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2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE)最新文献

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Research on Online Learning User Profile Based on K-means Algorithm 基于K-means算法的在线学习用户画像研究
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674507
Yan Wang, Qinglin Wu
Online learning is an important way for learners to acquire knowledge. It helps learners to choose time and place flexibly and improve the efficiency and autonomy of learning. Based on the analysis of the current situation of online learning and user portrait, the general process of online learning user portrait is proposed. Through the data collection of an online course, five dimensions of online learners are selected to cluster the online learners. The experimental results show that the clustering results are satisfactory when the learners of the course are clustered into four categories, Help to improve the effect of online learning. Through the in-depth analysis of the characteristics of online learning group portrait, it provides a reference basis for improving the effect of online learning.
在线学习是学习者获取知识的重要途径。它有助于学习者灵活地选择时间和地点,提高学习的效率和自主性。在分析在线学习和用户画像现状的基础上,提出了在线学习用户画像的一般流程。通过在线课程的数据收集,选择在线学习者的五个维度对在线学习者进行聚类。实验结果表明,将课程学习者分为四类,聚类结果令人满意,有助于提高在线学习的效果。通过对在线学习群画像特点的深入分析,为提高在线学习效果提供参考依据。
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引用次数: 0
Corpora-based Password Guessing: An Efficient Approach for Small Training Sets 基于语料库的密码猜测:一种小训练集的有效方法
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674437
Xiaochun Gan, Meng Chen, Dong Li, Zongyan Wu, Weili Han, Hu Chen
Password guessing plays an important role in studying the vulnerability of passwords to improve security. In modern password guessing methods, the patterns of passwords from users in specific regions are discovered from a large number of leaked passwords. Most traditional methods, such as PCFG, Markov process, and other deep learning methods rely only on the training set. Different from other application areas of machine learning, the training set of password guessing comes from leaked real password sets, such as Rockyou, CSDN, and VK. Traditional approaches of password guessing are effective for large-scale training sets. However, the size of leaked password sets leaked by users of small languages or users of specific organizations is very small, which makes it difficult for current password guessing methods which relying only on training sets to discover enough words in passwords. In order to solve this problem, this paper proposed a corpus-based password guessing method. First, we analyzed the common words and their categories in the leaked password sets from users in three different countries. On this basis, we proposed an organization method for multiple language corpora, and constructed corpora of more than 3 million words. Secondly, we improved the traditional PCFG password segmentation method and described password structure based on corpora. Third, we evaluated the probability of words in the corpora which are not appearing in the training set based on the Lapalace smoothing. Actual tests show that our method can produce a finer structure than the PCFG. When the size of the training set decreases, the cracking rate of the PCFG decreases significantly, while the impact of our method is not significant, and the cracking rate is significantly higher than that of the PCFG.
密码猜测对于研究密码的漏洞,提高密码安全性具有重要意义。在现代密码猜测方法中,从大量泄露的密码中发现特定区域用户的密码模式。大多数传统的方法,如PCFG、马尔可夫过程等深度学习方法只依赖于训练集。与机器学习的其他应用领域不同,猜密码的训练集来自泄露的真实密码集,如Rockyou、CSDN、VK等。传统的密码猜测方法对于大规模的训练集是有效的。然而,小语种用户或特定组织用户泄露的密码集的规模非常小,这使得目前仅依靠训练集来发现密码中足够单词的猜密码方法非常困难。为了解决这一问题,本文提出了一种基于语料库的密码猜测方法。首先,我们分析了三个不同国家用户泄露的密码集中的常用词及其类别。在此基础上,我们提出了一种多语言语料库的组织方法,构建了300多万字的语料库。其次,改进了传统的PCFG密码分割方法,采用基于语料库的密码结构描述。第三,我们基于拉普拉斯平滑评估语料库中没有出现在训练集中的词的概率。实际测试结果表明,该方法能产生比PCFG更精细的结构。当训练集的大小减小时,PCFG的开裂率明显减小,而我们的方法影响不显著,而且开裂率明显高于PCFG。
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引用次数: 1
DRM-VAE: A Dual Residual Multi Variational Auto-Encoder for Brain Tumor Segmentation with Missing Modalities 基于缺失模态的脑肿瘤分割的双残差多变分自编码器
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674673
Yian Zhu, Shaoyu Wang, Yun Hu, Xiao Ma, Yanxia Qin, Jianyun Xie
Brain tumor segmentation in multi-modal magnetic resonance images is an essential step in brain cancer diagnosis and treatment. Although the recent multi-modal fusion network has achieved impressive performance in brain tumor segmentation, we usually encounter the situations where certain acquired modalities cannot be obtained in advance in clinical practice. In this paper, we propose an advanced network composed of dual residual multi variational auto-encoder and the sub-model distribution loss, which is robust to the absence of any one modality in brain tumor segmentation. This network implements the information merging in both encoder and decoder through this dual residual multi variational auto-encoder and embeds it in latent space, and decodes the features in a residual form. In this way, the features as the input of the decoder will be consistent and the difficulty of learning will be reduced. We evaluate this network on BraTS2018 using subsets of the imaging modalities as input. The experimental results show that our method could achieve better segmentation accuracy compared with the current state-of-the art method UHVED.
多模态磁共振图像中脑肿瘤的分割是脑癌诊断和治疗的重要步骤。虽然近年来的多模态融合网络在脑肿瘤分割中取得了令人瞩目的成绩,但在临床实践中我们经常会遇到某些获得性模态无法提前获得的情况。本文提出了一种由对偶残差多变分自编码器和子模型分布损失组成的高级网络,该网络对脑肿瘤分割中任何一种模态的缺失都具有鲁棒性。该网络通过对偶残差多变分自编码器在编码器和解码器中实现信息合并,并将其嵌入到隐空间中,以残差形式对特征进行解码。这样,作为解码器输入的特征就会保持一致,学习的难度就会降低。我们使用成像模式子集作为输入,在BraTS2018上评估该网络。实验结果表明,与目前最先进的UHVED方法相比,我们的方法可以达到更好的分割精度。
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引用次数: 1
Color Constancy Based on Deep Residual Learning 基于深度残差学习的颜色恒常性
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674455
Mengyao Yang, K. Xie, Tong Li, Zepeng Yang
The purpose of color constancy algorithm is to eliminate the influence of illumination on the color of objects in the scene, so that the computer has the same color constancy ability as human visual system. In order to further improve the accuracy and robustness of the color constancy algorithm, this paper proposes a illumination estimation method based on deep residual learning, which fully extracts the illumination feature information in the image by deepening the number of network layers, and uses the residual module to prevent over fitting of the network model, At the same time, the local illumination estimates are integrated to obtain the global illumination estimation of the whole image. The experimental results on ColorChecker data set show that the estimation accuracy and robustness of this method are good, and can be applied to the fields of image processing and computer vision requiring color correction.
色彩恒定算法的目的是消除光照对场景中物体颜色的影响,使计算机具有与人类视觉系统相同的色彩恒定能力。为了进一步提高颜色不变算法的准确性和鲁棒性,本文提出了一种基于深度残差学习的照度估计方法,通过加深网络层数充分提取图像中的照度特征信息,并利用残差模块防止网络模型的过拟合,同时对局部照度估计进行整合,得到整个图像的全局照度估计。在ColorChecker数据集上的实验结果表明,该方法具有良好的估计精度和鲁棒性,可以应用于需要色彩校正的图像处理和计算机视觉领域。
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引用次数: 1
Cross Domain Clock Synchronization Based on Data Packet Relay in 5G-TSN Integrated Network 5G-TSN综合网络中基于数据包中继的跨域时钟同步
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674640
Zichao Chai, Wei Liu, Mao Li, Jing Lei
The 5G-TSN integrated network can effectively support time-critical industrial applications and realize real-time communication between all industrial equipment. However, the 5G-TSN integrated network involves different clock domains and the end-to-end cross domain clock synchronization problem needs to be solved. Based on detailed analysis of the synchronization process between 5G and TSN networks, this paper proposes a cross domain clock synchronization method based on data packet relay. The proposed method regards 5G network as logical TSN bridge which is only responsible for the forwarding of timestamped data packets. The clock domain compensation technology is introduced to estimate the residence time of 5G timing messages. The simulation results demonstrate that synchronization accuracy is significantly improved and complexity is reduced.
5G-TSN集成网络可以有效支持时间关键型工业应用,实现所有工业设备之间的实时通信。但5G-TSN集成网络涉及不同的时钟域,需要解决端到端的跨域时钟同步问题。在详细分析5G与TSN网络同步过程的基础上,提出了一种基于数据包中继的跨域时钟同步方法。该方法将5G网络视为逻辑上的TSN网桥,只负责转发带有时间戳的数据包。引入时钟域补偿技术估计5G定时报文的停留时间。仿真结果表明,该方法显著提高了同步精度,降低了同步复杂度。
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引用次数: 3
Resource allocation of Multi-service Network Slicing based on SCMA 基于SCMA的多业务网络切片资源分配
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674394
Yong Zhang, Zhenyu Zhang, Di Wu, Jie Bao
Sparse code multiple access (SCMA) is a non-orthogonal multiple-access technique, and how to apply it to enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) coexisting multiservice network slices is a serious challenge. The correlation among subcarriers, codebooks, users and power allocation are all issues that need to be solved by this system. Thus, we propose a SCMA-based slicing model for multi-service networks, and design a heuristic greedy algorithm and a power allocation algorithm based on Karush-Kuhn-Tucker conditions (KKT). The former obtains a sub-optimal solution of the problem by assigning codebooks to users and performing subcarrier matching, and the latter obtains the optimal solution of power allocation by the KKT condition. The simulation results demonstrate the feasibility of the algorithm and show that the SCMA scheme can achieve higher energy efficiency compared with the Orthogonal Multiple Access (OMA) scheme.
稀疏码多址(SCMA)是一种非正交多址技术,如何将其应用于增强型移动宽带(eMBB)和超可靠低延迟通信(URLLC)共存的多业务网络切片是一个严峻的挑战。子载波间、码本间、用户间的相互关系、功率分配等都是该系统需要解决的问题。因此,我们提出了一种基于scma的多业务网络切片模型,并设计了一种启发式贪婪算法和一种基于Karush-Kuhn-Tucker条件(KKT)的功率分配算法。前者通过给用户分配码本并进行子载波匹配得到问题的次优解,后者通过KKT条件得到功率分配的最优解。仿真结果验证了该算法的可行性,并表明与正交多址(OMA)方案相比,SCMA方案具有更高的能效。
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引用次数: 0
The Improved Decoding Algorithm of the (71, 36, 11) Quadratic Residue Code (71,36,11)二次剩余码的改进译码算法
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674498
Chunlan Luo, Xiaoxia Zhu
In the context of informatization and big data, the demands for the reliability and real-time of information transmission in wireless communication channels are increasing. Quadratic residue (QR) codes have a high prospect on error correction for reliable data conveyance over channel with noise. This paper proposes an optimized algebraic scheme for decoding (71, 36, 11) QR code for correcting up to 5 errors on one-case-by-one-case basis. This scheme is mainly to improve its algebraic decoding algorithm based on the Lin’s algorithm, only in this paper, the Lin’s algorithm is called LTC71 algorithm. The key technology is to use the algebraic structure of QR code and give a new discriminant condition by mathematical derivation, which can quickly detect whether there are four errors in the code. Simulation results show that the proposed decoding scheme achieves the same bit error-rate performance as LTC71 algorithm, and the decoding time is reduced by about 28.72% owing to the reduced complexity of the decoder when there are four errors in the code.
在信息化和大数据的背景下,对无线通信信道信息传输的可靠性和实时性的要求越来越高。二次残差码在有噪声信道上的可靠数据传输中具有很高的纠错前景。本文提出了一种优化的(71,36,11)QR码解码代数方案,可逐例修正最多5个错误。本方案主要是在Lin算法的基础上改进其代数解码算法,仅本文将Lin算法称为LTC71算法。其关键技术是利用QR码的代数结构,通过数学推导给出一个新的判别条件,可以快速检测出QR码中是否存在四种错误。仿真结果表明,该译码方案达到了与LTC71算法相同的误码率性能,并且在码中存在4个错误时,由于降低了译码器的复杂度,译码时间减少了约28.72%。
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引用次数: 1
A High-Level Model of the Read-Write Control System of a SRAM Chip SRAM芯片读写控制系统的高级模型
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674321
Di Wang, Tiehu Li
A high-level model of the read-write system for a synchronous-pipelined static random access memory (SRAM) was designed using Verilog hardware description language (HDL). The system is comprised of the host, the main controller and the SRAM chip, with the main controller further consisted of the signal source generator and the data transceiver controller. Three read-write modes, non-burst (regular), linear burst and interleaved burst, were realized in this model. The model was validated by behavioral simulations, which showed that the SRAM chip can be written and read correctly in all the operation modes. The SRAM read-write procedure is greatly simplified as the requirements for the source control signals are minimized. The stability and reliability of the system is improved by maximizing the timing margins of the data transmissions.
采用Verilog硬件描述语言(HDL)设计了同步流水线静态随机存取存储器(SRAM)读写系统的高级模型。该系统由主机、主控制器和SRAM芯片组成,主控制器又由信号源发生器和数据收发控制器组成。该模型实现了非突发(规则)、线性突发和交错突发三种读写模式。通过行为仿真验证了该模型的有效性,结果表明该SRAM芯片在所有工作模式下都能正常读写。SRAM读写过程大大简化,因为对源控制信号的要求被最小化。通过最大化数据传输的时间余量,提高了系统的稳定性和可靠性。
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引用次数: 0
A Blockchain-based Protocol for Evidence Preservation in Hengxing vs. Zhenhai Case 基于区块链的恒兴诉镇海案证据保全协议
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674252
Xinbo Wang, Xusheng Lu, Pengli Wang, Xin Huang, Haotian Yin, Bin Xing, Di Zhang, Zhao Wang, Jiaxing Wu
In 2006, Hengxing and Zhenhai signed a transportation service agreement via fax. Then the two parties disputed the authenticity of the agreement and went to court. This involves the legality, factuality, and relativity of the evidence provided by the double-issue, and we can use the advantages of the blockchain to assist in the collection of judicial evidence. We confirm the identity through a security authentication protocol, and then store data including timestamps and hashes in the blockchain after verification, so as to provide judicial validity to the evidence data.
2006年,恒兴与镇海通过传真签订了运输服务协议。随后,双方对协议的真实性产生了争议,并告上了法庭。这涉及到双重问题所提供证据的合法性、真实性和相对性,我们可以利用区块链的优势来辅助司法证据的收集。我们通过安全认证协议确认身份,验证后将时间戳、哈希等数据存储在区块链中,从而为证据数据提供司法效力。
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引用次数: 0
Electro-Thermal Model for Schottky Barrier Diode Based on Self-Heating Effect 基于自热效应的肖特基势垒二极管电热模型
Pub Date : 2021-12-17 DOI: 10.1109/ICECE54449.2021.9674655
Jianhang Cui, Yong Zhang, Haomiao Wei, Qingzhi Wu, Shuman Mao, Yuehang Xu, B. Yan, R. Xu
Terahertz frequency multiplier is very important for terahertz transceiver front-end to achieve terahertz high-speed wireless communication. In this paper, a study was conducted on the physical electro-thermal model of Schottky barrier diode developed on the basis of self-heating effect. The SPICE model and Symbolically Defined Devices (SDD) model of the diode with temperature-dependent saturation current and series resistance were constructed, respectively. Through Electromagnetic Heating multi-physics coupled with Heat Transfer in Solids and Electric Currents, an investigation was conducted into the temperature distribution and current distribution of the diode at dissipated powers. The SPICE model of the diode usually consisting of constant parameters was adopted to explore the I-V characteristics of the diode with temperature-dependent saturation current and series resistance at different temperatures. Given that the temperature, saturation current and series resistance of the diode have impact on each other, the diode SDD model with self-heating effect was further constructed by defining the port current related to temperature and the port voltage. The occurrence of current saturation phenomenon was demonstrated using the diode SDD model rather than the diode SPICE model, so as to validate this diode SDD model. The study of the diode physical electro-thermal model based on self-heating effect contributes a novel idea to the design of terahertz frequency multiplier and terahertz wireless communications.
太赫兹倍频器是实现太赫兹高速无线通信的关键。本文研究了基于自热效应建立的肖特基势垒二极管的物理电热模型。分别建立了温度相关饱和电流和串联电阻二极管的SPICE模型和SDD模型。通过电磁加热多物理场耦合固体传热和电流,研究了耗散功率下二极管的温度分布和电流分布。采用通常由常数参数组成的二极管SPICE模型,探索具有温度依赖性饱和电流和串联电阻的二极管在不同温度下的I-V特性。考虑到二极管的温度、饱和电流和串联电阻三者之间存在相互影响,通过定义与温度和端口电压相关的端口电流,进一步构建具有自热效应的二极管SDD模型。使用二极管SDD模型而不是二极管SPICE模型来演示电流饱和现象的发生,从而验证了该二极管SDD模型。基于自热效应的二极管物理电热模型的研究为太赫兹倍频器和太赫兹无线通信的设计提供了新的思路。
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引用次数: 0
期刊
2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE)
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