Decision tree-based classification of multiple operating conditions for power system voltage stability assessment

Vanfretti, Luigi; Arava, V.S. Narasimham

doi:10.1016/j.ijepes.2020.106251

Cited by 57 publications

(33 citation statements)

References 18 publications

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“…Figure 4 portrays a schematic view of DT. The main task of creating a DT is to locate a feature to be evaluated on one node repeatedly and then branch to the other node 58 …”

Section: Technical Backgroundmentioning

confidence: 99%

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

Cengil

Çınar

2021

Int J Imaging Syst Tech

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In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

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“…Figure 4 portrays a schematic view of DT. The main task of creating a DT is to locate a feature to be evaluated on one node repeatedly and then branch to the other node 58 …”

Section: Technical Backgroundmentioning

confidence: 99%

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

Cengil

Çınar

2021

Int J Imaging Syst Tech

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“…A decision tree model, as a supervised model, [26] has been successfully applied to solve the multiclass classification problems. It is a natural mechanism for human beings to deal with the decision making problem.…”

Section: Fundamental Theory Of Decision Tree Modelmentioning

confidence: 99%

Optimization of Dephosphorization Parameter in Consteel Electric Arc Furnace Using Rule Set Model

Chen

Wang

Chen

2021

steel research int.

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Consteel electric arc furnace, as one typical short-process smelting equipment, is widely applied in many steel plants. Nevertheless, similar to basic oxygen furnace (BOF), the steelmaking process in Consteel electric arc furnace is also a complicated multivariable high-temperature physicochemical process, and the fluctuation of process parameters in Consteel electric arc furnace would make it difficult to stably control the end-point target. The impurities such as phosphorus should be kept under control in the steelmaking process due to their possible adverse effects on the steel quality. [1] Thus, the endpoint phosphorus (P) content is one of the main target values at the end of smelting operation in the steelmaking process due to its significant influence on the steel products, [2][3][4][5] particularly for low phosphorous steels production. To enhance the dephosphorization efficiency, a lot of industrial experiment is carried out to determine the optimal parameters. For instances, Liu et al. [6] investigated the dephosphorization ratio of hot metal with 80 t BOF, and the experiment results showed that the suitable process conditions for obtaining a high dephosphorization ratio in the converter were that the basicity of slag was 1.2-2.0, and the temperature for the intermediate deslagging was 1600-1700 K. Wu et al., [7] through the industrial test of 120 t converter, determine the process of double slag operation in BOF to stabilize the dephosphorization effect. Yang et al. [8] applied the high-temperature laboratorial experiments to investigate the influence of temperature on the dephosphorization of hot metal at the low temperature range of 1300-1450 C with the slag of the low basicity (CaO/ SiO 2 ) of about 1.8. Tian et al. [9] reported that the effects of Fe 3þ content and the basicity of the dephosphorization slag on dephosphorization were greater than those of the decarbonization slag. Yang et al. [10] make a test in 180 t BOF and determine the range of smelting parameters to reduce the end phosphorus less than 0.007%. The reference mentioned earlier is to optimize the dephosphorization process parameter according to the industrial experimental data; however, optimizing the dephosphorization process parameter according to the actual production data is rare. With the improvement of data storage technology and information collection technology, a large number of actual production data can be obtained. [11,12] From these actual production data, we can obtain a lot of valuable information, which is benefit for adjusting the process parameters to improve the dephosphorization ratio. However, in the face of a large amount of data, it is impossible to mine the valuable information just by human. Data mining techniques are mainly used to mine the hidden information, and their patterns justify their importance in decision making. [13] For examples, Pal and Halder [14] utilized the multiobjective optimization technique to address important issue of achieving low phosphorus content along with high-end blow temperat...

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“…The consumer behavior is based on seasons and weather for influencing the energy consumption. Therefore, the design models are capable for analyzing the time series from smart meters for energy consumption [1]. The energy forecasting model is one of the components used for optimizing the energy model.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of demand forecasting in energy consumption based on ensemble model

Dhanalakshmi

Ayyanathan

2022

Bulletin EEI

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Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%.

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Decision tree-based classification of multiple operating conditions for power system voltage stability assessment

Cited by 57 publications

References 18 publications

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

Optimization of Dephosphorization Parameter in Consteel Electric Arc Furnace Using Rule Set Model

Performance analysis of demand forecasting in energy consumption based on ensemble model

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