Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Abstract

The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy can not always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pretrained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. Also, we compared the performance of other SOTA multivariate time series algorithms on predicting the working state of the mobile machines, which denotes that the CRDNNs are still the most suitable solution. As a by-product, we build up a human-machine communication system to label the dataset, which can be operated by engineers without knowledge about Artificial Intelligence (AI).

Smart System Design for College Physical Education Class Based on Abnormal Audio Detection Algorithm

“…The CRDNN performs a series of data enhancement calculations on the input abnormal audio data such as encoding, translation, and decoding [17]. It is experimentally proved that the CRDNN data enhancement module makes the model have stronger anti-noise performance, which proves that the addition of the module effectively suppresses environmental noise and improves the accuracy of abnormal audio event detection in different situations and real environments with different signal-to-noise ratios [18]. The literature introduces the related technologies used to construct the physical education network auxiliary system, such as the three-tier model, database access technology, and programming technology [19,20].…”

5G Meets Construction Machines: Towards a Smart Working Site

“…The command can then be directly sent to the machines. To avoid the additional cost, many scientists point out we can use a smartphone as an intermediary to transmit information instead of installing additional equipment [24].…”

KIT MOMA: A Mobile Machines Dataset

“…In this fashion, wireless communication should be developed for information sharing between machines and cameras. These researches can be found in [53], [54]. Consequently, a diversity of eight common categories across 5663 images was organized in the form of the PASCAL VOC dataset.…”