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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Savin, S. Southwest State Univ Dept Mech Mechatron & Robot Kursk Russia

ISBN: (纸本)9781538643075

In this paper, the problem of the model parameter estimation for in-pipe walking robots has been studied. Three estimators (regressor function-based method and the least squares-based methods with and without damping) are studied and analyzed. The study is focused on the influence the sensor resolution has on the accuracy of the estimation. The influence the accuracy of the initial estimation has on the methods' performance was also discussed. Two performance functions are proposed in order to simplify the analysis. It was shown that the least squares method has a significant advantage when the exact information about the robot's state is known;however, when the sensors have finite resolution, the performance of this method changes significantly while the regressor-based method is affected much less.

关键词： in-pipe robot model-based control model parameter estimation regressor function least squares method

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Designing an error model for navigation sensors using the Bayesian approach

Designing an error model for navigation sensors using the Ba...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Motorin, Andrei V. Stepanov, Oleg A. Cent State Res Inst Elektropribor JSC St Petersburg Russia Univ ITMO St Petersburg Russia

ISBN: (纸本)9781479977727

The paper considers a problem of designing error models for navigation sensors the data of which are used in fusion algorithms. Within the Bayesian approach, an algorithm is proposed that allows not only estimation of model parameters, but also identification of its structure. As a result of solving the joint problem of hypotheses recognition and parameter estimation, a shaping filter describing sensor errors is designed. An example is given to illustrate the performance of the proposed algorithm.

关键词： Bayes methods filtering theory parameter estimation sensor fusion Bayesian approach error model designing fusion algorithms hypotheses recognition model parameter estimation navigation sensors shaping filter Algorithm design and analysis Covariance matrices estimation error Kalman filters Navigation Sensors Bayes methods parameter estimation Algorithm design and analysis error model navigation filtering theory shaping filter estimation error Sensors Kalman filters Sensor fusion variance covariance matrix Covariance matrix

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ON THE estimation OF SURFACE ALBEDO USING SATELLITE DATE

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Journal of Geographical Sciences 1995年第2期5卷 59-76页

作者： Fang Xianjin(Dept. of Environmental Engineering, Tsinghua University, Beijing 100084People’s Republic of China)Zhang Ying(National Meteorological Center, Beijing 100081People’s Republic of China)

The method to determine the surface albedo using satellite date hasbeen developed and discussed. The relatons betwam the planetary albedo andsurfoce albedo in clear skies are derived with the help of the radiative transfermodelling. To improve the accuracy of the denved surfoce albedo, theparameter X0, i.e., the ratio of the reflected to the total extinction radiation isintroduced into this model. The influences of the model paramenters on thesurfoce albedo have been analyzed. It is found that the error in the derivedsurfoce albedo due to the paramaters together is less than that caused by oulyone parameter because of the complementarity of the parameters. Test resultsin 22 stations over China have been given using the Geostationary SatelliteGMS. The results are generally agreeable except for the case of snow/ice cover.

关键词： surface albedo satellite date model parameter estimation

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OCEAN WAVES estimation-AN ARTIFICIAL INTELLIGENCE APPROACH?

OCEAN WAVES ESTIMATION-AN ARTIFICIAL INTELLIGENCE APPROACH?

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作者： Andreas Ramberg Malardalen University

学位级别：硕士

This thesis aims to solve the mathematical inverse problem of characterizing sea waves based on the responses obtained from a marine vessel sailing under certain sea conditions. By researching this problem the thesis contributes to the marine industry by improving products that are using ocean behavior for controlling ship's dynamics. Knowledge about the current state of the sea, such as the wave frequency and height, is impor- tant for navigation, control, and for the safety of a vessel. This information can be retrieved from specialized weather reports. However, such information is not at all time possible to obtain during a voyage, and if so usually comes with a certain delay. Therefore this thesis seeks solutions that can estimate on-line the waves' state using methods in the? eld of Arti? cial Intelligence. The speci? c investigation methods are Transfer Functions augmented with Genetic Algorithm, Arti? cial Neural Networks and Case-Based Reasoning. These methods have been con? gured and validated using the n-fold cross validation method. All the methods have been tested with an actual implementation. The algorithms have been trained with data acquired from a marine simulation program developed in Simulink. The methods have also been trained and tested using monitored data acquired from an actual ship sailing on the Baltic Sea as well as wave data obtained from a buoy located nearby the vessel's route. The proposed methods have been compared with state-of-the art reports in order evaluate the novelty of the research and its potential applications in industry. The results in this thesis show that the proposed methods can in fact be used for solving the inverse problem. It was also found that among the investigated methods it is the Transfer Function augmented with Genetic Algorithm which yields best results. This Master Thesis is conducted under the Master of Engineering Program in Robotics at Malardalens hogskola in Vasteras, Sweden. The thesis was proposed by

关键词： Articial Intelligence Case-Based Reasoning Encounter Angle Encounter Spectrum Feedforward Neural Network Genetic Algorithm Inverse Problem Marine Vessels Master Thesis MATLAB model parameter estimation Ocean Theory Peak Period Response Amplitude Operator Ships Signicant Wave Height Transfer Function Wave Spectrum Waves

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parameter characterization of HT-PEMFC stack with a non-isothermal 3D model

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ELECTROCHIMICA ACTA 2023年第1期468卷

作者： Losantos, Raul Montiel, Manuel Mustata, Radu Zorrilla, Fernando Valino, Luis CSIC Inst Carboquim I Miguel Luesma Castan 4 Zaragoza 50018 Spain Fdn ARAID Avda Ranillas 1-D Zaragoza 50018 Spain

This paper proposes a methodology for characterizing electrochemical parameters in non-isothermal three-dimensional (3D) simulation models of fuel cell stacks. The proposed methodology involves utilizing only easily measurable construction and non-invasive operational data. In order to achieve a reasonable computational cost, an iterative method developed on various 3D computational domains is combined with a genetic algorithm optimization technique. The effectiveness of the methodology is demonstrated through its application on a real scale 40-cell high temperature PEM fuel cell (HTPEMFC) stack, with the results indicating good agreement between the model and experimental data. This approach has the potential to significantly reduce the computational cost of optimizing fuel cell designs while still maintaining accuracy.

关键词： PEMFC HTPEMFC model parameter estimation Genetic algorithm

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A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia estimation in Expensive Black-Box Design Problems

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第1期23卷 014501-014501页

作者： Biswas, Arpan Fuentes, Claudio Hoyle, Christopher Oregon State Univ Dept Mech Engn Corvallis OR 97331 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oregon State Univ Dept Stat Corvallis OR 97331 USA

We propose a nested weighted Tchebycheff Multi-objective Bayesian optimization (WTB MOBO) framework where we built a regression model selection procedure from the ensemble of models, toward better estimation of the uncertain parameters (utopia) of the weighted Tchebycheff expensive black-box multi-objective function. In our previous work, a weighted Tchebycheff MOBO approach has been demonstrated which attempts to estimate the model parameters (utopia) in formulating the acquisition function of the weighted Tchebycheff multi-objective black-box functions, through calibration using an a priori selected regression model. However, the existing MOBO model lacks flexibility in selecting the appropriate regression models given the guided sampled data and, therefore, can under-fit or over-fit as the iterations of the MOBO progress. This ultimately can reduce the overall MOBO performance. As, in general, it is too complex to a priori guarantee a best model, this motivates us to consider a portfolio of different families (simple-to-complex) of predictive models that have been fitted with current training data guided by the WTB MOBO, and the best model is selected following a user-defined prediction root-mean-square error-based approach. The proposed approach is implemented in optimizing a thin tube design under constant loading of temperature and pressure, minimizing the risk of creep-fatigue failure and design cost. Finally, the nested WTB MOBO model performance is compared with different MOBO frameworks with respect to accuracy in parameter estimation, Pareto-optimal solutions, and function evaluation cost. This approach is generalized enough to consider different families of predictive models in the portfolio for best model selection, where the overall design architecture allows for solving any high-dimensional (multiple functions) complex black-box problems and can be extended to any other global criterion multi-objective optimization methods where prior knowledge of uto

关键词： multi-objective Bayesian optimization nested design weighted techbycheff surrogate modeling model parameter estimation machine learning for engineering applications

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Estimating APC model parameters for Dynamic Intervals Determined Using Change-Point Detection in Continuous Processes in the Petrochemical Industry

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PROCESSES 2023年第8期11卷 2229页

作者： Yu, Yoseb Lee, Minyeob Lee, Chaekyu Cheon, Yewon Baek, Seungyun Kim, Youngmin Kim, Kyungmin Jung, Heechan Lim, Dohyeon Byun, Hyogeun Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea Infotrol Technol 159-1 Mokdongseo Ro Seoul 07997 South Korea Sungkyunkwan Univ Dept Stat 25-2 Sungkyunkwan Ro Seoul 03063 South Korea Sungkyunkwan Univ Dept Chem Engn 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Mech Engn 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Syst Management Engn 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Comp Sci & Engn 2066 Seobu Ro Suwon 16419 South Korea

Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute error (MAE) by utilizing statistical techniques such as pruned exact linear time, linear kernel, and radial basis function kernel of change-point detection (CPD). After that, we fix the hyper parameters at the minimum MAE value and estimate the APC model parameters by training with the data from the dynamic intervals. The estimated APC model parameters are applied to the APC program to compare the APC model fitting rate and verify the accuracy of the APC model parameters in the dynamic intervals obtained through CPD. The final validation of the model fitting rates demonstrates that the identification of the dynamic intervals and the estimation of the APC model parameters through CPD show high accuracy. We show that it is possible to estimate APC model parameters from dynamic intervals determined by CPD without a plant test.

关键词： petrochemical continuous process advanced process control change-point detection model parameter estimation

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model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal Optimization

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期53卷

作者： Rezaie, Mehrdad Azar, Keyvan Karamnejadi Sani, Armin Kardan Akbari, Ehsan Ghadimi, Noradin Razmjooy, Navid Ghadamyari, Mojtaba Malayer Univ Dept Elect Engn Malayer Iran Islamic Azad Univ Dept Elect Engn Urmia Branch Orumiyeh Iran Mazandaran Univ Sci & Technol Dept Elect Engn Babol Iran Islamic Azad Univ Young Researchers & Elite Club Ardabil Branch Ardebil Iran Lebanese French Univ Dept Comp Engn Kurdistan Region Iraq Shahid Beheshti Univ Dept Elect Engn Tehran *** Iran

Due to the growing use of fuel cell systems in electricity generation instead of conventional power generation systems for various reasons, such as high efficiency, non-environmental pollution, optimal dynamic response, and higher durability reliability, in this paper, by presenting an optimal model of the proton exchange membrane fuel cell (PEMFC) model and simulating it, the efficiency, and the output power of the fuel cell will be predicted. The main idea is to use a P SO-based Golden Jackal Optimization method to reduce the sum of squared errors (SSE) of the measured output voltage and the output voltage of the PEMFC stack. To validate the proposed approach, it is applied to two examined cases, and the results are compared with those of various new optimizers. The results reveal that the proposed ICSO outperforms the other comparison methods for the optimal estimate of the PEMFC model.

关键词： Proton exchange membrane fuel cell model parameter estimation Modified Golden Jackal Optimization Sum of squared error

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How to predict emissions of volatile organic compounds from solid building materials? A critical review on mass transfer models

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年第PartA期302卷 114054-114054页

作者： Liu, Zewei Yan, Yusen Liu, Tingting Zhao, Youcai Huang, Qifei Huang, Zechun Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China Tongji Univ Sch Environm Sci & Engn State Key Lab Pollut Control & Resource Reuse 1239 Siping Rd Shanghai 200092 Peoples R China Chinese Res Inst Environm Sci State Environm Protect Key Lab Hazardous Waste Id Beijing 100012 Peoples R China

Volatile organic compounds (VOCs) emitting from solid building materials can cause adverse human health and environmental climate effects. It's more cost effective and powerful for mass-transfer emission models to describe the emission characteristic of VOCs than emission chamber studies. In this review, the existing main physical mechanism-based models for predicting VOCs emissions from dry solid building materials have been discussed, as well as their differences and similarities. Ignoring internal diffusion and porosity of solid materials, singlephase model is generally quite safe for use in actual condition. Conversely, porous media model is good for understanding VOC-transfer principles in porous materials. Additionally, the porous media model and the singlephase model can be transformed mutually because their model parameters are correlative. The availability of emission models is largely determined by the reliable and useful model parameters. Therefore, substantial technologies and novel methods have been developed for parameter estimation, which have also been reviewed in this paper. How to readily and rapidly obtain model parameters is a future development direction. In addition, applying emission models to predict and control VOCs emission from other solid waste materials is another future research prospect.

关键词： VOCs emission models Mass transfer model parameter estimation

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Agent-Based modeling of Social Campaign Message Adoption: Problem of parameter’s Value Determination

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Procedia Computer Science 2021年 192卷 2460-2470页

作者： Anna Borawska Małgorzata Łatuszyńska University of Szczecin Institute of Management Cukrowa 8 71-004 Szczecin Poland

The paper addresses the issue of determining the parameter values of the agent-based model of social campaign message adoption relying on Bass’s classical innovation diffusion model. The problem concerns the parameters that reflect the influence of advertising and social communication on the adoption of the idea conveyed in the social campaign. Due to the fact that the factors influencing the behavior of message recipients are conditioned by their personality, circumstances or reaction to the environment, the conventional methods for estimating these parameters may not deliver a model reproducing reality with the required accuracy. Therefore, this paper proposes a procedure for determining the values of agent-based model parameters that relies on an experimental data acquisition procedure using a combination of cognitive neuroscience techniques and a survey method. The presented research examines a social campaign promoting road safety. The obtained results prove the suitability of the suggested solution for estimating the parameters of the agent-based model of social campaign message adoption. The proposed approach contributes to the methodology of data collection and parameter estimation in building agent-based models, although it is not without some limitations.

关键词： agent-based modeling simulation model parameter estimation cognitive neuroscience techniques

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