4 research outputs found
Artificial Intelligence in Supply Chain Management: Investigation of Transfer Learning to Improve Demand Forecasting of Intermittent Time Series with Deep Learning
Demand forecasting intermittent time series is a challenging business problem. Companies have difficulties in forecasting this particular form of demand pattern. On the one hand, it is characterized by many non-demand periods and therefore classical statistical forecasting algorithms, such as ARIMA, only work to a limited extent. On the other hand, companies often cannot meet the requirements for good forecasting models, such as providing sufficient training data. The recent major advances of artificial intelligence in applications are largely based on transfer learning. In this paper, we investigate whether this method, originating from computer vision, can improve the forecasting quality of intermittent demand time series using deep learning models. Our empirical results show that, in total, transfer learning can reduce the mean square error by 65 percent. We also show that especially short (65 percent reduction) and medium long (91 percent reduction) time series benefit from this approach
Fast CRDNN: Towards on Site Training of Mobile Construction Machines
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).Comment: 15 pages, 18 figure
AI and IoT Meet Mobile Machines: Towards a Smart Working Site
Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)