Fine tuning selection semantics in a structure editor based programming environment: some experimental results

1988 ◽  
Vol 20 (2) ◽  
pp. 38-43 ◽  
Author(s):  
Dennis R. Goldenson ◽  
Marjorie B. Lewis
Keyword(s):  
Experimental Results ◽  
Fine Tuning ◽  
Programming Environment ◽  
Structure Editor

scholarly journals Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model

2021 ◽  
Vol 2083 (4) ◽  
pp. 042017
Author(s):  
Yingdong Ru
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Training Model ◽  
Experimental Results ◽  
Fine Tuning ◽  
Computer Assisted ◽  
Single Voice ◽  
Music Information ◽  
Chord Recognition

Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.

scholarly journals Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2929 ◽  
Author(s):  
Yuanyuan Wang ◽  
Chao Wang ◽  
Hong Zhang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Fine Tuning ◽  
Classification Model ◽  
Great Success ◽  
Sar Images ◽  
Convolutional Networks ◽  
Ship Classification

With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.

scholarly journals Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 307
Author(s):  
Li Zhang ◽  
Haimeng Fan ◽  
Chengxia Peng ◽  
Guozheng Rao ◽  
Qing Cong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Hpv Vaccination ◽  
Experimental Results ◽  
Fine Tuning ◽  
Hpv Vaccines ◽  
Social Media Data ◽  
Public Sentiment ◽  
Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

scholarly journals Determination of Wheel-Rail Contact Characteristics by Creating a Special Program for Calculation

2014 ◽  
Vol 10 (3) ◽  
pp. 48-59 ◽  
Author(s):  
Ioan Sebeşan ◽  
Yahia Zakaria
Keyword(s):  
Normal Stress ◽  
Contact Zone ◽  
Experimental Results ◽  
Programming Environment ◽  
Special Program ◽  
Friction Coefficients ◽  
Future Developments ◽  
Working Quality ◽  
Contact Ellipse

Abstract The authors in this paper describe the steps of creating a special program in GUI tool in Matlab. The program is designed to calculate the main properties of wheel-rail contact zone, such as: contact ellipse dimensions, normal stress and friction coefficients. All the relevant equations, which were introduced by different researchers, are firstly presented and modified to be applicable to the programming environment, and then the program was built. In the end, the program working quality is discussed and some expected future developments on this program are suggested. The proposed program can make the comparison between theoretical and experimental results, when they are available, easier and faster.

scholarly journals The naturalness in the BLMSSM and B-LSSM

2020 ◽  
Vol 80 (12) ◽  
Author(s):  
Xing-Xing Dong ◽  
Tai-Fu Feng ◽  
Shu-Min Zhao ◽  
Hai-Bin Zhang
Keyword(s):  
Experimental Data ◽  
Higgs Boson ◽  
Neutrino Mass ◽  
Experimental Results ◽  
Fine Tuning ◽  
Boson Mass ◽  
Higgs Boson Mass ◽  
The Right ◽  
Mass Problems

AbstractIn order to interpret the Higgs boson mass and its decays naturally, we hope to examine the BLMSSM and B-LSSM. In the both models, the right-handed neutrino superfields are introduced to better explain the neutrino mass problems. In this paper, we introduce the fine-tuning to acquire the physical Higgs boson mass. Besides, the method of $$\chi ^2$$ χ 2 analyses will be adopted in the BLMSSM and B-LSSM to fit the experimental data. Therefore, we can obtain the reasonable theoretical values of the Higgs decays and muon $$g-2$$ g - 2 that are in accordance with the experimental results respectively in the BLMSSM and B-LSSM.

scholarly journals Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping

2018 ◽  
Vol 8 (9) ◽  
pp. 1618
Author(s):  
Weiwei Zhang ◽  
Zhe Chen ◽  
Fuliang Yin
Keyword(s):  
Dynamic Programming ◽  
Experimental Results ◽  
Fine Tuning ◽  
Dynamic Variation ◽  
Pitch Class ◽  
Polyphonic Music

A new architecture for melody extraction from polyphonic music is explored in this paper. Specifically, chromagrams are first constructed through the harmonic pitch class profile (HPCP) to measure the salience of melody, and chroma-level notes are tracked by dynamic programming. Then, note detection is performed according to chroma-level note differences between adjacent frames. Next, note pitches are coarsely mapped by maximizing the salience of each note, followed by a fine tuning to fit the dynamic variation within each note. Finally, voicing detection is carried out to determine the presence of melody according to the salience of fine-tuned notes. Note level pitch mapping and fine tuning avoids pitch shifting between different octaves or notes within one note duration. Several experiments have been conducted to evaluate the performance of the proposed method. The experimental results show that the proposed method can track the dynamic pitch changing within each note, and performs well at different signal-to-accompaniment ratios. However, its performance for deep vibratos and pitch glides still needs to be improved.

scholarly journals DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

2019 ◽  
Vol 33 ◽  
pp. 4031-4038
Author(s):  
Sunghwan Joo ◽  
Sungmin Cha ◽  
Taesup Moon
Keyword(s):  
Neural Network ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
State Of The Art ◽  
Unbiased Estimate ◽  
Experimental Results ◽  
Fine Tuning ◽  
Noisy Image ◽  
Supervised Training ◽  
The Given

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.

A Random Sampling Strategy for Tuning Contact Parameters of Underplatform Dampers

2015 ◽  
Author(s):  
Chiara Gastaldi ◽  
Muzio M. Gola
Keyword(s):  
Experimental Results ◽  
Fine Tuning ◽  
Dissipated Energy ◽  
Shape Similarity ◽  
Hysteresis Cycle ◽  
Friction Coefficients ◽  
Cycle Number ◽  
Contact Parameter ◽  
The Real ◽  
Contact Parameters

All numerical models of friction damped bladed arrays require knowledge of contact-friction parameters, which are established either through direct frictional measurements, done with the help of a separate single contact test arrangement, or by fine tuning the parameters in the numerical model of the real damping device and comparing the experimental response of a damped blade against its computed response. Recent results from direct measurements on underplatform dampers and the subsequent cross-comparison of experimental and numerical results have put into evidence several features which are usually neglected in FE models of damper-blade systems: – markedly different friction coefficients at different contact points; – friction coefficients evolving with time and cycle number towards a stable shape, in a systematic and repeatable manner with dramatic consequences on the shape of the hysteresis cycle and on dissipated energy; – particular cases where minimal variations in the friction coefficients lead to gross changes of the damper behavior; Identifying the contact parameters to assure the best match between model and experimental results becomes crucial to guarantee that the validated damper model will produce the correct cyclic forces on the blades during vibrational motion. While the tuning process described in the previous papers was a search based on the progressive refinement whose results depended on the operator’s ability to match different patterns of the model and experimental results, this paper dwells on a more objective and controllable method based on properly chosen indicators. The latter method is based on a sampling technique (Latin Hyper-cube) which produces a large number of solutions (in the present case 5000) on the basis of randomized extractions of contact parameters (in the present case 5) between given boundaries. An advantage of this method is a systematic exploration of the influence of each input contact parameter on the collection of output indicators, considered acceptable according to predetermined criteria. The main indicators suited for the purpose are three, i.e., the relative errors on the real and imaginary parts of the HBM complex spring equivalent to the hysteresis cycle and a measure of shape similarity between the experimental and simulated cycle. The paper shows the selection procedure which has been adopted to produce the final set of eligible solutions, which are further reduced by applying a secondary indicator based on the similarity of a kinematical parameter. In this paper this parameter is chosen to be a measure of the shape similarity of the damper rotation during the hysteresis cycle.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2021 ◽  
Vol 271 ◽  
pp. 01039
Author(s):  
Dongsheng Ji ◽  
Yanzhong Zhao ◽  
Zhujun Zhang ◽  
Qianchuan Zhao
Keyword(s):  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Fine Tuning ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples, the recognition accuracy is not ideal. In this paper, a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, small-sample image enhancement and extension are performed on the transformed image, such as staggered transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning. Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images, even with only a small number of CT image samples.

scholarly journals Research Concerning the Correlations Between Some Experimental Results in the Case of a Sucker Rod Pumping Installation

2018 ◽  
Vol 69 (11) ◽  
pp. 3060-3063
Author(s):  
Dorin Badoiu ◽  
Georgeta Toma
Keyword(s):  
Computer Program ◽  
Rotation Speed ◽  
Experimental Results ◽  
Programming Environment ◽  
Sucker Rod ◽  
Pumping Unit ◽  
Polished Rod

In the paper are analyzed the correlations between the experimental results obtained for the instantaneous rotation speed of the cranks shaft of a conventional sucker rod pumping installation and the speed and the acceleration at the end of the polished rod. The correlations have been established by analyzing the kinematics of the mechanism of the sucker rod pumping unit. The experimental records have been processed with the program Total Well Management. A computer program for performing the simulations has been developed by the authors using Maple programming environment.

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