|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Multi-agent reinforcement learning for edge information sharing in vehicular networks显示文摘To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments. | Ruyan Wang Xue Jiang Yujie Zhou Zhidu Li Dapeng Wu Tong Tang Alexander Fedotov Vladimir Badenko | 2022 | Digital Communications and Networks2022，8，3： | 3 |
| 2 | Foxp3<sup>+</sup> regulatory T cells are associated with the natural history of chronic hepatitis B and poor prognosis of hepatocellular carcinoma显示文摘 | XiangJing TaoWang ZhengyanZhu QinZhang YijunWang ZhiDu | 2012 | Liver Int2012，，4： | 1 |
| 3 | Improved projection-based energy weighting for spectral CT显示文摘Purpose Conventional X-ray CT scanners have limited ability to distinguish low-contrast substances.However,spectral CTs with photon counting detectors can identify photon energy and utilize spectral information,which is expected to achieve improved contrast.Energy weighting is a kind of reconstruction method for spectral CT.By assigning appropriate weight for each energy channel,the image contrast can be improved.Hence,how to determine the optimal weights is very important.Methods In this paper,we developed an improved projection-based energy weighting model for spectral CT.In this model,the object thickness of low-density materials is assumed as a constant,and the measured spectrum distribution is used to calculate the weight coefficients.Both phantom and tissue experiments were conducted in spectral CT scanner.Results The results showed that the thickness of low-density materials has little influence on the energy weight,so it can be regarded as a constant.For low-contrast phantom,the contrast-to-noise ratio was improved~32%by the proposed projectionbased weighting method.Conclusions The improved projection-based energy weighting model is effective in practice.It can increase the contrast of low-density materials. | Zhiwei Cheng Mohan Li Qiong Xu Zhidu Zhang Jinming Hu Cunfeng Wei Long Wei Zhe Wang | 2019 | Radiation Detection Technology and Methods2019，3，3： | 0 |
| 4 | A study on noise reduction for dual-energy CT material decomposition with autoencoder显示文摘Purpose A major challenge for the material decomposition task of the dual-energy computed tomography(DECT)is the algorithm often suffers from heavy noise in the results.The purpose of this study is to propose a scheme to increase the noise performance of material decomposition.Methods The scheme we propose in this paper is to apply an autoencoder-based denoising procedure to the photon-counting DECT images before they are fed into the material decomposition algorithm.We implement the autoencoder(AE)by stacking a series of convolutional and deconvolutional layers.The decomposition technique adopted in our work is an iterative method using least squares estimation with the Huber loss function.The noises of the input and the output of material decomposition are analyzed with both simulated data and real data.Phantom and chicken wing experiments are conducted with a photoncounting-based spectral CT scanner to evaluate the proposed material decomposition scheme.Results The noise analysis of the input and the output of material decomposition demonstrates a positive correlation between them.Comparative experiment indicates a noise reduction in the output density maps for 26.07%to 35.65%after the autoencoder pre-processing is applied.The resultant contrast-to-noise ratio is largely increased,correspondingly.Conclusions By utilizing the additional autoencoder denoising step,the material decomposition algorithm achieves an improvement in the noise performance of the resultant density maps. | Mohan Li Zhe Wang Qiong Xu Zhidu Zhang Zhiwei Cheng Shuangquan Liu Baodong Liu Cunfeng Wei Long Wei | 2019 | Radiation Detection Technology and Methods2019，3，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员