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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKiiE), International conference on

作者： Qihan Bao University Of Calgary Calgary Canada

Quality control mathematical model is a mathematical model based on the principle of statistical process control. Its application in modern quality management provides a scientific basis for quality management. The establishment process includes three parts: one is to make statistical analysis from a large number of actual data, the other is to make use of statistical data to establish a mathematical model. Based on support vector machine regression, logistic regression and neural network in machine learning, the mathematical modeling of quality control is studied in this paper. Experimental results show that machine learning based quality control mathematical model can achieve the highest accuracy of 94.4%. It is concluded that machine learning has high application potential in quality management and can provide a scientific basis for quality management.

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15th International conference on Subject-Oriented Business process Management, S-BPM ONE 2024

15th International Conference on Subject-Oriented Business P...

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15th International conference on Subject-Oriented Business process Management, S-BPM ONE 2024

ISBN: (纸本)9783031720406

The proceedings contain 22 papers. The special focus in this conference is on Subject-Oriented Business process Management. The topics include: data Mesh: How to Implement the Paradigm Shift;improving Agile Maturity in data Teams: A Framework for Enhanced Business process Agility;Synergizing data Contracts and BPM to Improve process Interoperability: An analysis of Gaps and Opportunities of data Exchange Agreements in BPM Models;an Aspect-Oriented Extension of the Parallel Activity Specification Schema: A First Draft;how Business process modeling Can Benefit from Rhetorical Structure Theory;modeling of IoT Systems Behavior: A Subject-Oriented Reference Model;Subject-Oriented modeling Workflow control Patterns with PASS;subject- and process-Oriented Comparison of Multi-factor Authentication Methods;S-BPM as an Alternative to BPMN in the Context of Low-Code: Applicability, User Experience and Transformation from S-BPM to BPMN;facilitating the Preparation of Life Cycle Assessment Through Subject-Oriented process modeling: A Methodological Framework;a Framework for Sustainable Web Design in the Era of Digital Transformation;green Thoughts and Creative Spaces: An Experimental Study on Influence of Innovation Labs on Productivity and Sustainability of process Teams;towards Resilient Digital Supply Chains;Walking Away from Omelas: Towards a Comprehensive Model for Successful Adoption of Industry 4.0 Technologies in SMEs;interoperable Product Change Management Within Engineering: A Digital Twin Approach;Next-Generation Business process Management (BPM): A Systematic Literature Review of Cognitive Computing and Improvements in BPM;three Stances in Enterprise System Design;process Orientation in Authorities: Opportunities, Challenges and Best Practices;Which Good Practices Minimize Procedural Problems in IT Workflows Between Authorities?;Meta-prompt Engineering in ChatGPT-4 for AI-Generated BPM Reference Models.

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Sparse Gaussian process Regression for Residual Dynamics Learning in Multi-rotor Aerial Vehicles control

Sparse Gaussian Process Regression for Residual Dynamics Lea...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Geesara Kulathunga Marc Hanheide Alexandr Klimchik Lincoln Institute for Agri-Food Technology University of Lincoln LN1 United Kingdom School of Computer Science University of Lincoln LN6 United Kingdom

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This paper introduces a method for modeling residual dynamics between a high-level planner and a low-level controller, using reference trajectory tracking in a cluttered environment as a case study. We aim to mitigate residual dynamics resulting solely from the kinematical modeling employed in high-level planning. Our high-level planner utilizes a simplified motion model for quadrotor motion. We propose a Sparse Gaussian process Regression-based technique to model residual dynamics. In contrast, data-Driven MPC, a recent technique, targets aggressive maneuvers without obstacle constraints. Our proposed method is compared with data-Driven MPC in estimating residual dynamics error, including obstacle constraints. Comparative analysis indicates that our technique reduces nominal model error by an average factor of 2. Furthermore, we evaluate our complete framework against four other trajectory-tracking approaches in terms of tracking reference trajectory while avoiding collisions. Our approach demonstrates superior performance, achieving shorter flight times without sacrificing computational efficiency.

关键词： Trajectory tracking Computational modeling Dynamics Training data Gaussian processes Predictive models data models Trajectory Vehicle dynamics Predictive control

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A hybrid modeling approach integrating first-principles with data for nonlinear process monitoring

A hybrid modeling approach integrating first-principles with...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Yijia Zhang Shaojun Ren Baoyu Zhu Qingyang Li Fengqi Si Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Fault monitoring plays a crucial role in modern industrial systems. Timely and accurate monitoring is essential to mitigate economic losses and protect life safety. Traditional data-driven methods, such as auto-encoder (AE) and variational autoencoder (VAE), are widely utilized in nonlinear process monitoring due to their simplicity and efficiency. However, the interpretability and generalization capabilities of data-driven models are constrained by the absence of physical knowledge. To address this limitation, we have developed a physics-informed variational auto-encoder (PI-VAE) method in this study. This method incorporates physical mechanism information into the model’s loss function through equality constraints. The results obtained from applying the PI-VAE method to a 1000MW ultra-supercritical coal-fired power plant demonstrate its exceptional capability in fault monitoring. By integrating reliable physical data with the VAE model, the model ensures enhanced stability and improved generalization.

关键词： process monitoring Biological system modeling Noise Neural networks Interference data models Stability analysis Safety Reliability Power generation

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Gaussian process-Based Integral Sliding Mode control for Robust AUV Trajectory Tracking

Gaussian Process-Based Integral Sliding Mode Control for Rob...

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IEEE International conference on Unmanned Systems (ICUS)

作者： Yongxu He Yuxin Zhao Geng Xu College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China School of Automation Harbin University of Science and Technology Harbin China Qitong Technology Development Co. LTD Weihai China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper proposes a novel robust control method for autonomous underwater vehicle (AUV) trajectory tracking, specifically addressing challenges arising from sudden changes in desired trajectories and significant modeling uncertainties. The method leverages sparse online Gaussian process (SOGP) to learn unknown dynamics online, given a prior AUV model. An exponential forgetting scheme is integrated into the SOGP algorithm to ensure the selected training dataset incorporates both temporally and spatially informative data. By incorporating Gaussian process (GP) posterior information into an integral sliding mode control (ISMC) framework, the proposed GP-ISMC scheme enhances robustness and adaptability to uncertainties and disturbances encountered during diverse motion patterns. Lyapunov analysis guarantees system stability and tracking error convergence with a specific probability. Simulation results demonstrate that the proposed method has superior performance in tracking accuracy and prediction reliability.

关键词： Robust control Uncertainty Trajectory tracking Simulation process control Gaussian processes Predictive models Robustness Stability analysis Sliding mode control

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Spatial-temporal analysis of urban environmental quality of Tehran, Iran

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ECOLOGICAL INDICATORS 2021年 120卷 106901-106901页

作者： Javanbakht, Mohammad Boloorani, Ali Darvishi Kiavarz, Majid Samany, Najmeh Neisany Zebardast, Lobat Zangiabadi, Mahdieh Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran Iran Univ Tehran Dept Environm Planning Management & Educ Sch Environm Coll Engn Tehran Iran Tehran Traff Control Co Tehran Municipal Tehran Iran East China Univ Technol Key Lab Digital Land & Resources Nanchang Jiangxi Peoples R China

Urban environmental quality (UEQ) is a quantifiable measure of the capacity of an environment to cope with the requirements of an urban population. UEQ can be mapped by way of modeling separate domains of a specific residential area, such as physical environment, buildup areas, infrastructure, natural hazards, etc. Along this thread, the present study sets out to design a unprecedented approach to spatial-temporal mapping of UEQ based on Fuzzy logic, analytical network process, remote sensing imagery, and field-collected data. The methods were implemented for the case of districts 3, 6, and 11 of Tehran. As maintained by the results of sensitivity analysis, the introduction of a 30 percent error to the input parameters, resulted in a mere 16 percent change in output, signifying high reliability of the developed spatial temporal modeling proposed in this study as a reasonably suitable model for environmental quality mapping in heterogeneous urban areas. The following conclusions were made based on the study findings: (i) the spatial pattern of UEQ follows along a north-south trend, (ii) UEQ is higher in spring and summer compared to winter and autumn, (iii) air quality index and vegetation cover are the most influential indicators for UEQ mapping and modeling.

关键词： Urban environmental quality Spatial-temporal modeling Fuzzy-ANP Tehran

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Empirical Study on the Acceleration/Deceleration Constraints Under Commercial Adaptive Cruise control 25

Empirical Study on the Acceleration/Deceleration Constraints...

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IEEE 25th International conference on Intelligent Transportation Systems (ITSC)

作者： Zhou, Hao Zhou, Anye Ding, Zijian Laval, Jorge Peeta, Srinivas Georgia Inst Technol Dept Civil & Environm Engn Atlanta GA 30332 USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This paper aims to identify the characteristics of acceleration/deceleration (accel/decel) constraints in commercial adaptive cruise control (ACC) and investigate their potential impact on traffic flow. We have collected empirical accel/decel data in Massachusetts and Georgia from various ACC-equipped vehicles including Tesla Model X/3, Civic, and Prius. Unlike constant values adopted in the literature so far, the observed maximum accel/decel rates across different ACC manufactures all suggest that the accel/decel constraints decrease linearly with vehicle speeds. Based on the data we also find: i) different ACC manufacturers seem to adopt different accel/decel constraints, ii) maximum accel rates do not vary significantly with the engine mode but differ in free-motion and car-following regimes iii) maximum decel rates show a strong correlation with minimum times to collision. We further demonstrate that accel/decel constraints in the ACC can contribute to speed overshootings/undershootings, causing capacity loss in the accelerating process and deteriorating congestion in the decelerating case. To reveal the underlying mechanisms, the paper develops an ODE model to analytically approximate the speed overshootings/undershootings and correlate them with the accel/decel constraints as well as ACC control parameters. Besides speed over/undershootings, simulation results also show that accel/decel constraints in ACCs can undermine the string stability.

关键词： Adaptation models Analytical models Correlation Adaptive systems Simulation Stability analysis data models

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Analyzing Model Order Estimation in Industrial Hydraulics System using Open Loop Identification

Analyzing Model Order Estimation in Industrial Hydraulics Sy...

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IEEE International conference on control System, Computing and Engineering (ICCSCE)

作者： Nur Husnina Mohamad Ali Rozaimi Ghazali Nayli Rahzani Hazriq Izzuan Jaafar Muhammad Fadli Ghani Chong Chee Soon Fakulti Teknologi dan Kejuruteraan Elektrik Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Engineering and Built Environment Tunku Abdul Rahman University of Management and Technology (TAR UMT) Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This paper employs system identification to characterize Industrial Hydraulics System (IHS) system using an open-loop method. It utilizes a black box approach for estimating the model and parameters of the system using MATLAB System Identification Toolbox. The paper presents three data sets and validates the estimated model using statistical methods such as percentage best fit, Root Mean Square Error (RMSE), histogram analysis, and correlation analysis. The process began by obtaining the input-output data from the experimental work. The validation results obtained from the input-output data reveals an average best fit percentage for the three data sets is approximately $85 \%$. The data sets show small RMSE values, histogram analysis reveals a Gaussian distribution and strong evidence of the validity of the IHS model in the correlation analysis. Various orders of analysis were conducted, with the results indicating that a third-order linear system offers the closest representation of the IHS. The IHS has been modeled and validated using black box system identification has proven successful as there is no significant disparity in the outcomes.

关键词： Analytical models Histograms Correlation Computational modeling Hydraulic systems Gaussian distribution Systems modeling Mathematical models data models System identification

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Approaches to the Selection of Predictive Models Based on Associative Search for Assessing the Risk Potential of the Technological process at the NPP Power Unit

Approaches to the Selection of Predictive Models Based on As...

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International conference on Management of Large-Scale System Development (MLSD)

作者： Ekaterina Abdulova Laboratory of Security of Complex Systems V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

This article aims to systematize approaches to selecting predictive models based on associative search and analyze their effectiveness and applicability in the operating conditions of nuclear power plants (NPP). The study examines the main approaches and algorithms used for associative data analysis. In addition, the article includes a review of existing work in this area, evaluates its results, and suggests new promising directions for further research. The article is devoted to the issue of selecting a predictive model based on associative search for a technological process (TP) parameter to predict the risk potential of the TP of an NPP power unit using the example of one of the parameters of the information task of the upper level control system of the automated process control system of an NPP - cooling water heating.

关键词： Cooling Reviews Biological system modeling Water heating process control Predictive models Prediction algorithms Safety Reliability Power generation

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Application and Uncertainty analysis of Modern Estuary Research in Tide control Reservoir modeling - Take M Oilfield as an Example 11th

Application and Uncertainty Analysis of Modern Estuary Resea...

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11th International Field Exploration and Development conference, IFEDC 2021

作者： Chaoqian, Zhang Likun, Xu Xianglin, Xu Zhangcong, Liu Zheng, Meng Kexin, Zhang Fang, Xu Heping, Chen Ying, Xin Yusheng, Wang Xueqi, Liu PetroChina Research Institute of Petroleum Exploration and Development Beijing China PetroChina China National Oil and Gas Exploration and Development Corporation Beijing China

ISBN: (纸本)9789811921483

The oil reservoir of M Oilfield locates in the eastern part of Ecuador, belongs to the typical tidal-controlled deposition. As the reservoir is affected by tidal action, the spatial distribution of sand bodies is extremely complex, and it extremely difficult to model the reservoir in detail. In this paper, the study of the characteristics of modern tidal control sedimentation is used to guide the characterization of underground reservoirs. We use satellite images to make fine geometric measurements of sand bodies in tidal-domain estuaries, and count the spatial and geometric characteristics of those sand bodies. Then, we apply such geometric characteristics to guide the variogram analysis in the reservoir geological modeling process. This method establishes the relationship between modern sediments and reservoir characteristics. The variogram values we used for geological modeling will be more accurate, and the final model result will contain the actual sedimentary geological features. In contrast, the common modeling method only relies on interpolation of logging data, and cannot guarantee the accuracy of the simulation result. This paper also analyzes the uncertainty of the application of this method on the modeling results. The analysis results show that the width of modern tidal-controlled sand bodies has a relatively large impact on the geological modeling result. This is because the width of tidal sand bodies varies greatly under different tidal environments. This indicates that we should focus on the accurate measurement and statistics of the width of sand bodies under different tidal conditions in future research, to obtain more accurate modeling results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deposition

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