National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Diversity in Machine Learning

Abstract: Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine learning system is composed of plentiful training data, a good model training process, and an accurate inference. Many factors can affect the performance of the machine learning process, among which the diversity of the machine learning process is an important one. The diversity can help each procedure to guarantee a totally good machine learning: diversity of the training data ensures that the training data can provide more discriminative information for the model, diversity of the learned model (diversity in parameters of each model or diversity among different base models) makes each parameter/model capture unique or complement information and the diversity in inference can provide multiple choices each of which corresponds to a specific plausible local optimal result. Even though diversity plays an important role in the machine learning process, there is no systematical analysis of the diversification in the machine learning system. In this paper, we systematically summarize the methods to make data diversification, model diversification, and inference diversification in the machine learning process. In addition, the typical applications where the diversity technology improved the machine learning performance have been surveyed including the remote sensing imaging tasks, machine translation, camera relocalization, image segmentation, object detection, topic modeling, and others. Finally, we discuss some challenges of the diversity technology in machine learning and point out some directions in future work. Our analysis provides a deeper understanding of the diversity technology in machine learning tasks and hence can help design and learn more effective models for real-world applications.

Electrified Vehicles and the Smart Grid: The ITS Perspective

Abstract: Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.

Wood-Inspired Anisotropic Cellulose Nanofibril Composite Sponges for Multifunctional Applications.

Abstract: Nanocellulose-based porous materials have been recently considered as ideal candidates in various applications. However, challenges on performances remain owing to the disorderly structure and the limited transport specificity. Herein, wood-inspired composite sponges consisting of cellulose nanofibrils (CNFs) and high-aspect-ratio silver nanowires (AgNWs) were generated with anisotropic properties by the directional freeze-drying. The obtained composite sponges exhibited attractive features, such as an excellent compressive stress of 24.5 kPa, low percolation threshold of 0.1 vol % AgNWs, and high electrical conductivity of 1.52 S/cm. Furthermore, the self-assembled ordered structure in the longitudinal direction and synergistic effect between CNFs and AgNWs benefited the sponge interesting anisotropic electrical conductivity, thermal diffusivity, ultrafast electrically induced heating (<5 s), sensitive pressure sensing (errors <0.26%), and electromagnetic interference (EMI) shielding for special practical demands. This multifunctional material inspired by natural woods is expected to broaden new applications as electronic devices for an intelligent switch or EMI shielding.

Efficient available bandwidth estimation for network paths

Abstract: For estimating the available bandwidth along a network path,a novel active probing methodology COPP(chirp of packet paris)is presented combining self-induced congested principle with packet pair technique.After a sequence of packet pairs with gradually reduced intra-pair spacing was transmitted,COPP process would discover all turning bandwidth.Then every bandwidth was bound with a distinct weight according of the degree of interference of the underlying turning point and its neighbors to yield the final estimate by weighted adding up all turning bandwidth.Based on analysis of inter-packet spacing and one-way delay of single packet in a packet pair,the decision rules for determining whether a packet pair is turning point were shown.We also described the weighting process to obtain different weights for different kinds of turning point and discussed the effect of different thresholds on estimation.Simulation results in several cross traffic conditions indicate that COPP provides accurate estimation with relatively fewer overhead compared to other estimating tools.