110 research outputs found
On the definition of Alexandrov space
This paper shows that, in the definition of Alexandrov space with lower
([BGP]) or upper ([AKP]) curvature bound, the original conditions can be
replaced with much weaker ones, which can be viewed as comparison versions of
the second variation formula in Riemannian geometry (and thus if we define
Alexandrov spaces using these weakened conditions, then the original definition
will become a local version of Toponogov's Comparison Theorem on such spaces).
As an application, we give a new proof for the Doubling Theorem by Perel'man.Comment: 12 page
KIT Bus: A Shuttle Model for CARLA Simulator
With the continuous development of science and technology, self-driving vehicles will surely change the nature of transportation and realize the automotive industry\u27s transformation in the future. Compared with self-driving cars, self-driving buses are more efficient in carrying passengers and more environmentally friendly in terms of energy consumption. Therefore, it is speculated that in the future, self-driving buses will become more and more important. As a simulator for autonomous driving research, the CARLA simulator can help people accumulate experience in autonomous driving technology faster and safer. However, a shortcoming is that there is no modern bus model in the CARLA simulator. Consequently, people cannot simulate autonomous driving on buses or the scenarios interacting with buses. Therefore, we built a bus model in 3ds Max software and imported it into the CARLA to fill this gap. Our model, namely KIT bus, is proven to work in the CARLA by testing it with the autopilot simulation. The video demo is shown on our Youtube
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
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