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SSRN 2023年

作者： Shi, Qiqi Gao, Pei Shen, Boxiong Zhang, Xiao Li, Shuhao Lyu, Honghong Liu, Shiyao Kang, Dongrui Zhang, Chenguang Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China Department of Chemistry and Biotechnology School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia CSIRO Manufacturing Private Bag 10 Clayton SouthVIC3169 Australia

Achieving efficient purification of Hg0 and CVOCs (chlorinated volatile organic compounds) in flue gas with the presence of SO2 is still an urgent problem. In this work, we developed a mesoporous MnCo2O4@TiO2 core-shell catalyst using hydrolysis method and applied it to catalytic purification of Hg0 and CVOCs (chlorobenzene as a simulated molecule). The results showed that MnCo2O4@TiO2 had strong SO2 resistance for Hg0 and chlorobenzene conversion. This could be attributed that the electronic interactions between MnCo2O4 and TiO2 induced the generation of more Co3+ active sites, moderate acidic sites, and weakened the deposition of sulfate. In addition, the enhanced SO2-resistance mechanism on the MnCo2O4@TiO2 core-shell catalysts for Hg0 and chlorobenzene conversion is discussed in detail. We deeply appreciate your consideration of our manuscript, and we look forward to receiving comments from the reviewers. If you have any queries, please don’t hesitate to contact me at the address below. © 2023, The Authors. All rights reserved.

关键词： Volatile organic compounds

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Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier

Influence of the variables describing brain signals on the p...

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2022 Progress in Applied Electrical engineering, PAEE 2022

作者： Kawala-Sterniuk, Aleksandra Zygarlicki, Jaroslaw Lysiak, Adam Grochowicz, Barbara Pelc, Mariusz Bauer, Waldemar Baczkowicz, Dawid Krecisz, Krzysztof Dylag, Katarzyna A. Wieczorek, Wiktoria Kahankova, Radana Martinek, Radek Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland University of Greenwich School of Computing and Mathematical Sciences London United Kingdom Agh University of Science and Technology Department of Automatic Control and Robotics Krakow Poland Opole University of Technology Faculty of Physical Education and Physiotherapy Opole Poland St. Louis Children Hospital Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Pathophysiology Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Bioinformatics and Telemedicine Krakow Poland Vsb - Technical University Ostrava Department of Cybernetics and Biomedical Engineering-FEECS Ostrava-Poruba Czech Republic

ISBN: (纸本)9781665455152

In this paper various variables describing brain signals such as near-infrared spectroscopy (NIRS) and electroencephalography (EEG) on the Naive Bayesian Classifier were presented. Analysis of biomedical signals is a very challenging task - therefore the authors of this work decided to analyse two types of brain signals - EEG and NIRS, which have different features, character, nature and are differently prone to various artifacts occurrence. The applied classifiers can be applied for various features extraction and pattern comparisons, which is useful in diagnostic processes. © 2022 IEEE.

关键词： Near infrared spectroscopy

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Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity

arXiv

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arXiv 2024年

作者： Min, Zhe Baum, Zachary M.C. Saeed, Shaheer U. Emberton, Mark Barratt, Dean C. Taylor, Zeike A. Hu, Yipeng School of Control Science and Engineering Shandong University Jinan China Centre for Medical Image Computing and Wellcome EPSRC Centre for Interventional & Surgical Sciences University College London London United Kingdom Division of Surgery & Interventional Science University College London London United Kingdom CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine Institute of Medical and Biological Engineering University of Leeds Leeds United Kingdom

This paper investigates both biomechanical-constrained nonrigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-informed neural networks (PINNs). The nonlinear elasticity theory is leveraged to formally establish the partial differential equations (PDEs) representing physics laws of biomechanical constraints to be satisfied, with which registration and identification tasks are treated as forward (i.e., data-driven solutions of PDEs) and inverse (i.e., parameter estimation) problems under PINNs respectively. While the forward problem has direct clinical applications in guiding targeted biopsy and treatment, the solution to the inverse problem may open new research directions in quantifying disease-indicative mechanical properties of in vivo tissues. Two net configurations (i.e., Cfg1 and Cfg2) have also been compared for both linear and nonlinear physics models, according to whether backbones are shared between branches or not. Two sets of experiments have been conducted, using pairs of undeformed and deformed MR images from clinical cases of prostate cancer biopsy. In the first experiment, against the finite-element-computed ground-truth, the root mean squared error (rmse) of registration for surface points was reduced from 1.83±0.51 mm without PINNs to 1.43±0.70 mm (Cfg1, p = 0.024) and 1.23 ± 0.69 mm (Cfg2, p © 2024, CC BY-NC-ND.

关键词： Biomechanics

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3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery 1

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4th Computational Methods in Systems and Software, CoMeSySo 2020

作者： Tomeš, Jakub Kohout, Jan Mareš, Jan Department of Computing and Control Engineering University of Chemistry and Technology in Prague Technická 1905/5 166 28 Praha 6 Prague Czech Republic

ISBN: (数字)9783030633226

ISBN: (纸本)9783030633219

The aim of this article is to show how modern sensors can be used in rehabilitation monitoring after a specific brain surgery that affects the facial muscles. Rehabilitation takes several months, it is important for both doctors and patients to be able to monitor progress quantitatively. To evaluate a patient’s facial disorders, the stereo-vision camera is used to detect and capture precise facial movements, which the subject is asked to perform. From these videos, specific patterns can be extracted for the rehabilitation process description. In this paper, we focused on a 3D analysis app for mobile phones. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stereo image processing

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Acid Etching-Induced Ce3+−O−Mn4+ Active Sites of SmCe0.1Mn1.9O5 Mullite for Enhanced Elimination of Hg0 and Chlorobenzene: Byproduct Analysis

SSRN

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SSRN 2023年

作者： Shi, Qiqi Zhang, Chenguang Shen, Boxiong Zhang, Xiao Lyu, Honghong Li, Shuhao Kang, Dongrui Bian, Yao Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China Department of Chemistry and Biotechnology School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia CSIRO Manufacturing Private Bag 10 Clayton SouthVIC3169 Australia

The efficient control of co-existing Hg0 and chlorobenzene in flue gas through catalytic oxidation is a major challenge in the field of energy and environmental engineering. In this study, a porous SmCe0.1Mn1.9O5 catalyst is prepared via a sol-gel method followed with acid etching for the synergistic elimination of Hg0 and chlorobenzene. The optimized Ce-SM-E exhibits near 100% removal efficiency of Hg0 within 100-400 oC and 90% chlorobenzene conversion above 275 °C, as well as excellent performance under complex flue gas conditions. The Ce substituting Mn3+ in the mullite structure enables favorable electron transfers from SmMn2O5 to CeO2 while causing more active defects. Subsequent acid etching removes the surface Sm species and modulates the electronic state of Mn atoms, inducing formation of more Ce3+-O-Mn4+ active sites, consequently enhancing the redox cycle. Especially, the generation of aldehydes, aromatic rings, maleate species and monodentate carbonate species are considered as the main rate-limiting steps in the CB degradation path. The effect of complex flue gas components (SO2, NO, H2O, etc) on the distribution of reaction byproducts is analyzed. The sulfation poisoning from SO2 consumes reactive oxygen species and promotes more dichlorobenzenes through electrophilic reactions. The presence of NH3/NO and H2O are found to promote the generation of NH4Cl and HCl, respectively, but the addition of which also generates much oxygen-containing byproducts such as acetophenone, benzoyl chloride and benzenecarboxylic acid via the Friedel-Crafts reaction. These results provide a promising approach for engineering efficient catalysts and benefit the evaluation of environmental risk under industrial conditions. © 2023, The Authors. All rights reserved.

关键词： Manganese compounds

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Optimizing temperature distributions for training neural quantum states using parallel tempering

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Physical Review E 2025年第5期111卷 055306-055306页

作者： Conor Smith Quinn T. Campbell Tameem Albash Center for Quantum Information and Control University of New Mexico University of New Mexico Albuquerque New Mexico 87131 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Center for Computational Quantum Physics Flatiron Institute New York New York 10010 USA Center for Computing Research Sandia National Laboratories Albuquerque New Mexico 87185 USA

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the J1−J2 model on a rectangular lattice.

关键词： Hamiltonians

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Latent multi-view subspace clustering based on Laplacian regularized representation

Latent multi-view subspace clustering based on Laplacian reg...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Guo Hangjun Che Man-Fai Leung Nankun Mu Xiangguang Dai Yuming Feng School of Electronic and Information Engineering Southwest University Chongqing China School of Electronic Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge UK Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Distributed Malicious Maneuvering Target Tracking Algorithm With Finite-Time Fusion Analysis

Distributed Malicious Maneuvering Target Tracking Algorithm ...

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Chinese control Conference (CCC)

作者： Zheng Zhang Dong Guo Hongwei Zheng Hong Wang Qingdong Li Xiwang Dong Zhang Ren Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing P.R. China China Academic of Launch Vehicle Technology Beijing P.R. china Beijing Academy of Blockchain and Edge Computing Beijing P.R. china Beijing Institute of Control Engineering Beijing P.R. China Institute of Artificial Intelligence Beihang University Beijing P.R. China

This paper proposes a distributed tracking framework for high-maneuvering and malicious maneuvering target based on the sensor network. Considering some of sensors in the network can only observe partial system output, the collective observability is guaranteed. Based on the collective observability, the distributed target tracking algorithm is designed by local filter phase and consensus fusion phase. In local filter phase, the local estimate results can be obtained by each sensor node. Based on the target kinematic and sensor observation equations, the Kalman-like estimation algorithm is proposed to obtain the local estimate results. In most of the exist distributed state estimation (DSE) algorithms, the excellent target tracking performance is based on the assumption that the fusion times is infinity in consensus fusion phase. In practical application, however, the consensus fusion phase should be completed during two sampling instants. Besides, based on the finite-time stability theory, the error covariance matrix is processed as the solution of discrete-time algebraic Riccati equation (DARE). Then the stability of the proposed algorithm with finite-time consensus fusion is analyzed. Finally, a numerical example for tracking maneuvering target is given to validate the effectiveness.

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ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data

ROCFL: A Robust Clustered Federated Learning Framework towar...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Zihan Li Zhitao Guan Shuai Yuan Ningyu An Xiao Liang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University Canada State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Beijing China

As a decentralized machine learning approach, federated learning has become a key solution for numerous applications. However, one primary challenge lies in the data heterogeneity issue when developing effective federated learning algorithms. While many existing frameworks address the concern of non-independent and identically distributed data(non-IID), they have not considered heterogeneity of data quality. Therefore, we propose ROCFL, a robust clustered federated learning method in this study, which can amplify the disparity in weight allocation between models trained on different quality data. We first develop an optimal clustering matching mechanism that groups clients with similar data distributions. This allows the optimal clustering model to be derived without specifying a predetermined number of clusters. Next, we introduce a personalized weight allocation strategy, which assigns a weight benchmark to each cluster based on its cluster importance index. This strategy mitigates the negative impacts of low-quality data during model aggregation. Finally, we design a federated aggregation strategy grounded in a sampling approach. It not only ensures unbiased sampling but also significantly reduces both computational and communication overheads. Experiments have been carried out and the results demonstrate that higher accuracy and robustness can be achieved by ROCFL in scenarios wherein data distribution and quality heterogeneity coexist, in comparison with the benchmarks.

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