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Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 6020-6026页

作者： Maric, Ante Li, Yiming Calinon, Sylvain Idiap Res Inst CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting from an arbitrary prior shape, our method incrementally constructs a continuous and smooth distance representation from incoming surface points, with analytical access to gradient information. The underlying model does not store training data for prediction, and its performance can be balanced through interpretable hyperparameters such as polynomial degree and number of segments. We assess the accuracy of the incrementally learned model on a set of household objects and compare it to neural network and Gaussian process counterparts. The utility of intermediate results and analytical gradients is further demonstrated in a physical experiment.

关键词： Polynomials Robots Computational modeling Gaussian processes Image color analysis Vectors Task analysis Signed distance fields incremental learning representation learning machine learning for robot control

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MSWI process parameter prediction based on improved case-based reasoning 36

MSWI process parameter prediction based on improved case-bas...

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36th Chinese control and Decision conference (CCDC)

作者： Cheng, Zijun Yan, Aijun Tang, Jian Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Univ Technol Beijing Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

To accurately predict the key parameters of the municipal solid waste incineration (MSWI) process, this paper proposes an improved case-based reasoning (CBR) predictive modeling method based on a deep Q network to realize the case adaptation process. First, the MSWI operation process is analyzed to screen out the relevant feature variables and build the corresponding case base. Second, the K-nearest neighbor (KNN) algorithm is used to realize the case retrieval process of the parameter prediction, and cases similar to the current incineration state are obtained. Then, based on the "Learning-Evaluation-Revision" idea, the case difference adaptation knowledge between similar cases and the feature variables of the current state is learned through the deep Q network to realize key parameter prediction. Finally, the actual data of a solid waste incineration plant are used to predict the key parameters of the furnace temperature and flue gas oxygen content. The results show that the proposed method can accurately predict the MSWI process parameters.

关键词： municipal solid waste incineration case-based reasoning deep Q network parameter prediction

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Optimal control of waste recovery process

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INTERNATIONAL JOURNAL OF DYNAMICS AND control 2024年第12期12卷 4244-4262页

作者： Cherkaoui-Dekkaki, Othman Djema, Walid Raissi, Nadia Gouze, Jean-Luc El Khattabi, Noha Mohammed V Univ Fac Sci Dept Math LAMA Lab Rabat Morocco Univ Cote Azur BIOCORE Project Team Ctr Inria InriaUniCA Valbonne France Univ Cote Azur MACBES Project Team Ctr Inria INRAeUniCA Valbonne France

As society transitions away from fossil fuels toward renewable energy sources, finding alternatives that are reliable becomes imperative. Waste-to-energy bioprocesses are promising options due to their ability to operate independently of weather conditions or time of day, making them sustainable and potentially lucrative solutions. This paper proposes an updated bioeconomic model, based on previous research (Cherkaoui Dekkaki et al. in Math Methods Appl Sci 45(1):468-482, 2022;Cherkaoui Dekkaki and Djema in American control conference pp. 2135-2140, 2023), to analyze investment in waste-to-energy technology and its associated valorization of waste treatment. This conceptual model represents a generic framework for studying waste-to-energy processes. By taking technological constraints into account, the updated model aims to optimize energy production processes and establish a sustainable business model. Indeed, using dynamic modeling, investment and valorization strategies will be evaluated through a maximization criterion over a finite time horizon, which is stated as an optimal control problem. The effective control strategies are then determined using the Pontryagin's maximum principle. Furthermore, direct optimization methods are applied to derive and validate the effectiveness of the obtained optimal strategy. This approach allows for a thorough evaluation of the economic and environmental impacts in waste-to-energy technologies, identifying optimal investment and valorization strategies to promote sustainable waste management practices. In addition, a sensitivity analysis is conducted to evaluate the robustness of the studied model, and provide insights into biotechnological limitations. Finally, an extensive numerical exploration of the turnpike-like features that characterize the optimal long-term behavior of the investment problem is widely discussed.

关键词： Optimal control Pontryagin principle Bioeconomic modeling Direct optimization Sensitivity analysis Turnpike

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Application of Recurrent Neural Networks for Predictive modeling of Electrical Submersible Pumps in Oil Extraction 11

Application of Recurrent Neural Networks for Predictive Mode...

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11th Vienna International conference on Mathematical Modelling (MATHMOD)

作者： Rebello, Carine Menezes Costa, Erbet Almeida Luiz de Abreu, Odilon Santana Reges, Galdir Viena, Vinicius Guimares Mendes, Tefilo Paiva Fontana, Marcio Ribeiro Bacellar, Francisco Raphael Ribeiro, Marcos Pellegrini Foresti, Bernardo Pereira Schnitman, Leizer Nogueira, Idelfonso B. R. Norwegian Univ Sci & Technol Dept Chem Engn Trondheim Norway Univ Fed Bahia Polytech Sch Dept Chem Engn Salvador BA Brazil Univ Fed Bahia Polytech Sch Dept Elect & Comp Engn Salvador BA Brazil Petrobras R&D Ctr CENPES Ilha Fundo Ave Horcio Macedo 950 Rio De Janeiro Brazil

This study presents a mathematical modeling framework that leverages recurrent neural networks (RNNs), specifically echo state networks (ESNs) and long short-term memory (LSTM) architectures, for the predictive modeling of electrical submersible pumps (ESPs) in offshore oil extraction operations. Utilizing real operational data from an offshore oil field, the research addresses the inherent complexity and nonlinear dynamics of ESP systems by employing these recurrent structures to capture and represent the temporal dependencies within the data. Key challenges, such as data noise, variability, and limited diversity, are systematically tackled to ensure robust dynamic modeling. A comparative analysis evaluates the performance of ESN and LSTM models under these constrained data conditions, aiming to identify the superior model in terms of predictive accuracy and resilience. The modeling approach emphasizes the formulation, parameterization, and validation processes essential for effective ESP optimization and control in real-world industrial settings. Findings reveal the distinct strengths and limitations of each RNN variant when applied to offshore operational data. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Electrical submersible pumps echo state network long short-term memory predictive modeling real data

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7th International conference on Aerospace System Science and Engineering, ICASSE 2023

7th International Conference on Aerospace System Science and...

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7th International conference on Aerospace System Science and Engineering, ICASSE 2023

ISBN: (纸本)9789819705498

The proceedings contain 22 papers. The special focus in this conference is on Aerospace System Science and Engineering. The topics include: A Review of Some Key Issues in CFD-Based Throughflow Simulation;influence of Multiple Parameters on the Efficiency of a Single Nozzle in a Heavy Gas Turbine Combustor;analysis of the Influence of Total Pressure Error on Air data Calibration;analysis of Impact Properties of Nanocomposites at Micron Scale;simulation Research on Air Distribution Optimization of Civil Aircraft Cabin Ventilation System;investigation of the 2-D Distribution Form of Bearing Stress in a Single-Bolt Single-Shear Metal-Composite Hybrid Joint;the Impact of Bleeding Slot Angles on the Performance of a Compressor;investigation into Provisions and Validation for Cockpit Smoke Evacuation on Civil Aircraft;numerical Investigation of Diffuser Curvilinear Meridional Shape on Centrifugal Compressor Stage Performance;modeling Method of Specimen Repair Techniques from Polymer Composite Material;process Approach as a Tool of the Knowledge-Intensive Industry Organization Management System;SysML-Based Approach for Functional Quantitative modeling of Civil Aircraft Systems;research on MagicGrid-Based Requirements Development process of Flight control System;airport Collaborative Decision-Making in Single Pilot Operations of Commercial Aircraft;Research on Capability Catalog Generation of UAV Intelligent System Based on DoDAF;research on the Contribution Rate of Shipboard Manned/Unmanned Aerial Vehicle Cooperative Operation Based on Wargame data Mining;cooperative Organization and Application Mechanism Based on Intention Environment Target for Maritime Ship-Aircraft Cooperation;LPV Robust Filter Based Fault Diagnosis Method for Aeroengine control System.

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Blockchain-based collaborative data analysis framework for distributed medical knowledge extraction

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Li, Zhi Li, Ming Li, Aofei Lin, Zhiyu Guangdong Univ Technol Sch Electromech Engn Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Adv Mfg Hung Hom Hong Kong Peoples R China

To ensure the privacy preservation and transparent use of regulated medical big data at decentralized and distributed medical institutions, this paper proposes a blockchain-based collaborative data analysis framework to realize multiparty secure data sharing and cooperative medical knowledge extraction through a transparent and regulatory machine learning approach. A smart contract is employed on the blockchain as the underlying technique to realize autonomous control and transparent regulation of closed-loop data acquisition and analysis. Considering the execution complexity of smart contracts for analysis collaboration, Petri net is adopted to formulize the workflows of smart contracts, and it acts as the underlying on-chain learning (OcL) approach. Finally, an experimental case study is conducted using real-life medical data to verify and evaluate the effectiveness and efficiency of our framework. A prototype system is established to demonstrate the real-life distributed knowledge extraction demand of our cooperating company. Four groups of experiments are designed and conducted to determine the effectiveness and efficiency of the learning process. The results show that the proposed framework significantly outperforms federated learning (FL) in terms of accuracy on small datasets, where the framework achieves an accuracy of 55.050% compared to FL. Meanwhile, the framework exhibits superior convergence in loss compared to FL, with a difference of 76.663%. In the case of big datasets, the framework achieves a faster completion of model training by 58.883%, with lower CPU utilization by 44.023% and lower memory utilization by 16.227% compared to FL.

关键词： Privacy protection Blockchain Smart contract Workflow modeling

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Connected Vehicle data in Systems modeling and Evaluation of Investments in Pedestrian Safety 10

Connected Vehicle Data in Systems Modeling and Evaluation of...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Johnson, DeAndre Marcellin, Megan C. Pennetti, Cody A. Wheeler, Rayshaun L. Clark, Collyn Jun, Jungwook Lambert, James H. Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA Univ Virginia Dept Civil & Environm Engn Charlottesville VA USA Virginia Dept Transportat Transportat & Mobil Planning Div Richmond VA USA

ISBN: (纸本)9798350373981;9798350373974

Ensuring pedestrian safety at high-risk, high-volume locations, such as hospitals, schools, stadiums, etc., requires consideration of the observed and possible future trends at access points of the transportation network. Existing performance metrics are often spatially and temporally aggregated, limiting their usefulness in assessing safety risks for time periods and locations of interest. Latest connected-vehicle (CV) technologies have improved both the volumes and resolutions of vehicle-movement data, with telemetry reported every few seconds. This study utilizes CV data in a multi- criteria analysis (MCA) to prioritize infrastructure improvements that improve pedestrian safety in school zones. The methods are demonstrated to update priorities for thirty-two candidate improvements at a single school vulnerable to evolving traffic and other conditions. Six performance criteria are addressed, including four criteria informed by CV event observations. The results highlight scenarios, articulated by the day of week and hour of the day, that are most disruptive to the priorities for improvements. The approach has interest across domains of systems engineering where trends and critical incidents of environment, markets, regulations, wear and tear, demographics, obsolescence, workforce etc. should influence systems evaluation and requirements.

关键词： multi-criteria analysis transportation systems risk analysis decision analysis systems evaluation requirements analysis

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Real-time monitoring of multiparameter in 1,3-propanediol production process with near-infrared spectroscopy

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VIBRATIONAL SPECTROSCOPY 2025年 136卷

作者： Liang, Zijian Zhang, Chijian Hu, Liyun Cai, Yunlong Deng, Maolang Guangdong C1 Life Biotech Co Ltd Guangzhou Peoples R China

1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocess control based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656 g/L, 1.502 g/L, 0.746 g/L, and 0.557 g/L in at-line measurement and 1.113 g/L, 1.581 g/L, 0.415 g/L, and 0.526 g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.

关键词： 1,3-propanediol Near-infrared spectroscopy Microbial fermentation process monitoring Chemometrics

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Physics-Informed data-Driven modeling for Engine Volumetric Efficiency Estimation 7

Physics-Informed Data-Driven Modeling for Engine Volumetric ...

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7th IFAC conference on Engine and Powertrain control, Simulation and modeling (E-COSM)

作者： Li, Qian Guo, Fan Song, Kang Xie, Hui Zhou, Shengkai Sang, Hailang Tianjin Univ State Key Lab Engines Tianjin Peoples R China Guangxi Yuchai Machinery Grp Co Ltd Yulin Peoples R China

Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Therefore, this paper proposes a physics-informed data-driven volumetric efficiency modeling method (PDM). Firstly, this paper constructs a model based on the simplified first law of physics to capture the main trends of volumetric efficiency changes. To improve the accuracy of the estimation, a PDM is proposed. This method includes a physical loss term and a data loss term. These loss terms are fused into a single fusion loss to train the neural network parameters, effectively merging the physical model with the neural network. The high correlation coefficient (R-2 = 0.958) between the PDM's volumetric efficiency estimates and the measured data demonstrates the robustness of the method. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Volumetric Efficiency Estimation Physics-Informed data-Driven Model Feedforward Neural Network Physical Model

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 1070-1082页

作者： Liu, Cong Zeng, Qingtian Cheng, Long Duan, Hua Cheng, Jiujun Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China

Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure process similarity by relying on control-flow structures only. This article investigates the role of data in process similarity measure. To incorporate data-flow information into business process control flow, it proposes a data-aware workflow net (DWF-net) by extending the classical workflow net with data reading and writing semantics. Then, we introduce three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, to quantify the similarity of data-aware business processes from different perspectives. Next, a methodology is introduced to help process analysts apply these three measures in a systematical way. Finally, we evaluate the effectiveness and applicability of the proposed similarity measures by a group of comparative experiments.

关键词： Business process control Petri nets Unified modeling language Computational modeling Semantics Task analysis data-aware business processes data-aware workflow nets (DWF-nets) Petri nets similarity measure

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