The paper is devoted to the development of a modeling complex for managing the development of fast-growing companies based on technologies for integrating, processing and visualizing big data about the country’s ente...
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ISBN:
(数字)9798350363708
ISBN:
(纸本)9798350363715
The paper is devoted to the development of a modeling complex for managing the development of fast-growing companies based on technologies for integrating, processing and visualizing big data about the country’s enterprises.
Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems...
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Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems including datamodeling and verification of digital model in the process of DTW construction. To solve these problem, multidimensional datamodeling and model validation methods of DTW are proposed in this article. First, five-order tensor models for representing manufacturing elements are established to unify the data from physical workshop (PW) and virtual workshop (VW). Then, the mathematical method for verifying DTW twin model is proposed from the recessive and explicit perspective. Finally, a case study of an aerospace machining workshop is carried out to verify the operability and effectiveness of the proposed method. The case analysis shows that the proposed methods can effectively evaluate whether the twin model accurately provides the description of the actual behavior process of physical workshop, and the proposed methods have good performance.
Food safety has always been an important concern of people. Under the rural revitalization strategy, as the main force in the process of agricultural and rural modernization agricultural product quality and safety con...
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In this paper, 3D image data obtained by Autodesk Map 3D flatbed scanning system was used to automatically extract point cloud data and conduct point cloud modeling. Firstly, this paper proposes a contour rendering me...
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ISBN:
(数字)9798350393682
ISBN:
(纸本)9798350393699
In this paper, 3D image data obtained by Autodesk Map 3D flatbed scanning system was used to automatically extract point cloud data and conduct point cloud modeling. Firstly, this paper proposes a contour rendering method based on elevation cloud image of data points to extract control points from the whole cloud image region. Then this paper uses the OpenGL graphics library provides rich graphics program interface. According to the principle of minimum distance and maximum Angle, a triangulation irregular network (TIN) model is established. Then, the elevation of data points and color RGB values are corresponded one by one to generate the elevation cloud map of data points. The experimental simulation analysis shows that the application of this method expands the search range of control points and increases the number of control points. In this way, the linearity of the contour map is better and more consistent with the actual terrain.
In this paper, we analyze the numerical characteristics of stopping spacing and starting interval of vehicles at signal intersections, explore the relationship between forward vision and stopping spacing and starting ...
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Although remarkable studies on dynamic processmodeling and monitoring in cyber-physical systems have been conducted, there are still several limitations to these approaches, rendering them unsuitable for practical ap...
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Although remarkable studies on dynamic processmodeling and monitoring in cyber-physical systems have been conducted, there are still several limitations to these approaches, rendering them unsuitable for practical applications. Firstly, the common dynamic models are built based on complete data, which means that their modeling performance usually deteriorates when there are missing measurements. Secondly, practical data often exhibits more complex characteristics, such as non-stationary and non-Gaussian noises. Therefore, a robust dynamic latent variable model is proposed to address the challenges of dynamic processmodeling and monitoring in complex measurement environments with missing values. The proposed method effectively handles non-Gaussian noises and recursively estimates missing values, thus enhancing the monitoring performance within the dynamic latent variable modeling framework. Finally, the feasibility and superiority of the proposed method are evaluated through a numerical example and a real wastewater treatment case, demonstrating its effectiveness and practicality in real-world scenarios.
In the last decade, machine learning (ML) has shown tremendous success in areas such as vision, language, strategic games, and more. Parallel to this, hospitals' capacity for data collection has greatly increased ...
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In the last decade, machine learning (ML) has shown tremendous success in areas such as vision, language, strategic games, and more. Parallel to this, hospitals' capacity for data collection has greatly increased with the adoption and continuing maturation of electronic health records (EHRs). The result of these trends has been a large degree of excitement and optimism about how ML will revolutionize healthcare once researchers get access to data. In this work, we present a cautionary tale of the instinct some computer scientists have to "let the data speak for itself." Using a popular, public EHR dataset as a case study, we demonstrate numerous examples where a non-clinician's intuition may lead to incorrect - and potentially harmful - modeling assumptions. We explore both non-obvious quirks in the data (i.e., hypothetical incorrect assumptions) and examples of published papers that misunderstood the data generating process (i.e., actual incorrect assumptions). This case study is meant to serve as a cautionary tale to encourage every data scientist to approach their projects with the humility to know what they can do well and what they cannot. Without the guidance of stakeholders that understand the data generating process, data scientists run the risk of "garbage-in, garbage-out" analysis because their models are not measuring meaningful relationships.
In today's industrial processes, data-driven soft sensors are a frequently used tool for predicting quality variables. Autoencoder (AE) is an unsupervised algorithm which can extract latent features from initial d...
In today's industrial processes, data-driven soft sensors are a frequently used tool for predicting quality variables. Autoencoder (AE) is an unsupervised algorithm which can extract latent features from initial data. However, during the feature extraction process, the traditional autoencoder does not consider the correlation between modeling input variables and quality variables to be predicted. To solve this issue, a novel autoencoder based on variable correlation analysis (VCA-AE) is proposed. In VCA-AE, the correlation of modeling input variables and quality variables to be predicted is performed by correlation analysis, and input variables are divided into two parts, which are input to the sub-autoencoder to extract latent features, respectively. In each sub-autoencoder, input variables and quality variables have the same correlation. Next, a feedforward neural network Extreme Learning Machine (ELM) is used to develop soft sensor model based on the extracted latent feature variables and quality variables. Finally, the effectiveness of the proposed soft sensor model combining VCA-AE and ELM is illustrated by an experiment of the industrial PTA process.
Today, the use of neural network technologies in solving practical problems in various fields of science and technology has become a topical issue. Such problems that can be successfully solved by artificial neural ne...
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This paper is devoted to the problem of magnetohydrodynamic stability (MHDS) in the energy-intensive process of primary aluminum production by electrolysis. Improving MHDS control is important because of the high cost...
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This paper is devoted to the problem of magnetohydrodynamic stability (MHDS) in the energy-intensive process of primary aluminum production by electrolysis. Improving MHDS control is important because of the high costs and reduced efficiency caused by the instability of magnetic and current fields. In this work, a methodological analysis of modern theoretical and numerical methods for studying MHDS was carried out, and approaches to optimizing magnetic fields and control algorithms aimed at stabilizing the process and reducing energy costs were considered. This review identified key challenges and proposed promising directions, including the application of computational methods and artificial intelligence to monitor and control electrolysis in real time. In this paper, it was revealed that wave MHD instability at the metal-electrolyte phase boundary is a key physical obstacle to further reducing specific energy costs and increasing energy stability. The novelty of this paper lies in an integrated approach that combines modeling and practical recommendations. The purpose of this study is to systematically summarize scientific data, analyze the key physical factors affecting the energy stability of electrolyzers, and determine promising directions for their solution. The results of this study can be used to improve the energy efficiency and environmental friendliness of aluminum production.
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