Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.
With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.
The powersphere is a device used for maximizing the conversion of light in wireless energy transmission via laser. It is a spherical structure made up of thousands of photovoltaic cells. Due to the large dimensions and existence of many holes in the spherical surface, there are some drawbacks in machining, such as limited movement space of the machines, long cycle, low precision, and high cost. In this context, with a powersphere irradiated by the laser as the model, the principle of powersphere is deduced theoretically. It is proven that the illuminance value at any position on the inner wall of the powersphere is equal, and the calculation formula of this value is derived. Based on this theory and the comparative analysis of processing methods and the results of processing experiments, the structure of the powersphere is designed. The experimental processing of the powersphere is carried out by selecting the welding method. Finally, two hemispherical powersphere frames are processed, which are connected by screws to form a ball frame for the installation of photovoltaic cells. The results show that the improved design and fabricating method can process the powersphere quickly, accurately, and economically. A comparative experiment of powersphere and photovoltaic panel was carried out. The experimental results show that the powersphere has the function of light uniformity and repeated use of laser. So, the designed and processed powersphere is consistent with the theoretical analysis.
Objective: To analyze the correlation between lymph node metastasis of thoracic esophageal squamous cell carcinoma (ESCC) and clinical and pathological factors, and to provide a reference for the outline of clinical target volume. Methods: The pathological characteristics of 1034 thoracic ESCC patients after surgery were described, and the correlations between clinical and pathological factors and lymph node metastasis were studied by univariate and Logistic multivariate analyses. Results: Lymph node metastasis was significantly correlated with tumor length, invasion depth and differentiation degree (P<0.01), but not gender, age, tumor site or pathological type (P>0.05). Logistic multivariate analysis showed that tumor length, invasion depth and differentiation degree were independent risk factors for thoracic ESCC. The lymph node metastasis rates of mid-thoracic ESCC in the middle mediastinum, lower-thoracic ESCC in the lower mediastinum and abdominal cavity were 18.5%, 35.3% and 19.7% respectively in the T1-T2 stage. In the T3-T4 stage, the lymph node metastasis rates of mid-thoracic ESCC in the middle mediastinum and abdominal cavity were 39.6% and 17.4% respectively, and those of lower-thoracic ESCC in middle and lower mediastina and abdominal cavity were 21.1%, 43.4% and 29.8% respectively. Highly/moderately differentiated mid-thoracic ESCC in the middle mediastinum, lower-thoracic ESCC in middle and lower mediastina and abdominal cavity had the lymph node metastasis rates of 34.7%, 15.1%, 33.5% and 23.7% respectively. Lowly differentiated mid-thoracic ESCC in the middle mediastinum and abdominal cavity had the lymph node metastasis rates of 46.9% a 29.6% respectively, and those of lower-thoracic ESCC in middle and lower mediastina and abdominal cavity were 25.5%, 49.1% and 27.3% respectively. Conclusion: During the outline of radiotherapy target volume for thoracic ESCC, tumor length, invasion depth and differentiation degree should be comprehensively considered to selectively irradiate the regions prone to lymph node metastasis. How to cite this:Pan G, Pan H, Zhang Y, Shuai H. Effects of lymph node metastasis of thoracic esophageal squamous cell carcinoma on design of radiotherapy target volume. Pak J Med Sci. 2019;35(1):177-182. doi: https://doi.org/10.12669/pjms.35.1.431 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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