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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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Highly-Compact Wideband High-Gain Four-Element MIMO Antenna for 5G New Radio IoT

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IEEE Internet of Things Journal 2025年第12期12卷 22350-22365页

作者： Khan, Shahid Khan, Owais Shah, Syed Ahson Ali Nasir, Jamal Malik, Bilal Tariq Khan, Salahuddin Koziel, Slawomir Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad-Campus Islamabad Pakistan Gwangju Institute of Science and Technology Graduate School of Energy Convergence Gwangju Korea Republic of King Saud University College of Engineering P.O. Box 800 Riyadh11421 Saudi Arabia Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Reykjavik University Engineering Optimization and Modeling Center Reykjavik101 Iceland

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37.40 GHz) and n259 (42.43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 × 3.3 λ2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls × Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4× 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 to 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of © 2014 IEEE.

关键词： Microwave antennas

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Joint Energy and Computation Workload Management for Geo-distributed Data centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

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Journal of Reliable Intelligent Environments 2023年第2期9卷 183-200页

作者： Rahman, Md. Mijanur Shakeri, Mohammad Khatun, Fatema Tiong, Sieh Kiong Alkahtani, Ammar Ahmed Samsudin, Nurul Asma Amin, Nowshad Pasupuleti, Jagadeesh Hasan, Mohammad Kamrul Department of Computer Science and Engineering Jatiya Kabi Kazi Nazrul Islam University Trishal Mymensingh2224 Bangladesh Department of Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science & Technology University Gopalganj Dhaka8100 Bangladesh Jalan Ikram-Uniten Selangor Kajang43000 Malaysia Center for Cyber Security School of Information Science and Technology Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia

The increasing energy demand and expansion of power plants are provoking the effects of greenhouse gas emissions and global warming. To mitigate these issues, renewable energies (like solar, wind, and hydropower) are blessings for modern energy sectors. The study focuses on wind-speed prediction in energy forecasting applications. This paper is a comprehensive review of deep neural network based approaches, like the "nonlinear autoregressive exogenous inputs (NARX)", "nonlinear input-output (NIO)" and "nonlinear autoregressive (NAR)" neural network models, in time-series forecasting applications. This study proposed NARX based prediction models in wind-speed forecasting for short-term scheme. The meteorological parameters related to wind time-series have been analyzed, and used for evaluating the performance of the proposed models. The experiments revealed the best performance of the prediction models in terms of "mean square error (MSE)", "correlation-coefficient (R2)", "auto-correlation", "error-histogram", and "input-error cross-correlation". Comparing with the other neural network models, like "recurrent neural network (RNN)" and "curve fitting neural network (CFNN)" models, the NARX-based prediction model achieved better performance in regard to "auto-correlation", "error-histogram", "input-error cross-correlation", and training time. The results also showed that the RNN and CFNN models performed better prediction accuracy with R2 and MSE values. While this performance index is slightly higher, it is negligible in forecasting applications and concluded that the proposed NARX-based model achieved the better prediction accuracy in terms of other performance evaluation measures. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Neural network models

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Moisture stable and ultrahigh-rate Ni/Mn-based sodium-ion battery cathodes via K^(+)decoration

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Nano Research 2023年第5期16卷 6890-6902页

作者： Tao Yuan Yuanyuan Sun Siqing Li Haiying Che Qinfeng Zheng Yongjian Ni Yixiao Zhang Jie Zou Xiaoxian Zang Shi-Hao Wei Yuepeng Pang Shuixin Xia Shiyou Zheng Liwei Chen Zi-Feng Ma School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China Zhejiang Natrium Energy Co. Ltd.Shaoxing 312000China In-situ Center for Physical Sciences Shanghai Electrochemical Energy Device Research CenterFrontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315000China Key Laboratory of Solar Energy Utilization&Energy Saving Technology of Zhejiang Province Zhejiang Energy R&D Institute Co.Ltd.Hangzhou 311121China

As one of the most promising cathodes for sodium-ion batteries(SIBs),the layered transition metal oxides have attracted great attentions due to their high specific capacities and facile ***,their applications are still hindered by the problems of poor moisture stability and sluggish Na^(+)diffusion caused by intrinsic structural Jahn–Teller ***,we demonstrate a new approach to settle the above issues through introducing K^(+)into the structures of Ni/Mn-based *** physicochemical characterizations reveal that K^(+)induces atomic surface reorganization to form the birnessite-type K_(2)Mn_(4)O_(8).Combining with the phosphate,the mixed coating layer protects the cathodes from moisture and hinders metal dissolution into the electrolyte ***,K^(+)substitution at Na site in the bulk structure can not only widen the lattice-spacing for favoring Na^(+)diffusion,but also work as the rivet to restrain the grain crack upon *** as achieved K^(+)-decorated P2-Na_(0.67)Mn_(0.75)Ni_(0.2)5O_(2)(NKMNO@KM/KP)cathodes are tested in both coin cell and pouch cell configurations using Na metal or hard carbon(HC)as ***,the NKMNO@KM/KP||Na half-cell demonstrates a high rate performance of 50 C and outstanding cycling performance of 90.1%capacity retention after 100 cycles at 5 ***,the NKMNO@KM/KP||HC fullcell performed a promising energy density of 213.9 Wh·kg^(−1).This performance significantly outperforms most reported state-ofthe-art ***,by adopting this strategy on O3-NaMn_(0.5)Ni_(0.5)O_(2),we further proved the universality of this method on layered cathodes for SIBs.

关键词： sodium-ion battery cathode layered oxide material K^(+)decoration

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Knowledge Graph Completion Based on Graph Attention Networks and Text Information 5

Knowledge Graph Completion Based on Graph Attention Networks...

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5th IEEE International Conference on computer and Communication engineering technology, CCET 2022

作者： Hong, Shengyi Qian, Heng Gao, Yongchao Lyu, Hongli Qilu University of Technology School of Computer Science and Technology Jinan China Shandong Computer Science Center National Supercomputing Center in Jinan Jinan China Lakehead University Department of Electrical and Computer Engineering Thunder Bay Canada

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

In knowledge graphs (KGs), there exist some unsolved problems such as incomplete data, hidden information with incomplete mining and so on. In the most completion models, the information of the triples in the KG is generally utilized, but the neighborhood information and rich entity description information are not included in the triples. In this paper, the knowledge graph completion (KGC) method is improved based on graph attention networks (GATs) with text information by using the neighborhood information of aggregated triples and entity description information. And the embedding capability of semantic information is enhanced in KGs. First, the feature vector of entity description information is extracted by the Bi-LSTM model and concatenated with the entity embedding in the triples. Then the joint vectors are trained by GATs to aggregate the neighborhood information. Next, the KGC task is realized by a decoder. Finally, the effectiveness of the proposed method is verified by the link prediction experiments in the public datasets FB15K-237 and WNISRR and comparison is investigated with several other existing methods. The test results show that most of the indicators in the two datasets are improved. Furthermore, it is proved that the model combined with multi-source information has better representation ability for entities, which can further improve the accuracy and comprehensive performance of KGC tasks. © 2022 IEEE.

关键词： Knowledge graph

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A Reliable Clinical Decision Support System for Posttraumatic Stress Disorder Using Functional Magnetic Resonance Imaging Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5605-5615页

作者： Bhattacharya, J. Gupta, A. Dretsch, M.N. Denney, T.S. Deshpande, G. TIET Department of Computer Science and Engineering Patiala147004 India Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Walter Reed Army Institute of Research U.S. Army Medical Research Directorate-West Joint Base Lewis McChordWA98433 United States Auburn University Auburn University Neuroimaging Center Department of Electrical and Computer Engineering AuburnAL36849 United States Alabama Advanced Imaging Consortium BirminghamAL36849 United States National Institute of Mental Health and Neurosciences Department of Psychiatry Bangalore560030 India Indian Institute of Technology Department of Heritage Science and Technology Hyderabad502284 India

In recent years, there has been an upsurge in artificial intelligence (AI) systems. These systems, along with efficient performance and predictability, also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems, given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of machine learning (ML)-based clinical decision support systems. As an illustrative example, we introduce a framework for ML-based posttraumatic stress disorder (PTSD) diagnostic classification that classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence ( © 2024 IEEE.

关键词： Decision support systems

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CdS/Ti-Si-O Composite Photoanode for Photoelectrochemical Hydrogen Generation

CdS/Ti-Si-O Composite Photoanode for Photoelectrochemical Hy...

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10th Symposium on Dielectrics for Nanosystems: Materials science, Processing, Reliability, and Manufacturing, held at the 245th ECS Meeting

作者： Feng, Weilong Ding, Dongyan Chen, Zhi SJTU-Zhongmu Joint Research Center for Materials and Technology of Intelligent and Connected Vehicles Institute of Electronic Materials and Technology School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical & Computer Engineering Center for Nanoscale Science & Engineering University of Kentucky LexingtonKY40506 United States

ISBN: (纸本)9781623326784

Advancements in clean energy drive socio-economic development and foster a sustainable future by restructuring energy systems and promoting low-carbon development. In this paper, we presented a comprehensive investigation on the design and performance characterization of CdS/Ti-Si-O composite photoanodes for photoelectrochemical (PEC) hydrogen generation. Silicon-doped TiO2 nanotube arrays were fabricated through anodization of Ti-5Si alloy, followed by the deposition of CdS using the SILAR (successive ionic layer adsorption and reaction) process, resulting in nanostructured Ti-Si-O photoanodes modified with CdS. The composite photoanodes were characterized in terms of their composition, microstructure, optical properties and photoelectrochemical hydrogen production performance. The composite photoanodes revealed a notable enhancement in PEC hydrogen generation properties with a photocurrent density of 7.86 mA/cm2,which was 19.15 times and 9.59 times to those of the undoped TiO2 photoanode and Ti-Si-O photoanode, respectively. The Si-doping mechanism of the photoanodes was elucidated through Density Functional Theory (DFT) calculations. The synergistic combination of Si-doping and CdS modification represents a novel and promising approach for the design of efficient nanostructured photoanodes. © The Electrochemical Society.

关键词： Cadmium sulfide

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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science China(Information sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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