检索结果-内蒙古大学图书馆

9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Evaluating Non-redundant Rules of Various Sequential Rule Mining Algorithms 6th

Evaluating Non-redundant Rules of Various Sequential Rule Mi...

引用

6th International Conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Youssef, Nesma Abdulkader, Hatem Abdelwahab, Amira Faculty of Computers and Information Minoufia University Shibin El Kom Egypt Department of Information System Sadat Academy for Management Science Cairo Egypt College of Computer Science and Information Technology King Feisal University Al-Hofuf Saudi Arabia

ISBN: (纸本)9783030586683

The data mining techniques help discover hidden knowledge from a huge database. In the pattern mining field, the main goal is to discover interesting patterns in large databases. The sequential pattern mining technique is specialized for discovering sequential patterns with only one measure called support. It is not sufficient and misleading for the user. Sequential rule mining is a good solution that takes another measure into an account called confidence. This paper presents a comparative analysis between two sequential rule mining algorithms, namely non-redundant with dynamic bit vector (NRD-DBV), and TRuleGrowth algorithm. The study clarifies the execution time, the number of rules, and the memory usage for each algorithm. In addition, exposure to the most proper field for each algorithm to achieve elevated efficiency. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Exploring Text-Guided Single Image Editing for Remote Sensing Images

arXiv

引用

arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

引用

7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Implementation of YOLOv5 and Vision OCR Hybrid Model for GD&T Recognition

Implementation of YOLOv5 and Vision OCR Hybrid Model for GD&...

引用

IEEE Symposium on computers & Informatics (ISCI)

作者： Muhammad Syukri Mohd Yazed Ezak Fadzrin Ahmad Shaubari Moi Hoon Yap Department of Multimedia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia (UTHM) Batu Pahat Johor Malaysia Department of Image Vision and Computing E116b John Dalton Building Manchester Metropolitan University United Kingdom

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

The interpretation of Geometric Dimensioning and Tolerancing (GD&T) in engineering drawings is a critical aspect of design and manufacturing processes. However, traditional methods of manual annotation are time-consuming and often lead to variability in understanding, which can impact product functionality and inspection outcomes. To address these challenges, this paper proposes an automated approach to recognize GD&T in engineering drawings by combining deep learning techniques. The primary objective of this paper is to develop a hybrid model that integrates the YOLOv5 object detection model and Vision OCR for symbol, text, and character extraction. The purpose is to streamline the interpretation process and improve the accuracy of GD&T recognition in engineering drawings. By training the YOLOv5 model on a diverse dataset and employing Vision OCR for text retrieval, the model aims to detect objects and extract relevant text efficiently. Performance evaluation metrics, including precision, recall, and mean Average Precision (mAP), are used to assess the effectiveness of the proposed hybrid model. Experimental results demonstrate promising outcomes, with the model achieving high precision and recall rates, as well as a strong mAP score. These results indicate that the hybrid model can accurately recognize objects and text within engineering drawings up to 80%, thereby addressing the problem of inefficiency and variability associated with manual GD&T interpretation. This paper offers a novel solution to automate GD&T recognition in engineering drawings, contributing to enhanced efficiency and accuracy in design interpretation. The proposed model has significant implications for engineering graphics and design practices, as it facilitates better communication and collaboration among engineers, designers, and manufacturers. By streamlining design documentation processes, the hybrid model can be integrated into manufacturing workflows to improve productivity and quality

关键词： YOLO Training Productivity Performance evaluation Accuracy Quality assurance Text recognition

来源：评论

学校读者我要写书评

暂无评论

Social Media Multi-Emotion Detection

Social Media Multi-Emotion Detection

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Soha S. Ibrahiem Bahy Ibrahim El-Sayed Mohammed Mohammed Ahmed El Sayed Ahmed Khaled ElBahnasy Computality R&D System Analyst & NLP Researcher Faculty of science Menofia university Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Computer Science and Information Technology Department Jeddah international College Jeddah KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states; however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection.

关键词： Support vector machines Technological innovation Accuracy Social networking (online) Smart cities Profitability Semantics Lexicon Feature extraction Vectors

来源：评论

学校读者我要写书评

暂无评论

Dealing with noise in crowdsourced GPS human trajectory logging data

Dealing with noise in crowdsourced GPS human trajectory logg...

引用

作者： Adhinugraha, Kiki Rahayu, Wenny Hara, Takahiro Taniar, David Department of Computer Science and Information Technology School of Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Multimedia Engineering Osaka University Osaka Japan Faculty of Information Technology Monash University MelbourneVIC Australia

As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real-world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non-map-matching approach convex hull-based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories. © 2020 John Wiley & Sons, Ltd.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Fire Segmentation in Still Images 20th

Fire Segmentation in Still Images

引用

20th International Conference on Advanced Concepts for Intelligent Vision systems, ACIVS 2020

作者： Mlích, Jozef Koplík, Karel Hradiš, Michal Zemčík, Pavel Department of Computer Graphics and Multimedia Faculty of Information Technology Brno University of Technology Božetěchova 2 Brno Czech Republic

ISBN: (纸本)9783030406042

In this paper, we propose a novel approach to fire in images based on a state of the art semantic segmentation method DeepLabV3. We compiled a data set of 1775 images containing fire from various sources for which we created polygon annotations. The data set is augmented with hard non-fire images from SUN397 data set. The segmentation method trained on our data set achieved results better than state of the art results on BowFire data set. We believe the created data set(http://***/research/view_***?id=12124) will facilitate further development of fire detection and segmentation methods, and that the methods should be based on general purpose segmentation networks. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

IoT for Water Quality Categorization

IoT for Water Quality Categorization

引用

International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometric behavior of the generalized Hadid-Luchko convolution operator in the open unit ball

Geometric behavior of the generalized Hadid-Luchko convoluti...

引用

International Conference on Fractional Differentiation and Its Applications (ICFDA)

作者： Samir B. Hadid Rabha W. Ibrahim Suzan J. Obaiys Al-Saidi Nadia M. G. Department of Mathematics and Sciences College of Humanities and Sciences Ajman University Ajman UAE Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman UAE Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Computer System & Technology Faculty of Computer Science and Information Technology University of Malaya KL Malaysia Department of Applied Sciences University of Technology Iraq

In this effort, by consuming the k-symbol calculus, we generalize the Hdid-Luchko function (the Mittag-Leffler function of several variables). Some inequalities are established containing the normalized type of this function. In addition, we illustrate a list of conditions on the function to recognize some special geometric properties in a unit ball. Our theory is based on the differential subordination of several variables.

关键词：

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：