A New Method for Ocean Wind Direction Retrieval from Delay-Doppler Maps Using Stare Processing and Machine Learning: Preliminary Simulation Results

2021 ◽  
Author(s):  
Ian Collett ◽  
Yunxiang Liu ◽  
Y. Jade Morton
Keyword(s):  
Machine Learning ◽  
Wind Direction ◽  
New Method ◽  
Simulation Results ◽  
Ocean Wind

A New Method of Rolling Prediction for Gas Emission Based on Wavelet Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 1500-1506
Author(s):  
Yu Min Pan ◽  
Xiao Yu Zhang ◽  
Peng Qian Xue
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Wavelet Neural Network ◽  
New Method ◽  
Neutral Network ◽  
Gas Emission ◽  
Sample Data ◽  
Simulation Results

A new method of rolling prediction for gas emission based on wavelet neural network is proposed in this paper. In the method, part of the sample data is selected, which length is constant, and the data is reselected as the next prediction step. Then a wavelet neutral network is adopted to prediction which input data is rolling, the sequence model of rolling prediction is thus constructed. Simulation results have proved that the method is valid and feasible.

A Reformative Simulation Method of Motor Pole Core Vibration Characteristics

2013 ◽  
Vol 331 ◽  
pp. 118-123
Author(s):  
Tian Hui Ding ◽  
Yun Hua Chen ◽  
Lei Tian
Keyword(s):  
Reference Value ◽  
Simulation Method ◽  
Vibration Characteristics ◽  
New Method ◽  
Motor Design ◽  
Simulation Results ◽  
Vibration Performance

As to directing motor design, it is very important to make sure that the motor’s forecast of vibration performance has reference value. So, it must need motor’s parts vibration characteristics simulation results are so close to their characteristics. This paper puts forward a new simulation method of motor pole core vibration characteristics, this method includes setting anisotropy material attributes multipartite, getting attributes parameters values which are based on recommended fitting curves, modeling and equating windings, equating dipping lacquer and so on. Combining with experiments, the new method is validated its availability.

scholarly journals Study On Setting Method of Flatness Target Curve of SUNDWIG 20-High Mill Considering Target Crown

2021 ◽  
Author(s):  
Jianliang Sun ◽  
Mingze Yan ◽  
Mingyuan Li ◽  
Tongtong Hao
Keyword(s):  
Control Theory ◽  
Cold Rolling ◽  
Hot Rolling ◽  
New Method ◽  
Discrimination Model ◽  
Flatness Control ◽  
Simulation Results ◽  
The Stability ◽  
And Control

Abstract The flatness target curve is important in the flatness control theory. The accuracy of flatness target curve is an important factor to determine the load of flatness control means and flatness quality. Aiming at the defect that crown of each pass after rolling cannot be controlled quantitatively in the traditional target curve formulation of cold rolling, a new method considering the target crown was proposed. Specifically, the target crown of each pass can be set by combining the total proportional crown change in hot rolling field to each pass and the instability discrimination model in cold rolling field. the total proportional crown change of incoming material and finished product is allocated to each pass, and the instability discrimination model is applied to ensure the stability of the plate. The purpose of new method is to control of the crown of each pass quantitatively, so that the flatness and thickness of plate can meet the production requirements. Taking SUNDWIG 20-high mill and typical rolling products as an example, the simulation results show that, on the basis of ensuring the flatness and obtaining the minimum available crown after rolling, the model can make the flatness and crown meet the production requirements at the same time and control the crown of each pass after rolling quantitatively by setting the target crown of each pass.

A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haoran Zhu ◽  
Lei Lei
Keyword(s):  
Machine Learning ◽  
Noun Phrases ◽  
New Method ◽  
Learning Models ◽  
Linguistic Features ◽  
Research Topics ◽  
Content Type ◽  
Research Areas ◽  
Information Retrieval Methods ◽  
Machine Learning Models

PurposePrevious research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.Design/methodology/approachFirst, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.FindingsThe new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.Originality/valueThis study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

scholarly journals Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

Batteries ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 54 ◽  
Author(s):  
Yoichi Takagishi ◽  
Takumi Yamanaka ◽  
Tatsuya Yamaue
Keyword(s):  
Machine Learning ◽  
Process Parameters ◽  
Volume Fraction ◽  
Specific Resistance ◽  
Active Material ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion ◽  
Simulation Results ◽  
Charge Discharge

We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

scholarly journals TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiali Sun ◽  
Qingtai Wu ◽  
Dafeng Shen ◽  
Yangjun Wen ◽  
Fengrong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
The Other ◽  
New Method ◽  
Genome Wide Association ◽  
High Dimensional ◽  
Simulation Experiments ◽  
Machine Learning Methods ◽  
Least Angle Regression ◽  
Genome Wide

AbstractOne of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population structure and polygenic effects, then selected the SNPs that were potentially related to target traits by using least angle regression (LARS), furtherly analyzed this variable subset using random forest (RF) to detect quantitative trait nucleotides (QTNs) associated with target traits. The new method has more powerful detection in simulation experiments and real data analyses. The results of simulation experiments showed that, compared with the existing approaches, the new method effectively improved the detection ability of QTNs and model fitting degree, and required less calculation time. In addition, the new method significantly distinguished QTNs and other SNPs. Subsequently, the new method was applied to analyze five flowering-related traits in Arabidopsis. The results showed that, the distinction between QTNs and unrelated SNPs was more significant than the other methods. The new method detected 60 genes confirmed to be related to the target trait, which was significantly higher than the other methods, and simultaneously detected multiple gene clusters associated with the target trait.

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