriculture vertical use cases such as those related to agricultural logistics or configuration of machinery and diagnostics in 65.8-99% of the cases;but it will be necessary to wait for 5G network upgrades and coming 5G Releases in order to operate the more low latency demanding use cases.

关键词： 5G mobile communication Agriculture Farming Data models Servers quality of service Low latency communication Satellites Reliability engineering Machinery IoF data-driven agriculture 5G quality of service multi-connectivity live measurement trial

来源：评论

学校读者我要写书评

暂无评论

Study and Analysis of Various Crop Prediction Techniques in IoT network: An Overview

Study and Analysis of Various Crop Prediction Techniques in ...

引用

2022 International conference on Automatic control and Intelligent systems

作者： Rajathi, S. Anitha Sahayadhas, Arun Melvin, Allan Vels Inst Sci Technol & Adv Studies VISTAS Sch Engn Dept CSE Chennai Tamil Nadu India Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra Campus Arau Perlis Malaysia

ISBN: (纸本)9781665495813

In the agriculture domain, the main issues determined are knowledge deficiencies regarding alterations in climate. As each crop poses its own climatic features, but the issues confronted in agriculture are managed using precise farming methods. The precision farming assists in fulfilling the demand for food, maintaining crop productivity, and increasing the yield rate. In India, the emerging needs require sustainable agriculture. The two main issues in agriculture are the selection of crops and altering climatic conditions, which are solved by examining prediction and monitoring techniques, but there is no solution for the crop suggestion. This survey examines several techniques based on crop prediction in the IoT network. This study utilizes 25 research papers focused on several methods, and review of researches based on the classical technique is devised. The assessment is done based on publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards crop prediction techniques in IoT network. At the end, the research gaps and issues of the existing techniques are devised in such a way that the motivation for developing an effective method for crop prediction techniques in IoT network is revealed.

关键词： Crop prediction internet of Things Smart agriculture Production IoT sensors

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Deep Learning Approach for Enhanced network Slicing in 6G

A Hybrid Deep Learning Approach for Enhanced Network Slicing...

引用

2024 IEEE Middle East conference on Communications and networking, MECOM 2024

作者： Mahmoud, Haitham Zhang, Yunsheng Guan, Mingxiang Lu, Chen Ismail, Tawfik Idrissi, Moad Mi, De Daraz, Umar Birmingham City University College of Computing Birmingham United Kingdom Shenzhen Institute of Information Technology Shenzhen China Taibah University College of Engineering Madinah Saudi Arabia

ISBN: (纸本)9798350376715

Future generation networks need to have improved security, reduced latency and more communication capacity. Some instances of these networks are millimeter-wave LAN, broadband wireless access systems, and 6G networks. One essential element of 6G technology is effective congestion control, which allows operators to administer several network instances with a single infrastructure while providing a higher quality of Service (QoS). The paper introduces a hybrid deep learning approach, combining Convolutional Neural networks (CNNs) for feature selection with various machine learning (ML) models for network slice classification. Using real-world network traffic data, the model accurately allocates network slices to incoming requests, enhancing resource utilization and QoS. The evaluation shows significant performance from RF, 5NN, XGBoost, and Naive Bayes, with all achieving the highest accuracy of 94.44%. However, Logistic Regression exhibits lower accuracy at 72%, indicating a significant misclassification rate. In terms of precision, RF, 5-NN, XGBoost, and Naive Bayes demonstrate outstanding precision of 100%, while the Bagging Trees model has lower precision at 64.3%. Models such as DT, RF, 5-NN, LR, XGBoost, and Naive Bayes achieve perfect recall, effectively classifying all instances into the correct slices. However, MLP displays slightly lower recall rates. F1 scores of RF, 5NN, XGBoost, and Naive Bayes indicate a robust balance between precision and recall, while the Bagging Trees model exhibits a lower F1 score, suggesting a trade-off. Logistic Regression and MLP demonstrate average performance in balancing precision and recall. © 2024 IEEE.

关键词： quality of service

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 16th International conference on Signal-Image Technology and internet-Based systems, SITIS 2022

Proceedings - 16th International Conference on Signal-Image ...

引用

16th IEEE International conference on Signal-Image Technology and internet-Based systems, SITIS 2022

ISBN: (纸本)9781665464956

The proceedings contain 94 papers. The topics discussed include: answering an Amharic language semantic question over interlinked data;query management for a decentralized enterprise knowledge graph;towards interpretable probabilistic classification models for knowledge graphs;a corpus of encoded malware byte information as images for efficient classification;digital health data, a way to take under control the quality during the elaboration processes;machine learning for physical layer security: limitations, challenges and recommendation;implementing passive authentication with enhanced risk-based security;a shared pose regression network for pose estimation of objects from RGB images;few-shot object detection in unseen domains;improving gesture recognition with object tracking and image stabilization to communicate with industrial cobots;neural network based landing assist using remote sensing data;hierarchical DNN-based image segmentation algorithm using texton, superpixels, and layer-adaptive loss functions;and an investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adapting tree algorithms for partial enumeration of Extreme Pathways sets 10

Adapting tree algorithms for partial enumeration of Extreme ...

引用

10th IFAC conference on Foundations of systems Biology in Engineering (FOSBE)

作者： Mores, Wannes Bhonsale, Satyajeet S. Logist, Filip Van Impe, Jan F. M. Katholieke Univ Leuven Dept Chem Engn BioTeC Ghent Belgium

Analysis of metabolic networks through characterising flux vectors such as Elementary Flux Modes and Extreme Pathways has shown to be valuable in many application within systems biology. However, their application is still limited to medium-scale metabolic networks due to the combinatorial explosion of the set size. Enumeration of the full set in genome-scale metabolic networks remains impossible, but few techniques exist for partial enumeration. Currently, an adapted version of the Canonical Basis Approach for generation of Extreme Pathways has allowed partial enumeration with good sampling quality through a filter. However, computational efficiency currently limits its application, especially when moving towards larger networks. One approach to increase this efficiency significantly is through candidate narrowing based on bit pattern trees. Here, many of the candidate modes are removed every iteration based on their ability to pass a rank test. However, the depth of the trees should be adapted based on the filter setting and the current amount of candidates to process. In this work, a novel algorithm is presented using candidate narrowing in a stochastic partial enumeration of Extreme Pathways. A case study for the central carbon metabolism of Escherichia coli showcases the adaptive tree depth and the overall performance increase for different settings. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： metabolic network extreme pathway constraint-based modelling model-based optimisation

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：