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arXiv 2022年

作者： Abdullah, Qazwan Shah, Nor Shahida Mohd Hamzah, Shipun Salh, Adeb Mohamad, Mahathir Nordin, Shahilah Abu, Maisarah Albaom, Mohammed Abdo Sadeq, Safwan Community College of Yareem IBB Yemen Faculty of Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Muar Pagoh Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Muar Pagoh Malaysia Malaysia Pulau Pinang Malaysia Faculty of Electronic and Computer Engineering University Technical Malaysia Malacca Malacca Malacca City Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Serdang Malaysia Department of Mechanical Engineering Universiti Teknologi Petronas Perak Malaysia

Massive multiple-input multiple-output (MIMO) is a critical technology for future fifth-generation (5G) systems. Reduced pilot contamination (PC) enhanced system performance, and reduced inter-cell interference and improved channel estimation. However, because the pilot sequence transmitted by users in a single cell to neighboring cells is not orthogonal, massive MIMO systems are still constrained. We propose channel evaluation using orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques to eliminate channel quality in end users with poor channel quality based on channel evaluation, large-scale shutdown evaluation, and analysis of maximum transmission efficiency. We derived the lower bounds on the downlink data rate (DR) and signal-to-interference noise ratio (SINR) that can be achieved based on PRS assignment to a group of users where the number of antenna elements mitigated the interference when the number of antennas reaches infinity. The channel coherence interval limitation, the orthogonal PRS cannot be allocated to all UEs in each cell. The short coherence intervals able to reduce the PC and improve the quality of channel. The results of the modelling showed that higher DR can be achieved due to better channel evaluation and lower loss. © 2022, CC BY.

关键词： Fading channels

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FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

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IEEE International Conference on Systems, Man and Cybernetics

作者： KaKit Chio Wenhao Zhu Lihua He Dian Zhang Xu Yang Wuman Luo Faculty of Applied Sciences Macao Polytechnic University Macao S.A.R China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Department of Computer Science and Software Engineering Shenzhen University Shenzhen China

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net.

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A Dynamic Interval Multi-Objective Evolutionary Algorithm Based on Multi-Task Learning and Inverse Mapping

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Feimeng Sun, Jing Gan, Xingjia Gong, Dunwei Wang, Gaige Guo, Yongde Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao Shandong266061 China Qingdao University of Science and Technology School of Data Science Qingdao Shandong266061 China Central South University School of Automation Changsha Hunan410083 China Ocean University of China Department of Computer Science and Technology Qingdao Shandong266071 China City University of Macau Faculty of Data Science Macau999078 China

Dynamic interval multi-objective optimization problems, such as those encountered in wireless sensor network scheduling and portfolio selection, are increasingly prevalent. However, they present significant challenges due to the inherent uncertainty and variability of their parameters. This paper proposes a dynamic interval multi-objective evolutionary algorithm in terms of multi-task learning and inverse mapping. The algorithm employs an interval-based MOEA/D as its foundational framework. Upon encountering environmental changes, prediction models for the midpoints and widths of intervals are developed using multi-task learning combined with a self-evolving fuzzy system. These models generate a predicted Pareto front (PF) in the objective space. Subsequently, inverse mappings of the objective functions at the new time are established to identify solutions that correspond to the predicted PF in the decision space. Finally, these solutions are expanded to form an initial population at the new time. Testing on eighteen benchmark optimization problems and an uncertain scheduling of underwater wireless sensor networks, the proposed algorithm demonstrates both competitiveness and superiority when compared to five state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Contrastive Learning

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Examining the optoelectronic and photovoltaic characteristics of Mg₃SbM₃ (M = F, Cl, Br) perovskites with diverse charge transport layers through numerical optimization and machine learning techniques

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Inorganic Chemistry Communications 2025年 179卷

作者： Shimul, Asadul Islam Sarker, Swarna Rani Ghosh, Avijit uz Zaman, Md Towfiq Alrafai, H.A. Hassan, Abeer A. Department of Electrical and Electronic Engineering Gopalganj Science and Technology University Gopalganj 8100 Bangladesh Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur 5400 Bangladesh Department of Electrical and Computer Engineering Lamar University Beaumont 77710 TX United States Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study employs first-principles density functional theory (DFT) to investigate the structural, optical, electrical, and mechanical properties of magnesium-based halide perovskites, specifically Mg₃SbM₃ (M = F, Cl, Br). The computations indicate that the direct band gaps are 3.374 eV for Mg₃SbF₃, 1.589 eV for Mg₃SbCl₃, and 0.879 eV for Mg₃SbBr₃. The compounds exhibit mechanical stability. The optical study identifies Mg₃SbCl₃ as a viable candidate for photovoltaic (PV) applications owing to its elevated dielectric constant, conductivity, reflectivity, and absorption coefficients, essential for photon absorption in solar devices. The SCAPS-1D simulation software was employed to assess the PV performance of solar cells utilizing Mg₃SbCl₃ as the absorber. Various hole transport layers (HTLs), specifically Cu₂O and CuI, were evaluated in conjunction with CdS and WS₂ as electron transport layers (ETLs). Considerations included layer thickness, doping, defects, temperature, carrier concentration, quantum efficiency (QE), and current density–voltage (J-V) properties. The optimization of HTL and ETL layers resulted in a maximum PCE of 27.41 % using Cu₂O as HTL and WS₂ as the ETL, achieving a J_SC of 24.81 mA/cm², an FF of 89.42 %, and a V_OC of 1.23 V. These findings illustrate the capability of Mg₃SbCl₃ to function as an absorber layer, with WS₂ and Cu₂O serving as the ETL and HTL, respectively. Additionally, we developed a machine learning algorithm that predicts solar cell performance with an accuracy of 86.5 %, underscoring the potential of high-performance perovskite heterostructure solar cells and future innovations in solar energy technology. © 2025 Elsevier B.V.

关键词： DFT Machine Learning Mg3SbCl3 Mg3SbM3 (M = F, Cl, Br) SCAPS-1D

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The Angular Block OTSU for Canopy Porosity of Hemisphere Method

The Angular Block OTSU for Canopy Porosity of Hemisphere Met...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Yu Feng Haiyin Lin Faculty of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665413657

The leaf area index reflects the growth of vegetation. The hemispheric image method is a common method for measuring the leaf area index. However, fisheye lenses, containing a large number of mixed cells, would change the luminosity of images. Traditional threshold methods will increase the error in calculating the key variable of the leaf area index, the canopy porosity. So, they cannot distinguish the sky and the leaves efficiently. This paper proposes the Angular Block Otsu algorithm, an improved algorithm based on Otsu for canopy fisheye images. Compared with previous methods, it can retain or highlight the original detail information of the image better, so that the accuracy of the canopy porosity calculation is greatly improved.

关键词： Area measurement Vegetation mapping Information processing Media Indexes Lenses

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Self Decoupling-Reconstruction Network for Facial Expression Recognition

Self Decoupling-Reconstruction Network for Facial Expression...

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International Joint Conference on Neural Networks (IJCNN)

作者： Linhuang Wang Xin Kang Fei Ding Hai-Tao Yu Yunong Wu Kazuyuki Matsumoto Satoshi Nakagawa Fuji Ren Graduate School of Advanced Technology and Science Tokushima University Tokushima Japan Faculty of Library Information and Media Science University of Tsukuba Tsukuba Japan Dataa Robotics Chengdu China Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Facial Expression Recognition (FER) poses significant challenges due to various imaging conditions, including diverse head poses, lighting conditions, resolutions, and occlusions. Additionally, different personal attributes such as age, gender, and racial background further contribute to the complexity of FER. To accurately extract meaningful expression features amidst these interfering factors to enhance recognition accuracy and the model’s generalization, we propose a Self Decoupling-Reconstruction Network (SDRNet). Specifically, our approach involves two learning processes. In the first phase, the network is trained to decouple facial images with expressions into expression and neutral components. This process involves reconstructing neutral facial images and the original input, ensuring the preservation of meaningful expression components devoid of interference in the decoupling process. In the second learning phase, we employ simple convolutional neural networks (CNNs) to recognize the extracted expression components. Our method has achieved state-of-the-art results across multiple widely used datasets, providing substantial evidence of its effectiveness. Additionally, we demonstrate the robust generalization performance of our approach through cross-database evaluations.

关键词： Image resolution Image recognition Head Face recognition Neural networks Lighting Imaging

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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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PAPR Reduction in DCO-OFDM and PAM-DMT Based VLC Systems

PAPR Reduction in DCO-OFDM and PAM-DMT Based VLC Systems

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hussien Alrakah Tilahun Gutema Funmilayo Offiong Wasiu Popoola School of Engineering The University of Edinburgh Edinburgh UK Faculty of Computer Science and Information Technology Jazan University Jazan KSA Department of Electronic and Electrical Engineering Obafemi Awolowo University Nigeria

In this paper, a pilot-assisted scheme is used to reduce the peak-to-average power ratio of multicarrier signalling based visible light communication systems and the system throughput is maximised with bit and power l... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Electric potential Power lasers Loading Peak to average power ratio Throughput Light emitting diodes Reliability

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A Hybrid Clean Energy System Based Thermal Solar Technologies 3

A Hybrid Clean Energy System Based Thermal Solar Technologie...

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3rd International Sustainability and Resilience Conference: Climate Change, ISRC 2021

作者： Al-Sharai, Abdo Ali Muneer, Amgad Nataraj, Chandrasekharan Salman, Abdulsalam Faculty of Electrical Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia Universiti Teknologi Petronas Department of Computer and Information Sciences Perak Malaysia School of Engineering Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9781665416320

Renewable energy, often described as clean energy, is derived from natural sources or mechanisms that are regenerated on a regular basis. Sunlight and heat continue to shine and blow even though their availability is dependent on time and temperature. While capturing nature's power is typically considered a novel technology, it has traditionally been used for transportation, lighting, and other purposes. This study aims to design a clean energy system by using solar and thermal techniques that will generate a hybrid, clean energy continuously from the solar panels and Thermoelectric generators. In this study, we revealed that the design characteristics of the clean energy system's stability after the first conversion of solar and thermal energies can have a big impact on overall efficiencies, which dictate both economics and lifetime. The applications of this system will be highly used in commercial, residential, and industrial sectors. © 2021 IEEE.

关键词： Simulink

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Information Theoretic Weighted Fuzzy Clustering Ensemble

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computers, Materials & Continua 2021年第4期67卷 369-392页

作者： Yixuan Wang Liping Yuan Harish Garg Ali Bagherinia Hamïd Parvïn Kim-Hung Pho Zulkei Mansor Department of Electronic Information Engineering School of Information EngineeringWuhan University of TechnologyWuhanChina School of Information Engineering Wuhan University of TechnologyWuhanChina School of Information Engineering Wuhan Huaxia University of TechnologyWuhanChina School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjabIndia Department of Computer Science Dehdasht BranchIslamic Azad UniversityDehdashtIran Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan Malaysia 43600 UKM BangiSelangorMalaysia

In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble *** clustering ensemble approaches attempt to improve the performance of fuzzy clustering ***,in these approaches,cluster(or clustering)reliability has not paid much attention *** cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering *** this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus *** fuzzy clustering ensemble approach works based on fuzzy cluster unreliability *** unreliability is estimated according to an entropic criterion using the cluster labels in the entire *** do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data *** and robustness of the proposed method are experimentally evaluated for some benchmark *** experimental results demonstrate efciency and suitability of the proposed method.

关键词： Fuzzy clustering ensemble cluster unreliability consensus function

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