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Cloud-enabled Load Estimation of Heavy-Duty Truck via Sparse Vehicle Information

Cloud-enabled Load Estimation of Heavy-Duty Truck via Sparse...

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International conference on Intelligent Transportation

作者： Mingxuan Guo Zhiwu Zhang Weichao Zhuang School of Mechanical Engineering Southeast University Nanjing China Core competency center Smartlink Nanjing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The load estimation of heavy-duty trucks is crucial for vehicle condition monitoring and motion control. The development of cloud-based platforms for the connected vehicle provides sufficient data for vehicle condition estimation, while the sparse and poor vehicle information poses a great challenge for accurate and stable load estimation. In this paper, through feature extraction and anomaly analysis of massive sparse vehicle driving data, we propose processing measures such as interpolation/filtering for missing, noise and other anomalies in connected vehicle information. Then an online real-time load estimation model based on the Recursive Least Squares method with a forgetting factor is established. The test results of the sparse connected vehicle information show that the load estimation model established in this paper can realize less than 10 % of the average error of estimation, and more than 900/0 of the estimation results converge within 15% of the error, which can meet the real-time and accurate load supervision of freight vehicles in the process of traveling.

关键词： Adaptation models Accuracy Connected vehicles Roads Vehicle driving Estimation Real-time systems data models Vehicle dynamics Load modeling

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Evaluation method and input selection for the model to predict the maximum and minimum ambient temperature parameters

Evaluation method and input selection for the model to predi...

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International conference on Engineering and Emerging Technologies (ICEET)

作者： Dinh Do Van Sao Do University Hai Duong Viet Nam

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

The input selection is a very important step in the process of building the model for identification, control or prediction,… A good set of input will ensure a compact model with acceptable accuracy. This paper develops a method based on linear decomposition of output signal into input signals to evaluate, compare and select the input for the forecasting model. The experimental results are compared with other methods such as the correlation-based selection, the Principal Component analysis (PCA), Linear Discriminat analysis method (LDA). Experimental results show that with the problem of selecting input parameters for the problem of predicting the maximum and minimum temperature in Hai Duong, the method of expanding linear components (SVD) gives better results. The main content of the article: (i) presents an overview of input feature extraction methods; (ii) common methods of selecting input characteristics for predictive models; (iii) simulation results and discussion; (iv) conclusion.

关键词： Temperature distribution Simulation process control Predictive models data models Numerical models Reliability

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Application of Stator Current Spectral analysis Method for High Capacity Turbogenerators Diagnostics

Application of Stator Current Spectral Analysis Method for H...

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International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Aleksandr Danilov Aleksandr Zimin Denis Shiryaev Voronezh State Technical University Voronezh Russia EC JCS «Concern Rosenergoatom» «Novovoronezh Nuclear Power Plant» Novovoronezh Russia Novovoronezh Nuclear Power Plant Novovoronezh Russia

The wide application of VVER-1200 reactors in global energy sector poses a challenge of increasing their reliability and operational safetу. The main requirement in this case is the referentiality of the Units, as well as the availability of all post-Fukushima requirements to ensure the safety of advanced generation 3+ Power Units. In this regard, the article considers the issue of processing and deep analysis of data of electrical parameters of turbine generator, which is one of the main ones due to the large amount of information and the complexity of obtaining the desired result without operator involved. Alarm event recorders are started when the monitored value reaches the preset value, or the entire operation process is recorded, but with a very long sampling time. While turbine generators operation there are known cases of serious accidents, which led to financial losses and reduced production of electricity. State-of-the-art generator monitoring and diagnosis systems are indispensable and essential for NPP operation. To date, the methods of diagnosis of electric motors, based on tracking and evaluation of current consumption, with further spectral analysis of the information obtained are widely used. This type of analysis allows to determine with a high probability the type, type and nature of damage to the equipment, both mechanical and electrical parts. At this method of diagnostics the subject of study is harmonic components in stator currents, this type of observation is especially relevant for high capacity turbogenerators. At present, the spectral analysis of turbine-generator currents is not fully applied at the latest generation Power Units with VVER-1200 type reactors, and more precisely, there is no systematic approach to the processing of the obtained results for a long period of time. To implement this important diagnostic function and fully process the results, it is necessary to create a methodology allowing fully assess the condition of the tu

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modeling Hydrodynamic Diffusion processes using Spatio-Temporal Deep Neural Networks with Environmental Physical-Coupled Constraints

Modeling Hydrodynamic Diffusion Processes using Spatio-Tempo...

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IEEE International conference on Systems, Man and Cybernetics

作者： Lei Jia Neil Yen Yan Pei Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The simulation and analysis of complex spatiotemporal systems are crucial for expressing and solving chaotic dynamical systems such as those in Earth and environmental sciences. Understanding and computing physical processes, re-actions, or substance transport typically relies on control equations. This paper aims to explore a novel research paradigm by enhancing the physical network coupling structure to construct predictive models for fluid dynamics systems, simulating spatiotemporal dynamical processes of substance transformation in the domain of environmental physics. In particular, when addressing problems involving the non-homogeneous 2D fluid dynamics equations, the characteristic parameters of the phys-ical processes were redefined. This was achieved by encoding hard boundary conditions and designing appropriate neural network architectures to mitigate over-fitting issues during the prediction of parameterized dynamical systems. Comparative experiments involving five benchmark physics-informed neural network methods emphasize the significant improvement in capturing time-varying features and prediction accuracy brought by the proposed approach. Through various water cycle scenarios, the model's estimation ability for diffusion fields is validated, focusing on analyzing the influence of data errors and sample size on the computational results of this deep neural network. Notably, the proposed method exhibits higher robustness to outlier observations under extreme conditions.

关键词： Fluid dynamics Computational modeling process control Artificial neural networks Predictive models Mathematical models Robustness Spatiotemporal phenomena Dynamical systems Water cycle

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Partially-Observed Discrete Dynamical Systems

Partially-Observed Discrete Dynamical Systems

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American control conference (ACC)

作者： Imani, Mahdi Ghoreishi, Seyede Fatemeh George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

ISBN: (纸本)9781665441971

This paper introduces a new signal model called partially-observed discrete dynamical systems (PODDS). This signal model is a special case of the hidden Markov model (HMM), where the state is a vector containing the information of different components of the system, and each component takes its value from a finite real-valued set. This signal model is currently treated as a finite-state HMM, where maximum a posteriori (MAP) criterion is used for state estimator purpose. This paper takes advantage of the discrete structure of the state variables in PODDS and develops the optimal component-wise MAP (CMAP) state estimator, which yields the MAP solution in each state variable. A fully-recursive process is provided for computation of this optimal estimator, followed by introducing a specific instance of the PODDS model suitable for regulatory networks observed through noisy time series data. The high performance of the proposed estimator is demonstrated by numerical experiments with a PODDS model of random regulatory networks.

关键词： Computational modeling Time series analysis Hidden Markov models data models Numerical models Noise measurement Dynamical systems

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Determination of error of sector location of subterrene stabilizing section based on coordinate control data 3

Determination of error of sector location of subterrene stab...

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3rd International Scientific and Practical conference on Mathematical modeling, Programming and Applied Mathematics, MMPAM 2021

作者： Valter, A.V. Aksenov, V.V. Beglyakov, V.Yu. Chazov, P.A. Efremenkov, A.B. Yurga Technological Institute Branch of the National Research Tomsk Polytechnic University Yurga Russia Sibirskoe NPO Kemerovo Russia Institute of Coal FITs UUKh so RAN The Federal Research Center for Coal and Coal Chemistry Siberian Branch Russian Academy of Sciences Kemerovo Russia T.F. Gorbachev Kuzbass State Technical University Branch in the City of Prokopyevsk Prokopyevsk Russia T.F. Gorbachev Kuzbass State Technical University Kemerovo Russia National University of Science and Technology MISIS Gubkin Branch Gubkin Russia Yaroslav-the-Wise Novgorod State University Veliky Novgorod Russia

Results of the study of actual accuracy of the stabilizing section shell of the prototype subterrene are presented. The problem of experimental verification of the assumptions made in modeling is formulated. The research described in the article was carried out on the basis of experimental data obtained by coordinate control of the shell of the prototype subterrene. data analysis was carried out by mathematical modeling of the surface of the shell of the stabilizing section as a whole and the surfaces of each individual sector. The obtained mathematical models are based on the approximation of sets of points obtained in the process of coordinate control by cylindrical surfaces. The article shows that at least a significant part of the deviations of the geometric shape of the section shell (from 30.3 to 52.3%) is explained by errors in the location of the sectors and errors in their radii. On the basis of the performed modeling, absolute values of the corresponding errors and actual values of the dimensions and deviations were determined. Studies confirmed the possibility of ensuring the specified accuracy of the shell surface when implementing the assembly technology used in a pilot production. At the same time, the proximity of actual deviations to maximum permissible values can lead to problems in ensuring the stable quality of subterrene case products in mass production. Correlation analysis of coordinate control data and statistical analysis of the series of residuals of developed models were carried out. Correlation analysis confirmed the dependence of deviations of experimental points on their cylindrical coordinates, which confirms the significance of the error in the location of the sectors in deviations from the geometric accuracy of the shell. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Errors

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Network modeling and analysis of COVID-19 Testing Strategies 43

Network Modeling and Analysis of COVID-19 Testing Strategies

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43rd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Zhang, Siqi Ventura, Marta J. Yang, Hui Penn State Univ Complex Syst Monitoring Modeling & Control Lab University Pk PA 16802 USA

ISBN: (纸本)9781728111797

The COVID-19 preparedness plans by the Centers for Disease control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can't be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.

关键词： Epidemic modeling network models testing strategies data-driven risk scoring systems COVID-19

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60th IEEE conference on Decision and control, CDC 2021

60th IEEE Conference on Decision and Control, CDC 2021

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60th IEEE conference on Decision and control, CDC 2021

ISBN: (纸本)9781665436595

The proceedings contain 938 papers. The topics discussed include: application of state estimation methods to low-temperature plasma dynamics;applications of gaussian process regression in the aero-thermo-servo-elastic analysis towards integrated hypersonic flight dynamic analysis;advances in system identification: theory and applications;data-based modeling and control of dynamical systems: parameter estimation;progressive graph partitioning based on information diffusion;imitation learning from inconcurrent multi-agent interactions;adversarial attacks on computation of the modified policy iteration method;and human-behavior learning for infinite-horizon optimal tracking problems of robot manipulators.

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Ultra-Local Model Based Fixed- Time control for the Wearable Exoskeleton with Prescribed Performance

Ultra-Local Model Based Fixed- Time Control for the Wearable...

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Chinese control conference (CCC)

作者： Xianlei Zhang Yan Zhang Qing Hu Anjie Yang Xuan Li College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China

This paper studies the problem of trajectory tracking control for the wearable exoskeleton system subject to unknown dynamics. An ultra-local model is adopted to describe the dynamic relationship between control input and output. To achieve high-precision fast tracking, a new fixed-time control method is designed. Not only can the convergence time and tracking accuracy be given in advance, but also the complicated modeling process is avoided. A new performance function is constructed and the specified tracking accuracy is achieved within a given time. Through rigorous theoretical analysis, it can be concluded that all signals of the control system are fixed-time convergent, and the tracking error converges to a small neighborhood of the equilibrium within a fixed-time. Finally, the effectiveness of the control scheme is verified through a practical simulation example.

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Knowledge-Aided Generative Adversarial Network: A Transfer Gradient-Less Adversarial Attack for Deep Learning-Based Soft Sensors

Knowledge-Aided Generative Adversarial Network: A Transfer G...

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Asian control conference

作者： Runyuan Guo Qingyuan Chen Shuo Tong Han Liu School of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

With the increasing complexity of modern industrial and process data, deep learning-based soft sensor (DLSS) models have provided more efficient sensing results than those of traditional soft sensors, but their vulnerability to adversarial attacks has hindered their deployment and safe application. Because it is difficult for the existing DLSS attack methods to accurately estimate the transfer gradient, this study proposes an adversarial attack method called knowledge-aided generative adversarial network (KAGAN) to directly generate adversarial samples by using deep neural networks, thereby avoiding the estimation and use of the transfer gradient. Firstly, the KAGAN utilize the proposed automatic peak-finding algorithm to get the auxiliary knowledge from the modeling data of the original DLSS. The auxiliary knowledge is the fake sensing results expected to be realized by the attack. In the KAGAN structure, a GAN and a proxy soft sensor model are fused, and the loss function of this structure is redesigned based on the set fake sensing results. After the KAGAN is trained and convergent, the generator part of the KAGAN is extracted and used to directly generate adversarial samples. The effectiveness of the proposed attack method is verified using the thermal deformation sensing data of an air preheater rotor. The experimental results show that the KAGAN attack is superior in terms of imperceptibility, rationality, and stability than the transfer gradient-based attack methods, thus proving that DLSS attacks can be realized without relying on the transfer gradient estimation. Moreover, KAGAN can reveal additional vulnerabilities of DLSSs in the face of adversarial attacks, thereby laying the foundation for further research on defense methods, which is crucial for improving the security of DLSSs.

关键词： Solid modeling Soft sensors Estimation Generative adversarial networks Solids Generators Stability analysis

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