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Furthermore, we introduce an effective feature representation for infrared objects.
However, many challenges exist in extracting the effective feature from vibration signals for fault recognition.
One of the core problems to FDP is to design effective feature selection algorithms.
Therefore, it is useful to develop effective feature extraction techniques for soft sensor design.
Thus, accurate score evaluation is a key factor for obtaining an effective feature subset.
We also present an effective feature aggregation method by exploring two variations of network features.
Thus, an effective feature space could be simply a relatively small number of time intervals between extrema in a suitably filtered signal.
High-performance object detection is usually carried out in feature space and effective feature representation can improve the performance significantly.
Then we propose two simple and effective feature selection algorithms based on this framework and Kullback Leibler divergence.
Effective Feature Subset Selection (FSS) is an important step when designing engineering systems that classify complex data in real time.
It performs an effective feature extraction approach based on norm entropy in order to obtain the features represented main frequency, harmonic and transient characteristics of the fault signals.
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