2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)最新文献

Design and Development of 4-DOF Robot Manipulator with Intermediate Link Motions for Cycle Time Reduction in Automated Production 面向自动化生产周期缩短的四自由度中间连杆机器人机械手的设计与开发

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188219

Md Al Fayshal bin Shiraj Shuvo, Riady Siswoyo Jo, H. S. Jo

引用次数: 1

Active Vibration Control of an Inertia-type Piezoelectric Actuator based Suspended Handle using PID-AFC Controller 基于PID-AFC控制器的悬柄惯性型压电作动器振动主动控制

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188127

Cheah Cheng Theik, Ahmad Zhafran Ahmad Mazlan

引用次数: 2

Low Cost Extended Wireless Network Using Raspberry Pi 3B+ 使用树莓派3B+的低成本扩展无线网络

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188215

N. S. Ismail, N. A. Rashid, N. A. Zakaria, Z Ismail Khan, A. R. Mahmud

引用次数: 0

Overview of Machine Learning Approaches Applied in Disease Profiling 机器学习方法在疾病分析中的应用概述

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188140

Ricardo Buettner, T. Kuri, Andreas Feist, Jannik Hudak

引用次数: 1

RFID Tracking and Localization Technologies in Healthcare 医疗保健中的RFID跟踪和定位技术

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188076

Ricardo Buettner, Timon Clauß, Minh Thuan Huynh, D. Koser

引用次数: 3

A Hybrid Chaotic Particle Swarm Optimization with Differential Evolution for feature selection 基于差分进化的混合混沌粒子群特征选择算法

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188198

S. Ajibade, Norhawati Binti Ahmad, A. Zainal

{"title":"A Hybrid Chaotic Particle Swarm Optimization with Differential Evolution for feature selection","authors":"S. Ajibade, Norhawati Binti Ahmad, A. Zainal","doi":"10.1109/ISIEA49364.2020.9188198","DOIUrl":"https://doi.org/10.1109/ISIEA49364.2020.9188198","url":null,"abstract":"The selection of feature subsets has been broadly utilized in data mining and machine learning tasks to produce a solution with a small number of features which improves the classifier's accuracy and it also aims to reduce the dataset dimensionality while still sustaining high classification performance. Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. Particle Swarm Optimization (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, since feature selection is a challenging task with a complex search space, PSO has problems with pre-mature convergence and easily gets trapped at local optimum solutions. Hence, the need to balance the search behaviour between exploitation and exploration. In our previous work, a novel chaotic dynamic weight particle swarm optimization (CHPSO) in which a chaotic map and dynamic weight was introduced to improve the search process of PSO for feature selection. Therefore, this paper improved on CHPSO by introducing a hybrid of chaotic particle swarm optimization and differential evolution known as CHPSODE. The search accuracy and performance of the proposed (CHPSODE) algorithms was evaluated on eight commonly used classical benchmark functions. The experimental results showed that the CHPSODE achieves good results in discovering a realistic solution for solving a feature selection problem by balancing the exploration and exploitation search process and as such has proven to be a reliable and efficient metaheuristics algorithm for feature selection.","PeriodicalId":120582,"journal":{"name":"2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Identification of seizure from single channel EEG using Support Vector Machine & Hilbert Vibration Decomposition 基于支持向量机和希尔伯特振动分解的单通道脑电图癫痫识别

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188137

A. Z. Karim, Shikder Shafiul Bashar, Md. Sazal Miah, Md. Abdullah Al Mahmud, M. A. Al Amin

{"title":"Identification of seizure from single channel EEG using Support Vector Machine & Hilbert Vibration Decomposition","authors":"A. Z. Karim, Shikder Shafiul Bashar, Md. Sazal Miah, Md. Abdullah Al Mahmud, M. A. Al Amin","doi":"10.1109/ISIEA49364.2020.9188137","DOIUrl":"https://doi.org/10.1109/ISIEA49364.2020.9188137","url":null,"abstract":"A well-known neurological brain dysfunction named epilepsy which is caused by recrudescent seizures. Because of higher temporal resolution, brain activities measured by electroencephalography (EEG) are usually utilized for confinement of seizures and distinguishing proof of epileptic dysfunctions. Detection of EEG seizures by using traditional Fourier-based methods and manual interpretation is tedious and challenging because of non-linear and non-stationary dynamics of EEG. In our research, at first, we have done robust statistical analysis to detect and classify the seizure and nonseizure. But, the result was not accurate enough to detect and classify seizure effectively. For identification of ordinary and epileptic EEG measurement, we approached a novel algorithm based on Hilbert vibration decomposition (HVD). HVD accomplishes Hilbert transform demonstration of instantaneous frequency and bring outs mono components that have particular time-differing amplitudes and instantaneous frequencies from non-stationary signals. Least squares support vector machine (LS-SVM) is used for identifying epileptic seizures in this research. In addition, it is attracting for real-time physiological signal processing applications because of its lower mathematical complexity. The classification accuracy of 97.66% was attained on a test, which was conducted on a benchmark EEG data set. In addition, area of 0.9914 under the receiver operating characteristics (ROC) curve utilizing the delta, theta & alpha rhythms.","PeriodicalId":120582,"journal":{"name":"2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129417718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Spectral Analysis of EEG Signals obtained during Mirror Therapy and Imagined Hand Movements 在镜像治疗和想象手部运动中获得的脑电图信号的频谱分析

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188071

A. A. Rahim, W. Mansor, D. P. Morawakage, F. H. Kamaru Zaman, N. Jaafar, A. Z. Che Daud, N. F. Ahmad Roslan

引用次数: 1

A Review of Virtual Reality and Augmented Reality Literature in Healthcare 虚拟现实和增强现实在医疗保健方面的文献综述

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188211

Ricardo Buettner, H. Baumgartl, Tim Konle, Patrick Haag

引用次数: 5

A Lightweight Deep Learning Model for COVID-19 Detection 新型冠状病毒检测的轻量级深度学习模型

2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2020-07-01 DOI: 10.1109/ISIEA49364.2020.9188133

Siti Raihanah Abdani, M. A. Zulkifley, Nuraisyah Hani Zulkifley

{"title":"A Lightweight Deep Learning Model for COVID-19 Detection","authors":"Siti Raihanah Abdani, M. A. Zulkifley, Nuraisyah Hani Zulkifley","doi":"10.1109/ISIEA49364.2020.9188133","DOIUrl":"https://doi.org/10.1109/ISIEA49364.2020.9188133","url":null,"abstract":"COVID-19 is a contagious disease that has caused more than 230,000 deaths worldwide at the end of April 2020. Within a span of just a few months, it has infected more than 4 million peoples across the globe due to its high transmittance rate. Thus, many governments have tried their best to increase the diagnostic capability of their hospitals so that the disease can be identified as early as possible. However, in most cases, the results only come back after a day or two, which directly increases the possibility of disease spreadness because of the delayed diagnosis. Therefore, a fast screening method using existing tools such as x-ray and computerized tomography scans can help alleviate the burden of mass diagnosis tests. A chest x-ray is one of the best modalities in diagnosing a pneumonia symptom, which is the primary symptom for COVID-19. Hence, this paper proposes a lightweight deep learning model to screen the possibility of COVID-19 accurately. A lightweight model is important, as such it allows the model to be deployed on various platforms that include mobile phones, tablets, and normal computers without worrying about the memory storage capacity. The proposed model is based on 14 layers of convolutional neural network with a modified spatial pyramid pooling module. The multiscale ability of the proposed network allows it to identify the COVID-19 disease for various severity levels. According to the performance results, the proposed SPP-COVID-Net achieves the best mean accuracy of 0.946 with the lowest standard deviation among the training folds accuracy. It comprises of just 862,331 total number of parameters, which uses less than 4 MegaBytes memory storage. The model is suitable to be implemented for fast screening purposes so that better-targeted diagnoses can be performed to optimize the test time and cost.","PeriodicalId":120582,"journal":{"name":"2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124912674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26