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Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address...
Multi-step ahead forecasting is an important issue for organizations, often used to assist in tactical decisions. Such forecasting can be achieved by adopting time series forecasting methods, such as the classical Holt-Winters (HW) that is quite popular for seasonal series. An alternative forecasting approach comes from the use of more flexible learning algorithms, such as Neural Networks (NN) and...
A new model is introduced in this paper to construct the input-output relation in the prediction and control problem of non-analytic systems. The historical input-output data of general system is de-noised with wavelet transformation and SVM, and the input-output variables which can reflect the features of the system are determined with correlation analysis and sensitivity analysis. With the historical...
Tool wear monitoring is an integral part of modern CNC machine control. This paper presents a new tool wear predictive model by combination of workpiece surface texture analysis and support vector machine with genetic algorithm (SVMG). Firstly, the column projection method and the Gabor filter method are proposed to extract texture features of machined surfaces. Then, SVMG-based tool wear predictive...
Due to time series forecasting involves a rather complex data pattern, there are lots of novel forecasting approaches to improve the forecasting accuracy. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the...
Accelerated Degradation Testing (ADT) is now adopted frequently to verify the reliability and life of high-reliable, long-life product. But ADT data analysis methods are still deficiency. Due to the excellent capable of little sample learning and nonlinear mapping, SVM prediction model is widely used in many fields. In this paper, a new degradation prediction method based on Support Vector Machines...
Software project development is a risky process with high failure rate. This paper proposes an intelligent model that can predict and control software development risks from an overall project perspective rather than focusing only on the single factor, project output. In this study, we first constructed a formal model for risk identification, and then collected actual cases from software development...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
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