Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Abstract

Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

“…Accordingly, the LOG LOSS was lower in the testing phase from 2 to 5.5%, while the F1 score and classification accuracy were higher in the testing phase from 0.7 to 2.3 and 0.5 to 1.6%, respectively. All of these results are coming in high contrast to those of the previous studies which have found either similar [4] or higher values of classification errors and lower values in terms of classification accuracy in the testing phase [24,52].…”

“…Therefore, the best model architecture was selected using the first criterion, namely the error during training which was found to be the lowest for an NN architecture when using the ReLU activation function and α set at 0.01. It turns out that the best-performing models reported in other studies checking the effect of classification performance on acceleration data had similar architectures, placing the use of the ReLU activation function and of the regularization terms of up to 0.1 among the best options in terms of classification performance [24,52]. However, the performance of NN depends also on several other factors [53,54], including signal quality and other issues specific to classification tasks such as intra-class variability and inter-class similarity.…”

“…Several studies have tested the capability of NN models in correctly predicting classbased outcomes with various applications in forestry, some of which have focused on using acceleration signals to make predictions by machine learning [4,24,52]. Most of them agree that general classification accuracies of up to 100% may be achieved depending on several factors such as the complexity of classification, signal quality and accuracy of data labeling.…”

“…As such, the two classes characterized by the highest performance will dominate the data, potentially making the model more effective in classification. In relation to the SWITCH events which were poorly classified, previous studies have already described how the inter-class similarity, which was typical to this event, may affect the classification performance [4,24,52].…”

Optimized Design of Touching Parts of Soil Disinfection Machine Based on Strain Sensing and Discrete Element Simulation

“…Soil disinfection combined with tillage is an effective solution to continuous cropping obstacles, which can be used to kill most of the virus bacteria in the soil at the same time as cultivation. At present, the vertical spiral blade roll has been widely used in afforestation and reforestation operations due to its reliability and ability to crush soil quickly [ 8 , 9 ]. The principle of vertical rotary tillage is precisely based on the use of the mechanical vertical spiral blade roll to grind and suspend the soil naturally, integrating the functions of plowing, harrowing, crushing, and loosening the soil.…”