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58th International Universities Power engineering Conference, UPEC 2023

作者： Al Shibli, Abdullah Mohammed Tanavade, Satish Al Kalbani, Saif Mohammed Petroleum Development Oman Oil North Operation Department Muscat Oman College of Engineering National University Science & Technology Department of Electrical & Communication Engineering Muscat Oman Petroleum Development Oman Infrastructure Power System Department Muscat Oman

ISBN: (纸本)9798350316834

This study investigates the future expansion in the load, its effects on the voltage stability of the grid and impact of Static Var Compensator (SVC) on a real 132 kV power system grid in Oman. The power grid consists of 55 busbars, over 90 transmission lines, and a generation capacity exceeding 1500 MW. The north region of the grid was selected for analysis, focusing on the worst five buses with high voltage drop. Load Flow Analysis (LFA) approach located voltage profile issues and assessed the impact of SVC on voltage profile. The optimal location for SVC installation in the region was determined as T bus. Installing SVC at T bus maintained voltage drop and minimized reactive power losses by 21.7%, reducing overall losses from 59.8 MVAr to 46.8 MVAr. Additionally, SVC installation improved the system power factor from 0.909 to 0.931. The SVC was designed based on the system's compensation requirements for voltage drop scenarios, resulting in the fixed capacitor ratings of 160 MVAr and thyristor controlled reactor rating of 250 MVAr, respectively. This study demonstrates the effectiveness of SVC in maintaining voltage stability, reducing reactive power losses, and improving the power factor in the analyzed 132 kV power system grid in Oman. © 2023 IEEE.

关键词： Static Var compensators

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Deep Learning Based Spectrum Sensing Method for Cognitive Radio system

Deep Learning Based Spectrum Sensing Method for Cognitive Ra...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Hussein, Ahmed T. Kivanc, Didem Abdullah, Hikmat Falih, Muntaser S. Department of Advanced Electronics and Communication Technology Istanbul Okan University Istanbul34959 Turkey Department of Information and Communication Engineering Al-Nahrain University Baghdad10072 Iraq Department of System Engineering Al-Nahrain University Baghdad10072 Iraq

ISBN: (纸本)9783031270987

Cognitive radio (CR) network is the promised paradigm to resolve the spectrum shortage and to enable the cooperation in heterogeneous wireless networks in 5G and beyond. CR mainly relays on Spectrum Sensing (SS) strategy by which the vacant spectrum portion is identified. Therefore, the sensing mechanism should be accurate as much as possible, as long as the subsequent cognition steps are mainly depended on it. In this paper, an efficient and blind SS algorithm called Deep Learning Based Spectrum Sensing (DBSS) is proposed. This algorithm utilizes the deep learning approach in SS by using Convolutional Neural Network (CNN) as a detector instead of energy thresholding. In this algorithm, the computed energies of the received samples are used as dataset to feed the optimized CNN model in both training and testing phases. The proposed algorithm is simulated by MATLAB, the simulation scenarios divided into: CNN optimization (training) and SS. The last scenario shows the detection ability of the proposed algorithm for PU under noisy environment. The simulation results show that the proposed algorithm reached high detection probability (Pd) with low sensing errors at low SNR. In addition, high recognition ability to identifying Primary User (PU) signal form noise only signal is achieved as well. Finally, the proposed algorithm is validated with respect to real spectrum data that supported by SDR in an experimental signal transmission and reception scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cognitive radio

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India 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 Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & technology 2023.

关键词： Smart city

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PRI-based radar signal binning algorithm and simulation implementation 7

PRI-based radar signal binning algorithm and simulation impl...

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7th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2023

作者： He, Yonghua Sang, Xin Li, Yonggang Wang, Jiangyu Guo, Qianyong Wu, Peng University of Aerospace Engineering Department of Electronic and Optical Engineering Beijing China Key Lab. of Electron. Info. Equipment System Research for National Defense Science and Technology Beijing China

ISBN: (纸本)9798350334197

In a radar signal sorting system, sorting based on PRI features is an important part of the system, and PRI classification is generally divided into two parts, one is to determine the PRI value, and the other is to perform sequence retrieval based on PRI. This paper first describes the PRI characteristics of several typical radar signals, and then introduces in detail several classical PRI feature extraction algorithms, such as the dynamic extended correlation algorithm, the statistical histogram algorithm and the CDIF algorithm, and carries out theoretical analysis and experimental validation of these algorithms. © 2023 IEEE.

关键词： Graphic methods

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Infrared and visible image fusion using a feature attention guided perceptual generative adversarial network

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9099-9112页

作者： Chen, Yunfan Zheng, Wenqi Shin, Hyunchul Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan430068 China Department of Electrical Engineering Hanyang University Ansan15588 Korea Republic of

In recent years, the performance of infrared and visible image fusion has been dramatically improved by using deep learning techniques. However, the fusion results are still not satisfactory as the fused images frequently suffer from blurred details, unenhanced vital regions, and artifacts. To resolve these problems, we have developed a novel feature attention-guided perceptual generative adversarial network (FAPGAN) for fusing infrared and visible images. In FAPGAN, a feature attention module is proposed to incorporate with the generator aiming to produce a fused image that maintains the detailed information while highlighting the vital regions in the source images. Our feature attention module consists of spatial attention and pixel attention parts. The spatial attention aims to enhance the vital regions while the pixel attention aims to make the network focus on high frequency information to retain the detailed information. Furthermore, we introduce a perceptual loss combined with adversarial loss and content loss to optimize the generator. The perceptual loss is to make the fused image more similar to the source infrared image at the semantic level, which can not only make the fused image maintain the vital target and detailed information from the infrared image, but also remove the halo artifacts by reducing the discrepancy. Experimental results on public datasets demonstrate that our FAPGAN is superior to those of state-of-the-art approaches in both subjective visual effect and objective assessment. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image fusion

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Effect of Delignification Method on the type of Cellulose Water Hyacinth ( Eichornia crassipes) as a Material for Sustainable Sodium-Ion Battery technology 3

Effect of Delignification Method on the type of Cellulose Wa...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Anugerah Putra Pradana, Revaldo Rafi Setopratama, Andyan Zulaiha Ahmad, Chanifa Prasetyo, Andry Nur Abti, Salsabilla Darminto Department of Physics Faculty of Science and Data Analytics Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Department of Chemistry Faculty of Science and Data Analytics Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Department of Material Engineering and Metallurgy Faculty of Industrial Technology and System Engineering Institute Technology Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Biopolymers developed for solid electrolyte materials of sodium-ion batteries are of great interest these days. The main precursor in the form of cellulose biopolymers has been successfully isolated from water hyacinths (Eichornia crassipes). The first stage is maceration using 2% NaOH to produce cellulose-Iα and Ethanol 60% to produce cellulose-Iβ by hydrothermal reaction process at 150 °C and continued at the bleaching stage with H2O2 solution at 50 °C until it changes color. Then the sample is washed to a neutral pH and dried in an oven at 60 °C. Cellulose-Iα yields were obtained with a yield of 33.98% and cellulose-Iβ of 39.11%. The cellulose-I that has been obtained is modified to obtain cellulose-II type by mercerization method, where cellulose-I type is reacted with 20% NaOH for 5 hours. The mixture is then washed to neutral and dried. Cellulose-II yield was obtained with a yield of 69.21%. Samples were characterized by XRD, FTIR, and SEM. The cellulose content of hyacinths before delignification was 36.69%. The cellulose content of hyacinths after delignification for cellulose-Iα and cellulose-Iβ types was 64.26% and 48.58% respectively. FTIR analysis proved the presence of hydroxyl and carboxyl functional groups in hyacinth cellulose. XRD analysis showed that all three samples were identified with cellulose-Iα, cellulose-Iβ, and cellulose-II amorphous structures with a crystallinity index of 28.62% and the largest crystallite size based on the hkl field (121) of cellulose-Iβ type samples was ∼10 nm. SEM analysis shows that cellulose-Iα, cellulose-Iβ has a slim fiber diameter size and straight, smooth surface and microfibrils around the fiber. While cellulose-II shows visualization of a diameter that looks larger and twisted, the surface is rough and there are no microfibrils around the fibers. © Published under licence by IOP Publishing Ltd.

关键词： Sodium hydroxide

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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3D Virtual Reality Application with Design Approach for Introduction of Istano Basa Pagaruyung Tourist Attractions 23

3D Virtual Reality Application with Design Approach for Intr...

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11th International Conference on Computer and Communications Management, ICCCM 2023

作者： Adrian, Qadhli Jafar Hidayat, Robby Rahmat Pasha, Donaya Maharini, Anggita Septia Lubis, Muharman Lubis, Arif Ridho Department of Information System Universitas Teknokrat Indonesia Indonesia Department of Information Technology Universitas Teknokrat Indonesia Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia

ISBN: (纸本)9798400707735

This research is about the development of proper media for the tourist attraction, which in this case related to the Istano Basa Pagaruyung in order to increase the engagement during Covid-19 pandemic, which is the Android-based. It aims to produce interactivity and innovation product service by using Virtual Reality (VR) technology through design approach method as cognitive and practical process. For the purpose of testing, there are three aspects that were evaluated namely the functional conformity, the usability and the portability. In the first aspect, the results indicated almost 100% successful presentation, while the usability aspect provide the presentation value of 86.7% and the last aspects show zero errors result. Thus, it can conclude that the application can be one alternative solution to promote tourist attraction in the midst of the Covid-19 pandemic in term of marketing or learning mechanism. It also presents the useful practice on the utilization of design approach that can be aligned with multimedia development life cycle (MDLC) to have non-linear and iterative process in order to redefine the problem for designing the alternative solution © 2023 ACM.

关键词： Virtual reality

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DENSE-Guided Deep Motion Networks Accounted by Large Rotations to Improve Myocardial Strain Analysis from Routine Cine MRI 22

DENSE-Guided Deep Motion Networks Accounted by Large Rotatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lei, Pengcheng Xing, Jiarui Fang, Faming Epstein, Frederick H. Zhang, Miaomiao University of Virginia Department of Electrical and Computer Engineering United States School of Computer Science and Technology East China Normal University Shanghai China University of Virginia Health System Department of Biomedical Engineering United States

ISBN: (纸本)9798331520526

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations. © 2025 IEEE.

关键词： Rotation

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Internal Leakage Diagnosis of a Hydraulic Cylinder Using C-LSTM Neural Network 5th

Internal Leakage Diagnosis of a Hydraulic Cylinder Using C-L...

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5th International Conference on Reliability, Safety and Hazard, ICRESH 2024

作者： Prakash, Jatin Kankar, P.K. Miglani, Ankur Tamhankar, Ravindra Matter Motor Works Pvt. Ltd. Gujarat Ahmedabad382481 India System Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Indore Indore453552 India Microfluidics and Droplet Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Indore Indore453552 India DRDO Pune India

ISBN: (纸本)9789819730865

The leakage in hydraulic cylinders is of utmost importance due to its critical nature and potential consequences like reduction in system efficiency and performance. It results in the loss of hydraulic pressure which leads to diminished power output. The present manuscript discusses the leakage detection of a hydraulic cylinder utilising pressure signals. For this, pressure signals of a 2-stage hydraulic cylinder for no leakage and leakage condition (5% wear in the nominal diameter of the seal) are acquired. A total of 100 samples (50 for each condition) are divided into 80:20 for training and testing. The training data is used to train Convolution Long Short Term Memory (C-LSTM) neural network which captures spatial along with temporal features. The layer of 1-D convolution extracts the features from the signals and is then fed to the LSTM layer. The model is trained for 30 epochs and achieves the validation loss © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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