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Mechanically scanned leaky-wave antenna based on a topological one-way waveguide

Frontiers of physics 2020年第3期15卷 97-103页

作者： Qian Shen Yun You Jie Xu Yun Shen Xiaohua Deng Zhuoyuan Wang Weidong Min Linfang Shen Sanshui Xiao Information Engineering School Nanchang UniversityNanchang330031China Institute of Space Science and Technology Nanchang UniversityNanchang330031China Department of Photonics Engineering Technical University of DenmarkDK-2800Kgs.LyngbyDenmark Electronic and Information Engineering College Ningbo University of TechnologyNingbo315016China School of Software Nanchang UniversityNanchang330031China Department of Applied Physics Zhejiang University of TechnologyHangzhou310023China

We propose a uniform backfire-to-endfire leaky-wave antenna(LWA)based on a topological one-way waveguide under external bias magnetic *** systematically analyze the dispersion,showing that the proposed structure supports leaky mode arisen from total internal *** means of tuning frequency or magnetic field,we obtain fixed-bias frequency and fixed-frequency bias LWA with continuous beam scanning from backward,broadside to forward *** importantly,we,for the first time,demonstrate that this proposed LWA shows mechanical tunability,allowing us to manipulate the radiation direction from backward,broadside to forward direction by mechanically tuning the air layer *** simulated results show that our system exhibits super low 3dB beam width,high radiation efficiency as well as high antenna *** provided such multiple controlled(especially mechanically)beam scanning manners,the present LWA paves an advanced approach for continuous beam scanning,holding a great potential for applications in modern communication and radar system.

关键词： leaky-wave antenna one-way waveguide magneto-optic materials

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Hierarchical Optimization-Derived Learning

arXiv

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arXiv 2023年

作者： Liu, Risheng Liu, Xuan Zeng, Shangzhi Zhang, Jin Zhang, Yixuan The DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian116024 China The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China The Department of Mathematics and Statistics University of Victoria VictoriaBCV8P5C2 Canada The Department of Mathematics SUSTech International Center for Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen Peng Cheng Laboratory Shenzhen518055 China The Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks. Although having achieved relatively satisfying practical performance, there still exist fundamental issues in existing ODL methods. In particular, current ODL methods tend to consider model constructing and learning as two separate phases, and thus fail to formulate their underlying coupling and depending relationship. In this work, we first establish a new framework, named Hierarchical ODL (HODL), to simultaneously investigate the intrinsic behaviors of optimization-derived model construction and its corresponding learning process. Then we rigorously prove the joint convergence of these two sub-tasks, from the perspectives of both approximation quality and stationary analysis. To our best knowledge, this is the first theoretical guarantee for these two coupled ODL components: optimization and learning. We further demonstrate the flexibility of our framework by applying HODL to challenging learning tasks, which have not been properly addressed by existing ODL methods. Finally, we conduct extensive experiments on both synthetic data and real applications in vision and other learning tasks to verify the theoretical properties and practical performance of HODL in various application scenarios. © 2023, CC BY-NC-SA.

关键词： Constrained optimization

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Shunt Capacitor Bank Fault Detection and Localization Using Sub-Cycle Algorithm

Shunt Capacitor Bank Fault Detection and Localization Using ...

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IEEE Canada Electric Power Conference, EPEC

作者： Mohsen Tajdinian Behzad Behdani Ali Goodarzi Harold R. Chamorro Vijay K. Sood Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft the Netherlands Fars Electricity Distribution Company Fars Iran School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (纸本)9781665463195

Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.

关键词： Performance evaluation Fault detection Capacitors Discrete Fourier transforms Estimation Voltage Fault location

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A Simplified Hyperspectral Recognition System Based on Mathematical Transformation: An Example of Cordyceps Sinensis Geographical Origins

SSRN

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SSRN 2023年

作者： Chen, Xingfeng Zhou, Kaiwen Liu, Yun Du, Hejuan Wang, Donghong Liu, Shumin Liu, Shu Li, Jiaguo Zhao, Limin School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050000 China The School of Information Engineering Xizang Minzu University Xianyang712089 China Beijing Institute of Tracking and Telecommunications Technology Beijing100094 China Jilin Provincial Key Laboratory of Chinese Medicine Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun130022 China

Hyperspectral measurement system is an effective technique to acquire the information for identifying food quality and medical material authenticity. However, the high economic cost of hyperspectral equipment and the identification accuracy affect the application. In the study, we chose identifying the Nagqu Cordyceps sinensis geographical origin as the application example. First, we experimented three typical mathematical transformation on hyperspectral reflectance curves,that can help us to find the sensitive spectral range. Meanwhile, we combined hyperspectral characteristics and several popular machine learning models to recognize the authenticity of Cordyceps sinensis. Secondly, we analyzed the influence of different spectral resolution on the identification accuracy. Finally, we designed a simplified Hyperspectral recognition system, that the spectral range is SWIR1 (1000-1800nm) and spectral resolution is 1 nm, which can offer low cost but high identification accuracy. Based on our design, identification accuracy of the test sample is nearly 100%. © 2023, The Authors. All rights reserved.

关键词： Spectral resolution

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Machine Learning Technique for Analyzing the Behavior of Fish in an Aquarium 1st

Machine Learning Technique for Analyzing the Behavior of Fis...

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1st International Conference on Modeling, Machine Learning and Astronomy, MMLA 2019

作者： Bhaskaran, Rishabh Baskaran, Rajesh Kanna Vijayalakshmi, C. Software Engineer -II Walmartlabs Bengaluru India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Statistics and Applied Mathematics Central University of Tamil Nadu Thiruvarur India

ISBN: (纸本)9789813364622

Over the years lots of marine biologists and scientists have made efforts to study the various problem related to the ocean and its animals. One such problem faced by the fisheries is the change in the physical and mental state of the fishes when they are affected by natural calamities, external agents, change in environment, epidemic outbreak or many such factors. Under such circumstances it’s probable that the fishes in the region may get affected and due to a small amount of bad fish the whole population could suffer which is a huge loss both biologically and economically. There are numerous research and studies back on the fact that fishes tend to change their behaviour in the situation of distress. The behavioural change is just in response to the distress caused to them either physically or psychologically. The motion of the fish over a period of time interval can help us learn about the behaviour it is showing. And a significant change in its pattern of motion can alarm us that there is some issue with the fish. The main idea is to identify and establish the relationship between the movements and behaviour of the fish. The video footage of a Tilapia Genus fish in a standard size aquarium set up with the help of surveillance cameras, tracked the motion of the fish in 2-D space is collected and the movement of the fish over a period of time is categorised. Based on the output the losses can be reduced. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fish

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How the Apriori Algorithm Can Help to Find Semantic Duplicates in Ontology 1

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作者： Astrova, Irina Koschel, Arne Lee, Su Ling Department of Software Science School of IT Tallinn University of Technology Akadeemia tee 21 Tallinn12618 Estonia Faculty IV Department of Computer Science University of Applied Sciences and Arts Hannover Ricklinger Stadtweg 120 Hannover30459 Germany

ISBN: (数字)9783030539498

ISBN: (纸本)9783030539481;9783030539511

Ontology-based data integration attempts to overcome the semantic heterogeneity problem in data integration. Semantic heterogeneity refers to an ambiguous interpretation of terms that describes the meaning of data in heterogeneous resources. However, the presence of semantic duplicates such as similar attributes in the integrated ontologies can lead to incomplete query results. This paper proposes to use the Apriori algorithm from market basket analysis to find similar attributes in an ontology. © 2020, Springer Nature Switzerland AG.

关键词： Data integration

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Artificial Intelligence aware Knowledge Graphs and Deep Learning-Based Diagnostic Prediction Model in Healthcare

Artificial Intelligence aware Knowledge Graphs and Deep Lear...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ramenani Hari Babu Ismail Keshta Mukesh Soni Berdjouh Chafik Haewon Byeon Vertika Rai Department of Pharmacy Practice Teerthanker Mahaveer College of Pharmacy Teerthanker Mahaveer University Moradabad Uttar Pradesh India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India LIAP Laboratory University of El OuedPO Box 789 El Oued Algeria Department of AI and Software Inje University Gimhae Republic of Korea Department of Medical Laboratory Technology School of Allied Health Sciences Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Diagnostic prediction experts use a patient’s medical history to anticipate their future diagnosis. Attention mechanisms and recurrent neural networks are typically utilized for this purpose; however, data shortages often cause complications. Additionally, healthcare information can help diagnostic prediction algorithms. This study links healthcare ideas using AI-aware, intelligent healthcare ontologies and patient visit records as an intelligent graph. Next, utilize a graph convolution module to collect geographical attributes that show patient visit healthcare code linkages. Using a multihead attention technique, visit information and healthcare knowledge are compared to predict the patient’s future health. On two open-source healthcare datasets, the suggested solution outperforms state-of-the-art diagnostic prediction algorithms and better uses IMG data.

关键词： Accuracy Convolution MIMICs Medical services Knowledge graphs Predictive models Prediction algorithms History Clinical diagnosis Medical diagnostic imaging

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A Dynamic SDN-based Privacy-Preserving Approach for Smart City Using Trust Technique 9

A Dynamic SDN-based Privacy-Preserving Approach for Smart Ci...

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9th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2022

作者： Alzubi, Jafar A. Movassagh, AliAkbar Gheisari, Mehdi Najafabadi, Hamid Esmaeili Abbasi, Aaqif Afzaal Liu, Yang Pingmei, Zhou Izadpanahkakhk, Mahdieh Najafabadi, AmirHossein Pourishaban Al-Balqa Applied University Faculty of Engineering Salt Jordan Tehran University of Medical Sciences School of Medicine Department of Medical Physics and Biomedical Engineering Tehran Iran Islamic Azad University Young Researchers and Elite Club Parand Branch Parand Iran University of Calgary Canada Foundation University Department of Software Engineering Islamabad44000 Pakistan Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China Shenzhen BKD Company Shenzhen China La Sapienza University DIET Department Rome Italy University of Yazd Department of Electrical and Computer Engineering Yazd Iran

ISBN: (纸本)9781665478724

A smart city is an Internet-based application of things that automates city management with no need for human interference. Exchanging data via devices obviate some challenges in intelligent cities. In a smart city, Internet-of-Things (IoT) devices may detect sensitive data, posing a risk of privacy violation and system harm. We discover that existing solutions are either too expensive or ineffective at limiting unintended disclosure of sensitive data to build a dependable, smart city. The fact that they create static surroundings is the fundamental reason behind this. software-Defined Networking (SDN) technology has recently evolved to configure the network for performance and monitoring improvement. This study offers a work-in-progress that uses the SDN to protect the privacy of IoT devices by creating a dynamic SDN-based privacy-preserving ecology. The mechanism of the SDN controller performs under the nodes' mutual trust;it chooses various routes from the IoT device to the Cloud space destination dependent on the level of confidence. The packet is re-routed if the SDN controller identifies a device that does not trust its neighbor. Then it instructs the owner to deliver data over a different path. To demonstrate its improved performance, we are currently evaluating it from the perspective of overhead criteria in the future. © 2022 IEEE.

关键词： Smart city

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