Urban investment bonds (Chengtou bonds) refers to bonds issued for local economic and social development, are also a major component of implicit local government debt, which is one of the main financing channels for l...
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With the development of smart sensors,information storage,processing technology and computer performance,large amounts of operating data collected from production process provide opportunities as well as challenges in...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
With the development of smart sensors,information storage,processing technology and computer performance,large amounts of operating data collected from production process provide opportunities as well as challenges in remaining useful life(RUL) *** one hand,data-driven analysis approaches are experiencing a fast *** the other hand,the collected variables may be redundant,noisy and high-dimensional for ***,data dimension reduction is applied for eliminating useless *** from correlation-based methods,causal inference methods can obtain reliable models reflecting causal relationships among interesting ***,the latter is more suitable in data dimension *** this study,we use PCMCI+,a causal discovery method based on graph model,that handles both lagged and contemporaneous relationships in multi-variable time *** validate this method on time series data extracted directly from a medium frequency quenching *** obtained results confirm that PCMCI+is able to recognize causal associations among various sensor *** instance,variables in the same process have relatively larger causal relationships than those in different processes.
Source-Mask Co-Optimization (SMO) techniques have significantly supported semiconductor manufacturing quality by enhancing imaging contrast and lithographic processcontrol in advanced lithography nodes over the past ...
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ISBN:
(数字)9798331513351
ISBN:
(纸本)9798331513368
Source-Mask Co-Optimization (SMO) techniques have significantly supported semiconductor manufacturing quality by enhancing imaging contrast and lithographic processcontrol in advanced lithography nodes over the past decade. Through jointly optimizing the design of the illumination source and modifying the reticle patterns on the mask, the SMO techniques have provided a viable method to find the best lithography process for a given design rule. In SMO, process parameters such as the Exposure Latitude (EL), the Depth of Focus (DoF), the Mask Error Factor (MEF) can be improved through the definition of a cost function. In this presentation, we provide an example with a minimum pitch of 40 nm, which is commonly used for the 2~3 nm Back-End-Of-the-Line (BEOL) logic technology nodes. In this example, we will discuss the challenges and potential of our SMO technique and will offer recommendations for EUV SMO compensations for aberration. Our analysis indicates that SMO can continue to improve optimal pattern transfer capabilities by simultaneously optimizing both illumination source and mask design.
In this paper and its companion paper [1], we address the problem of modeling and robust adaptive tracking control for an overactuated vertical nanopositioning system. We first introduce the operation principle of the...
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ISBN:
(纸本)9781665473385
In this paper and its companion paper [1], we address the problem of modeling and robust adaptive tracking control for an overactuated vertical nanopositioning system. We first introduce the operation principle of the lifting modules. In relation to the integrated actuators, their dynamics and important parameters are obtained from experimental and projected data. We further derive a physical model from the relevant components of the motion system. The model is validated with measured data in order to illustrate that it adequately reflects the real behavior. Emphasis is placed on the merits of control allocation (CA) to handle the overactuated nature of the overall motion system. The proposed CA concept proves to be effective for the ongoing application since it demands low computational costs and fulfills the heat emission constraint. We close this paper with the formulation and analysis of a suitable model for the derivation of advanced control strategies.
Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing predic...
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Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing prediction methods attempt to use a single model to establish the relationship between variables. However, due to the strong volatility, uncertainty, and multivariable coupling of sintering process, the traditional prediction model cannot produce reliable predictions. In order to deal with the complex characteristics of sintering process, this paper proposes a decomposition-based encoder-decoder modeling framework, in which a sequence decomposition module is designed to decompose the input time series into different sub-sequences. Then, these sub-sequences are constructed by the encoder-decoder models separately. The effectiveness of the proposed multi-step ahead prediction modeling framework was evaluated in a real-world sintering process. Compared with the traditional prediction modeling framework, the proposed modeling framework has more accurate results in multi-step ahead prediction.
The allocation of CPU time and memory resources, are well known problems in organizations with a large number of users, and a single mainframe. Usually, the amount of resources given to a single user is based on its o...
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Artificial intelligence technology plays a critical role in the development of measurement. Focusing on the requirements of application in intelligent measurement, this paper explores the application direction of arti...
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Fatigue analysis of pistons is reliant on an accurate representation of the high temperatures to which they are exposed. It can be difficult to represent this accurately, because instrumented tests to validate piston ...
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Traffic state estimation is relevant for real-time traffic control, providing travel information as well as for expost analysis of traffic patterns. While the output is usually the average speed and vehicle flow along...
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ISBN:
(数字)9781665468800
ISBN:
(纸本)9781665468800
Traffic state estimation is relevant for real-time traffic control, providing travel information as well as for expost analysis of traffic patterns. While the output is usually the average speed and vehicle flow along street segments, the type of input data and the existing methods to obtain the output are diverse. Recently, physics-informed data-driven approaches started to emerge that enrich the estimation process with information taken from physical models. In traffic, so far, these have been the continuity equation and the fundamental diagram, designed to describe fully the traffic dynamics along links and corridors. In this paper, we propose a simpler and practiceready physics-informed machine learning approach that informs the estimation through the well-established fundamental diagram in a loss constraint. It is designed for a link-level analysis where traffic homogeneity along the considered link is assumed. We apply the proposed method to full-trajectory drone data from Athens, Greece, demonstrate the applicability of our proposed approach, and point out its potential to future applications, e.g., a filter for control algorithms.
Micro-milling technology is widely used in the manufacturing of micro three-dimensional complex parts in aerospace, mechanical equipment, biomedical devices, electronic components, and other fields due to its advantag...
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Micro-milling technology is widely used in the manufacturing of micro three-dimensional complex parts in aerospace, mechanical equipment, biomedical devices, electronic components, and other fields due to its advantages of high machining accuracy, small size, and complex surface. Consequently, understanding its machining process is essential. This study proposes a time-varying dynamic micro-milling process model with the effects of tool runout, tool wear, and cutting status and explores the stable cutting domain of real-time machining. Considering the time-varying characteristics of the cutting process, combined with measured data and particle filtering method, the cutting force coefficients and tool wear parameters in the dynamic model at different times are identified. The dynamic equation of micro-milling is derived using a discrete method, and the stability of the cutting process is evaluated. Multiple cutting experiments are conducted on the Al6061, and the test data are compared with simulated values. The comparison results show that the accuracy of the proposed model in predicting the cutting state can reach 100%, and the average error of cutting force prediction is 7.72%. The research results can provide theoretical guidance for the safety evaluation of micro-milling and the selection of machining conditions.
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