Geological drilling process is an industrial process that contains a lot of variables, and the relationships and characters among them are also complex. Accordingly, implementing operating performance assessment by co...
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Geological drilling process is an industrial process that contains a lot of variables, and the relationships and characters among them are also complex. Accordingly, implementing operating performance assessment by conventional methods has always been a problem that operators are dedicated to solving. In fact, geological drilling process is a complex industrial process involving multiple systems and stratigraphic uncertainties, which makes assessing its operating performance difficult. A decentralized operating performance assessment based on multi-block total projection to latent structures (T-PLS) and Bayesian inference is proposed for the geological drilling process. Utilizing the variational trends of the detection variables, the most related variables can be grouped in the same block, and the process capability index determines the operating performance grade. This is followed by a T-PLS algorithm-based operating performance assessment model and Bayesian inference. Each sub-block's assessment results are integrated to achieve a comprehensive performance assessment. Last but not least, drill data show that the proposed method is effective and superior. The proposed method provides better accuracy and generalizability in assessing drilling performance. The novelty of the assessment scheme involves that a decentralized framework is proposed for operating performance assessment by identifying normal operating conditions first and then constructing the multi-block T-PLS-based monitoring model on the local sub-blocks. (C) 2022 Elsevier Ltd. All rights reserved.
In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems, whose objective function is the separable sum of possibly...
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In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems, whose objective function is the separable sum of possibly nonconvex nonsmooth functions and a smooth function, coupled by a difference of functions. This structure encapsulates numerous significant nonconvex and nonsmooth optimization problems in the current literature including the linearly constrained difference-of-convex problems. Relying on the successive linearization and alternating direction method of multipliers (ADMM), the proposed algorithm exhibits the global subsequential convergence to a stationary point of the underlying problem. We also establish the convergence of the full sequence generated by our algorithm under the Kurdyka-& Lstrok;ojasiewicz property and some mild assumptions. The efficiency of the proposed algorithm is tested on a robust principal component analysis problem and a nonconvex optimal power flow problem.
Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive graph to obtain embedded representation for clustering. In terms of adaptive graph learning, the embedded representation is ...
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Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive graph to obtain embedded representation for clustering. In terms of adaptive graph learning, the embedded representation is usually treated as the principal component of the graph to help improve graph structure. However, most adaptive graph learning methods only use a single graph layer. Therefore, the extraction power of embedded representation is restricted to single graph layer and insufficient to explore the intrinsic information. To break through this limitation, this article proposes a stacked network to realize spectral clustering with adaptive graph learning (SCnet-AGL). Specifically, the network allows the development of latent embedded representation underlying the multiple graph layers to reveal the intrinsic information. Meanwhile, we have designed an adaptive graph learning scheme to exploit the latent embedded representation for graph learning. With the advantage of the network, an augmented graph is obtained by incorporating the representation information for graph learning layer by layer. Finally, an efficient algorithm with feedback training scheme is proposed for network training. Experiments on real datasets demonstrate the effectiveness of the proposed network, and show that it is feasible to develop latent embedded representation to improve clustering performance.
The amphiphilic behavior of poly(ethylene oxide)/poly(propylene oxide)/poly(ethylene oxide) block copolymers has been broadened by introducing new urethane-urea as well as poly(ethylene glycol) co-segments in polyuret...
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The amphiphilic behavior of poly(ethylene oxide)/poly(propylene oxide)/poly(ethylene oxide) block copolymers has been broadened by introducing new urethane-urea as well as poly(ethylene glycol) co-segments in polyurethane (PUR) and polyurethane-ureas (PURU) materials. Supermolecular organization and self-assembly was confirmed by electron microscopy transmission observations. The driving force for self-assembling is represented by the amphiphilic nature of these materials, the interactional segment length and stabilization through hydrogen bonding. FT-IR spectroscopy was used to investigate the hydrogen bonding related to phase segregation. Phase transitions and phase segregation was evidenced by DSC analyses. The relationship between structure and thermal stability was investigated by thermogravimetric analysis.
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