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2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)最新文献

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Electromyography and Speech Controlled Prototype Robotic Car using CNN Based Classifier for EMG 基于CNN分类器的肌电图与语音控制原型机器人车
Zahid Ul Hassan, Nouman Bashir, Afaq Iltaf
Wearable electronic equipment is continually improving and becoming more integrated with technology for prosthesis control. These devices, which come in a variety of shapes and sizes, can detect, quantify, and perhaps use signals generated by the human body's physiological and muscular changes to control machinery. One such gadget, the MYO gesture/arm band, collects information from our forearm in the form of electromyographic (EMG) Signal, which is based on the measurement of small electrical impulses caused by ion exchange between muscle membranes, utilize these myoelectric impulses and converts them into input signals by using pre-defined motions. There is a range of tasks that may be carried out with this device and use of this device can give better results in a combination with another controlling modality. This paper addresses the use of several input modalities, including speech and myoelectric signals recorded through microphone and MYO band, respectively to control a robotic car. Hand gestures are used to control the car through MYO armband. The complete process is done by using Raspberry Pi. Classification of EMG signals is done by using Convolution Neural Network (CNN) classifier. Experimental results obtained as well as their accuracies for performance analysis are also presented.
可穿戴电子设备正在不断改进,并与假肢控制技术越来越融合。这些设备有各种形状和大小,可以检测、量化,也许还可以利用人体生理和肌肉变化产生的信号来控制机器。一个这样的小工具,MYO手势/臂带,以肌电图(EMG)信号的形式从我们的前臂收集信息,这是基于测量肌肉膜之间离子交换引起的小电脉冲,利用这些肌电脉冲,并通过使用预先定义的动作将其转换为输入信号。使用该装置可执行一系列任务,并且与另一种控制方式结合使用该装置可获得更好的结果。本文介绍了几种输入方式的使用,包括通过麦克风和MYO波段记录的语音和肌电信号,分别用于控制机器人汽车。手势是用来控制汽车通过MYO臂章。完整的过程是通过使用树莓派完成的。采用卷积神经网络(CNN)分类器对肌电信号进行分类。并给出了实验结果及其性能分析的准确性。
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引用次数: 0
Whispering gallery mode resonators: An alternate platform for Ti:sapphire lasers and amplifiers 窃窃私语画廊模式谐振器:Ti:蓝宝石激光器和放大器的替代平台
Farhan Azeem, L. Trainor, Ang Gao, Maya Isarov, D. Strekalov, H. Schwefel
Titanium doped sapphire (Ti:sapphire) is a ubiquitous gain medium. Due to its broadband gain it has been in use in the solid state laser industry for over three decades while still going strong. We recently demonstrated a Ti:sapphire laser using the whispering gallery mode (WGM) resonator platform. Here, we review some of our previous work, shedding light on the theoretical and experimental lasing threshold of this first high quality Ti:sapphire WGM laser and review amplification with this new platform as well. We also report on new results of multi-mode lasing observed with this system. These results can potentially be utilised in future to train neural networks to control the experimental parameters used to observe lasing.
掺钛蓝宝石(Ti:sapphire)是一种普遍存在的增益介质。由于其宽带增益,它已经在固体激光工业中使用了三十多年,同时仍然很强大。我们最近展示了使用窃窃私语画廊模式(WGM)谐振器平台的Ti:蓝宝石激光器。在这里,我们回顾了我们之前的一些工作,揭示了第一个高质量Ti:蓝宝石WGM激光器的理论和实验激光阈值,并回顾了这个新平台的放大。我们还报道了用该系统观测到的多模激光的新结果。这些结果有可能在未来用于训练神经网络来控制用于观察激光的实验参数。
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引用次数: 0
Classification of White Blood Cells Subtype Using MCNN 白细胞亚型的MCNN分类
Muhammad Sufyan Arshad, Jawad Arif
White Blood cells are building blocks of the immune system of humans as they fight different types of infections, which is vital for healthy recovery. Changes in the number of White Blood Cell subtypes (WBCs) rule out certain diseases such as infection, heart disease and diabetes in medical practices. Conventional methods of counting the number of WBCs are dependent on manual testing and have chances of human error and the automated method apparatus is very costly. Thus the classification of White Blood Cell subtypes is of vital importance. In this study, CV based solution is proposed for White Blood Cell subtype identification. Different MCNN-based models along with transfer learning-based models (VGG16 & Resnet50) are trained and implemented for performance comparison and the effect of different training parameters on the performance of the models is also explored in the study. It was observed that changing the training parameters also affects the accuracy of the model. The highest accuracy of 96.6% was achieved using the MCNN-based model for the classification of White Blood Cells.
白细胞是人类免疫系统的基石,因为它们对抗不同类型的感染,这对健康恢复至关重要。在医疗实践中,白细胞亚型(wbc)数量的变化排除了某些疾病,如感染、心脏病和糖尿病。传统的白细胞计数方法依赖于人工测试,有可能出现人为错误,而且自动化的方法设备非常昂贵。因此,白细胞亚型的分类是至关重要的。本研究提出了基于CV的白细胞亚型鉴定方法。本文训练并实现了不同的基于mcnn的模型以及基于迁移学习的模型(VGG16和Resnet50)进行性能比较,并探讨了不同训练参数对模型性能的影响。观察到,改变训练参数也会影响模型的准确性。使用基于mcnn的白细胞分类模型达到了96.6%的最高准确率。
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引用次数: 0
Controlling Tunneling of Atoms Through aHigh-Quality Cavity Via an External Driving Field 利用外部驱动场控制原子在高质量空腔中的隧穿
Fazal Badshah, Qing He, Zeyun Shi, Haiyang Zhang, Jin Xie, Rahmatullah, M. Yousaf, Muqaddar Abbas
In high quality micromaser cavities, we investigate the tunnelling and traversal of ultra-cold A-type three-level atoms. We specifically discuss how a coherent driving field affects the tunnelling atoms' traversal behaviour. Phase time, which is shown to be a suitable measure of atoms' transit time through a cavity, is affected by driving induced atomic coherence. For example, atomic coherence leads to negative phase times for atomic transmission on both the excited and ground levels. The phase tunnelling time also exhibits alternate subclassical and superclassical traversal tendencies depending on the driving field value as atomic momentum increases.
在高质量微脉泽腔中,研究了超冷a型三能级原子的隧穿和穿越。我们具体讨论了相干驱动场如何影响隧穿原子的遍历行为。相时间是原子通过空腔时的一个合适的度量,它受驱动诱导原子相干性的影响。例如,原子相干性导致原子在激发态和地能级上传输的相位时间为负。随着原子动量的增加,随着驱动场值的增加,相隧穿时间也表现出亚经典和超经典交替穿越的趋势。
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引用次数: 0
A Study on the Design and Analysis of a Bidirectional IPT System for EV wireless charging by Using Switch-Controlled Capacitor 基于开关控制电容的电动汽车无线充电双向IPT系统设计与分析
Tahir Hafeez, M. Numan, Akif Zia, H. A. Qureshi, Hasaan Farooq
For loosely coupled transformers, the leakage and magnetizing inductances vary due to the changing positions for the primary and the secondary sides of the transformer windings in inductive power transfer (IPT) systems. For reducing the effects of these varying inductances, various compensation networks have been proposed. These compensation networks are used to obtain a unity power factor and constant output voltage for power electronic applications. However, most of these compensation networks have a fixed compensation network designed for only a specific coupling coefficient. Therefore, a compensation network with variable components needs to be implemented to compensate for the changing inductances of the network. In this paper switched capacitor-based compensation network is proposed to match the resonant frequency of the network with the switching frequency of the converter. The proposed network compensates the magnetizing inductance for coupling coefficient in the range of 0.23 to 0,35 using a switched capacitor. Moreover, the proposed converter is symmetrical so that bidirectional power flow is possible while maintaining constant output voltage and unity power factor under zero voltage switching condition (ZVS).
对于松散耦合的变压器,在感应电力传输(IPT)系统中,由于变压器绕组一次侧和二次侧位置的变化,漏电电感和磁化电感会发生变化。为了减少这些电感变化的影响,已经提出了各种补偿网络。这些补偿网络用于电力电子应用中获得统一的功率因数和恒定的输出电压。然而,这些补偿网络大多只针对特定的耦合系数设计固定的补偿网络。因此,需要实现一个可变分量的补偿网络来补偿网络电感的变化。本文提出了一种基于开关电容的补偿网络,使网络的谐振频率与变换器的开关频率相匹配。该网络利用开关电容对耦合系数在0.23 ~ 0.35范围内的磁化电感进行补偿。此外,所提出的变换器是对称的,因此在零电压开关条件下,可以在保持恒定输出电压和单位功率因数的情况下实现双向潮流。
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引用次数: 0
Beetle Bee Algorithm Applied to Trajectory Tracking Control of OMNI Manipulator 甲虫蜂算法在OMNI机械臂轨迹跟踪控制中的应用
Xu Zhang, J. Gu, M. Asad, U. Farooq, G. Abbas
This paper proposed an improved beetle bee algorithm and applied it to the trajectory tracking control of the OMNI manipulator. A metaheuristic algorithm mimics the beetle's excellent nature of food foraging in an unknown environment by their two antennas, and based on the intensity of smell, beetles decide to move left or right until they reach the final desired location. The convergence speed for a typical Beetle Antennae Search (BAS) is not fast enough, which is time-consuming, especially when dealing with higher dimensional systems. This proposed Improved Beetle Bee algorithm overcomes this problem by adding the square in angular velocities in the objective function. Finally, the simulation results will be compared between the proposed and state-of-the-art metaheuristic algorithms.
提出了一种改进的甲虫蜂算法,并将其应用于OMNI机械手的轨迹跟踪控制中。一种元启发式算法通过甲虫的两根触角模仿甲虫在未知环境中觅食的优良特性,并根据气味的强度决定向左或向右移动,直到它们到达最终想要的位置。典型的甲虫天线搜索(BAS)收敛速度不够快,特别是在处理高维系统时,耗时较长。提出的改进甲虫蜂算法通过在目标函数中加入角速度的平方来克服这一问题。最后,仿真结果将比较所提出的和最先进的元启发式算法。
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引用次数: 0
State-of-the-Art Design Optimization of an IPM Synchronous Motor for Electric Vehicle Applications 电动汽车用IPM同步电机的优化设计
M. U. Sardar, Dou Manfeng, Umar Saleem, Mannan Hassan, Muhammad Kashif Nawaz
Nowadays, vehicles based on electric charge and operation due to the motor are more prevalent in use due to their superiority in zero-emission, lowest noise, higher power density to size or weight ratio, and higher performance. In this scenario, synchronous machines using permanent interior magnets (IPMSM) are a suitable candidate and a potential source of mechanical power generation in electric vehicles (EVs). They have unique merits over the other types of electric motor families. A rare earth permanent magnet material with an interior V-type rotor is used as a model motor, and its optimal design shows higher performance characteristics. This research paper presents and validates an optimally designed state-of-the-art IPMSM motor, which gives higher torque density, lower torque ripples, efficiency of the drive, lower magnet volume, and higher power factor. The design input parameters include magnet thickness, width, and pole V-angle. Using the initial model of a 60kW PMSM motor, the results are generated through FEA analysis, optimization with OptiSlang, and performance is validated with an input of urban drive cycle and operation in the wide speed range.
目前,由于零排放、噪音最低、功率密度与尺寸或重量比更高、性能更高等优点,以电动机为基础的充电和运行车辆在使用中更为普遍。在这种情况下,使用永久内磁铁(IPMSM)的同步电机是一种合适的候选者,也是电动汽车(ev)机械发电的潜在来源。与其他类型的电动机家族相比,它们具有独特的优点。采用带内v型转子的稀土永磁材料作为模型电机,其优化设计显示出更高的性能特点。本文提出并验证了一种优化设计的最先进的IPMSM电机,该电机具有更高的转矩密度,更低的转矩波动,驱动效率,更小的磁体体积和更高的功率因数。设计输入参数包括磁体厚度、宽度和磁极v角。采用60kW永磁同步电机初始模型,通过FEA分析、opti俚语优化得出结果,并通过城市驱动循环输入和宽速度范围内的运行验证了性能。
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引用次数: 1
Modelling and Optimal Control of Human Voluntary Motion in 3D for Bipedal 双足人体自主运动的三维建模与优化控制
Badar Ali, A. Mughal
Biomechanical modelling in three dimensions of human voluntary motion with motor control is an extremely important field that consists of human intended behaviors. The human body demonstrates extremely complicated motion trajectories with a very high level of mobility and degree of freedom (DOF). In this research, we have extended our 3D biomechanical model research to develop the optimal motor controls that exhibit biomechanical schemes for human sit to stand (STS) motion. The developed three modelling schemes are realized to analyze the motion constraints on rigid body model of human STS motion. Model developed in CAD software SOLIDWORKS Corp. comprising of a 3D 8-segment biped having 2 feet, 2 calf, 2 thigh, a pelvic and a HAT segment is utilized to generate the LQR based optimal control on the developed reference trajectories of each joint. Model having one foot fix and other a 1DOF prismatic joint is utilized for controller development due to its full rank controllability and observability. The optimal control is developed in MATLAB / SIMULINK after linearizing the model in SIMSCAPE / SIMULINK by importing the xml files from SOLIDWORKS. Control system utilized the feedback of position and speed of each joint and generates the torque inputs for the model based on the required reference trajectories. The developed model is of 22nd order and the results show that all the motor joints followed the reference trajectories.
具有运动控制的人体自主运动的三维生物力学建模是一个非常重要的领域,它包含了人类的预期行为。人体表现出极其复杂的运动轨迹,具有非常高的机动性和自由度(DOF)。在这项研究中,我们扩展了我们的3D生物力学模型研究,以开发最佳的运动控制,展示人类坐立(STS)运动的生物力学方案。实现了三种建模方案,分析了人体STS运动刚体模型的运动约束。在CAD软件SOLIDWORKS Corp.中开发的模型由一个3D 8节双足动物(2足、2小腿、2大腿、1骨盆和1 HAT节)组成,用于根据所开发的每个关节的参考轨迹生成基于LQR的最优控制。由于该模型具有全秩可控性和可观测性,因此采用一个足部固定和另一个1自由度移动关节的模型进行控制器开发。通过从SOLIDWORKS中导入xml文件,在SIMSCAPE / SIMULINK中对模型进行线性化,然后在MATLAB / SIMULINK中开发最优控制。控制系统利用每个关节的位置和速度反馈,根据所需的参考轨迹为模型生成扭矩输入。所建立的模型是22阶的,结果表明所有的电机关节都遵循参考轨迹。
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引用次数: 0
Cyberbullying Detection in Urdu Language Using Machine Learning 基于机器学习的乌尔都语网络欺凌检测
Sara Khan, Amna Qureshi
Cyberbullying has become a significant problem with the surge in the use of social media. The most basic way to prevent cyberbullying on these social media platforms is to identify and remove offensive comments. However, it is hard for humans to read and remove all the comments manually. Current research work focuses on using machine learning to detect and eliminate cyberbullying. Although most of the work has been conducted on English texts to detect cyberbullying, limited to no work can be found in Urdu. This paper aims to detect cyberbullying from the users' comments posted in Urdu on Twitter using machine learning and Natural Language Processing (NLP) techniques. To the best of our knowledge, cyberbullying detection on Urdu text comments has not been performed due to the lack of a publicly available standard Urdu dataset. In this paper, we created a dataset of offensive user-generated Urdu comments from Twitter. The comments in the dataset are classified into five categories. n-gram techniques are used to extract features at character and word levels. Various supervised machine-learning techniques are applied to the dataset to detect cyberbullying. Evaluation metrics such as precision, recall, accuracy and F1 scores are used to analyse the performance of machine learning techniques.
随着社交媒体使用的激增,网络欺凌已成为一个重大问题。防止这些社交媒体平台上的网络欺凌最基本的方法是识别和删除攻击性评论。然而,人类很难手动阅读和删除所有评论。目前的研究工作集中在使用机器学习来检测和消除网络欺凌。虽然大部分工作都是在英语文本上进行的,以检测网络欺凌,但乌尔都语的工作几乎没有。本文旨在利用机器学习和自然语言处理(NLP)技术,从Twitter上乌尔都语用户的评论中检测网络欺凌。据我们所知,由于缺乏公开可用的标准乌尔都语数据集,还没有对乌尔都语文本评论进行网络欺凌检测。在本文中,我们创建了一个来自Twitter的攻击性乌尔都语评论数据集。数据集中的评论分为五类。N-gram技术用于提取字符和单词级别的特征。各种监督机器学习技术被应用于数据集以检测网络欺凌。诸如精度、召回率、准确性和F1分数等评估指标用于分析机器学习技术的性能。
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引用次数: 0
The FE Approach for Data Cleaning of Phase Measurement Units 相位测量单元数据清洗的有限元方法
M. Yousaf, Muhammad Ahmad Khan, M. F. Tahir, Chen Zhichu, Fazal Badshah, S. Khalid
With the rising use of Phase Measurement Units (PMUs) in smart grid applications, it is important for PMUs to function in extreme circumstances, resulting in outliers and missing dataset. Traditional approaches take an inordinate amount of time to clear outliers and fill missing data to assure better accuracy. This study offers a flexible ensemble approach (FEA) to construct a precise, rapid, and sustainable data cleaning procedure with Apache Spark. To discover outliers in the suggested system, an ensemble model based on a soft voting technique employs PCA in combination with the K-means, GMM, and iForest approach. The suggested method fills the data with an improved gradient-boosting decision tree for each obtained PMUs characteristic after outlier detection. The test results demonstrate that the proposed model acquired good accuracy during comparing with LOF and DBSCAN techniques. To evaluate the suggested technique's data-filling outcomes against modern methods such as decision tree and linear regression techniques, the MAE and RMSE criteria are applied.
随着相位测量单元(pmu)在智能电网应用中的使用越来越多,pmu在极端情况下发挥作用非常重要,这会导致异常值和缺失数据集。传统的方法需要花费大量的时间来清除异常值和填充缺失的数据,以确保更好的准确性。本研究提供了一种灵活的集成方法(FEA)来构建一个精确、快速和可持续的Apache Spark数据清理过程。为了发现建议系统中的异常值,基于软投票技术的集成模型将PCA与K-means、GMM和ifforest方法相结合。该方法利用一种改进的梯度增强决策树来填充数据,该决策树是通过离群值检测得到的每个pmu特征。测试结果表明,与LOF和DBSCAN技术相比,该模型具有较好的精度。为了评估建议的技术的数据填充结果与现代方法,如决策树和线性回归技术,应用了MAE和RMSE标准。
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引用次数: 0
期刊
2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)
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