2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)最新文献_第3页

IoT Assisted Real Time PPG Monitoring System for Health Care Application 物联网辅助医疗保健实时PPG监测系统

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362852

Subhajit Bhowmick, P. Kundu, D. D. Mandal

{"title":"IoT Assisted Real Time PPG Monitoring System for Health Care Application","authors":"Subhajit Bhowmick, P. Kundu, D. D. Mandal","doi":"10.1109/CMI50323.2021.9362852","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362852","url":null,"abstract":"Photoplethysmography is an important area that measures heart rate and its variability for clinical diagnosis of cardiac illness and oxygen saturation level in blood. Nowadays biomedical signal transmission through IoT cloud provides an additional benefit in health monitoring especially for ailing senior citizens who are remotely located. The process of bioelectric signal transmission takes place at a very slow sampling rate. In the present work, a prototype system is proposed for PPG monitoring using Internet-of-Things (IoT). PPG data are captured by a reflectance-type PPG sensor with an embedded controller over a measured interval of time. PPG waveforms are then modeled using either Fourier or Gaussian method, the model parameters thus obtained are truly representing the sampled PPG Data. The computed model coefficients are then transmitted to the IoT cloud server (e.g. Dropbox) with WiFi connectivity. At the remote end, provision is made to access these model parameters from the cloud server and reconstructing the PPG waveform. The performance of the reconstruction process is evaluated by calculating mean square error (MSE) and percentage root mean squared difference (PRD). Experiments were performed on ten volunteers of different ages in order to assess the reliability of the entire method. Experimental results reveal the ruggedness of the proposed method, which can supplement the clinical diagnosis in cardiac ailments and facilitate the treatment of rural patients from any urban location through expert physicians.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632511","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: 6

Multivariate Analysis of Formalin Using UV-Vis Spectroscopy 紫外可见光谱法分析福尔马林的含量

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362970

S. Nag, D. Das, B. Tudu, R. B. Roy

{"title":"Multivariate Analysis of Formalin Using UV-Vis Spectroscopy","authors":"S. Nag, D. Das, B. Tudu, R. B. Roy","doi":"10.1109/CMI50323.2021.9362970","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362970","url":null,"abstract":"An investigation on formaldehyde detection using UV-Vis spectrophotometer has been explored in this work. A 37 % aqueous solution of formaldehyde known as formalin is categorized as potential carcinogen and a threat to human health. Formalin is a widely used food adulterant and excessive consumptions may adversely affect lifespan. In this study different concentrations of formalin solutions were prepared and examined using a UV-Vis spectrophotometer. During the experiment the absorbance spectra were recorded by UV Probe user interface within the wavelength range of 200400 nm. The absorbance peak obtained at a wavelength of 207 nm depicted a linear relation with concentration variation from $500-1000 mu mathrm{M}$. The qualitative data analysis tool, principal component analysis (PCA) was implemented and a successful data clustering was observed between four different concentrations $1000 mu mathrm{M}, 400 mu mathrm{M}, 200 mu mathrm{M}$ and $20 mu mathrm{M}$. A high measure of class separability index (SI) 87.7 was obtained from the PCA analysis. The quantitative estimation of formalin concentrations $1000 mu mathrm{M}, 400 mu mathrm{M}, 200 mu mathrm{M}$ and $20 mu mathrm{M}$ was carried out using partial least square regression (PLSR) analysis and principal component regression (PCR). In PLSR, the prediction analysis provided a high correlation factor 0.98 and a high average prediction accuracy of 84% while in case of PCR the correlation factor was 0.97 and average prediction accuracy was obtained as 83.4%. The experimental observations suggest that the UV-Vis technique may be applicable for qualitative analysis of formalin in food products.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114225531","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: 1

Performance Comparison of Nonlinear State Estimators for State-of-Charge Estimation of Supercapacitor 非线性状态估计器在超级电容器荷电状态估计中的性能比较

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362850

M. Saha, Pankaj Saha, M. Khanra

引用次数: 2

Decentralized Control of Multi-Input Multi-Output Processes Using Effective Transfer Function Method 基于有效传递函数法的多输入多输出过程分散控制

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362886

Sidhanta Mohanty, A. Sengupta

{"title":"Decentralized Control of Multi-Input Multi-Output Processes Using Effective Transfer Function Method","authors":"Sidhanta Mohanty, A. Sengupta","doi":"10.1109/CMI50323.2021.9362886","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362886","url":null,"abstract":"The design of controllers for multi-input multioutput systems are always complicated and difficult as compared to single-input single-output systems due to the interactions between inputs and outputs. These interactions are addressed in a quantitative manner using Interaction Measures. For ease of designing, the complex multi-input multi-output systems are assumed to be divided into several equivalent single loops and then the controllers are designed for these single loops differently. To simplify the tuning in case of failure tolerant systems, the decentralized controllers are used. The effective transfer function method is used for decentralized control of multi-input multi-output in this paper. This decentralized control method is compared with a centralized control which uses an interaction measure approach based on Hankel Interaction Index Array. Examples using both the methods are employed to show the effectiveness of the decentralized method over the centralized method. The effectiveness is shown by comparing the rise time, settling time and overshoot of the responses obtained from both the methods.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121685010","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: 0

Development of Molecularly Engraved Polymer Based Sensor for Detection of Theobromine in Tea 基于分子雕刻聚合物的茶叶中可可碱检测传感器的研制

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362831

D. Das, S. Nag, Upasana Saha, B. Tudu, R. B. Roy

{"title":"Development of Molecularly Engraved Polymer Based Sensor for Detection of Theobromine in Tea","authors":"D. Das, S. Nag, Upasana Saha, B. Tudu, R. B. Roy","doi":"10.1109/CMI50323.2021.9362831","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362831","url":null,"abstract":"A low cost sensor has been developed in this work for the detection of theobromine (ThBr) in tea utilizing molecularly imprinted polymer technique. At first, thorough mortaring of the materials prepared by co-polymerization of divinyl benzene (DVB) and polyacrylic acid on a graphite sheet is done. The total and clean removal of theobromine from the polymer matrix has been confirmed through use of ultra violet visible spectroscopic (UV-vis) analysis. The analytical and electrochemical characteristics of the sensor have been determined on application of differential pulse voltammetry (DPV). Acceptable repeatability of the sensor is confirmed with relative standard deviation (%RSD) value of 1.42. The quantitative measure for selectivity is the relative selectivity coefficient (RSC). RSC obtained in this case is 71.43%. Real time usability of the as-prepared electrode has been checked by subjecting it to eight grades of tea. To focus on segregation capability of the sensor based on its theobromine content in tea, a dimension reduction technique like principal component analysis (PCA) was performed using the experimental datasets. A quantitative measure of separation has been evaluated by calculating the separability index (SI). An SI value of 25.69 so obtained affirms a clear segregation among the samples.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122289250","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: 0

Robust Control of Coupled-Tank System using Uncertainty and Disturbance Estimator 基于不确定性和扰动估计的耦合油箱系统鲁棒控制

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362932

Priyanka Barat, S. Mandal

引用次数: 1

Time Series Load Forecasting using Multitask Deep Neural Network 基于多任务深度神经网络的时序负荷预测

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362936

D. Kiruthiga, V. Manikandan

{"title":"Time Series Load Forecasting using Multitask Deep Neural Network","authors":"D. Kiruthiga, V. Manikandan","doi":"10.1109/CMI50323.2021.9362936","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362936","url":null,"abstract":"This work presents a new framework based on multitasking with BiLSTM+dropout deep neural network for individual consumers’ load forecasting. The proposed framework quantifies the uncertainties of consumers’ load profiles in smart meter dataset. The hierarchical clustering algorithm is employed to group similar consumers based on consumption pattern. Furthermore, load profile pooling is carried out on each consumer group to increase the data diversity for addressing the overfitting issues. This framework is tested on 1031 randomly selected residential consumers’ of SGSC smart meter dataset, Australia and implemented through MATLAB platform. Compared to LSTM+dropout technique, the prediction accuracy of the proposed technique shows an improvement of 35.5% and 17.64% over RMSE and MAE respectively. In addition, in comparison to the pooling based LSTM technique, the enhancement in prediction accuracy is around 61.77% and 45.13% over RMSE and MAE respectively. The Experimental results show that the proposed model achieved high prediction accuracy by learning the shared features efficiently and account for stochastic environmental disturbances.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130809147","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: 1

A Sensitive Fiber Optic Technique for Remote Measurement of Liquid Flow 一种用于液体流量远程测量的敏感光纤技术

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362788

T. Islam, M. Rehman

引用次数: 0

IoT based Intelligent Parking Management System 基于物联网的智能停车管理系统

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362845

D. Annirudh, D. Arun kumar, Adabala Taraka Sai Raghava Kumar, K. V. Vijaya Chandrakala

引用次数: 0

Estimation of Driver Demand Torque using Parametric and Nonparametric Data-driven Model 基于参数和非参数数据驱动模型的驱动需求转矩估计

2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI) Pub Date : 2021-01-08 DOI: 10.1109/CMI50323.2021.9362860

A. Bhattacharjee, S. Saranya, Purushottam Kuntumalla

{"title":"Estimation of Driver Demand Torque using Parametric and Nonparametric Data-driven Model","authors":"A. Bhattacharjee, S. Saranya, Purushottam Kuntumalla","doi":"10.1109/CMI50323.2021.9362860","DOIUrl":"https://doi.org/10.1109/CMI50323.2021.9362860","url":null,"abstract":"The complexity of vehicle dynamics is increasing due to growing demands for the inclusion of a large number of functionalities in vehicle model. New generation electronic control units (ECUs) regulate different components of powertrain to produce optimum power and torque necessary to meet the complex functional requirements. But the lookup table or map used in different ECUs e.g., engine ECU, gearbox ECU to create these functionalities are not capable enough to capture the dynamic behavior of system. Thus, effective control of vehicle by ECUs requires a model that is able to accurately predict the dynamic behavior of the system over its complete operating range. The present work proposes both parametric and nonparametric data-driven models that can replace lookup tables or maps used for the estimation of driver torque request. The driver input module estimates the driver demand torque or driver torque request. The inputs to the driver input module are engine speed and accelerator pedal. A data-driven parametric polynomial regression model and nonparametric Volterra model are developed to describe the dynamic behavior of multivariable nonlinear driver input module. The parameters of both the models are estimated using least square optimization algorithm. The input-output data taken from real vehicle dataset is used for both identification and validation of the model. The validation experiments show good fit of the predicted output with actual output. The accuracy obtained from the Volterra and polynomial regression models are 98.27% and 98.6% respectively.","PeriodicalId":142069,"journal":{"name":"2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127584031","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: 0