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Optimal Location And Size of The STATCOM to Enhance The Power System Stability 优化STATCOM的位置和大小以提高电力系统的稳定性
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.604
Mohamad A. Khalaf, Abdulsatar H. Jasim, Bilal A. Nasir
The importance of power system operation is becoming increasingly significant due to the escalating demand observed in many nations. The exponential growth in load demand exerts substantial stress on electrical power systems, since they run in close proximity to their critical thresholds due to limitations imposed by environmental and economic factors. The basic goal of operating electrical power networks is to efficiently supply energy to users at optimal frequency and voltage levels, with a focus on cost minimization. The main objective of this study is to examine the augmentation of voltage stability margin by deploying Flexible AC Transmission Systems (FACTS) at their ideal positions. In order to determine the most efficient size of a Static Synchronous Compensator (STATCOM), it is necessary to explore the relationship between the loading factor and the capacity of the STATCOM. The case study utilized the Iraqi National Super Grid System (INSGS), which consisted of a 24-bus configuration. The effectiveness of the CPF technique has been assessed in the framework of the Iraqi Super Grid test System. The appropriate place for attaching STATCOM to enhance voltage stability in the Iraqi network system was identified to be Bus (20).
由于许多国家的需求不断增加,电力系统运行的重要性变得越来越重要。负荷需求的指数级增长对电力系统施加了巨大的压力,因为由于环境和经济因素的限制,电力系统在接近其临界阈值的情况下运行。运行电网的基本目标是以最佳频率和电压水平有效地向用户供应能源,并以成本最小化为重点。本研究的主要目的是通过在理想位置部署柔性交流输电系统(FACTS)来检验电压稳定裕度的增加。为了确定静态同步补偿器(STATCOM)的最有效尺寸,有必要研究负载因子与静态同步补偿器容量之间的关系。该案例研究利用了伊拉克国家超级电网系统(INSGS),该系统由24总线组成。在伊拉克超级电网测试系统的框架下,对CPF技术的有效性进行了评估。确定了在伊拉克电网系统中,为提高电压稳定性而安装STATCOM的合适位置为总线(20)。
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引用次数: 0
DESIGN OF A SCADA SYSTEM FOR A SOLAR PHOTOVOLTAIC POWER PLANT 太阳能光伏电站监测系统的设计
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.598
Majeed Ismail Mohammed, Ahmed M. T. Ibraheem Al-Naib
This paper presents the design and implementation of a solar panel data monitoring system using a SCADA (Supervisory Control and Data Acquisition) system. The system is built via the Siemens S7-1200 Programmable Logic Controller (PLC) and programmed using TIA (Totally Integrated Automation) Portal V17 software. The system includes various sensors and instrumentation for monitoring solar panel performance, such as temperature sensors, current and voltage sensors, and irradiance sensors. The PLC collects data from these sensors and transmits it to the SCADA software, which displays real-time data on the graphical user interface (GUI). The system also includes features such as alarms and notifications for abnormal conditions, data logging for historical analysis, and remote access for monitoring and control from a central location. The system's reliability and efficiency are enhanced through the use of the S7-1200 PLC and TIA Portal V17 software, which provide advanced programming and automation capabilities. Therefore, this solar panel data monitoring system provides a comprehensive solution for monitoring and optimizing the performance of solar panel systems, helping to increase efficiency, reduce downtime, and improve overall system performance.
本文介绍了一个利用SCADA (Supervisory Control and data Acquisition)系统的太阳能电池板数据监控系统的设计与实现。该系统通过西门子S7-1200可编程逻辑控制器(PLC)构建,并使用TIA(完全集成自动化)Portal V17软件进行编程。该系统包括用于监测太阳能电池板性能的各种传感器和仪器,如温度传感器、电流和电压传感器以及辐照度传感器。PLC从这些传感器收集数据并将其传输到SCADA软件,SCADA软件在图形用户界面(GUI)上显示实时数据。该系统还包括异常情况的警报和通知、历史分析的数据记录以及从中心位置进行监控的远程访问等功能。通过使用S7-1200 PLC和TIA Portal V17软件,系统的可靠性和效率得到了提高,这些软件提供了先进的编程和自动化功能。因此,该太阳能电池板数据监测系统为监测和优化太阳能电池板系统的性能提供了全面的解决方案,有助于提高效率,减少停机时间,并改善整体系统性能。
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引用次数: 0
RF-RFE-SMOTE: A DoS And DDoS Attack Detection Framework RF-RFE-SMOTE: DoS和DDoS攻击检测框架
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.436
Nora Rashid Najam, Razan Abdulhammed Abduljawad
Denial of service and Distributed denial of service (Dos/DDos) attacks continue to be one of the most significant dangers in cybersecurity. Many efforts are being put into developing defenses against these types of attacks. The tools used by attackers to perform these types of attacks increase day-to-day. Thus, a countermeasure is necessary. For this reason, this thesis utilized one of the most recent datasets (CSE-CICIDS2018 and CIC-DDoS2019) containing most Dos/DDoS attacks. This study proposed a framework based on Machine Learning for detecting denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. The framework comprises three main modules: feature selection method using Random Forest—Recursive Feature Elimination (RF-RFE), handling the Imbalanced class distributions using Synthetic Minority Oversampling Technique (SMOTE), and classification. This study used five classifiers to make comparisons that include Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Linear and Quadratic Discriminant Analysis (LDA, QDA)”. Framework empirical findings reveal that the RF-RFE_SMOTE_RF outperformed all other models by obtaining an accuracy of 100% for CSE-CIC-IDS2018 and 0.99% for CIC-DDoS2019.
拒绝服务和分布式拒绝服务(Dos/DDos)攻击仍然是网络安全中最重要的危险之一。人们正在努力开发针对这类攻击的防御措施。攻击者用于执行这些类型攻击的工具每天都在增加。因此,有必要制定对策。出于这个原因,本文使用了包含大多数Dos/DDoS攻击的最新数据集之一(CSE-CICIDS2018和CIC-DDoS2019)。本研究提出了一个基于机器学习的框架,用于检测拒绝服务(DoS)和分布式拒绝服务(DDoS)攻击。该框架包括三个主要模块:基于随机森林-递归特征消除(RF-RFE)的特征选择方法、基于合成少数派过采样技术(SMOTE)的不平衡类分布处理方法和分类方法。本研究使用五种分类器进行比较,包括随机森林(RF)、朴素贝叶斯(NB)、逻辑回归(LR)和线性和二次判别分析(LDA, QDA)。框架实证结果表明,RF-RFE_SMOTE_RF优于所有其他模型,CSE-CIC-IDS2018的准确率为100%,CIC-DDoS2019的准确率为0.99%。
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引用次数: 0
STUDYING OF DUAL DEEP PERIOCULAR PARTS FOR PERSONS RECOGNITION 双深眼周区域对人识别的研究
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.483
Safa N. H. Al-Moktar, Raid Al-Nima
Recently, periocular region has been employed in recognitions and it can be so effective especially in wearing a face mask as happened during the Coronavirus pandemic. In this study, a new method is proposed for recognizing persons based on their perioculars. It is named the Dual Deep Periocular Parts (DDPP). In this method, two deep learning networks are employed, where each network is determined for a certain periocular side (right or left). They are termed the Deep Network for the Right Periocular (DNRP) and Deep Network for the Left Periocular (DNLP). Both the DNRP and DNLP are fused together to construct the proposed DDPP approach. Also in this paper, a database called the Northern Technical University Periocular Database (NTUPD) is collected from scratch. Persons recognition based on the proposed periocular approach shows further performance enhancements as we obtained results of accuracy that reached 98.7% and Equal Error Rate (EER) 1.3%.
最近,眼周区域被用于识别,特别是在冠状病毒大流行期间戴口罩时,它可以非常有效。本研究提出了一种基于眼周的人脸识别新方法。它被命名为双深眼周部(DDPP)。在该方法中,使用了两个深度学习网络,其中每个网络针对特定的眼周侧(右或左)确定。它们被称为右眼周深度网络(DNRP)和左眼周深度网络(DNLP)。将DNRP和DNLP融合在一起,构建了所提出的DDPP方法。此外,本文还从头开始收集了一个名为北方工业大学眼周数据库(NTUPD)的数据库。基于所提出的眼周方法的人识别性能进一步提高,我们获得的准确率达到98.7%,平均错误率(EER)为1.3%。
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引用次数: 0
Skin Cancer Classification: A Transfer Learning Approach Using Inception-v3 皮肤癌分类:使用Inception-v3的迁移学习方法
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.532
Yaarob Younus Al Badrani, Abbas Mgharbel
In the human body, the skin serves as the primary layer of defense for essential organs. However, as a result of ozone layer degradation, exposure to UV radiation, fungal and viral infections. Skin cancer is becoming more common. This study proposes a novel deep learning-based framework for the multi-classification of eight different types of skin cancer. The suggested framework is divided into several steps. The initial phase is the data augmentation of images. In the second step, deep models are fine-tuned. The model is opted, for Inception-v3, and updated their layers. In the third step, The suggested model has been applied to train both fine-tuned on augmented datasets. After optimization, the pre-trained model performs well for classifying skin tumors, with Inception-v3 having accuracy and an F-score of 81% and 81%, respectively.
在人体中,皮肤是重要器官的主要防御层。然而,由于臭氧层退化,暴露于紫外线辐射,真菌和病毒感染。皮肤癌正变得越来越普遍。本研究提出了一种新的基于深度学习的框架,用于八种不同类型的皮肤癌的多分类。建议的框架分为几个步骤。初始阶段是图像的数据增强。第二步,对深度模型进行微调。对于Inception-v3,选择了该模型,并更新了它们的层。在第三步中,将建议的模型应用于增强数据集上的两种微调训练。优化后,预训练模型对皮肤肿瘤的分类效果良好,其中Inception-v3的准确率和f分分别为81%和81%。
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引用次数: 0
Fog Computing: A Comprehensive Review of Architectures, Applications, and Security Challenges 雾计算:架构、应用和安全挑战的全面回顾
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.614
Shatha A. Baker, Salar Jamal Rashid, Omar I. alsaif
Fog computing has emerged as a promising paradigm for bringing capabilities of cloud computing closer to the edge computing. It tries to overcome the limits of traditional cloud designs by putting storage, computing, and resources of networking closer to the data source. This results in accelerated processing, decreased latency, and enhanced system performance. Fog computing designs use a hierarchical approach, where the fog nodes act as an intermediary layer for local data processing and the cloud infrastructure acts as the top layer to support the fog nodes while the devices and sensors generate the data at the bottom layer. The paper discusses the uses of fog computing, and security issues,and suggests countermeasures including encryption, intrusion detection, and access control to reduce risks. It is anticipated that as fog computing develops further, it will spur creativity and efficiency in the linked world because to its adaptability and versatility. In order to properly utilize the promise of fog computing and handle security concerns, the paper underlines the significance of ongoing research and development.
雾计算已经成为一种很有前途的范例,它使云计算的功能更接近边缘计算。它试图通过将存储、计算和网络资源更靠近数据源来克服传统云设计的局限性。这样可以加快处理速度、减少延迟并增强系统性能。雾计算设计使用分层方法,其中雾节点作为本地数据处理的中间层,云基础设施作为支持雾节点的顶层,而设备和传感器在底层生成数据。本文讨论了雾计算的用途和安全问题,并提出了包括加密、入侵检测和访问控制在内的对策来降低风险。预计随着雾计算的进一步发展,由于其适应性和多功能性,它将激发互联世界的创造力和效率。为了正确利用雾计算的前景并解决安全问题,本文强调了正在进行的研究和开发的重要性。
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引用次数: 0
Intelligent System to Transformer Slang Words into Formal Words 将俚语转化为正式词汇的智能系统
Pub Date : 2023-10-17 DOI: 10.56286/ntujet.v2i2.689
Ahmed Abdulstar Ibrahim, Ban Shareef Mustafa
Understanding and utilizing informal words not recognized in standard dictionaries poses challenges for users. These words are specific to certain communities, hindering comprehension for those outside. Natural language processing tasks, like translation and summarization, struggle with informal vocabulary and local dialects. Although existing models can translate informal words, comprehensive solutions are elusive due to regional and contextual variations. Developing natural language processing models that consider informal words and local dialects is crucial for future research. This paper presents an updated dataset of informal English words tailored to current usage. Multiple models from the Transformer core library on the Hugging Face platform were trained and evaluated, with the facebook/bart-base model demonstrating high accuracy (training data loss: 0.05299). Continued research and innovation in this field are imperative for effective cross-cultural and intercommunity communication
理解和使用标准词典中不认识的非正式词汇对用户来说是一个挑战。这些词是特定于某些社区的,阻碍了外界的理解。自然语言处理任务,如翻译和总结,与非正式词汇和当地方言作斗争。虽然现有的模型可以翻译非正式词汇,但由于区域和上下文的差异,全面的解决方案难以捉摸。开发考虑非正式词和当地方言的自然语言处理模型对未来的研究至关重要。本文提出了一个更新的非正式英语单词数据集,以适应当前的用法。对来自hug Face平台上Transformer核心库的多个模型进行了训练和评估,facebook/bart-base模型显示出较高的准确性(训练数据丢失:0.05299)。在这一领域的持续研究和创新是有效的跨文化和跨社区交流的必要条件
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