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Defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

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Coordination Chemistry Reviews 2025年 541卷

作者： Amit Kumar Julaiba Tahsina Mazumder Kenza Joyen Frédéric Favier Ali Mirzaei Jin-Young Kim Monika Kwoka Mikhael Bechelany Ravindra Kumar Jha Mahesh Kumar Hyoun Woo Kim Sang Sub Kim Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Mangaldai Assam 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS 34090 Montpellier France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS 34090 Montpellier France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Islamic Republic of Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 44-100 Gliwice Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. Despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO 2 , ZnO, Co 3 O 4 , ZrO 2 , WO 3 , and CeO 2 . It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities.

关键词： Chemiresistive gas sensing Defect engineering Metal-oxide semiconductor

Prediction of fitness in bacteria with causal jump dynamic mode decomposition

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arXiv 2020年

作者： Balakrishnan, Shara Hasnain, Aqib Boddupalli, Nibodh Joshy, Dennis M. Egbert, Robert G. Yeung, Enoch Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Mechanical Engineering University of California Santa Barbara United States Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Mechanical Engineering Center for Control Dynamical Systems and Computation Biomolecular Science and Engineering University of California Santa Barbara United States

— In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Bacteria

Mind the performance gap: Examining dataset shift during prospective validation

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arXiv 2021年

作者： Ötles, Erkin Oh, Jeeheh Li, Benjamin Bochinski, Michelle Joo, Hyeon Ortwine, Justin Shenoy, Erica Washer, Laraine Young, Vincent B. Rao, Krishna Wiens, Jenna Department of Industrial & Operations Engineering University of Michigan Medical Scientist Training Program University of Michigan Medical School Division of Computer Science & Engineering University of Michigan Nursing Informatics Michigan Medicine Department of Anesthesiology University of Michigan Medical School Department of Learning Health Sciences University of Michigan Medical School Infection Control Unit Division of Infectious Diseases Massachusetts General Hospital Harvard Medical School Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Department of Microbiology & Immunology University of Michigan Medical School

Once integrated into clinical care, patient risk stratification models may perform worse compared to their retrospective performance. To date, it is widely accepted that performance will degrade over time due to changes in care processes and patient populations. However, the extent to which this occurs is poorly understood, in part because few researchers report prospective validation performance. In this study, we compare the 2020-2021 (’20-’21) prospective performance of a patient risk stratification model for predicting healthcare-associated infections to a 2019-2020 (’19-’20) retrospective validation of the same model. We define the difference in retrospective and prospective performance as the performance gap. We estimate how i) "temporal shift", i.e., changes in clinical workflows and patient populations, and ii) "infrastructure shift", i.e., changes in access, extraction and transformation of data, both contribute to the performance gap. Applied prospectively to 26,864 hospital encounters during a twelve-month period from July 2020 to June 2021, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.767 (95% confidence interval (CI): 0.737, 0.801) and a Brier score of 0.189 (95% CI: 0.186, 0.191). Prospective performance decreased slightly compared to’19-’20 retrospective performance, in which the model achieved an AUROC of 0.778 (95% CI: 0.744, 0.815) and a Brier score of 0.163 (95% CI: 0.161, 0.165). The resulting performance gap was primarily due to infrastructure shift and not temporal shift. So long as we continue to develop and validate models using data stored in large research data warehouses, we must consider differences in how and when data are accessed, measure how these differences may negatively affect prospective performance, and work to mitigate those differences. Copyright © 2021, The Authors. All rights reserved.

关键词： Data warehouses

MDFN: Multi-scale deep feature learning network for object detection

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arXiv 2019年

作者： Ma, Wenchi Wu, Yuanwei Cen, Feng Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China

This paper proposes an innovative object detector by leveraging deep features learned in high-level layers. Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information. The proposed deep feature learning scheme shifts the focus from concrete features with details to abstract ones with semantic information. It considers not only individual objects and local contexts but also their relationships by building a multi-scale deep feature learning network (MDFN). MDFN efficiently detects the objects by introducing information square and cubic inception modules into the high-level layers, which employs parameter-sharing to enhance the computational efficiency. MDFN provides a multi-scale object detector by integrating multi-box, multi-scale and multi-level technologies. Although MDFN employs a simple framework with a relatively small base network (VGG-16), it achieves better or competitive detection results than those with a macro hierarchical structure that is either very deep or very wide for stronger ability of feature extraction. The proposed technique is evaluated extensively on KITTI, PASCAL VOC, and COCO datasets, which achieves the best results on KITTI and leading performance on PASCAL VOC and COCO. This study reveals that deep features provide prominent semantic information and a variety of contextual contents, which contribute to its superior performance in detecting small or occluded objects. In addition, the MDFN model is computationally efficient, making a good trade-off between the accuracy and speed. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Modeling and control of multimass systems in terms of 2D systems*

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Modeling and control of multimass systems in terms of 2D sys...

Mechatronics Systems and Materials (MSM)

作者： Bartłomiej Sulikowski Daniel Trzciński Krzysztof Gatkowski Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 Zielona Góra Poland Eric Rogers is with the School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

ISBN: (数字)9781728169569

ISBN: (纸本)9781728169576

In this paper modelling and efficient controller design for multi-mass systems is considered by writing the dynamics as a 2D state-space model representation of a spatially interconnected system. The 2D structure arises since there are two independent variables in the model. One of these is time and the other is the number (location) of the subsystem in the sequence. This paper uses 2D systems theory to develop a control law design, which stabilizes the dynamics and enforces tracking of a reference signal.

关键词： Two dimensional displays Force control systems Interconnected systems Springs Vehicle dynamics Numerical stability

H∞ Model-free Reinforcement Learning with Robust Stability Guarantee

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arXiv 2019年

作者： Han, Minghao Zhang, Lixian Tian, Yuan Wang, Jun Pan, Wei Department of Control Science and Engineering Harbin Institute of Technology Department of Cognitive Robotics Delft University of Technology Department of Computer Science University College London

Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in the real-world environment, it is difficult to guarantee the successful generalization and sim-to-real transfer of learned policies theoretically. In this paper, we introduce and extend the idea of robust stability and H∞ control to design policies with both stability and robustness guarantee. Specifically, a sample-based approach for analyzing the Lyapunov stability and performance robustness of a learning-based control system is proposed. Based on the theoretical results, a maximum entropy algorithm is developed for searching Lyapunov function and designing a policy with provable robust stability guarantee. Without any specific domain knowledge, our method can find a policy that is robust to various uncertainties and generalizes well to different test environments. In our experiments, we show that our method achieves better robustness to both large impulsive disturbances and parametric variations in the environment than the state-of-art results in both robust and generic RL, as well as classic control. Anonymous code is available to reproduce the experimental results at https://***/RobustStabilityGuaranteeRL/RobustStabilityGuaranteeRL. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

Fault-tolerant control-based flexible AGV transportation in a seat assembly system ⁎

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IFAC-PapersOnLine 2019年第10期52卷 67-72页

作者： Marcin Witczak Bogdan Lipiec Zbigniew Banaszak Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland

The cyclic nature of the production flow enables just-in-time planning and organization of the associated supply chains. In turn, disruptions in the operation of machinery and equipment, which occur in practice, lead to deviations from nominal operation times. That lead to changes in production takt time, making it necessary to adjust previously created schedules for delivery/reception of materials. Consequently since a control action should be taken to adjust transport operation times within a specified time range, the following problem of fault-tolerant cyclic scheduling boils down to the dual stage task composed of linear- and mixed-linear-integer programming. Its advantage is that it can compensate the effects of certain faults such as delays present in the assembly station or reduced velocities of mobile robots enabling the system to return to the previously scheduled delivery times.

关键词： Fault-tolerant control Assembly Systems Max-Plus algebra AGVs system Model Predictive control

Solving non-monotone equilibrium problems via a DIRECT-type approach

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arXiv 2020年

作者： Lucidi, Stefano Passacantando, Mauro Rinaldi, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Roma00185 Italy Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Mathematics "Tullio Levi-Civita" University of Padova via Trieste 63 Padova35121 Italy

A global optimization approach for solving non-monotone equilibrium problems (EPs) is proposed. The class of (regularized) gap functions is used to reformulate any EP as a constrained global optimization program and some bounds on the Lipschitz constant of such functions are provided. The proposed global optimization approach is a combination of an improved version of the DIRECT algorithm, which exploits local bounds of the Lipschitz constant of the objective function, with local minimizations. Unlike most existing solution methods for EPs, no monotonicity-type condition is assumed in this paper. Preliminary numerical results on several classes of EPs show the effectiveness of the *** Codes 90C26, 90C33, 90C56 Copyright © 2020, The Authors. All rights reserved.

关键词： Global optimization

Action Recognition by Weighted Averaged Hidden Markov Models

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Action Recognition by Weighted Averaged Hidden Markov Models

International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Aleksandra Postawka Przemysław Śliwiński Department of Computer Engineering Wrocław University of Science and Technology Poland Department of Control Systems and Mechatronics Wrocław University of Science and Technology Poland

In the paper the problem of action recognition with the help of Markov models is considered. We propose an algorithm aiming at reduction of the false positive rate. We state a hypothesis that these errors are caused by learning gesture sequences representing rare movements and not that containing popular ones. Our algorithm translates the sequence of gestures into the corresponding Markov models which are used for a preliminary classification. The obtained evaluation coefficients (numbers of proper classifications) are then used to determine the best weights in a combined model composed of these models. In order to find a compromise between the number of true positives and the number of false positives, the power function of weights is examined.

关键词： Hidden Markov models Mathematical model Markov processes Sensors Biological system modeling Real-time systems Measurement uncertainty