Optimization of operation strategy for primary torque based hydrostatics drivetrain using artificial intelligence

Xiang, Yusheng; Geimer, Marcus

doi:10.25368/2020.13

Cited by 8 publications

(10 citation statements)

References 10 publications

(11 reference statements)

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“…Thanks to the transfer learning, the system can be adapted to a new machine, even where there has a different distribution as the source dataset, without a complicated calibration process. The test accuracy, i.e., micro F1, of this working state recognition system can reach 98% on the challenging dataset [1], which achieves the humanlevel performance and guarantees accurate recognition. The strong ability of generalization of transfer-learning-based CRDNN is proven.…”

Section: Evaluation the Benefits Of Transfer Learningmentioning

confidence: 89%

“…I N the previous study [1], the CRDNN shows excellent performance in detecting the Y cycles of primary-torquebased mobile machines. To date, we believe that CRDNN is a promising method to solve the problem.…”

Section: Introductionmentioning

confidence: 88%

“…In our previous works, the CRDNN is validated as an excellent AI tool to solve the detection problem so that the corresponding operation strategy can be easily designed. A concrete description of how we regenerate the energy can be found in [1] with more details. Also, the working process detection is used as a vital criterion to predict the intention of the drivers.…”

Section: B Y Cycles Detection Of Wheel Loadersmentioning

confidence: 99%

“…The model is built with Kereas API in Tensorflow [11]. A more detailed description of how we built up the dataset and the CRDNN can be found in our previous study [1]. In this study, we use CRDNN with 2 LSTMs as the base network based on its micro F1, back-and forward propagation duration so that the networks can be further trained directly on the working site using transfer learning.…”

Section: What Is Crdnn For Y Cycles?mentioning

confidence: 99%

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Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Xiang¹,

Tang

et al. 2021

IEEE Access

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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).

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Section: Evaluation the Benefits Of Transfer Learningmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 88%

Section: B Y Cycles Detection Of Wheel Loadersmentioning

confidence: 99%

Section: What Is Crdnn For Y Cycles?mentioning

confidence: 99%

See 2 more Smart Citations

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Xiang¹,

Tang

et al. 2021

IEEE Access

Self Cite

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“…Besides artificial intelligence [1], the fleet management of mobile machines is the principal research direction of the internet of things in the fields of mobile working machinery. Currently, the mobile machines are distributed sparsely in the working site and working at low transport speed to avoid a collision.…”

Section: Introductionmentioning

confidence: 99%

Realtime Estimation of IEEE 802.11p for Mobile working Machines Communication respecting Delay and Packet Loss

Xiang

Brach

et al. 2020

Preprint

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The fleet management of mobile working machines with the help of connectivity can increase not only safety but also productivity. However, rare mobile working machines have taken advantage of V2X. Moreover, no one published the simulation results that are suitable for evaluating the performance of the ad-hoc network at a working site on the highway where is congested, with low mobility, and without building. In this paper, we suggested that IEEE 802.11p should be implemented for fleet management, at least for the first version. Furthermore, we proposed an analytical model for machines to estimate the ad-hoc network performance about the expected delay and the probability of packet loss in real-time based on the simulation results we made in ns3. The model of this paper can be used for determining when shall ad-hoc or cellular network be used in the corresponding scenario.

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An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

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Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

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Optimization of operation strategy for primary torque based hydrostatics drivetrain using artificial intelligence

Cited by 8 publications

References 10 publications

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Realtime Estimation of IEEE 802.11p for Mobile working Machines Communication respecting Delay and Packet Loss

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

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