检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Ding, Lifeng Song, Yuru Li, Qiang Guo, Jinqing Zhang, Jie Wang, Qi Dong, Hongmei Song, Yongbo Song, Zhengwei Zhao, Xiaohong Niu, Yulan Department of Chemistry and Chemical Engineering Taiyuan Institute of Technology Taiyuan030008 China School of Chemical Engineering and Technology North University of China Taiyuan030051 China MicroNano System Research Center College of Information and Computer Key Laboratory of Advanced Tran sducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan University of Technology Taiyuan030024 China

In this work, a ZIF-67/MWCNT/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and square wave stripping voltammetry (SWSV) were used to characterize and test ZIF-67/MWCNT/Nafion/GCE. The results show that ZIF-67/MWCNT/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (4.3 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. Finally, the modified electrode has good repeatability, reproductivity, anti-interference and stability and can be applied to test domestic water. © 2023, The Authors. All rights reserved.

关键词： Electrochemical sensors

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Spatial non-locality induced non-markovian EIT in a single giant atom

arXiv

引用

arXiv 2021年

作者： Zhu, Y.T. Wu, R.B. Xue, S. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

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Antitumor Effects of Cds-Ppix Adducts for Nuclear-Targeted Sonodynamic Therapy

SSRN

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SSRN 2023年

作者： Xue, Juanjuan Jia, Muyue Sang, Shengbo Li, Huaqian Wang, Xiaojuan Guo, Li Duan, Qianqian Xi, Yanfeng Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China Department of Pathology Shanxi Cancer Hospital Shanxi Hospital Affiliated to CancerHospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Shanxi Taiyuan030013 China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of Orthopaedics Second Hospital of Shanxi Medical University Taiyuan030001 China Department of Pathology Shanxi Tumor Hospital Taiyuan030013 China

To achieve high-efficiency treatment to diseases, accurate delivery of drugs with a carrier into the specific organelles is of great importance. The nucleus is the ultimate target for a large number of drugs and direct delivery of drugs into nucleus can greatly improve the therapeutic treatment. Whereas, in sonodynamic therapy (SDT), the fact that most drug delivery systems can only deliver drugs to the cytoplasm of the cells and cannot act directly on the nucleus. In this paper, nucleolus-targeted carbondots (CDs) are prepared. Also, the conjugate of CDs and PpIX is synthesized by amide cross-linking and then release singlet oxygen to kill cancer cells under ultrasound radiation. The resulting CDs-PpIX nanoparticles showed significant nuclear targeting properties, and greatly improved the efficiency of acoustic dynamics treatment, as the nucleus exhibited poor tolerance to reactive oxygen species (ROS) generated during acoustic dynamics treatment. In addition, ROS as an extracellular signal can activate the p38MAPK protein pathway, which is the main mechanism of ultrasound killing of cancer cells. Our results demonstrate the great potential of CDs-PpIX in targeting cell nuclei for acoustic dynamics treatment. © 2023, The Authors. All rights reserved.

关键词： Cancer cells

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NeRF in Robotics: A Survey

arXiv

引用

arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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New approach for constructing BDD from a complex fault tree

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2007年第SUPPL. 1期27卷 140-144页

作者： Zhu, Lin Chen, Jie Chen, Wen-Jie Department of Automatic Control School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China

A new conversion method for fault tree (FT) to binary decision diagram (BDD) was introduced. Firstly, FT was reduced and the reduction rule was proposed, share-node conception was introduced on the basis of FT decomposition, then the rules of connection of component BDD was proposed. Furthermore the route-based BDD reduction rule was proved, with which reduction can be done at the same time with BDD composition, and the route sequence and cut sets can be obtained, while computation and storage is greatly reduced during the process. Finally, the procedure of the transformation is presented. The affectivity effectiveness and practicability is thereby proved.

关键词： Fault tree analysis

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Backlash neural network model for hysteresis nonlinearity

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2007年第SUPPL. 1期27卷 226-230页

作者： Liao, Xiao-Zhong Lu, Guo-Hui Liu, Xiang-Dong Department of Automatic Control School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China

A Backlash neural network model is proposed for the systems with hysteresis nonlinearity. In order to approximate the hysteresis nonlinearity, Backlash-type operators are used as one layer of the neural network. Then, the number of Backlash operators and neurons of hidden layer are gotten through simulation. Compared with the normal BP network, the results show the improvement validity of the proposed Backlash neural network model for describing the hysteresis nonlinearity.

关键词： Nonlinear network analysis

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Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

arXiv

引用

arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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Algorithm of improved marriage in honey bees optimization and convergence analysis

引用

Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2007年第SUPPL. 1期27卷 80-84页

作者： Yang, Chen-Guang Cai, Tao Tu, Xu-Yan Department of Automatic Control School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China

One problem of marriage in honey bees optimization (MBO) is that its complex computation process will limit its applications. The paper proposed an improved marriage honey bees optimization (IMBO). By randomly initializing drones and restricting the condition of iteration, the calculation process becomes easier. The global convergence characteristic of IMBO is also proved based on the Markov chain theory. With different number of nodes, traveling salesman problem(TSP) is used to compare the IMBO with MBO and genetic algorithm(GA). Simulation results show that IMBO has better convergence performance.

关键词： Genetic algorithms

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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