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Dielectric metasurface evolution from bulk to monolayer by strong coupling of quasi-BICs for second harmonic boosting

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Photonics Research 2024年第4期12卷 784-792页

作者： yinong XIE QIANTING chen JIN YAO XUEYING LIU ZHAOGANG DONG JINFENG ZHU Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen UniversityXiamen 361005China Shenzhen Research Institute of Xiamen University Shenzhen 518057China Department of Electrical Engineering City University of Hong KongHong Kong 999077China Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore

2D materials are promising candidates as nonlinear optical components for on-chip devices due to their ultrathin structure. In general, their nonlinear optical responses are inherently weak due to the short interaction thickness with light. Recently, there has been great interest in using quasi-bound states in the continuum (q-BICs) of dielectric metasurfaces, which are able to achieve remarkable optical near-field enhancement for elevating the second harmonic generation (SHG) emission from 2D materials. However, most studies focus on the design of combining bulk dielectric metasurfaces with unpatterned 2D materials, which suffer considerable radiation loss and limit near-field enhancement by high-quality q-BIC resonances. Here, we investigate the dielectric metasurface evolution from bulk silicon to monolayer molybdenum disulfide (MoS2), and discover the critical role of meta-atom thickness design on enhancing near-field effects of two q-BIC modes. We further introduce the strongcoupling of the two q-BIC modes by oblique incidence manipulation, and enhance the localized optical field on monolayer MoS2dramatically. In the ultraviolet and visible regions, the MoS2SHG enhancement factor of our design is 105times higher than that of conventional bulk metasurfaces, leading to an extremely high nonlinear conversion efficiency of 5.8%. Our research will provide an important theoretical guide for the design of high-performance nonlinear devices based on 2D materials.

关键词： monolayer harmonic quasi

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A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

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Machine Intelligence Research 2024年第2期21卷 400-410页

作者： Dezheng Wang Yinglong Wang Fan Yang Liyang Xu yinong Zhang Yiran chen Ning Liao School of Automation Southeast UniversityNanjing 210096China Software and Artificial Intelligence College Chongqing Institute of EngineeringChongqing 400056China Beijing National Research Center for Information Science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China Liangjiang International College Chongqing University of TechnologyChongqing 401135China Smart City College Beijing Union UniversityBeijing 100101China

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

关键词： Multi-scale feature extractor deep neural network(DNN) multirate sampled industrial processes prediction

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SEVAR: a stereo event camera dataset for virtual and augmented reality

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 755-762页

作者： Yuda DONG Zetao chen Xin HE Lijun LI Zichao SHU yinong CAOl Junchi FENG Shijie LIU Chunlai LI Jianyu WANG Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences Bejing 100049China Yongjiang Laboratory Ningbo 130021China

Event cameras,characterized by their low latency,large dynamic range,and extremely high temporal resolution,have recently received increasing attention.These features make them particularly well-suited for virtual/augmented reality(VR/AR)applications.To facilitate the development of three-dimensional(3D)perception and navigation algorithms in VR/AR applications using event cameras,we introduce the Stereo Event camera dataset for Virtual and Augmented Reality(SEVAR),which comprises a wide variety of head-mounted indoor sequences,including scenarios with rapid motion and a large dynamic range.

关键词： mounted dynamic camera

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Novel multi‐domain attention for abstractive summarisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 796-806页

作者： Chunxia Qu Ling Lu Aijuan Wang Wu Yang yinong chen College of Computer Science and Engineering Chongqing University of TechnologyChongqingChina School of Computing and Augmented Intelligence Arizona State UniversityTempeArizonaUSA

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

关键词： abstractive summarisation attention mechanism Bi‐LSTM coverage mechanism pointer network

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Bayesian estimation-based sentiment word embedding model for sentiment analysis

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 144-155页

作者： Jingyao Tang Yun Xue Ziwen Wang Shaoyang Hu Tao Gong yinong chen Haoliang Zhao Luwei Xiao Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhouChina College of Mathematics and Informatics&College of Software Engineering South China Agricultural UniversityGuangzhouChina School of Foreign Languages Zhejiang University of Finance&EconomicsHangzhouZhejiangChina Educational Testing Service PrincetonNew JerseyUSA School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Sentiment word embedding has been extensively studied and used in sentiment analysis tasks.However,most existing models have failed to differentiate high-frequency and low-frequency words.Accordingly,the sentiment information of low-frequency words is insufficiently captured,thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis.A Bayesian estimation-based sentiment word embedding(BESWE)model,which aims to precisely extract the sentiment information of low-frequency words,has been proposed.In the model,a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment proba-bilities of words,and a novel loss function is defined for sentiment word embedding learning.The experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods,for example,C&W,CBOW,GloVe,SE-HyRank and DLJT1,in sentiment analysis tasks,which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss function.In addition,replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.

关键词： function embedding estimation

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Guest Editorial： Internet of things and intelligent devices and services

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CAAI Transactions on Intelligence Technology 2018年第2期3卷 73-74页

作者： MIN HUANG yinong chen School of Software Engineering South China University of Technology Guangzhou China Arizona State University Tempe AZ 85287-8809 USA

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Visual programming language environment for different IoT and robotics platforms in computer science education

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CAAI Transactions on Intelligence Technology 2018年第2期3卷 119-130页

作者： Gennaro De Luca Zhongtao Li Sami Mian yinong chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA School of Information Science and Engineering University of Jinan Shandong People's Republic of China

This study presents the authors’ recent research and application of a new visual programming language and its development environment: VIPLE (Visual IoT/Robotics Programming Language Environment) at Arizona State University (ASU). ASU VIPLE supports a variety of IoT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100: Introduction to Engineering and in CSE 446: Software Integration and Engineering, as well as in several other universities worldwide.

关键词：工作流程编程概念计算机技术人工智能

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence Technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding yinong chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of words.However,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are limited.In order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word embedding.Instead of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each topic.Effective integrating strategies are designed to obtain high quality contextual vectors.Extensive experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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Technologies Supporting Artificial Intelligence and Robotics Application Development

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Journal of Artificial Intelligence and Technology 2021年第1期1卷 1-8页

作者： yinong chen Gennaro De Luca School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityTempeAZ 85287USA Faculty Ambassador Amazon Web Services at ASUTempeAZ 85287USA Polytechnic School Arizona State UniversityMesaAZ 85212USA

Artificial intelligence(AI)and robotics have gone through three generations of development,from Turing test,logic theory machine,to expert system and self-driving car.In the third-generation today,AI and robotics have collaboratively been used in many areas in our society,including industry,business,manufacture,research,and education.There are many challenging problems in developing AI and robotics applications.We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies.In this inaugural issue,we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms.We present examples of such development environments created by both industry and academia.We also selected eight papers in the related areas to celebrate the foundation of this journal.

关键词： artificial intelligence robotics machine learning education

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Explainable Artificial Intelligence for Workflow Verification in Visual IoT/Robotics Programming Language Environment

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Journal of Artificial Intelligence and Technology 2021年第1期1卷 21-27页

作者： Gennaro De Luca yinong chen Arizona State University TempeAZ 85281 USA

Teaching students the concepts behind computational thinking is a difficult task,often gated by the inherent difficulty of programming languages.In the classroom,teaching assistants may be required to interact with students to help them learn the material.Time spent in grading and offering feedback on assignments removes from this time to help students directly.As such,we offer a framework for developing an explainable artificial intelligence that performs automated analysis of student code while offering feedback and partial credit.The creation of this system is dependent on three core components.Those components are a knowledge base,a set of conditions to be analyzed,and a formal set of inference rules.In this paper,we develop such a system for our own language by employing π-calculus and Hoare logic.Our detailed system can also perform self-learning of rules.Given solution files,the system is able to extract the important aspects of the program and develop feedback that explicitly details the errors students make when they veer away from these aspects.The level of detail and expected precision can be easily modified through parameter tuning and variety in sample solutions.

关键词： explainable AI π-calculus VIPLE education

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