检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Song, Chunshan Wang, Jiajie Wang, Xiaoxing AlQahtani, Mohammad S. Knecht, Sean D. Bilén, Sven G. Chu, Wei Department of Chemical Engineering Sichuan University Chengdu610065 China Clean Fuels & Catalysis Program EMS Energy Institute Department of Energy and Mineral Engineering The Pennsylvania State University University ParkPA16802 United States Department of Chemical Engineering The Pennsylvania State University University ParkPA16802 United States School of Engineering Design Technology and Professional Programs The Pennsylvania State University University ParkPA16802 United States School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Department of Chemistry Faculty of Science The Chinese University of Hong Kong NT Hong Kong Shatin China

Nonthermal plasma can provide an alternative to CO 2 utilization under mild conditions. Understanding the effect of plasma on catalysis is highly desired. Herein, a comparative study was performed on CO 2 hydrogenation under thermal catalytic and plasma catalytic conditions using an alumina-supported cobalt catalyst. Significant plasma-catalyst synergy was attained at the low-temperature range between 423 K and 498 K where the CO 2 conversion increased up to approximately 3.6 times of the sum of that using plasma or catalyst individually. The plasma-catalytic reaction showed a significantly lower activation energy (~40 kJ/mol), which is only half of that in the thermal catalytic reaction (~80 kJ/mol). Kinetic analysis and DRIFTS results indicate that CO 2 activation is promoted by plasma with the assistance of active H species from gas phase. Furthermore, the total H 2 adsorption capacity and the coverage of unrecyclable adsorbed H species on the metal surface were decreased under plasma. The promotion effect is very likely due to a new reaction pathway introduced by plasma. In addition, the structure and morphology changes of spent catalysts after thermal or plasma reaction were not significant, suggesting they contribute little to the synergetic effect observed under the conditions. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

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Adaptive feature selection with weighted fuzzy rough sets for noisy data

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Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

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A Novel Robust Visible Light Positioning Based on Sparsity-Adaptive Compressed Sensing

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Journal of Lightwave technology 2025年

作者： Xu, Shiwu Wu, Yi Xing, Song Zhang, Longteng Fujian Normal University Concord University College Fuzhou350117 China Fujian Normal University Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fuzhou350117 China California State University Department of Information Systems Los AngelesCA90032 United States Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China

The fingerprint-based visible light positioning (VLP) method offers high-precision indoor positioning with centimeter-level accuracy, making it well-suited for applications like robot navigation. However, the sensitivity of photodiodes (PDs) to natural light compromises the system's robustness. To address this challenge, we construct a robust received signal strength (RSS) database that leverages the properties of pulse width modulation (PWM) technology to minimize interference from natural light. This robust RSS database serves as the foundation for a novel, low-complexity, sparsity-adaptive compressed sensing (CS) algorithm, which enables accurate signal reconstruction without requiring prior knowledge of sparsity. Experimental results show that the proposed VLP system effectively mitigates the impact of natural light and maintains consistent positioning performance across varying natural lighting conditions. With a grid spacing of 20 cm, the proposed RSS database construction method achieves an average positioning error (APE) of 1.36 cm in VLP, marking a 75.54% reduction compared to the traditional RSS database construction method. Furthermore, the proposed VLP system maintains an APE of 2.15 cm even at a 40 cm grid spacing. This demonstrates that our system achieves excellent positioning accuracy while maintaining low computational and storage complexity. © 1983-2012 IEEE.

关键词： Image reconstruction

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SpEx+: A Complete Time Domain Speaker Extraction Network

arXiv

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arXiv 2020年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker’s reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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Superior performance of ZnGaO solar-blind photodetectors by Implementing TFT structure and tunable ZnO cycle ratio

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Materials Today Chemistry 2024年 38卷

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Ting-Zhu Kuo, Hao-Chung Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen 361024 China School of Ocean Information Engineering Jimei University Jimei District Xiamen 361021 Fujian China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen 361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou 54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua 51591 Taiwan

In this study, plasma enhanced atomic layer deposition was used to prepare ZnGaO thin films with different ZnO cycle ratios from 0 % to 50 %, which were employed to fabricate the MSM-type and TFT-type solar-blind photodetector. The properties of the films and the performance of the solar blind photodetector are greatly affected by the ZnO cycle ratio. Appropriate Zn doping content decreases the ratio of oxygen vacancies and Zn interstitials in Ga₂O₃, while excessive Zn doping degrades the quality of the film. Compared to MSM-type photodetector, an obviously improved performance of ZnGaO TFT-type photodetector has been observed due to its self-amplified photocurrent and the reduced dark current controlled by gate voltage. At a ZnO cycle ratio of 40 %, a superior performance of TFT-type solar-blind photodetector with an ultra-low dark current ∼4.5 × 10⁻¹³ A, an excellent light switching ratio ∼2.2 × 10⁷, and an outstandingly high sensitivity ∼34.31 A/W has been obtained. These results demonstrate that the incorporation of Zn dopants can effectively modulate the performance of Ga₂O₃ TFT-type photodetector providing a great perspective for cost-effective and high-performance solar-blind PDs. © 2024 Elsevier Ltd

关键词： High performance Plasma enhanced atomic layer deposition TFT-Type solar-blind photodetector ZnGaO film ZnO cycle ratio

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Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals

Neuroscience Informatics

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Neuroscience Informatics 2025年第2期5卷 100203-100203页

作者： Dahiya, Ritu G, Mamatha Jawale, Shila Sumol Das, Santanu Choudhary, Sagar Rathod, Vinod Motiram Rajput, Bhawna Janghel Dept. of Chemistry Chhotu Ram Arya College Haryana Sonepat India Department of Management Studies Sri Siddhartha Institute of Business Management Karnataka Tumkur India Department of Computer Engineering Somaiya School of Engineering (formerly K J Somaiya College of Engineering) Somaiya Vidyavihar University Vidyavihar Maharashtra Mumbai India Department of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Department of Mathematics Bioinformatics and Computer Application MANIT Madhya Pradesh Bhopal India Bharati Vidyapeeth Deemed University Department of Engineering and Technology Maharashtra Navi Mumbai India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India

Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions. © 2025 The Author(s)

关键词： Capsule network Clinical emotion recognition Deep learning EEG Transactional NIRS

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Transformation between elastic dipoles, quadrupoles, octupoles and hexadecapoles driven by surfactant self-assembly in nematic emulsion

arXiv

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arXiv 2022年

作者： Senyuk, Bohdan Mozaffari, Ali Crust, Kevin Zhang, Rui de Pablo, Juan J. Smalyukh, Ivan I. Department of Physics and Soft Materials Research Center University of Colorado Boulder CO United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL United States Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center for Molecular Engineering Argonne National Laboratory LemontIL United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado Boulder CO United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO United States

Emulsions comprising isotropic fluid drops within a nematic host are of interest for applications ranging from biodetection to smart windows, which rely on changes of molecular alignment structures around the drops in response to chemical, thermal, electric and other stimuli. We show that absorption or desorption of trace amounts of common surfactants can drive continuous transformations of elastic multipoles induced by the droplets within the uniformly aligned nematic host. Out-of-equilibrium dynamics of director structures emerge from a controlled self-assembly or desorption of different surfactants at the drop-nematic interfaces, with ensuing forward and reverse transformations between elastic dipoles, quadrupoles, octupoles and hexadecapoles. We characterize inter-transformations of droplet-induced surface and bulk defects, probe elastic pair interactions and discuss emergent prospects for fundamental science and applications of the reconfigurable nematic emulsions. © 2022, CC BY-NC-ND.

关键词： Surface active agents

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Big data driven product design: A survey

arXiv

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arXiv 2021年

作者： Quan, Huafeng Li, Shaobo Zeng, Changchang Wei, Hongjing Hu, Jianjun College of Big Data and Statistics Guizhou Universify of Finance and Economics Guiyang550050 China State Key Laboratory of Public Big Data Guizhou University Guiyang550050 China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu610041 China School of Mechanical Engineering Guizhou Institute of Technology Guiyang550050 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29201 United States

With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements;product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design. © 2021, CC BY-NC-SA.

关键词： Product design

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User Experience Metric for Augmented Reality application: A Review 3

User Experience Metric for Augmented Reality Application: A ...

引用

3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Arifin, Yulyani Sastria, Thomas Galih Barlian, Edo Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Game Application and Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Augmented Reality technology is developing rapidly and has been widely used in the field of education. Due to the ease of developing AR by non-professionals, User Experience (UX) is often not considered in the application. Currently, there are no standard measurements of UX for AR applications especially in Education. The authors reviewed previous research to obtain UX references and the existing measurement standards. From the results of the review, the metrics were analyzed based on the characteristics of AR, especially in the field of education. The metrics will be recommended for the UX measurement of an AR application. The available standard metrics can be used to determine the UX quality of an AR application and contribute to the improvement of UX in AR applications, especially in the field of Education. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Natural language processing systems

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FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

FaSTrack:A Modular Framework for Real-Time Motion Planning a...

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作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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