There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Frechet distance is an effective metric for measuring curve similarity, a novel 3D face similarity measure method based on Frechet distances of geodesics is proposed in this paper. In our method, the surface similarity between two 3D faces is measured by the similarity between two sets of 3D curves on them. Due to the intrinsic property of geodesics, we select geodesics as the comparison curves. Firstly, the geodesics on each 3D facial model emanating from the nose tip point are extracted in the same initial direction with equal angular increment. Secondly, the Frechet distances between the two sets of geodesics on the two compared facial models are computed. At last, the similarity between the two facial models is computed based on the Frechet distances of the geodesics obtained in the second step. We verify our method both theoretically and practically. In theory, we prove that the similarity of our method satisfies three properties: reflexivity, symmetry, and triangle inequality. And in practice, experiments are conducted on the open 3D face database GavaDB, Texas 3D Face Recognition database, and our 3D face database. After the comparison with iso-geodesic and Hausdorff distance method, the results illustrate that our method has good discrimination ability and can not only identify the facial models of the same person, but also distinguish the facial models of any two different persons.

3D Face Similarity Measure by Fréchet Distances of Geodesics

This exploration is aimed at the system prediction and safety performance of the Digital Twins (DTs) of autonomous cars based on artificial intelligence technology, and the intelligent development of transportation in the smart city. On the one hand, considering the problem of safe driving of autonomous cars in intelligent transportation systems, it is essential to ensure the transmission safety of vehicle data and realize the load balancing scheduling of data transmission resources. On the other hand, convolution neural network (CNN) of the deep learning algorithm is adopted and improved, and then, the DTs technology is introduced. Finally, an autonomous cars DTs prediction model based on network load balancing and spatial-temporal graph convolution network is constructed. Moreover, through simulation, the performance of this model is analyzed from perspectives of Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that in comparative analysis, the accuracy of road network prediction of the model reported here is 92.70%, which is at least 2.92% higher than that of the models proposed by other scholars. Through the analysis of the security performance of network data transmission, it is found that this model achieves a lower average delay time than other comparative models. Besides, the message delivery rate is basically stable at 80%, and the message leakage rate is basically stable at about 10%. Therefore, the prediction model for autonomous cars constructed here not only ensures low delay but also has excellent network security performance, so that information can interact more efficiently. The research outcome can provide an experimental basis for intelligent development and safety performance improvement in the transportation field of smart cities. 

Artificial intelligence enabled Digital Twins for training autonomous cars

In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes. Our dataset contains both long and short video sequences, and enables the quantitatively evaluation for RGB based tracking and registration methods. To construct ground truth for the RGB sequences, we simultaneously capture Near-Infrared (NIR) image sequences where dense markers - visible only in NIR - represent ground truth positions. This allows for comparison with automatically tracked RGB positions and the formation of error metrics. Most previous datasets containing nonrigidly deforming sequences are based on synthetic data. Our capture protocol enables us to acquire real-world deforming objects with realistic photometric effects - such as blur and illumination change - as well as occlusion and complex deformations. A public evaluation website is constructed to allow for ranking of RGB image based optical flow and other dense tracking algorithms, with various statistical measures. Furthermore, we present an RGB-NIR multispectral optical flow model allowing for energy optimization by adoptively combining featured information from both the RGB and the complementary NIR channels. In our experiments we evaluate eight existing RGB based optical flow methods on our new dataset. We also evaluate our hybrid optical flow algorithm by comparing to two existing multispectral approaches, as well as varying our input channels across RGB, NIR and RGB-NIR.

Dense Nonrigid Ground Truth for Optical Flow in Real-World Scenes.

Multiple-antenna systems and multiuser communications: Fundamentals and an overview of software-based modeling techniques

A balance measurement software based on Kinect2 sensor is evaluated by comparing to golden standard balance measure platform intuitively. The software analysis the tracked body data from the user by Kinect2 sensor and get user's center of mass(CoM) as well as its motion route on a plane. The software is evaluated by several comparison tests, the evaluation results preliminarily prove the reliability of the software.

Zhihan Lv

Papers

3D Face Similarity Measure by Fréchet Distances of Geodesics

Artificial intelligence enabled Digital Twins for training autonomous cars

Dense Nonrigid Ground Truth for Optical Flow in Real-World Scenes.

Multiple-antenna systems and multiuser communications: Fundamentals and an overview of software-based modeling techniques

Intuitive Evaluation of Kinect2 based Balance Measurement Software