scholarly journals Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

2022 ◽  
Vol 16 ◽  
pp. 30-37
Author(s):  
J. V. Alamelu ◽  
A. Mythili
Keyword(s):  
Machine Learning ◽  
Optimal Control ◽  
Rise Time ◽  
Infusion Pump ◽  
Drug Dosage ◽  
Settling Time ◽  
Support Vector ◽  
Response Analysis ◽  
Linear Quadratic ◽  
Pump Speed

In the current scenario, usage of the smart medical pump is predominant in the medical field. The precise drug dosage, flow accuracy should be maintained to increase the performance of an infusion pump. In this work, an attempt has been made to predict and control the speed of the infusion pump for suitable infusion flowrate using machine learning technique and Linear Quadratic Gaussian (LQG) controller. The data for this study is considered from the publicly available online database, electronic Medicines Compendium (eMC). The speed of the infusion pump has been calculated using the drug dosage and flow rate for two different drugs. The prediction of infusion pump speed is achieved using Linear regression with Principal Component analysis (PCR) and Support Vector Machine Regression (SVR). The performance of the prediction schemes is evaluated using standard metrics. To validate the optimal control of the predicted speed, two different medical graded motors are considered. Further, the optimal control of the pump speed is investigated using Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and LQG controllers for its stability criteria. The prediction of the pump speed using regression models PCR, SVR has been verified and then the transient response analysis with rise time, settling time for both the motors have been examined. Results demonstrate that the LQG optimal control strategy achieves fast rise time, settling time of motor1 with 0.653s, 1.15s, and 0.22, 0.392s for motor2 respectively.

scholarly journals Perancangan Kendali Optimal pada Motor Arus Searah Tanpa Sikat melalui Metode LQRI

2019 ◽  
Vol 7 (2) ◽  
pp. 377 ◽  
Author(s):  
KHOIRUDIN FATHONI ◽  
ARYO BASKORO UTOMO
Keyword(s):  
Rise Time ◽  
Load Condition ◽  
Linear Quadratic Regulator ◽  
Settling Time ◽  
Control Signal ◽  
Bldc Motor ◽  
Linear Quadratic ◽  
Cost Matrix ◽  
R Matrix ◽  
Set Point

ABSTRAKArtikel ini akan menjelaskan perancangan kendali kecepatan MASTS  dengan tujuan diperoleh respon kecepatan MASTS yang tanggap serta memiliki sinyal kendali dan arus minimal. Untuk mencapai hal ini MASTS akan dikendalikan melalui metode Linear Quadratic Regulator (LQR) dengan state yang dipilih adalah arus, kecepatan, dan state integral galat kecepatan. Diperlukan penalaan nilai parameter Q matriks bobot state dan R matriks bobot input untuk mendapatkan performa kecepatan dan arus yang terbaik. Berdasarkan pengujian diperoleh bahwa dengan kendali LQR-I, kecepatan MASTS dapat mengikuti set point dengan respon rise time Tr = 0,03 detik, settling time Ts=0,044 detik, overshoot (OS) 1,6 %, arus Imax=0,16 A dan dutycycle sinyal kontrol umax 56% pada kondisi tanpa beban dan Tr = 0,03 detik, Ts=0,044 detik, OS 1,6 %, Imax=0,16 A dan umax 56% pada kondisi berbeban. Dibandingkan dengan kendali PID ketika tanpa beban mempunyai Tr=0,0176 Ts=0,075 %OS=3,9% umax=96% Imax=0,35 A, LQRI mempunyai respon settling time, sinyal kendali dan arus yang lebih baik.Kata kunci: Motor Arus Searah Tanpa Sikat, Kendali Optimal, Linear  Quadratic Regulator dan Integral ABSTRACTThis paper aimed to discuss further research about BLDC motor speed control so that BLDC not only has fast speed response but also has minimum control signal and current using LQR (Linear Quadratic Regulator) control with chosen states are current, speed of BLDC, and speed error integral state. Tuning of Q and R matrix is required to reach the best speed and current performance. Where Q and R matrix is state cost matrix and input cost matrix, respectively. Result show that LQR-I control can track set point with rise time Tr = 0.03 s, settling time Ts=0,044 s, overshoot (OS) 1,6 %, current Imax=0,16 A and dutycycle control signal umax 56% in no load condition, and Tr = 0,03 s, Ts=0,044 s, OS 1,6 %, Imax=0,16 A dan umax 56% in the load condition. Compared to PID controller which has Tr=0,0176 Ts=0,075 %OS=3,9% umax=96% Imax=0,35 A in no load condition, proposed controller has a better settling time, control signal and current.Keywords: BLDC Motor, Optimal Control, LQR and Integral

scholarly journals The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

2021 ◽  
Author(s):  
Tanmoy Chatterjee ◽  
Aniekan Essien ◽  
Ranjan Ganguli ◽  
Michael I. Friswell
Keyword(s):  
Machine Learning ◽  
Rotor Blade ◽  
Nonlinear Response ◽  
Real Life ◽  
Helicopter Rotor ◽  
Training Dataset ◽  
Support Vector ◽  
Response Analysis ◽  
Helicopter Rotors ◽  
Temporal Models

AbstractThis paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Desain dan Komparasi Kontrol Kecepatan Motor DC

2020 ◽  
Vol 7 (2) ◽  
pp. 127-134
Author(s):  
Safah Tasya Aprilyani ◽  
Irianto Irianto ◽  
Epyk Sunarno
Keyword(s):  
Steady State ◽  
Rise Time ◽  
Settling Time ◽  
Proportional Integral

Penggunaan kontrol sangat diperlukan dalam pengaturan kecepatan motor DC. Dalam pengaturan kecepatan motor DC, salah satu jenis kontrol yang digunakan adalah kontrol Proportional Integral (PI). Untuk 4 jenis metode pada kontrol PI yang digunakan adalah metode Ziegler Nichole, Chien Servo 1, Chien Regulator 1 dan perhitungan secara analitik yang telah diperoleh dari data yang sudah ada.  Namun kontrol dengan PI 4 metode yang digunakan  sebagai pembanding memiliki waktu respon kecepatan saat stabil cenderung lambat baik dari nilai settling time, rise time dan steady state. Maka dari itu dilakukan komparasi antara 4 metode kontrol PI dengan penggunaan kontrol fuzzy. Dalam membandingkan antara 4 metode kontrol PI dan kontrol fuzzy terdapat beberapa parameter sebagai perbandingan yaitu maximum overshoot, steady state, rise time dan settling time. Hasil dari perbandingan tersebut adalah kontrol fuzzy dapat menghasilkan performa lebih baik jika dibandingkan dengan 4 metode pada kontrol PI. Kontrol fuzzy memiliki nilai rise time sebesar 0,015 detik, nilai settling time sebesar 0,025 detik dengan kecepatan sebesar 2900 rpm serta error steady state sebesar 3,33% tanpa adanya overshoot dan osilasi.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Phum Tachachartvanich ◽  
Denis Fourches ◽  
Anatoly Soshilov ◽  
Jennifer C.Y. Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Androgen Receptor ◽  
Endocrine Disruptors ◽  
Hormone Receptors ◽  
Steroid Hormone Receptors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Polyfluoroalkyl Substances ◽  
Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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