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2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)最新文献

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Indoor Fingerprinting Localization Based on Fine-grained CSI using Principal Component Analysis 基于主成分分析的细粒度CSI室内指纹定位
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528612
Jingjing Wang, Xianqing Wang, Jishen Peng, J. Hwang, J. Park
With the development of Wi-Fi technology, the IEEE 802.11n series communication protocol and the subsequent wireless LAN protocols use multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies. Channel state information (CSI) fingerprint positioning technology based on fine-grained channel state information is widely used in the field of WIFI indoor positioning. However, the propagation of CSI is still affected by indoor multipath, and we cannot obtain signals in some corner areas. Therefore, CSI needs a suitable calibration method to improve the accuracy of the position estimation system. This paper proposes a fine-grained CSI fingerprint location algorithm based on Principal Component Analysis (PCA). This novel algorithm uses a dimensionality reduction method on the basis of the Discrete Wavelet Transform (DWT) to optimize, eliminate the noise and redundancy of the original data and reduce the positioning error. Experimental results show that the proposed approach achieves significant localization accuracy improvement over using the RSSI fingerprint method and original CSI fingerprint method, while it incurs much less computational complexity. Meanwhile, the algorithm improves the influence of multiple paths in a complex indoor environment on location, and the method can obtain more accurate location results.
随着Wi-Fi技术的发展,IEEE 802.11n系列通信协议及其后续无线局域网协议采用了多输入多输出(MIMO)和正交频分复用(OFDM)技术。基于细粒度通道状态信息的通道状态信息(CSI)指纹定位技术被广泛应用于WIFI室内定位领域。但是,CSI的传播仍然受到室内多径的影响,在一些角落无法获得信号。因此,CSI需要一种合适的标定方法来提高位置估计系统的精度。提出了一种基于主成分分析(PCA)的细粒度CSI指纹定位算法。该算法采用基于离散小波变换(DWT)的降维方法对原始数据进行优化,消除了原始数据的噪声和冗余,减小了定位误差。实验结果表明,与RSSI指纹方法和原始CSI指纹方法相比,该方法在显著提高定位精度的同时,大大降低了计算复杂度。同时,该算法改善了复杂室内环境中多条路径对定位的影响,可以获得更准确的定位结果。
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引用次数: 5
Intelligent Learning Architecture with Hybrid Features for Phishing Detection 基于混合特征的网络钓鱼检测智能学习架构
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528537
Yu-Hung Chen, Jiann-Liang Chen
This study proposes a novel machine learning architecture that uses deep learning technology to extract features from the structure of a web page and construct a model for phishing detection. Hackers can commit crimes through a variety of Internet technologies. In recent years, phishing incidents have become more frequent, and the rapid development of information technology has enabled hackers to develop more advanced phishing attacks. Furthermore, the release of phishing toolkits, which are collections of software tools, make it easier for people with minimal technical skills to launch their own phishing attacks. Therefore, more attention must be paid to the prevention of such attacks. Protection from phishing websites has various aspects, including user training, public awareness, technical security measures and others. In this research, we further improve the phishing detection on phishing kits. This research proposes to use the combination HTML structural feature with the features proposed by AI@ntiPhish1.0 to train the phishing detection model. Relevant experimental results demonstrate that the combination of AI@ntiPhish1.0 features with extracted HTML structural features is more effective on detecting the phishing kits, increasing the accuracy thereof from 82% to 87.2%.
本研究提出了一种新的机器学习架构,该架构使用深度学习技术从网页结构中提取特征,并构建网络钓鱼检测模型。黑客可以通过各种互联网技术进行犯罪。近年来,网络钓鱼事件日益频繁,信息技术的快速发展使黑客能够开发出更高级的网络钓鱼攻击。此外,网络钓鱼工具包的发布,即软件工具的集合,使具有最低技术技能的人更容易发起自己的网络钓鱼攻击。因此,必须更加重视预防此类攻击。防范钓鱼网站有多方面的内容,包括用户培训、公众意识、技术安全措施等。在本研究中,我们进一步改进了网络钓鱼工具的网络钓鱼检测。本研究提出将HTML结构特征与AI@ntiPhish1.0提出的特征相结合来训练网络钓鱼检测模型。相关实验结果表明,将AI@ntiPhish1.0特征与提取的HTML结构特征相结合,可以更有效地检测出钓鱼工具,准确率从82%提高到87.2%。
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引用次数: 2
Optimal Energy Management Among Multiple Households with Integrated Shared Energy Storage System (ESS) 基于集成共享储能系统(ESS)的多户能源管理优化
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528536
Md. Morshed Alam, Md. Osman Ali, M. Shahjalal, Byung-deok Chung, Y. Jang
The integration of artificial intelligence with home energy management systems (HEMS) due to the development of advanced metering infrastructure is a promising scheme to improve the usage of renewable energy in a residential application. In the paper, energy management among multiple co-operative households with PV-Storage integrated generation system in a home micro-grid in the presence of short-term prediction of power generation and consumption is studied. In such a home microgrid system, the central energy storage system (C.ESS) is considered that is connected with multiple household and PV panels. The key parameters that are responsible for optimum scheduling of C.ESS are forecasted PV power generation, forecasted household energy consumption, dynamic state of charge (SOC), and base level of energy consumption. In this paper, firstly, the prediction of short-term generation and consumption based on the long short-term memory (LSTM) algorithm is done. Then, this forecasted data is used as the constraint to the control algorithm for optimum scheduling. Therefore, the amount of power that will be supplied from C.ESS is also determined for properly utilizing the stored energy. The simulation results of the proposed scheme show the robustness and effectiveness in the home microgrid environment.
由于先进计量基础设施的发展,人工智能与家庭能源管理系统(HEMS)的集成是一种有前途的方案,可以提高可再生能源在住宅应用中的使用。本文研究了在短期发电量和用电量预测的情况下,家庭微电网中多户光伏-储能联合发电系统的能源管理问题。在这种家庭微电网系统中,中央储能系统(C.ESS)被认为是与多个家庭和光伏板连接。光伏发电预测、家庭用电预测、动态荷电状态(SOC)和基础用电水平是影响光伏发电系统优化调度的关键参数。本文首先对基于长短期记忆(LSTM)算法的短期发电量和用电量进行了预测。然后,将该预测数据作为控制算法的约束,以实现最优调度。因此,为了正确利用储存的能量,也确定了将由cess提供的电量。仿真结果表明了该方案在家庭微网环境下的鲁棒性和有效性。
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引用次数: 0
Residual Frequency offset Estimation Scheme for 5G NR System 5G NR系统剩余频偏估计方案
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528769
Yong-An Jung, Sang-Bong Byun, H. Shin, D. Han, Soo-Hyun Cho, Sung-Hun Lee
The primary synchronization signal (PSS) and secondary synchronization signal (SSS) transmitted in the 5G are used to perform a synchronization procedure. This paper proposes an effective residual frequency offset (RFO) estimation method in a 5G new radio (NR) system. The proposed RFO estimation method applies two branches correlation using PSS and SSS sequence. This paper shows via the simulation results that the inherent property of the PSS and SSS signals is exploited for a robust RFO estimation at various delay spread of wireless environments. It is demonstrated that the proposed RFO estimation scheme is efficient for the 5G NR system.
在5G中传输的主同步信号(PSS)和从同步信号(SSS)用于执行同步过程。提出了一种5G新空口系统中有效的剩余频偏估计方法。提出的RFO估计方法利用PSS序列和SSS序列进行两分支相关。仿真结果表明,利用PSS和SSS信号的固有特性,可以在各种无线环境下进行鲁棒的RFO估计。实验结果表明,所提出的RFO估计方案对于5G NR系统是有效的。
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引用次数: 1
Lidar Upsampling Using HSD Color Space Guided Image 使用HSD色彩空间引导图像的激光雷达上采样
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528800
Sang-Hobn Oh, Soon-Yong Park
This paper proposes a 3D spatial upsampling algorithm using a 2D LiDAR and a single camera. These two devices are placed on the same line, and both data are acquired by rotating the stage 360° around a vertical axis using a step motor. The obtained data is used to calibrate between the LiDAR and the camera. And a high-density 3D map is generated through a proposed two-step upsampling method using HSD-based guide image.
本文提出了一种基于二维激光雷达和单摄像头的三维空间上采样算法。这两个设备放在同一条线上,两个数据都是通过使用步进电机围绕垂直轴旋转360°来获取的。获得的数据用于在激光雷达和相机之间进行校准。利用基于hsd的导图,通过提出的两步上采样方法生成高密度三维地图。
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引用次数: 0
A Weighted Multi-band Algorithm Using Estimation BER in Underwater Acoustic Communication 基于估计误码率的水声通信加权多波段算法
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528650
Ji-Eun Shin, Hyun-Woo Jeong, Jiwon Jeong
The multi-band UWAS communication techniques are effective in terms of performance and throughput efficiency. However, the multi-band configuration in a particular band affects the output from the entire bands. This problem can be solved through a receiving end that analyzes error rates of each band. In this paper, we proposed an estimation BER algorithm which get the reliability of received data to set the weighting value to each band. Therefore, we analyzed the efficiency of multi-band transmission scheme with estimation BER and 3 [dB] performance gain is obtained.
多频段UWAS通信技术在性能和吞吐量效率方面是有效的。但是,特定频段的多频段配置会影响整个频段的输出。这个问题可以通过接收端分析每个频段的错误率来解决。本文提出了一种估计误码率算法,该算法通过获取接收数据的可靠度来设置各波段的权重值。因此,我们分析了估计误码率的多波段传输方案的效率,并获得了3 [dB]的性能增益。
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引用次数: 0
A High Power High Efficient 5.8 GHz CMOS Class-A Power Amplifier for a WPT Application 用于WPT应用的高功率高效率5.8 GHz CMOS A类功率放大器
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528778
Reza E. Rad, Sungjin Kim, B. S. Rikan, Kangyoon Lee
This paper presents a high power and highly efficient 5.8 GHz differential two-stage cascode Class-A Power Amplifier (PA) for a Wireless Power Transfer (WPT) system. The PA is designed in a standard General Purpose (GP) 180 nm CMOS technology. The process does not apply any Radio Frequency (RF) devices such as inductor nor transformer which are essential for an RF design. A full custom-made transformer is proposed and optimized at 5.8 GHz which is modeled using EMX analysis. The proposed transformer shows 1.5 nH and 1.28 nH inductance at the primary and secondary sides of the transformer while their quality factor reaches up to 11.4 and 11 at 5.8 GHz, respectively. Even though reaching higher efficiencies in CMOS processes is more challenging than the GaN processes, the proposed PA has a relatively high Power Added Efficiency (PAE) of 33%. The power gain of the PA is 19.47 dB at 5.8 GHz. The average current consumption of the PA is 144 mA while the power supply is 1.8V.
介绍了一种用于无线传输系统的高功率、高效率5.8 GHz差分级联码a类功率放大器(PA)。PA采用标准通用(GP) 180纳米CMOS技术设计。该过程不适用任何射频(RF)设备,如电感器或变压器,这是射频设计所必需的。提出并优化了5.8 GHz的全定制变压器,并利用EMX分析建模。该变压器的主、次侧电感分别为1.5 nH和1.28 nH,在5.8 GHz频段的质量因数分别达到11.4和11。尽管在CMOS工艺中达到更高的效率比GaN工艺更具挑战性,但所提出的PA具有相对较高的功率附加效率(PAE),为33%。扩音器在5.8 GHz时的功率增益为19.47 dB。当电源为1.8V时,PA的平均电流消耗为144ma。
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引用次数: 2
Performance Analysis of Cell-Free mmWave Massive MIMO with Low-Resolution DAC Quantization 低分辨率DAC量化的无小区毫米波海量MIMO性能分析
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528671
Seung‐Eun Hong
This paper focuses on the downlink of a cell-free massive multiple-input multiple-output (MIMO) system in which each of the access nodes (ANs) is equipped with low-resolution digital-to-analog converters (DACs). In particular, the system is assumed to operate over mmWave frequency with practical considerations such as pilot contamination and hybrid precoding scheme. By mimicking the effect of few-bit DACs with the additive quantization noise model (AQNM), a tight approximate rate expression is derived and analyzed with some simulation results, which provide deep understanding of the impacts from the few-bit quantization as well as channel estimation error on the performance of cell-free mmWave massive MIMO.
本文主要研究无小区大规模多输入多输出(MIMO)系统的下行链路,其中每个接入节点(ANs)都配备了低分辨率数模转换器(dac)。特别是,该系统假定在毫米波频率上工作,并考虑了导频污染和混合预编码方案等实际问题。利用加性量化噪声模型(AQNM)模拟少量dac的影响,推导了一个严密的近似速率表达式,并结合仿真结果进行了分析,深入了解了少量量化和信道估计误差对无小区毫米波大规模MIMO性能的影响。
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引用次数: 0
Deep Learning-Based 3D Printer Fault Detection 基于深度学习的3D打印机故障检测
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528692
Mark Verana, C. I. Nwakanma, Jae-Min Lee, Dong Seong Kim
The development of intelligent manufacturing and 3D printers is rapidly engaging in the industry. However, 3D printers are challenged by occasional anomalies due to leading to failure in 3D performance. In this work, a fault diagnosis based on a convolutional neural network (CNN) for 3D printers is proposed. We have leveraged an online repository of a set of data streams collected from working 3D printers. The CNN was used to process, detect and classify anomalies in 3D printing with appreciable accuracy. The proposed CNN outperformed the support vector machine (SVM), and artificial neural network (ANN) by 5.1% and 25.7%, respectively.
智能制造和3D打印机的发展正在迅速进入这个行业。然而,由于导致3D性能失败,3D打印机偶尔会遇到异常的挑战。本文提出了一种基于卷积神经网络的3D打印机故障诊断方法。我们利用了从3D打印机收集的一组数据流的在线存储库。CNN被用于处理、检测和分类3D打印中的异常,精度相当高。本文提出的CNN比支持向量机(SVM)和人工神经网络(ANN)分别高出5.1%和25.7%。
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引用次数: 11
Comparative Analysis of IEC 62439–3 (HSR) and IEEE 802.1CB (FRER) Standards IEC 62439-3 (HSR)和IEEE 802.1CB (FRER)标准的比较分析
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528562
Duc L. N. Hoang, J. Rhee
Because more and more on-board electronics devices are being used inside such complicated systems as cars or spacecraft, Ethernet-based protocol with high bandwidth capability might be an alternative to legacy low-speed protocols. Many efforts have been made to improve the reliability of Ethernet for safety-critical networks, including the IEC 62439–3 (High-availability Seamless Redundancy) and IEEE 802.1CB (Frame Replication and Elimination for Reliability) standards. This paper is a comparative study of the two aforementioned protocols. Although both achieve seamless redundancy by replicating and sending multiple copies of the same frame over disjointed paths, their characteristics might be suited for different network configurations.
由于越来越多的车载电子设备被用于汽车或航天器等复杂系统,基于以太网的具有高带宽能力的协议可能是传统低速协议的替代方案。为了提高用于安全关键网络的以太网的可靠性,已经做出了许多努力,包括IEC 62439-3(高可用性无缝冗余)和IEEE 802.1CB(可靠性帧复制和消除)标准。本文对上述两种协议进行了比较研究。虽然两者都通过在不连接的路径上复制和发送同一帧的多个副本来实现无缝冗余,但它们的特性可能适合不同的网络配置。
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引用次数: 4
期刊
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)
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