This paper investigates the influence of end-milling process parameters such as spindle speed (rpm), feed rate (mm/min), and depth of cut (mm) on the response parameters such as surface roughness (SR) in μm and mater...
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Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dyn...
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Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an "almost-periodic" character, i.e., a continuous Fourier spectrum punctuated by dominant peaks, which capture regular (e.g., daily or weekly) recurrences in the dynamics. The forecasting method presented takes advantage of this property by (i) regressing to a deterministic linear model whose eigenspectrum maps onto those peaks, and (ii) simultaneously learning a stochastic Gaussian process regression (GPR) process to actuate this system. Our forecasting algorithm is compared against state-of-the-art forecasting techniques not using additional explanatory variables and is shown to produce superior performance. We further show that this joint approach offers marked improvement over existing methods that use purely DMD or GPR. Moreover, its use of linear intrinsic dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid. Load forecasting is an essential challenge in power systems engineering, with major implications for real-time control, pricing, maintenance, and security decisions.
The proceedings contain 45 papers. The topics discussed include: research on mobile robot localization algorithm based on multi-sensor fusion;decision trees based multi-factor remote technical diagnostics of car retar...
ISBN:
(纸本)9781510664845
The proceedings contain 45 papers. The topics discussed include: research on mobile robot localization algorithm based on multi-sensor fusion;decision trees based multi-factor remote technical diagnostics of car retarders;decision trees based multi-factor remote technical diagnostics of car retarders;the construction of expert systems based on the theory of fuzzy sets as a trend in modern science and education;development of an adaptive control system for the quality parameter in the lack of information;point cloud analysis in a production quality guarantor computer vision system;using mathematical statistics to optimize the process of crossovers using data center infrastructure management;simulation modeling photovoltaic modules;machine learning techniques combination for selective and hierarchical analysis of psycho diagnostic data;rationalization of neural network training parameters on WWii poster classification example;and development of software and hardware complex for control and access management based on face recognition algorithms and neural networks.
Language models have recently been applied to binary analysis tasks, such as function similarity detection and function signature recovery. These models typically employ a two-stage training process: pre-training via ...
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A process that is almost always present in the chemical industry is distillation. The plant used in this study is a batch-type distillation column system located in the ITB Honeywell control Systems Laboratory, capabl...
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This paper conducted a questionnaire survey on the use of baggage handling system in 42 terminals of 37 airports in China, and divided the stages affecting the smoothness of baggage handling system into 4 parts: Bagga...
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The conventional positive solution approach for determining the forward motion of the kinematic mechanism relies primarily on the outcomes of the inverse solution of motion, which is not universally applicable to flex...
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Industrial process tomography (IPT) is a specialized imaging technique widely used in industrial scenarios for process supervision and control. Today, augmented/mixed reality (AR/MR) is increasingly being adopted in m...
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ISBN:
(数字)9783031422836
ISBN:
(纸本)9783031422829;9783031422836
Industrial process tomography (IPT) is a specialized imaging technique widely used in industrial scenarios for process supervision and control. Today, augmented/mixed reality (AR/MR) is increasingly being adopted in many industrial occasions, even though there is still an obvious gap when it comes to IPT. To bridge this gap, we propose the first systematic AR approach using optical see-through (OST) head mounted displays (HMDs) with comparative evaluation for domain users towards IPT visualization analysis. The proof-of-concept was demonstrated by a within-subject user study (n = 20) with counterbalancing design. Both qualitative and quantitative measurements were investigated. The results showed that our AR approach outperformed conventional settings for IPT data visualization analysis in bringing higher understandability, reduced task completion time, lower error rates for domain tasks, increased usability with enhanced user experience, and a better recommendation level. We summarize the findings and suggest future research directions for benefiting IPT users with AR/MR.
In conventional exponentially weighted moving average (EWMA) control charts, a basic premise is that the data are independent of each other. However, in the era of big data, with the popularization and application of ...
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ISBN:
(纸本)9798400711831
In conventional exponentially weighted moving average (EWMA) control charts, a basic premise is that the data are independent of each other. However, in the era of big data, with the popularization and application of automatic data collection technology, there are often correlations between data, making conventional EWMA control chart monitoring ineffective. To solve this problem, an AEWMA control chart for autocorrelation processes (adaptive EWMA control chart) is proposed in this study, that is, the AEWMA control chart is constructed by fitting the residual of the time series model, and the optimal parameters of the control chart under autocorrelation conditions are found by combining the three principles with the search algorithm, so that the control limits can be set more accurately for monitoring. Then, by comparing the performance of conventional control chart and AEWMA control chart in terms of average running length, it is concluded that AEWMA control chart is better than conventional EWMA control chart in the case of non-strong correlation regardless of the size of the mean offset. Finally, the application effect of AEWMA control chart in the process of monitoring autocorrelation is demonstrated by an example, which fully proves its superiority in solving the quality control problem under the condition of autocorrelation.
With the rapid promotion of hydrogen energy, hydrogen production technology becomes a key area of future energy research. Herein, alkaline water electrolysis (AWE) is a mainstream technology, and the study and optimiz...
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ISBN:
(纸本)9798350349047;9798350349030
With the rapid promotion of hydrogen energy, hydrogen production technology becomes a key area of future energy research. Herein, alkaline water electrolysis (AWE) is a mainstream technology, and the study and optimization of its performance are of great significance. However, in the process of coupling electric hydrogen production and power electronic devices, different subsystems have different mathematical properties, which is difficult to construct models together. And the strong coupling between electricity and heat affects the overall analysis of AWE systems. In order to establish an isomorphic model with the circuit network, a cross-scale heat current model of AWE system is established to describe heat transfer and electrochemical processes following a unified equivalent circuit form. The influence of parameters such as temperature and pressure on the system performance and internal characteristics is investigated. As the cooling water flow rate increases from 0.3 to 0.7kg/s, the cell voltage rises by 0.1 V, and voltage efficiency decreases by 2.7%. Comparing the current-voltage polarization curve with single-cell experimental data conducted by HRI, the maximum relative errors is less than 0.5%, comparing the system result with Aspen simulation, the maximum relative errors is less than 0.43% and 0.1%, validating the effectiveness of the proposed model.
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