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Optimal Design of Aperture Illuminations for Microwave Power Transmission with Annular Collection Areas

engineering 2023年第11期30卷 63-74页

作者： Xun Li Baoyan Duan Yiqun Zhang Yongxin Guo Shaanxi Key Laboratory of Space Solar Power Station System Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore

This work presents an optimal design method of antenna aperture illumination for microwave power transmission with an annular collection *** objective is to maximize the ratio of the power radiated on the annular collection area to the total transmitted *** formulating the aperture amplitude distribution through a summation of a special set of series,the optimal design problem can be reduced to finding the maximum ratio of two real quadratic *** on the theory of matrices,the solution to the formulated optimization problem is to determine the largest characteristic value and its associated characteristic *** meet security requirements,the peak radiation levels outside the receiving area are considered to be extra constraints.A hybrid grey wolf optimizer and Nelder–Mead simplex method is developed to deal with this constrained optimization *** order to demonstrate the effectiveness of the proposed method,numerical experiments on continuous apertures are conducted;then,discrete arrays of isotropic elements are employed to validate the correctness of the optimized ***,patch arrays are adopted to further verify the validity of the proposed method.

关键词： Microwave power transmission Beam collection efficiency Ring-shaped beam Annular collection area Grey wolf optimizer Nelder-Mead simplex method

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FCA-based θ-iceberg core decomposition in graphs

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1423-1428页

作者： Hao, Fei Xinchang, Khamphaphone Park, Doo-Soon Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, θ-Iceberg Core decomposition in graphs, which is able to incorporate a parameter θ (0 © Springer-Verlag GmbH Germany, part of Springer Nature 2017.

关键词： Formal concept analysis

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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2024 International Research Conference on Smart Computing and systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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Multi-Stage Transfer Learning Evolutionary Algorithm for Dynamic Multiobjective Optimization 13

Multi-Stage Transfer Learning Evolutionary Algorithm for Dyn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Qianhui Zhu, Qingling Ji, Junkai College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite. © 2024 IEEE.

关键词： Dynamic multiobjective optimization evolutionary algorithm transfer learning

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Multi‐granularity re‐ranking for visible‐infrared person re‐identification

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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Attack-detection and multi-clock source cooperation-based accurate time synchronization for PLC-AIoT in smart parks

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Digital Communications and Networks 2024年第6期10卷 1732-1740页

作者： Zhigang Du Sunxuan Zhang Zijia Yao Zhenyu Zhou Muhammad Tariq State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Electrical Engineering Department National University of Computer and Emerging SciencesIslamabad 44000Pakistan

Power Line Communications-Artificial Intelligence of Things(PLC-AIo T)combines the low cost and high coverage of PLC with the learning ability of Artificial Intelligence(AI)to provide data collection and transmission capabilities for PLC-AIo T devices in smart *** the development of smart parks,their emerging services require secure and accurate time synchronization of PLC-AIo T ***,the impact of attackers on the accuracy of time synchronization cannot be *** solve the aforementioned problems,we propose a tampering attack-aware Deep Q-Network(DQN)-based time synchronization ***,we construct an abnormal clock source detection ***,the abnormal clock source is detected and excluded by comparing the time synchronization information between the device and the ***,the proposed algorithm realizes the joint guarantee of high accuracy and low delay for PLC-AIo T in smart parks by intelligently selecting the multi-clock source cooperation strategy and timing *** results show that the proposed algorithm has better time synchronization delay and accuracy performance.

关键词： Smart park Power line communications Artificial intelligence of things Tampering attack awareness Abnormal clock source detection Multi-clock source cooperation

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