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...
详细信息
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.
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 rea...
详细信息
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.
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 oper...
详细信息
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 controlconference 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.
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 modeli...
详细信息
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/)
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 ...
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.
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 dataanalysis framework ...
详细信息
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 dataanalysis 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.
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 netw...
详细信息
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.
1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol based on real-time information of feedstock and main products is cru...
详细信息
1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol 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.
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. Th...
详细信息
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/)
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 proc...
详细信息
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 processcontrol 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.
暂无评论