Current Location:home > Browse

1. chinaXiv:202112.00003 [pdf]

基于因果网络的驾驶人感知-行为模式研究.pdf

徐庆; 王文傲; 彭喆
Subjects: Computer Science >> Other Disciplines of Computer Science

驾驶人是“人-车-环境”道路交通系统的核心,研究驾驶人的感知-行为模式对于规范驾驶行为、提高安全水平具有重要意义。然而,当前的研究很少涉及驾驶中头动与器官协调性,尤其缺乏量化计算的研究。因此,本文设计了虚拟现实下的匀速平稳驾驶实验,并使用信息论工具进行建模、分析。我们研究了头、眼、手、脚四种运动器官的协调性,提出了基于传递熵的因果网络用来描述四者间的协作模式,提出了使用网络平均传递熵作为评估驾驶中器官协调性的一种指标。最后,我们还发现,在驾驶中头-眼协调性非常强,配合紧密度高;在转弯时各器官间的协调性比在直行时强;在平稳匀速驾驶任务中,驾驶人对行为任务的优先级高于感知任务。

submitted time 2021-11-25 Hits56Downloads22 Comment 0

2. chinaXiv:202110.00073 [pdf]

基于主动信息存储的驾驶员头动行为和驾驶方向平稳度的量化研究在VR驾驶中

王文傲; 彭喆; 徐庆
Subjects: Computer Science >> Other Disciplines of Computer Science

主动信息存储是重要的信息论工具,其优点在于易于获取复杂在系统中的可解释的信息存储。驾驶员的头动行为对于其把控方向具有重要作用,然而这种作用没有得到量化的衡量与解释。在本文中,我们将主动信息存储应用在驾驶员头动研究上,研究其与驾驶方向平稳度的关系。具体而言,我们设计了包含直道和转弯的VR驾驶实验,获取驾驶员头动主动信息存储序列和车辆偏转角序列,并研究二者的量化关系。我们证明了二者具有高度的时序相关性,并且用二者的联合熵作为驾驶表现的一种指标,最后我们用驾驶员的头动实时预测车辆偏转角,得到了88.56%的准确率。该工作有望帮助监测驾驶员状态,提高驾驶安全性。

submitted time 2021-10-21 Hits1671Downloads190 Comment 1

3. chinaXiv:202110.00015 [pdf]

基于公益事业的第二代数字货币原理分析

张靖博; 吴博凡
Subjects: Computer Science >> Other Disciplines of Computer Science

区块链是随着比特币等数字加密货币的日益普及而逐渐兴起的一种全新的去中心化基础架构与分布式计算范式, 目前已经引起政府部门、金融机构、科技企业和资本市场的高度重视与广泛关注。以比特币为代表的数字货币在获得巨大成功的同时,也具有许多缺陷并带来了一系列社会问题。本文提出了新的数字货币与非对称代币(NFT)发行模式,可以有效的弥补当前数字货币的缺陷并使其创造更大的社会价值。

submitted time 2021-10-07 Hits2382Downloads193 Comment 0

4. chinaXiv:202110.00004 [pdf]

深度学习在低频非侵入式负荷分解中的应用与比较

Li, Yiming; Ju, Yuntao; Qu, Liao
Subjects: Computer Science >> Other Disciplines of Computer Science

非侵入式负荷分解能够充分解析用户用电数据,是分析评估用户柔性调控潜力的关键技术。鉴于深度学习方法在负荷分解的广泛应用,首先深入探讨了降噪自编码器、循环神经网络、卷积神经网络等主流深度学习网络结构应用在负荷分解问题时的工作机理,并对它们在负荷分解领域中的应用与发展进行了展望分析;之后,提出了基于不同维度的分解算法评价框架,并补充了评价过程中测试数据的选择规范;最后,利用该评价框架对主流的深度学习分解模型进行评价,并对模型代码进行了开源,评价结果证明所提框架更能综合地评价给定超参设置下的深度学习分解模型,并揭示模型性能关于网络结构、特征输入等因素的敏感性。

submitted time 2021-10-01 Hits2257Downloads166 Comment 0

5. chinaXiv:202108.00103 [pdf]

A New Interpolation Approach and Corresponding Instance-Based Learning

廉师友
Subjects: Computer Science >> Other Disciplines of Computer Science

Starting from finding approximate value of a function, introduces the measure of approximation-degree between two numerical values, proposes the concepts of "strict approximation" and "strict approximation region", then, derives the corresponding one-dimensional interpolation methods and formulas, and then presents a calculation model called "sum-times-difference formula" for high-dimensional interpolation, thus develops a new interpolation approach ? ADB interpolation. ADB interpolation is applied to the interpolation of actual functions with satisfactory results. Viewed from principle and effect, the interpolation approach is of novel idea, and has the advantages of simple calculation, stable accuracy, facilitating parallel processing, very suiting for high-dimensional interpolation, and easy to be extended to the interpolation of vector valued functions. Applying the approach to instance-based learning, a new instance-based learning method ? learning using ADB interpolation ? is obtained. The learning method is of unique technique, which has also the advantages of definite mathematical basis, implicit distance weights, avoiding misclassification, high efficiency, and wide range of applications, as well as being interpretable, etc. In principle, this method is a kind of learning by analogy, which and the deep learning that belongs to inductive learning can complement each other, and for some problems, the two can even have an effect of “different approaches but equal results” in big data and cloud computing environment. Thus, the learning using ADB interpolation can also be regarded as a kind of “wide learning” that is dual to deep learning.

submitted time 2021-08-17 Hits3722Downloads292 Comment 0

6. chinaXiv:202108.00061 [pdf]

一种改进Transformer的电力负荷预测方法

黄飞虎; 赵红磊; 弋沛玉; 李沛东; 彭舰
Subjects: Computer Science >> Other Disciplines of Computer Science

负荷预测是电网系统中很多应用的关键部分,具有重要作用。然而,由于电网负荷的非线性、时变性和不确定性,使得准确预测负荷具有一定的挑战。充分挖掘负荷序列的潜在特征是提升预测准确率的关键。本文认为在特征提取时应该充分利用负荷序列的位置信息、趋势性、周期性和时间信息,同时还应构建更深层次的神经网络框架进行特征挖掘。因此,本文提出了基于特征嵌入和Transformer框架的负荷预测模型,该模型由特征嵌入层,Transformer层和预测层组成。在特征嵌入层,模型首先对历史负荷的位置信息、趋势性、周期性和时间信息进行特征嵌入,然后再与天气信息进行融合,得到特征向量。Transformer层则接受历史序列的特征向量并挖掘序列的非线性时序依赖关系。预测层通过全连接网络实现负荷预测。从实验结果来看,本文模型的预测性能优于对比模型,体现了该模型的可行性和有效性。

submitted time 2021-08-11 Hits4658Downloads353 Comment 0

7. chinaXiv:202106.00009 [pdf]

Complex-valued Deng Entropy

Lipeng Pan; Yong Deng
Subjects: Computer Science >> Other Disciplines of Computer Science

Complex evidence theory has been applied to several fields due to its advantages in modeling and processing uncertain information. However,to measure the uncertainty of the complex mass function is still an open issue. The main contribution of this paper is to propose a complex-valued Deng entropy. The complex-valued Deng entropy can effectively measure the uncertainty of the mass function in the complex-valued framework. Meanwhile, the complex-valued Deng entropy is a generalization of the Deng entropy and Shannon entropy. That is, the complex-valued Deng entropy can degenerate to classical Deng entropy when the complex-valued mass function degenerates to a mass function in real space. In addition, the proposed complex-valued Deng entropy can also degenerates to Shannon entropy when the complex-valued mass function degenerates to a probability distribution in real space. Some numerical examples demonstrate the compatibility and effectiveness of the complex-valued Deng entropy.

submitted time 2021-06-03 Hits6428Downloads557 Comment 0

8. chinaXiv:202106.00005 [pdf]

Complex-valued Renyi Entropy

Lipeng Pan; Yong Deng
Subjects: Computer Science >> Other Disciplines of Computer Science

Complex-valued expression models have been widely used in the application of intelligent decision systems. However, there is a lack of entropy to measure the uncertain information of the complex-valued probability distribution. Therefore, how to reasonably measure the uncertain information of the complex-valued probability distribution is a gap to be filled. In this paper, inspired by the Renyi entropy, we propose the Complex-valued Renyi entropy, which can measure uncertain information of the complex-valued probability distribution under the framework of complex numbers, and is also the first time to measure uncertain information in the complex space. The Complex-valued Renyi entropy contains the features of the classical Renyi entropy, i.e., the Complex-valued Renyi Entropy corresponds to different information functions with different parameters q. Meanwhile, the Complex-valued Renyi entropy has some properties, such as non-negativity, monotonicity, etc. Some numerical examples can demonstrate the flexibilities and reasonableness of the Complex-valued Renyi entropy.

submitted time 2021-05-31 Hits5663Downloads510 Comment 0

9. chinaXiv:202007.00047 [pdf]

鲁棒模式识别研究进展

张煦尧; 刘成林
Subjects: Computer Science >> Other Disciplines of Computer Science

目前诸多模式识别任务的识别精度获得不断提升,在一些任务上甚至超越了人的水平。单从识别精度的角度来看,模式识别似乎已经是一个被解决了的问题。然而,高精度的模式识别系统在实际应用中依旧会出现不稳定和不可靠的现象。因此,开放环境下的鲁棒性成为制约模式识别技术发展的新瓶颈。实际上,在大部分模式识别模型和算法背后蕴含着三个基础假设:封闭世界假设、独立同分布假设、以及大数据假设。这三个假设直接或间接影响了模式识别系统的鲁棒性,并且是造成机器智能和人类智能之间差异的主要原因。本文简要论述如何通过打破三个基础假设来提升模式识别系统的鲁棒性。

submitted time 2020-07-29 Hits16309Downloads2565 Comment 0

10. chinaXiv:202006.00176 [pdf]

Automated Radiological Impression Generation for Plain Chest X-rays with End to End Deep Learning

Zhang, Shuai; Xin, Xiaoyan; Shen, Jingtao; Guo, Yachong; Wang, Yang; Yang, Xianfeng; Wang, Jun; Zhang, Jian; Zhang, Bing
Subjects: Computer Science >> Other Disciplines of Computer Science

The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge. Therefore, an automated diagnosis system that is able to save the effort of doctors is of great value. At present, the applications of artificial intelligence in CXR diagnosis usually use pattern recognition to classify the scans. However, such methods rely on labeled databases. They are costly and usually have a high error rate. In this work, we built a database containing more than 12,000 CXR scans and radiological reports, and developed a model based on deep convolutional neural network and recurrent network with attention mechanism. The model learns features from the CXR scans and the associated raw radiological reports directly; no additional labeling required. The model provides automated recognition of given scans and generation of impression. The quality of the generated impression was evaluated with both the CIDEr scores and by radiologists as well. The CIDEr scores were found to be around 5.8 on average for the testing dataset. Further blind evaluation suggested a comparable performance against radiologists.

submitted time 2020-06-09 Hits16952Downloads1160 Comment 0

12  Last  Go  [2 Pages/ 13 Totals]