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SSRN 2024年

作者： Zhang, Shasha Zhou, Jie Ma, Yanfang Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Mathematics and Science College Shanghai Normal University Shanghai200234 China Computer Science and Information Engineering College Changzhou Institute of Technology Jiangsu Changzhou213032 China

Fuzzy concurrent game structures (FCGSs), an extension of concurrent game structures in the fuzzy setting, combine both fuzzy and nondeterministic choices. They are therefore a powerful semantic foundation for modeling fuzzy systems that interact with their environments. In recent years, refinement relations from the field of formal verification, including trace containment and simulation, have been successfully introduced to special classes or related ones of FCGSs, such as fuzzy transition systems and fuzzy automata, and have been developed quickly. However, these refinement relations are not directly applicable to FCGSs as they constrain the possible behaviors of the environment. To further satisfy the need for analysis and verification of fuzzy systems, this paper introduces alternating refinement relations for FCGSs. First, we present two general notions of alternating trace containment for FCGSs. Although these notions coincide with the corresponding classical ones, we show that they do not coincide within FCGSs. Then, we provide algorithms to solve the problems of deciding two types of alternating trace containments in finite FCGSs under memoryless strategies. On the other hand, we propose two types of alternating simulations for FCGSs via weight functions and closed sets, and discover a novel result that they have different discriminatory abilities, with one alternating simulation implying the other. Finally, we investigate the relationships between two types of alternating trace containments and two types of alternating simulations. © 2024, The Authors. All rights reserved.

关键词： Game theory

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Real-time manifold regularized context-aware correlation tracking

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Frontiers of computer Science 2020年第2期14卷 334-348页

作者： Jiaqing FAN Huihui SONG Kaihua ZHANG Qingshan LIU Fei YAN Wei LIAN Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT) Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 211800China Department of Computer Science Changzhi UniversityChangzhi 046011China

Despite the demonstrated success of numerous correlation filter(CF)based tracking approaches,their assumption of circulant structure of samples introduces significant redundancy to learn an effective *** this paper,we develop a fast manifold regularized context-aware correlation tracking algorithm that mines the local manifold structure information of different types of ***,different from the traditional CF based tracking that only uses one base sample,we employ a set of contextual samples near to the base sample,and impose a manifold structure assumption on ***,to take into account the manifold structure among these samples,we introduce a linear graph Laplacian regularized term into the objective of CF ***,the optimization can be efficiently solved in a closed form with fast Fourier transforms(FFTs),which contributes to a highly efficient *** evaluations on the OTB100 and VOT2016 datasets demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms in terms of accuracy and ***,our tracker is able to run in real-time with 28 fps on a single CPU.

关键词： visual tracking manifold regularization correlation filter graph Laplacian

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Uv-Triggered Carrier Transport Regulation of Fibrous Nio/Sno2 Heterostructures for Triethylamine Detection

SSRN

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SSRN 2023年

作者： Zhao, Huiyi Sun, Jianhua Liu, Jinmei Zhang, Haowei He, Huaguang Liu, Xuemei Liao, Dankui Tong, Zhangfa Sun, Lixia Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology School of Chemistry and Chemical Engineering Guangxi University Nanning530004 China School of Computer and Electronic Information Guangxi University Nanning530004 China

Constructing heterojunctions by using the synergistic and complementary effects of different metal oxide semiconductors (MOSs) properties can effectively increase catalytic activity, enhance adsorption capacity and improve carrier transport characteristics. Therefore, a series of NiO/SnO2 nanofibers with p-n heterojunction were prepared by coaxial electrospinning method, and triethylamine (TEA) detection was realized at low temperature or even room temperature by UV light (375 nm) photoactivation. Gas sensing measurements revealed that NiO/2SnO2 showed high response of 262 at 75 ℃ towards 50 ppm TEA, which is 6 times higher than that of pure SnO2. And NiO/2SnO2 based gas sensor has excellent repeatability and stability. The enhanced gas sensing mechanism was also discussed in detail, which is attributed to the synergistic effects aroused by p-n heterojunction and photoactivation. The present work provides a promising strategy for improving the gas sensitive characteristics of MOSs as well as a new idea for the development and application of room temperature triethylamine gas sensor. © 2023, The Authors. All rights reserved.

关键词： Heterojunctions

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Fully Differentiable Correlation-Driven 2D/3D Registration for X-Ray to CT Image Fusion

Fully Differentiable Correlation-Driven 2D/3D Registration f...

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IEEE International Symposium on Biomedical Imaging

作者： Minheng Chen Zhirun Zhang Shuheng Gu Zhangyang Ge Youyong Kong School of Computer Science and Engineering Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University China Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables the network to extract and separate low-frequency global features from high-frequency local features. A correlation-driven loss is further proposed for low-frequency feature and high-frequency feature decomposition based on embedded information. Besides, a training strategy that learns to approximate a convex-shape similarity function is applied in our work. We test our approach on a in-house dataset and show that it outperforms both existing fully differentiable learning-based registration approaches and the conventional optimization-based baseline. Our code is available at https://***/m1nhengChen/cdreg.

关键词： Training Correlation Computed tomography Surgery process control Feature extraction Controllability

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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High-entropy ceramics:Present status,challenges,and a look forward

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Journal of Advanced Ceramics 2021年第3期10卷 385-441页

作者： Huimin XIANG Yan XING Fu-zhi DAI Hongjie WANG Lei SU Lei MIAO Guojun ZHANG Yiguang WANG Xiwei QI Lei YAO Hailong WANG Biao ZHAO Jianqiang LI Yanchun ZHOU Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing 100076China New Energy Technology Engineering Laboratory of Jiangsu Province Nanjing University of Posts and TelecommunicationsNanjing 210023China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy and MaterialSchool of Material Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Institute of Functional Material College of Material Science and EngineeringDonghua UniversityShanghai 201620China Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijing 100081China College of Metallurgy and Energy North China University of Science and TechnologyTangshan 063210China College of Material Science and Engineering Shenzhen UniversityShenzhen 518060China School of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001China Henan Key Laboratory of Aeronautical Materials and Application Technology School of Material Science and EngineeringZhengzhou University of AeronauticsZhengzhou 450046China National Engineering Laboratory for Hydrometallurgical Cleaner Production Technology CAS Key Laboratory of Green Process and EngineeringInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China

High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal *** in the infant stage,the emerging of this new family of materials has brought new opportunities for material design and property *** from metals,the diversity in crystal structure and electronic structure of ceramics provides huge space for properties tuning through band structure engineering and phonon *** from strengthening,hardening,and low thermal conductivity that have already been found in high-entropy alloys,new properties like colossal dielectric constant,super ionic conductivity,severe anisotropic thermal expansion coefficient,strong electromagnetic wave absorption,etc.,have been discovered in *** a response to the rapid development in this nascent field,this article gives a comprehensive review on the structure features,theoretical methods for stability and property prediction,processing routes,novel properties,and prospective applications of *** challenges on processing,characterization,and property predictions are also ***,future directions for new material exploration,novel processing,fundamental understanding,in-depth characterization,and database assessments are given.

关键词： high-entropy ceramics(HECs) processing structure properties applications

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Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural process Approach

arXiv

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arXiv 2023年

作者： Zhao, Zicheng Luo, Linhao Pan, Shirui Nguyen, Quoc Viet Hung Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Department of Data Science and AI Monash University Australia School of Information and Communication Technology Griffith University Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

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computers, Materials & Continua 2021年第8期68卷 2651-2663页

作者： Zhen-Zhen Li Zi-Chen Li Xiu-Bo Chen Zhiguo Qu Xiaojun Wang Haizhu Pan School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China School of Electronic Engineering Dublin City UniversityDublin9Ireland College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China

In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.

关键词： Quantum network coding non-maximally entangled state quantum grail network practical protocol

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Ensemble Selector Mixed with Pareto Optimality to Feature Reduction

SSRN

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SSRN 2023年

作者： Yin, Zhenyu Yang, Xibei Wang, Pingxin Yu, Hualong Qian, Yuhua School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China School of Science Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China School of Economics and Management Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China Institute of Big Data Science and Industry Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Among various tasks of feature reduction, how to search qualified features and then construct the corresponding reduct which satisfies a given constraint is a hot topic. Constraint is the pre-defined condition in feature reduction, it rules two crucial aspects: 1) when the process of searching can be terminated to construct reduct;2) whether a feature/set of features should be added into reduct pool or not. Obviously, such a task of feature reduction is constraint-dependent. However, some limitations may naturally emerge: 1) constraint-independent features are ignored;2) fixed constraint hinders the diverse evaluations of features;3) selected features based on single constraint are powerless to data perturbation. Therefore, an Ensembler Mixed with Pareto Optimality (EmPo) is developed in this study. Firstly, the principle of Restricted Pareto Optimality is proposed to identify constraint-independent features before performing constraint related searching, which indicates a two-stages strategy to feature reduction. Naturally, diverse evaluations of features are achieved in such a process. Secondly, a data perturbation w.r.t. either sample or feature aspect is employed to obtain multiple reductions of features. The objective is to further improve the stability of the classification results based on those multiple reducts. It should also be emphasized that EmPo is a general framework, and most existing approaches to feature reduction can be embedded into it to further improve their performances. Finally, the effectiveness of our EmPo is validated over 20 datasets within 4 different ratios of noisy label. © 2023, The Authors. All rights reserved.

关键词： Pareto principle

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