Control Chart Patterns Recognition Based on Optimized Deep Belief Neural Network and Data Information Enhancement
نویسندگان
چکیده
منابع مشابه
Pattern Recognition in Control Chart Using Neural Network based on a New Statistical Feature
Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...
متن کاملControl Chart Recognition Patterns using Fuzzy Rule-Based System
Control Chart Patterns (CCPs) recognition is one the most important concepts in control chart application. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this study, a fuzzy rule-based system is developed for X ̅ control charts to prioritize the control chart causes based on the accumulated e...
متن کاملA Bayesian Approach for the Recognition of Control Chart Patterns
In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...
متن کاملFace Recognition based on Deep Neural Network
In modern life, we see more techniques of biometric features recognition have been used to our surrounding life, especially the applications in telephones and laptops. These biometric recognition techniques contain face recognition, fingerprint recognition and iris recognition. Our work focuses on the face recognition problem and uses a deep learning method, convolutional neural network, to sol...
متن کاملDeep neural network-based bandwidth enhancement of photoacoustic data.
Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed netwo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3036006