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Multi-Vital Signs System with Sensor Fusion 传感器融合的多生命体征系统
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971588
Chun-Liang Yang, Yi-Chen Lin, Ming-Che Chiang
This paper proposes a scheme with sensor fusion that enhances a traditional multi-vital signs system’s performance by predicting the user’s physiological state. Detecting the user’s carbon dioxide, temperature, and humidity produced by breathing can effectively estimate whether the user has enough resting-state time before conducting measurements. Additionally, when users are not using the system, it can monitor ambient parameters, such as carbon dioxide, temperature, and humidity. More importantly, it can collect the user’s physiological state to achieve the higher performance of an intelligent multi-vital signs system.
本文提出了一种传感器融合方案,通过预测用户的生理状态来提高传统多生命体征系统的性能。检测用户呼吸产生的二氧化碳、温度和湿度,可以有效地估计用户在进行测量之前是否有足够的休息状态时间。此外,当用户不使用该系统时,它可以监测环境参数,如二氧化碳、温度和湿度。更重要的是,它可以收集用户的生理状态,实现智能多生命体征系统的更高性能。
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引用次数: 0
Grid-Forming Inverter Control for Power Sharing Simulation in Microgrid 微电网电力共享仿真成网逆变器控制
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971695
Yu-Jen Liu, Pei-Hao Sun, Po-Yu Hou
For the operation of the microgrid system, it must de-energize from utility grid when the system meets fault conditions due to the safety and stability reasons. Meanwhile, it also need to guarantee continued power supply for the remaining facilities in microgrid. To achieve this operation task, inverters with grid-forming control are considered as mature solutions in recent years. In this paper, a microgrid system with a 30kVA and a 5kVA grid-forming inverters that integrated in two energy storage systems are modelling and the droop control with virtual impedances are designed for the inverters to operate in off-grid state. MATLAB/Simulink is implemented to carry out the power sharing simulation for the validation of the performance of proposed microgrid and grid-forming inverters models.
对于微电网系统的运行来说,出于安全稳定的考虑,当系统出现故障时,必须从电网中退电。同时,还需要保证微电网剩余设施的持续供电。为了实现这一运行任务,具有成网控制的逆变器被认为是近年来较为成熟的解决方案。本文对集成在两个储能系统中的30kVA和5kVA并网逆变器微电网系统进行了建模,并设计了虚拟阻抗下垂控制,使逆变器在离网状态下运行。利用MATLAB/Simulink进行功率共享仿真,验证所提出的微电网和并网逆变器模型的性能。
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引用次数: 0
Bilingual Fake News Detection Algorithm Using Naïve Bayes and Support Vector Machine Models 基于Naïve贝叶斯和支持向量机模型的双语假新闻检测算法
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971596
Paolo Joshua R. Billones, Dailyne D. Macasaet, Shearyl U. Arenas
This study aims to mitigate the absorption of fraudulent news by exploring the feasibility of using Naive Bayes and SGD classifier models in predicting whether the English or Filipino article is real or fake. This is accomplished by training the models through large pre-processed datasets. After evaluation, both models have achieved an accuracy of 93% and 95% accuracy respectively.
本研究旨在通过探索使用朴素贝叶斯和SGD分类器模型预测英语或菲律宾文章是真还是假的可行性,来减轻对虚假新闻的吸收。这是通过通过大型预处理数据集训练模型来完成的。经过评估,两种模型的准确率分别达到93%和95%。
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引用次数: 1
An RF-DC Converter IC for Power Charging Application 一种用于电源充电的RF-DC变换器集成电路
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971475
Pin-You Chen, Bo-Yuan Chen, Chia-Hung Chang, Jheng-Yu Cheng, Syuan-Sou Chen, Meng-Man Yang, Wei-Wen Hu
RF-DC converter integrated circuits (ICs) are presented for RF energy harvesting and power charging. To achieve wide incident RF signal variations that sketch the directing frequency band, an adaptive impedance matching network is used. The proposed RF signal to DC converter is fabricated in 0.18-um CMOS process. The simulated performances present the proposed circuit achieves Peak Power Converting Efficiency (PPCE) of 27% at 0 dBm input power, across 50 k$Omega$ load resistance and 1 pF load capacitance and can provide an output voltage of higher than 2 V.
提出了一种用于射频能量采集和功率充电的RF- dc变换器集成电路。为了实现宽入射射频信号的变化,勾画了指导频带,自适应阻抗匹配网络被使用。所提出的射频信号到直流转换器采用0.18 μ m CMOS工艺制作。仿真结果表明,该电路在0 dBm输入功率下,负载电阻为50 k$Omega$,负载电容为1 pF,峰值功率转换效率(PPCE)为27%,输出电压高于2 V。
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引用次数: 0
Exploiting Discrete Cosine Transform Features in Speech Enhancement Technique FullSubNet+ 利用离散余弦变换特征的语音增强技术FullSubNet+
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971683
Yu-sheng Tsao, Berlin Chen, J. Hung
The highly effective deep learning-based technique FullSubNet+ employs a full-band and sub-band fusion model to fulfill the speech enhancement task. FullSubNet+ exploits the short-time magnitude spectrogram, real-and imaginary parts of the complex-valued spectrogram to learn the deep neural network that mainly comprises multi-scale time-sensitive channel attention (MulCA) modules and stacked temporal convolution network (TCN) blocks. To capture the phase information of input time-domain signals more simply, we propose using the short-time DCT-based spectrogram as an alternative for the real and imaginary spectrograms to be an input source to learn the FullSubNet+ framework. The preliminary experiments conducted with the VoiceBank-DEMAND task indicate that exploiting STDCT spectrograms in FullSubNet+ achieves higher objective speech quality and intelligibility in terms of PESQ and STOI metric scores, respectively, for the test set compared with the original FullSubNet+ arrangement. In addition, the STDCT-wise FullSubNet+ obtains a real-time factor (RTF) of 0.229, lower than 0.260, the RTF for the original FullSubNet+.
基于深度学习的高效技术FullSubNet+采用全带和子带融合模型来完成语音增强任务。FullSubNet+利用短时幅度谱图、复值谱图的实部和虚部来学习主要由多尺度时敏信道注意(MulCA)模块和堆叠时间卷积网络(TCN)模块组成的深度神经网络。为了更简单地捕获输入时域信号的相位信息,我们建议使用基于短时dct的频谱图作为替代实谱图和虚谱图的输入源来学习FullSubNet+框架。VoiceBank-DEMAND任务的初步实验表明,与原始的FullSubNet+安排相比,在FullSubNet+中利用STDCT频谱图分别在PESQ和STOI度量分数方面获得了更高的客观语音质量和可理解性。此外,STDCT-wise FullSubNet+的实时因子RTF (real-time factor)为0.229,低于原始FullSubNet+的RTF 0.260。
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引用次数: 0
A 5.2 GHz Differential Down Conversion Mixer Design 5.2 GHz差分下变频混频器设计
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971575
Jia-Min Chiau, Min‐Hua Ho, W. Lai
This letter presents a 5.2 GHz differential mixer design that uses MOS switch and differential CS amplifier. The fully integrated mixer is fabricated by the tsmc 0. 1S$mu$m BiCMOS process with its IIP3 of -13dBm, conversion gain of 15 dB, and the radio frequency (RF) and local oscillator (LO) to an intermediate frequency (IF) isolation of 15S and 139 dB, respectively. Overall chipset consumes 30. SmW with a supply voltage of 1.SV.
本文介绍了一种采用MOS开关和差分CS放大器的5.2 GHz差动混频器设计。完全集成的混合器由台积电制造。bimos工艺的IIP3为-13dBm,转换增益为15db,射频(RF)和本振(LO)到中频(IF)的隔离度分别为15S和139db。整个芯片组消耗30。电源电压为1 sv的SmW。
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引用次数: 0
Magnitude to Digital Converter with Latch-Type Comparator and Dynamic Switching Current Scheme 具有锁存式比较器和动态开关电流方案的幅值到数字转换器
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971646
Hsin-Liang Chen, Yin-Qin Ye, Jen-Shiun Chiang
A magnitude to digital converter is proposed using a latch-type comparator to replace the conventional opamp-based comparator. The PVT-dependent timing error can be relieved by employing the latch-type comparator and rearranging the decision control circuits. Besides, the power efficiency can be improved within the low and high speed operations. For increasing the linearity of the converting process, a dynamic current source is also developed to obtain the best coefficient of determination. A prototype of 10-bit converter was designed to operate at 40-kS/s with only 56.S-$mu$W of power dissipations, respectively.
提出了一种用锁存式比较器代替传统的基于运放的比较器的幅度-数字转换器。通过采用锁存式比较器和重新排列决策控制电路,可以消除pvt相关的定时误差。此外,在低速和高速运行时,可以提高功率效率。为了提高转换过程的线性度,还开发了动态电流源,以获得最佳的决定系数。一个10位转换器的原型被设计为工作在40-kS/s,只有56。S-$mu$W分别表示功耗。
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引用次数: 0
Systematic and Flexible Genetic-Algorithm-Based Feature Reduction for Decision Tree ML-Validation 基于遗传算法的决策树ml验证系统灵活特征约简
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971631
Xin-Yu Shih, Yao Lu
In this paper, we propose a systematic genetic-algorithm-based feature reduction method. It has a high design flexibility based on 5-tuple parameter adjustment. The users can decide these 5 parameters to satisfy the demands of making the focus on accuracy or reduced feature amount. The proposed algorithm is verified by decision-tree models with different data sets. As for the data set, ala, the number of features is reduced from 123 to 53 while the accuracy performance has an increase of 4.2%. In addition, for other data sets, the maximum accuracy loss is no more than 3.1% while the feature reduction ratio achieves 41.9%. Its advantage is to provide a design trade-off between accuracy and reduced feature amount.
本文提出了一种系统的基于遗传算法的特征约简方法。它具有基于5元组参数调整的高设计灵活性。用户可以自行决定这5个参数,以满足关注精度或减少特征量的需求。采用不同数据集的决策树模型对算法进行了验证。对于数据集,ala,特征数量从123个减少到53个,而准确率性能提高了4.2%。此外,对于其他数据集,最大准确率损失不超过3.1%,特征约简率达到41.9%。它的优点是提供了精度和减少特征量之间的设计权衡。
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引用次数: 0
Solar Photovoltaic Power Generation Prediction based on Deep Learning Methods 基于深度学习方法的太阳能光伏发电预测
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971676
Mu-Yen Chen, Hsiu-Sen Chiang, Chih-Yung Chang
In recent years, renewable energy power generation has received more and more attention. Since the forecast of electricity generation is helpful for properly using and managing electricity. Therefore, this study uses time series analysis and deep learning methods, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU), to forecast solar power generation. Furthermore, this study also uses different time intervals (every ten minutes, every thirty minutes, hourly, daily) to forecast the power generation and evaluate their performances. In comparing the four deep learning models, the prediction performance of LSTM is the best, while the performance of the TCN model is poor. In addition, the time interval length greatly influences the prediction performance. The time interval is divided into smaller, and the performance of various deep learning models is relatively good and stable; otherwise, the performance of the models is poor.
近年来,可再生能源发电受到越来越多的关注。因为发电量预测有助于合理用电和管理。因此,本研究使用时间序列分析和深度学习方法、长短期记忆(LSTM)、时间卷积网络(TCN)和门控循环单元(GRU)来预测太阳能发电。此外,本研究还采用不同的时间间隔(每十分钟、每三十分钟、每小时、每天)来预测发电量并评估其性能。对比四种深度学习模型,LSTM的预测性能最好,而TCN模型的预测性能较差。此外,时间间隔长度对预测性能影响很大。时间间隔被划分得更小,各种深度学习模型的性能相对较好且稳定;否则,模型的性能很差。
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引用次数: 0
Design of a Low-power Compass FIR Filter for Electrocardiogram Signals Noise Removal 用于心电图信号去噪的低功耗罗经FIR滤波器设计
IF 1.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-10-14 DOI: 10.1109/IET-ICETA56553.2022.9971525
Kuang-Hao Lin, Guan-Zhou Lai, Bowen Peng, Ping-Seng Tseng
In recent years, electrocardiogram measuring instruments have been developing towards miniaturization, which has also promoted the development of miniaturization of circuits and low power consumption. FIR filters are suitable for filtering out noise and revealing the obvious characteristic waveforms in an electrocardiogram, but they have high power consumption. Therefore, under the premise of maintaining stable performance and the advantages of low power consumption, this paper proposed a new architecture named the compass form FIR filter. When the high order number M = 256, the power consumption of the linear phase form was 28.2112 mW, while the power consumption of the compass form FIR was 24.0749 mW. The power consumption of the compass FIR was lower than that of the linear phase form FIR, with a difference of 4.1363 mW. Therefore, the compass form FIR filter designed in this paper had the advantages of low power consumption and high filtering performance under set conditions and high order specifications.
近年来,心电图测量仪器向着小型化方向发展,这也推动了电路小型化和低功耗的发展。FIR滤波器适用于滤除噪声和显示心电图中明显的特征波形,但其功耗较高。因此,在保持性能稳定和低功耗优点的前提下,本文提出了一种新的结构,命名为罗盘型FIR滤波器。当高阶数M = 256时,线性相位形式的功耗为28.2112 mW,而罗盘形式的FIR功耗为24.0749 mW。罗盘型FIR的功耗比线性相位型FIR低,相差4.1363 mW。因此,本文设计的圆规型FIR滤波器具有功耗低、在设定条件下、高阶规格下滤波性能好的优点。
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引用次数: 0
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IET Networks
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