Comparison of Wind Turbine Performance Prediction and Measurement

1982 ◽  
Vol 104 (2) ◽  
pp. 84-88 ◽  
Author(s):  
J. L. Tangler
Keyword(s):  
Test Data ◽  
Wind Turbines ◽  
Performance Prediction ◽  
State Of The Art ◽  
Correlation Study ◽  
Performance Models ◽  
Rocky Flats ◽  
Turbine Performance ◽  
Horizontal Axis Wind Turbines ◽  
Development Center

The purpose of this work was to evaluate the state-of-the-art of performance prediction for small horizontal-axis wind turbines. This effort was undertaken since few of the existing performance methods used to predict rotor power output have been validated with reliable test data. The program involved evaluating several existing performance models from four contractors by comparing their predictions for two wind turbines with actual test data. Test data were acquired by Rocky Flats Test and Development Center and furnished to the contractors after submission of their prediction reports. The results of the correlation study will help identify areas in which existing rotor performance models are inadequate and, where possible, the reasons for the models shortcomings. In addition, several problems associated with obtaining accurate test data will be discussed.

STATE OF THE ART OF SMALL WIND ENERGY ANALYSING DIFFERENT CONTROLS

10.6036/10376 ◽  
2022 ◽  
Vol 97 (1) ◽  
pp. 11-11
Author(s):  
MARLON GALLO TORRES ◽  
ENEKO MOLA SANZ ◽  
IGNACIO MUGURUZA FERNANDEZ DE VALDERRAMA ◽  
AITZOL UGARTEMENDIA ITURRIZAR ◽  
GONZALO ABAD BIAIN ◽  
...  
Keyword(s):  
Wind Turbines ◽  
State Of The Art ◽  
Vertical Axis ◽  
Energy Conversion Efficiency ◽  
Horizontal Axis ◽  
Prevailing Wind ◽  
Vertical Axis Wind Turbines ◽  
Axis Of Rotation ◽  
Horizontal Axis Wind Turbines ◽  
Installation Cost

There are two wind turbine topologies according to the axis of rotation: horizontal axis, "Horizontal Axis Wind Turbines" (HAWT) and vertical axis, "Vertical Axis Wind Turbines" (VAWT) [2]. HAWT turbines are used for high power generation as they have a higher energy conversion efficiency [2]. However, VAWTs are used in mini wind applications because they do not need to be oriented to the prevailing wind and have lower installation cost.

Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

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