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Orthorhombic Phase MoO3 Nanobelts Gas Sensor for Ultra-sensitive Detection of Triethylamine

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Meng, Fanli Cui, Weijia Liu, Yang Shen, Yanbai Yuan, Zhenyu National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Shenyang110819 China Ministry of Education Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University School of Resources and Civil Engineering Department of Minerals Processing Engineering Shenyang110819 China

In view of the poor sensitivity and upper detection limit of triethylamine gas sensors, high performance triethylamine sensor was prepared by adjusting the crystalline phase of MoO3. In this work, the orthorhombic phase MoO3 nanobelts were synthesized through hydrothermal method and used for the detection of triethylamine. The experimental outcomes indicate that the orthorhombic phase MoO3 gas sensor exhibits the peak responsiveness, which reaches 774.47 when the concentration of triethylamine is 100 ppm at 220°C accompanied by a theoretical detection threshold as minute as 10 ppb. The orthorhombic phase MoO3 sensor has superior selectivity for triethylamine stemming from the robust Lewis acid-base interplay with MoO3 and triethylamine and the C-N of triethylamine is easily broken. Finally, the sensing mechanism of orthorhombic phase MoO3 gas sensor is deeply analyzed. Since orthorhombic phase MoO3 possesses a narrow band gap and small size effect, and has a high adsorption energy for triethylamine, the gas sensor exhibits excellent performance. The strategic design of crystalline phase modulation serves as a pivotal approach to enhancing the properties of gas sensors. © 1963-2012 IEEE.

关键词： Nanobelts

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An Improved PSO for Clock Synchronization Subject to Disturbances

An Improved PSO for Clock Synchronization Subject to Disturb...

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Smart World Congress (SWC), IEEE

作者： Shuxin Liu Xuewu Dai Zhian Jia Dong Zhou Dongliang Cui The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Faculty of Engineering and Environment Northumbria University Newcastle Upon Tyne UK Qiqihar Heavy CNC Equipment.Ltd Qiqihar China

To improve the clock synchronization accuracy of wireless networks subject to the period disturbance caused by the wireless transceiver, this paper develops a perturbation compensation loop with the capability of compensating the periodic perturbations. The stability of the closed-loop system is analyzed. Improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the parameters of the disturbance compensation system. The proposed scheme is based on the General Proportional Integral Observer (GPIO). Compared with the traditional protocol without a GPIO compensator by numerical simulation, the results show that the proposed scheme eliminates the impact of low-frequency disturbance during packet exchange on synchronization error, and improves clock synchronization accuracy. The maximum synchronization error is 387.6ns reduced from 913.7ns.

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Dynamic Ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology

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Applied Soft Computing 2024年 152卷

作者： Liu, Jinze Feng, Jian Yang, Shengxiang Zhang, Huaguang Liu, Shaoning College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China

Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic Ε-Multilevel Hierarchy Constraint Optimization (ΕMHCO). Firstly, we propose the dynamic constraint tolerance factor Ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of ΕMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that ΕMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of ΕMHCO are verified on the real-world problem of the pipeline inner detector speed control. © 2023 Elsevier B.V.

关键词： Swarm intelligence

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Variational Disentangled Graph Auto-Encoders for Link Prediction

arXiv

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arXiv 2023年

作者： Fu, Jun Zhang, Xiaojuan Li, Shuang Chen, Dali The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Computer Science and Technology Beijing Institute of Technology Beijing China The College of Information Science and Engineering Northeastern University Shenyang China

With the explosion of graph-structured data, link prediction has emerged as an increasingly important task. Embedding methods for link prediction utilize neural networks to generate node embeddings, which are subsequently employed to predict links between nodes. However, the existing embedding methods typically take a holistic strategy to learn node embeddings and ignore the entanglement of latent factors. As a result, entangled embeddings fail to effectively capture the underlying information and are vulnerable to irrelevant information, leading to unconvincing and uninterpretable link prediction results. To address these challenges, this paper proposes a novel framework with two variants, the disentangled graph auto-encoder (DGAE) and the variational disentangled graph auto-encoder (VDGAE). Our work provides a pioneering effort to apply the disentanglement strategy to link prediction. The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors, which contributes to improving the performance of link prediction. To further encourage the embeddings to capture mutually exclusive latent factors, we introduce mutual information regularization to enhance the independence among different channels. Extensive experiments on various real-world benchmarks demonstrate that our proposed methods achieve state-of-the-art results compared to a variety of strong baselines on link prediction tasks. Qualitative analysis on the synthetic dataset also illustrates that the proposed methods can capture distinct latent factors that cause links, providing empirical evidence that our models are able to explain the results of link prediction to some extent. All code will be made publicly available upon publication of the paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Multistep Asymptotic Pre-Training Strategy Based on Pinns for Solving Steep Boundary Singular Perturbation Problems

SSRN

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SSRN 2024年

作者： Cao, Fujun Gao, Fei Yuan, Dongfang School of Science Inner Mongolia University of Science and Technology Baotou014010 China School of Information Engineering Inner Mongolia University of Science and Technology Baotou014010 China Key laboratory of Process industry Integrated automation of Inner Mongolia Colleges and Universities Baotou014010 China

The singularly perturbed problem is characterized by the presence of narrow boundary layers, which poses challenges for traditional numerical methods due to complexity and high costs. The contemporary deep learning physics-informed neural networks (PINNs) suffer from accuracy issues while learning initial conditions, fail to capture the sharp gradient behaviors, and provide inadequate approximations to rapidly oscillating solutions. To manage singularly perturbed parabolic problems with a strong gradient in the spatio-temporal domain, a novel time and parameter multi-step asymptotic pre-training technique based on PINNs is proposed. The presented technique can assist the model in learning the system dynamic behavior and improve the accuracy of the initial conditions. It also enables PINNs to capture abrupt changes in the solution with out prior knowledge of the boundary layer position, boosting its ability to approximate oscillatory solutions. This innovative approach does not require hyperparameter fine-tuning and provides a dependable deep learning approach for handling evolutionary singular perturbation problems. The proposed method is compared to PINNs and pre-training PINNs (PTPINNs) by solving singular convection-diffusion-reaction equations and magnetohydrodynamic equations. The results show that the proposed strategy outperforms PINNs and PTPINNs in capturing the boundary layer gradient, improving the approximation accuracy and accelerating the training process, in addition to significantly improving the accuracy of PINNs in approximating the initial conditions. © 2024, The Authors. All rights reserved.

关键词： Boundary layers

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Condition Identification of Copper Flotation process based on Foam Image and VGG16 Network

Condition Identification of Copper Flotation Process based o...

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第33届中国控制与决策会议

作者： Zhiqiang Wang Qiang Li Xu Wang Dakuo He Xiang Ma School of Information Science and Engineering Northeastern University Beijing Key Laboratory of Automation of Mining and Metallurgy Process State Key Laboratory of Synthetical Automation for Process Industries Northeastern University SINTEF Industry

In the flotation process,the foam in the flotation tank can reflect the characteristic information of the flotation condition *** on the characteristic information of the foam,the identification of working conditions and the operational optimization can be carried *** this paper,the research on image processing and working condition recognition based on deep learning is ***,the flotation foam image is preprocessed through the gray scale,image size processing and filtering ***,the neural network visual geometry group(VGG) is built and trained to realize the recognition of flotation foam image *** VGG16 network is based on the Sequential model in the Keras *** main structure of the network consists of 13 convolutional layers,5 pooling layers and 3 fully connected *** VGG16 data set is a collection of 43,800 foam images under six conditions,dividing the data set into a training set,a validation set,and a test ***,the VGG16 network is used for image feature learning to realize six classifications of bubble images and working condition *** is verified that the proposed neural network has high precision and can be used in flotation industrial process.

关键词： copper flotation process bubble image gray scale Gaussian filtering VGG16

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Data-Driven Output Containment Control of Heterogeneous Multiagent Systems: A Hierarchical Scheme

Data-Driven Output Containment Control of Heterogeneous Mult...

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IEEE Conference on Decision and Control

作者： Malika Sader Wenyu Li Yanhui Yin Zhongmei Li Dexian Huang Zhongxin Liu Xiao He Chao Shang Department of Automation Tsinghua University Beijing China College of Artificial Intelligence Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China School of Automation Engineering North-East Electric Power University Jilin China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed.

关键词： Regulators Protocols System dynamics Distributed feedback devices Observers Physical layer Mathematical models State-space methods Trajectory Multi-agent systems

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Robust Iterative Learning Control Design for a Class of Uncertain Batch processes

Robust Iterative Learning Control Design for a Class of Unce...

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European Control Conference (ECC)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of designing robust iterative learning control laws for discrete-time batch processes with norm-bounded parameter uncertainties. A law of proportional-differential type is designed to achieve robust convergence of the tracking error in the batch-to-batch direction. It is shown that the design problem can be written as a two-dimensional system. Then, the recently developed non-conservative conditions for (structural) stability analysis for a linear Roesser model are used. The conditions for the existence and computation of the required control law matrices are linear matrix inequality-based. Finally, comparative simulation results show the effectiveness of the new design.

关键词： Uncertain systems Analytical models Simulation Computational modeling Batch production systems Europe Linear matrix inequalities

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Assistive Classification Modeling of Laryngeal Squamous Cell Carcinoma based on data expansion and SSA-SVM 23

Assistive Classification Modeling of Laryngeal Squamous Cell...

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Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science

作者： Chunni Ren Shuting Yu Guimei Cui School of Information Engineering Key Laboratory of Integrated Automation for Process Industry Inner Mongolia University of Science and Technology China Department of Otolaryngology-Head and Neck Surgery Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences China

ISBN: (纸本)9798400708138

Laryngeal squamous cell carcinoma (LSCC) is one of the major diseases causing death in the world, and early diagnosis of the disease is beneficial to later treatment. Aiming at the characteristics of small sample size and many variables of LSCC data, this paper proposes a prediction method of LSCC incidence risk based on data expansion and SSA-SVM (Sparrow Search Algorithm Optimized Support Vector Machine). Firstly, the microbiota of patient samples was ranked in order of importance using the random forest feature algorithm to extract the features of typical microbiota; secondly, the basic features of patients and microbiota were combined to build a data expansion model based on CGAN (Conditional Generative Adversarial Network); and lastly, SSA was used to find the optimization of C and g of the SVM, and to build the prediction model for the risk of LSCC development by SSA-SVM. In this study, we compare the predictive results and evaluation metrics of multiple methods using actual patient data from a well-known tertiary hospital. The results show that the prediction accuracy of the prediction model based on data expansion and SSA-SVM is better than other methods, which verifies the feasibility of the proposed method.

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