Showing 7 results for Regression
Z. Gallehdari, M. Dehghani, S. K. Nikravesh,
Volume 10, Issue 2 (6-2014)
Abstract
The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is considered as the input and the rotor angle and the active power are considered as the generator outputs. In fact, the third order nonlinear state space model is converted to only two linear regression equations. Then, easy-implemented regression equations are used to estimate the unknown parameters of the nonlinear model.
The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.
S. Abolmaali,
Volume 15, Issue 4 (12-2019)
Abstract
Accurate delay calculation of circuit gates is very important in timing analysis of digital circuits. Waveform shapes on the input ports of logic gates should be considered, in the characterization phase of delay calculation, to obtain accurate gate delay values. Glitches and their temporal effect on circuit gate delays should be taken into account for this purpose. However, the explosive number of combinations of waveform shapes, which can be applied to the input ports of logic gates, causes existing lookup-based methods to have huge space requirements. In this article, instead of considering all possible combinations of waveform shapes in the characterization phase of delay calculation process, the least number of combinations, which are dominant in determining the waveform shape of gate output, is presented. Multivariate Polynomial Regression (MPR) method is used to further reduce the required memory space. Exploration of the possible MPR analyses is performed to find the best regression case with proper memory space reduction and precision. Attained results show a 1.013E6 times reduction in storage space required for storing parameters utilized in extraction of output waveform characteristics in comparison to a state of the artwork, accompanied by acceptable precision.
P. O. Oluseyi, J. A. Adeagbo, D. D. Dinakin, O. M. Babatunde,
Volume 17, Issue 1 (3-2021)
Abstract
The philosophy of efficient energy consumption is vitally crucial to profitable production cost in manufacturing industries. This is because the unit production cost is largely determined by the cost of unit energy supply; which is quite higher than the cost of raw materials in Nigeria. It has been established that the Nigerian industrial sector is responsible for 8.7% of the total energy consumption in the nation. Out of this chunk, the food and beverage industry appropriates approximately 2%. Meanwhile, it is observed that the energy consumption trend in most industrial electric motors is always high due to continuous operation even during the idle time/period in production. In this study, data gathered has a coefficient of determination of 99.7%. This is, thus, subjected to regression analysis which assists in predicting the energy consumption trend for a period of one year. Further to this, the capacity of control principles in efficient energy consumption is demonstrated by practical real time implementation of a smart energy saving in the food industries using PLClogicx software. In this sense, the developed programmable logic control (PLC) ladder diagram was further designed and implemented using fuzzy logic control (FLC). This is simulated using MATLAB/Simulink toolbox. By this arrangement; it is observed that there was a significant reduction in energy consumption. This is obviously revealed in the obtained results. In this case, there was an average electrical energy savings of 65.59% in the plant’s case sealing section while an energy saving of approximately 0.13% was achieved in reference to the overall energy consumption of the industrial plant’s processes. Finally, based on the mathematical calculations obtained from observations of typical production processes in the multinational food and beverage company, the FLC is discovered to provide 99.83% efficiency in optimizing energy consumption.
S. P. Ramezanzadeh, M. Mirzaie, M. Shahabi,
Volume 19, Issue 2 (6-2023)
Abstract
Due to the role of renewable energy sources in providing energy in future power systems, multi-terminal HVDC (MTDC) systems have attracted the attention of utilities and decision-makers. The reliability study of MTDC grids is critical for analyzing electrical power systems and providing a reliable power delivery system. Reliability modeling and study of six MTDC transmission networks containing hybrid DC circuit breakers for interrupting transmission line contingencies is presented in this paper. This study incorporates precise reliability models of MTDC grid configurations and describes a step-by-step grid expansion. Considering these reliability models, critical reliability indices of the demand bus of the grid have been obtained to calculate the amount of energy not supplied. Also, the influence of the tapping stations on the demand bus reliability features has been investigated. Since the components' characteristics significantly affect the system's reliability, the impact of the transformer and DC circuit breaker's failure rate and repair time on the reliability features of the demand bus of all MTDC grids have been assessed. The obtained results are employed to forecast the effect of simultaneous change of the repair time and failure rate of the transformer, the most influential component in determining the reliability indices, on the proposed configuration by incorporating multivariate linear regression.
Biswapriyo Sen, Maharishi Kashyap, Jitendra Singh Tamang, Sital Sharma, Rijhi Dey,
Volume 20, Issue 2 (6-2024)
Abstract
Cardiovascular arrhythmia is indeed one of the most prevalent cardiac issues globally. In this paper, the primary objective was to develop and evaluate an automated classification system. This system utilizes a comprehensive database of electro- cardiogram (ECG) data, with a particular focus on improving the detection of minority arrhythmia classes.
In this study, the focus was on investigating the performance of three different supervised machine learning models in the context of arrhythmia detection. These models included Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). An analysis was conducted using real inter-patient electrocardiogram (ECG) records, which is a more realistic scenario in a clinical environment where ECG data comes from various patients.
The study evaluated the models’ performances based on four important metrics: accuracy, precision, recall, and f1-score. After thorough experimentation, the results highlighted that the Random Forest (RF) classifier outperformed the other methods in all of the metrics used in the experiments. This classifier achieved an impressive accuracy of 0.94, indicating its effectiveness in accurately detecting arrhythmia in diverse ECG signals collected from different patients.
Sandra D’souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, Aneesha Acharya K,
Volume 20, Issue 4 (11-2024)
Abstract
The incidence of heart-related illnesses is on the rise worldwide. Heart diseases are primarily caused by a multitude of parameters, including high blood pressure, diabetes, and excessive cholesterol, which are controlled by poor dietary and lifestyle choices. The growth in cardiovascular diseases (CVD) is mostly due to several other behaviors, such as smoking, drinking, and sleeplessness. In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. The data set consists of 831 normal and 260 abnormal data, and the duration of each sample is 5 seconds. Features extracted from the data are up-sampled and applied to the logistic regression and random forest classification models. The developed models record a classification accuracy of 71% for logistic regression and 94% for the random forest model. Further, artificial neural networks (ANN) and Deep learning networks have been trained to improve performance and demonstrated an accuracy of 94.5%.
Somayeh Talebzadeh, Reza Radfar, Abbas Toloei Ashlaghi,
Volume 21, Issue 3 (8-2025)
Abstract
The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.
