检索结果-内蒙古大学图书馆

22nd International Middle East Power systems Conference, MEPCON 2021

作者： Zaky, Alaa A. Rashwan, Yasser Elhossieni, Mostafa El-Sehiemy, Ragab A. Kafrelsheikh University Electrical Engineering Department Kafr el-Sheikh Egypt Mansoura University Computers Engineering and Control Systems Department Mansoura Egypt

ISBN: (纸本)9781665419987

Recently, renewable energy resources have been widely penetrated because of their technical advantages and environmental benefits. So, modeling and simulation of this resource are very important. Obtaining accurate photovoltaic (PV) solar cell's parameters is very significant to monitor maximum power point, performance assessment, regulation, efficiency measurements, and diagnose problems. In this paper, three photovoltaic parameters estimation models are considered. These three models, single-diode, two-diode, and triple-diode models, are presented in this work to investigate the effect of the model on precision. In this, work the Elephant Herding Optimization (EHO) algorithm is employed for the PV three models' parameters estimation. Real experimental data of a 55 mm diameter commercial Radiotechnique-Coprim (RTC France) solar cell was used for the EHO implementation. The suggested algorithm was assessed compared with several recent optimizers like the crow search algorithm, spotted hyena optimization, moth flame optimization, grasshopper optimization algorithm, and Harris hawk's optimization. The results revealed that the estimated I-V and P-V curves are very close to the experimental data at very low error. Also, the results demonstrate different models' effects in fitting the curves and proved the efficiency of the suggested EHO algorithm over other algorithms. © 2021 IEEE.

关键词： Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Classification of Breast Ultrasound Images Based on Convolutional Neural Networks - A Comparative Study

Classification of Breast Ultrasound Images Based on Convolut...

引用

2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Badawy, Samir M. Mohamed, Abd El-Naser A. Hefnawy, Alaa A. Zidan, Hassan E. Gadallah, Mohammed T. El-Banby, Ghada M. Menoufia University Faculty of Electronic Engineering Industrial Electronics and Control Engineering Department Menoufia Menouf Egypt Computers and Systems Department Cairo El Nozha Egypt

ISBN: (纸本)9781665445733

Computer aided diagnosis (CAD) helps physicians towards an early characterization of tumors in different biomedical tissues, including Breast. Deep learning (DL) based image classification, especially convolutional neural networks (CNNs), has achieved a noticeable success in automatic differentiation of breast ultrasound (BUS) images through the last few years. In this paper: ten well-known pretrained CNNs classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify BUS images by transfer learning (TL). A dataset of 780 BUS images (133 normal, 437 benign, and 210 malignant) has been utilized in training and validation. A selected 375 BUS images from the dataset have been utilized to evaluate the classification's accuracy of each CNN model after TL process. Ultrasound's speckle noise effect on classification has been studied by applying a simulated multiplicative speckle noise to the mentioned 375 BUS images before being classified by the ten CNN models. Three restoration approaches have been applied to treat speckled BUS images before being classified. Accuracy evaluation results have represented different values for each CNN model over all 375 BUS images through different studied circumstances. The best accuracy's value was for: ResNet101 when the input images were clear with high resolution, SqueezeNet in case of speckled input images, and InceptionResNetV2 in case of restored images using the three applied restoration schemes. So, in case of speckled BUS images, it is recommended to utilize a proper preprocessing step for image enhancement before applying a CNN based classification model (InceptionResNetV2 is preferred). Ten modified and trained CNNs models for BUS images' classification (input image size: 128 by 128 by 3) utilized in this study are available to researchers at: https://***/mohammedtgadallah/ten-cnns-for-breast-us-image

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Adaptive PI-Fuzzy Like control of a Stack Pneumatic Actuators Testbed for Multi-Configuration Small Scale Soft Robotics 6

Adaptive PI-Fuzzy Like Control of a Stack Pneumatic Actuator...

引用

6th International Conference on Manipulation, Automation, and Robotics at Small Scales, MARSS 2023

作者： Al Mhdawi, Ammar K. Wright, Nick Humaidi, Amjad J. Azar, Ahmad Taher Newcastle University Department of Electrical and Electronic Engineering Newcastle United Kingdom Edge Hill University United Kingdom Prince Sultan University Asscl Lab Saudi Arabia University of Technology Control and Systems Engineering Department Baghdad Iraq College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Egypt

ISBN: (纸本)9798350330397

It has become evident that pneumatic muscle-based robots are the most compliant of all soft and semi-soft robotic testbeds under investigation, despite the fact that the artificial muscle-based robot is the most challenging due to its hyperelastic behavior resulting from the constitutive elastic material. The purpose of this paper is to examine the development of a physical pneumatic actuation testbed and the application of control strategies for precision small scale soft robotics control. Moreover, an adaptive PI-Fuzzy like control is proposed for reference signal tracking. It is imperative to note that in this application, the controller's performance is determined by its ability to track a reference signal as well as its ability to minimize the absolute tracking error. The Fuzzy Logic controller (FLC) controller, including the nature of the membership functions and the rule base, is described in detail. Simulations and hardware testing have revealed that the adaptive PI-Fuzzy like controller outperforms the traditional FLC controller in terms of experimental results. Additionally, the actuation speed is controlled under different loading conditions. It is considered that the proposed testbed is capable of controlling and managing multi-configurations of medium to small scale soft-based robots efficiently and effectively. © 2023 IEEE.

关键词： controllers

来源：评论

学校读者我要写书评

暂无评论

Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

引用

12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Intelligent fault tolerance control using long short-term memory for efficient system performance under fault conditions

引用

Scientific Reports 2025年第1期15卷 1-16页

作者： El-Mahdy, Mostafa H. Ali, Abdelrahman O. Hassan, O.H. El-Gendy, Eman M. Saafan, Mahmoud M. Mechatronics Department Faculty of Engineering Ahram Canadian University Cairo Egypt Mechanical Power Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Mansoura Egypt Computers Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Fault-Tolerant control (FTC) is a crucial field within control systems engineering that focuses on designing systems capable of maintaining desired performance and stability even in the presence of faults. This study introduces a data-driven fault-tolerant control system that enhances the operation of control systems in the presence of faults. The system is designed on a single Long Short-Term Memory (LSTM), which replaces the units responsible for diagnosis and control reconfiguration. The LSTM-FTC system does not require diagnostic and process models, which is a significant advantage over traditional model-based methods. The factory I/O is interfaced with MATLAB through the implementation of the digital twin idea, which allows for the simulation and validation of the suggested approaches. These approaches are then applied to an assembler case study that included both faultless and multiple faulty sensors. The training process reaches 6553 iterations with Root Mean Square Error (RMSE) equal to at six minutes and 17 s. The results of the simulation demonstrate the effectiveness of the proposed approaches. The accuracy of the system outputs in the faultless and worst-case scenarios are 92.81% and 67.16% respectively. © The Author(s) 2025.

关键词： And long short-term memory network Assembler Passive fault tolerant controllers Robust controller Sensors faults

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Networks Based Classification of Segmented Breast Ultrasound Images – A Comparative Preliminary Study

Convolutional Neural Networks Based Classification of Segmen...

引用

Intelligent Methods, systems, and Applications (IMSA)

作者： Mohammed Tarek GadAllah Abd El-Naser A. Mohamed Alaa A. Hefnawy Hassan E. Zidan Ghada M. El-Banby Samir Mohamed Badawy Computers and Systems Department Electronics Research Institute (ERI) Cairo Egypt Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt Industrial Electronics and Control Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt

Computer-aided diagnosis (CAD) helps physicians in tumors’ identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), have obtained noticeable success in differentiating breast ultrasound images automatically. More researches, concerned with breast cancer detection, are representing the output as a black and white segmented image (mask) or a selected region on the breast ultrasound image which will in role submitted to physicians to help them in making a better diagnosis decision. Our paper represents the second step. In this paper: ten pre-trained Convolutional neural networks (CNNs) classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify segmented breast ultrasound images by transfer learning (TL). A dataset of 375 breast ultrasound images' masks (ground truths), divided as 125 normal, 125 benign, and 125 malignant, has been utilized in training and validation. A dataset of 255 breast ultrasound images' ground truths (masks), divided as 85 normal, 85 benign, and 85 malignant, has been utilized to evaluate the classification accuracy of each CNN model after TL process. Each CNN model's evaluation (over the 255 breast ultrasound images' masks) has represented different accuracy values evaluated. The best accuracy value was for ResNet50 with an accuracy of 97.25 %. The proposed classification scheme for segmented ultrasound images can be regarded as a step that may help in an automatic breast cancer diagnosis system. The produced trained ten CNNs models in this study (input image size: 128 by 128 by 3) are not in their optimum case but it can be considered as a start to any researcher interests in studying the classification of segmented binary images of breast cancers, the ten produced CNNs models are available to researchers at: https://***/

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

引用

SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

引用

SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument maintenance support engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Photoacoustic Sensing for Differentiating Breast Cancer Sizes – A Numerical Simulation Study

Photoacoustic Sensing for Differentiating Breast Cancer Size...

引用

Electronic engineering (ICEEM), International Conference on

作者： Mohammed Tarek GadAllah Abd El-Naser A. Mohamed Alaa A. Hefnawy Hassan E. Zidan Ghada M. El-Banby Samir Mohamed Badawy Computers and Systems Department Electronics Research Institute (ERI) Cairo El Nozha Egypt Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt Industrial Electronics and Control Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt

Photoacoustic (PA) sensing is a crossbred technique relying on light-produced ultrasonic signals due to a phenomenon called the PA effect. PA imaging (PAI), which is based originally on PA sensing, has been utilized efficiently in breast cancer identification and characterization over the last few years. In this paper, a numerical simulation study in silico has been applied, utilizing the COMSOL Multiphysics platform based on the Finite Element Method (FEM), for breast cancer sizes’ differentiating by PA sensing. A time-dependent study has been applied on COMSOL resulting in different measured PA signals from different tumor sizes measured at a constant measuring point on the surface of the simulated breast, showing the different shapes for each PA signal and its frequency spectrum for the different tumor sizes. Statistical analysis for some resulting simulated PA signals has been performed by the MATLAB software, deducing that the produced standard deviation values may be considered as a quantitative indication mark of the breast cancer’s size and hence its dangerous degree. In conclusion, the presented study has succeeded qualitatively and quantitatively in differentiating breast cancer tissue sizes in silico. This, in role, may help in giving more insights for in vitro and in vivo PA experiments for breast cancer characterization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer

Awakened at CheckThat! 2022: Fake News Detection using BiLST...

引用

2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian Department of Information Technology Uppsala University Lägerhyddsvägen 1 Uppsala75105 Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei 313 Bucharest060042 Romania Fraunhofer Institute for Open Communication Systems Berlin10589 Germany

In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task);and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19. © 2022 Copyright for this paper by its authors.

关键词： Statistical tests

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：