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Circuits Techniques for Dynamic Power Reduction
Published in Christian Piguet, Low-Power CMOS Circuits, 2018
Note that signal d, which has the highest switching activity, is injected last in configuration A, thus implying better power performance for this configuration. This technique has been found as being optimal for dynamic CMOS circuits, but also produces very good results for static CMOS circuits. In general, the low-power technology decomposition procedure reduces the total switching activity in the circuits by 5% over the conventional balanced tree decomposition method.
Pre- and post-filter selection for image segmentation
Published in Rajesh Singh, Anita Gehlot, Intelligent Circuits and Systems, 2021
Splitting is a recursive procedure, in which splitting of the image is done using the quad tree decomposition method. It splits the image into four equal quadrants. It is observed that boundaries of the image segments are not very sharp, therefore to sharpen and preserve the edge information pre-filtering is required. Laplacian, median and min spatial filters have been used as pre-filter to remove noise present in the image.
Batching and scheduling in a continuous-discrete hybrid flowshop: Lagrangian relaxation-based heuristic algorithms
Published in International Journal of Production Research, 2023
The major contributions of our study can be summarised as follows. We build a MILP model for this NP-hard problem and develop Lagrangian relaxation-based algorithms to deal with the problem. We propose a novel decomposition method by relaxing constraints on machine capacities to obtain tractable family-level subproblems with a tree structure.We transform the subproblem into a set-partitioning problem with each column representing a partial schedule of a batch and the jobs in this batch. The schedule of each batch is then carefully constructed, and the branch-and-price method is adopted to obtain a valid lower bound of the original problem so as to obtain a near-optimal feasible solution.We also develop two simple heuristic algorithms to obtain a good feasible solution at a mild cost of computational effort. Computational experiments show that our approach outperforms existing solution approaches.
Low frequency scattering simulation of homogeneous objects in layered medium by Muller formed SIE
Published in Electromagnetics, 2023
For low frequency problems, a variety of sophisticated methods have been presented to attempt to solve the low frequency breakdown problem (Chen et al. 2011; Ling, Liu, and Jin 2002; Qian and Chew 2010; Ren et al. 2018; Taskinen and Vanska 2007; Taskinen and Yl¨a-Oijala 2006). The most famous one is the quasi-Helmholtz decomposition method, which is represented by the loop-tree/star scheme (Burton and Kashyap 1995; Chen et al. 2009; Ling, Liu, and Jin 2002; Vecchi 1999). However, most of them have only been produced in free space background and very few have been extended to LM cases. To the best of our knowledge, there has been very little work regarding dielectric problems in LM cases. In fact, only PEC problems can be found in literature at present. Ling utilized the loop-tree/star to analyze a circuit embedded in LM (Ling, Liu, and Jin 2002), while Chen used a similar approach to simulate the low frequency scattering of PEC objects in LM (Chen et al. 2009). There have no dielectric object analyses in LM are reported in open literature. Therefore, due to the complexity of layered medium Green’s functions (LMGFs) and low frequency breakdown, the low frequency scattering of dielectric object in LM is still a challenging question faced by the electromagnetic society.
Real-time monitoring and diagnosis scheme for IoT-enabled devices using multivariate SPC techniques
Published in IISE Transactions, 2023
Zhenyu Wu, Yanting Li, Fugee Tsung, Ershun Pan
Second, T2 decomposition has no clear physical meaning, which makes it difficult to both understand the interaction and transmission relationship between variables and detect the root cause of the fault. To counter this, Shang et al. (2013) proposed a diagnosis framework based on the hierarchical likelihood, which incorporates physical laws and engineering knowledge. Also, the Bayesian method is widely applied to the diagnostics of multivariate SPC, giving a more specific meaning to the identification of faulty variables. Verron et al. (2010) introduced an indicator variable for each mean shift, taking into account prior knowledge and the direction of the mean shift, and obtained the posterior distribution of indicator variables using the Bayesian method. On this basis, Wang et al. (2020) diagnosed the shift in the covariance matrix. When there is a causal relationship between variables, the Bayesian Network (BN) provides a solution. Li et al. (2008) proposed a causation-based T2 decomposition method, and used the directed acyclic graph based on BN to decompose a multivariate process and identify the root cause. Li et al. (2017) have applied the BN-based method to diagnose Multivariate Classification Processes (MCPs) with a causal relationship. Xian et al. (2019) integrated ordinal information based on causal relationships to establish a diagnostic control diagram for the MCP, in which BN was used to describe the causal relationships between variables, and a latent continuous variable was used to model the orders of the attribute levels of the ordinal variables.