A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds(PIB)which have been identified as the endangered bird species.The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected(BNDFC)layers to enhance the baseline model of transfer learning.The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network(CNN)based model to improve the classification accuracy,especially for image-based species classification problems.The experiment results show that the proposed sequence of BNDFC layers outperform other combination of BNDFC.The addition of BNDFC can improve the model’s performance across ten different CNN-based models.On average,BNDFC can improve by approximately 19.88%in Accuracy,24.43%in F-measure,17.93%in G-mean,23.41%in Sensitivity,and 18.76%in Precision.Moreover,applying fine-tuning(FT)is able to enhance the accuracy by 0.85%with a smaller validation loss of 18.33%improvement.In addition,MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07%in the validation set.