scholarly journals Modeling of Scramjet Combustors Based on Model Migration and Process Similarity

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2516 ◽  
Author(s):  
Tao Cui ◽  
Yang Ou
Keyword(s):  
Machine Learning ◽  
Development Strategy ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Low Cost ◽  
Simulation Method ◽  
Migration Strategy ◽  
Model Migration ◽  
Scramjet Combustors ◽  
Few Data

Contributed by the low cost, the simulation method is considered an attractive option for the optimization and design of the supersonic combustor. Unfortunately, accurate and satisfactory modeling is time-consuming and cost-consuming because of the complex processes and various working conditions. To address this issue, a mathematical modeling for the combustor on the basis of the clustering algorithm, machine learning algorithm, and model migration strategy is developed in this paper. A general framework for the migration strategy of the combustor model is proposed among the similar combustors, and the base model, which is developed by training the machine learning model with data from the existing combustion processes, is amended to fit the unexampled combustor using the model migration strategy with a few data. The simulation results validate the effectiveness of the development strategy, and the migrated model is proved to be suitable for the new combustor in higher accuracy with less time and calculation.

scholarly journals Using Machine Learning Algorithms to Recognize Shuttlecock Movements

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Wang
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Research Strategies ◽  
Image Capture ◽  
Motion Recognition ◽  
Motion Characteristics

Shuttlecock is an excellent traditional national sport in China. Because of its simplicity, convenience, and fun, it is loved by the broad masses of people, especially teenagers and children. The development of shuttlecock sports into a confrontational event is not long, and it takes a period of research to master the tactics and strategies of shuttlecock sports. Based on this, this article proposes the use of machine learning algorithms to recognize the movement of shuttlecock movements, aiming to provide more theoretical and technical support for shuttlecock competitions by identifying features through actions with the assistance of technical algorithms. This paper uses literature research methods, model methods, comparative analysis methods, and other methods to deeply study the motion characteristics of shuttlecock motion, the key algorithms of machine learning algorithms, and other theories and construct the shuttlecock motion recognition based on multiview clustering algorithm. The model analyzes the robustness and accuracy of the machine learning algorithm and other algorithms, such as a variety of performance comparisons, and the results of the shuttlecock motion recognition image. For the key movements of shuttlecock movement, disk, stretch, hook, wipe, knock, and abduction, the algorithm proposed in this paper has a good movement recognition rate, which can reach 91.2%. Although several similar actions can be recognized well, the average recognition accuracy rate can exceed 75%, and even through continuous image capture, the number of occurrences of the action can be automatically analyzed, which is beneficial to athletes. And the coach can better analyze tactics and research strategies.

Automatic microseismic event picking via unsupervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1750-1764 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Earthquake Data

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Exploring simple K-means clustering algorithm for automating segregation of colors in leaf of Axonopus compressus: Towards maintenance of an urban landscape

2021 ◽  
Vol 40 (1) ◽  
pp. 1219-1243
Author(s):  
Lovepreet Singh ◽  
He Huang ◽  
Sanandam Bordoloi ◽  
Ankit Garg ◽  
Mingjie Jiang
Keyword(s):  
Machine Learning ◽  
Green Infrastructure ◽  
Clustering Algorithm ◽  
Urban Landscape ◽  
Learning Algorithm ◽  
Community Gardens ◽  
Spatial And Temporal Variation ◽  
Plant Leaves ◽  
Learning Program ◽  
Green Corridor

Images of green infrastructure (gardens, green corridor, green roofs and grasslands) large area can be captured and processed to provide spatial and temporal variation in colours of plant leaves. This may indicate average variation in plant growth over large urban landscape (community gardens, green corridor etc). Towards this direction, this short technical note explores development of a simple automated machine learning program that can accurately segregate colors from plant leaves. In this newly developed program, a machine learning algorithm has been modified and adapted to give the proportion of different colors present in a leaf. Python script is developed for an image processing. For validation, experiments are conducted in green house to grow Axonopus compressus. Script first extracts different RGB (Red Green and Blue) colors present in the leaf using the K-means clustering algorithm. Appropriate centroids required for the clusters of leaf colors are formed by the K-means algorithm. The new program provides saves computation time and gives output in form of different colors proportion as a CSV (Comma-Separated Values) file. This study is the first step towards the demonstration of using automated programs for the segregation of colors from the leaf in order to access the growth of the plant in an urban landscape.

Classifying Exoplanets with Machine Learning

2020 ◽  
Author(s):  
Ana Barboza ◽  
Solène Ulmer-Moll ◽  
João Faria
Keyword(s):  
Machine Learning ◽  
Parameter Space ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Giant Planets ◽  
Dimensional Parameter ◽  
Hot Jupiters ◽  
Core Composition ◽  
Rocky Planets ◽  
Large Groups

<div class="gmail_default">More than 4200 exoplanets have been detected and their diversity is remarkable, ranging from very small rocky planets, to<br />puffed gas giants. Several of their types are unknown in our Solar System, hence new classes have been defined to understand this<br />diversity and the similarities within each group, such as their formation mechanism or core composition.<br />We aimed to determine the main types of exoplanets, develop a method that automatically associates exoplanets to their type,<br />classifying them into labels with a machine learning algorithm. We also worked to further understand each group, analysing their<br />characteristics, and exploring correlations within each group.<br />Given the planetary mass and orbital period of a large number of exoplanets, we used a K-Means clustering algorithm to classify three large groups: Hot Jupiters, Long Period Giants and Small Planets. In order to take into account more planetary and stellar parameters, we also work with the Uniform Manifold Approximation and Projection (UMAP) technique to visualize<br />data on a 2D map, aiming to find structures within the high dimensional parameter space. We identified different clusters of<br />exoplanets on this map with the help of groups already described in the literature. We explored how different sets of input parameters<br />impact the clustering of exoplanets and studied, in particular, the effect of stellar metallicity.<br />We were able to identify 5 different groups: Hot Jupiters, Longer Period Giants, sub-Jupiters, sub-Neptunes and Rocky Planets.<br />We described these groups in terms of values for each parameter, and discussed outliers. We also analysed metallicity separately and verified<br />that, on average, giant planets orbit around higher metallicity stars than non giant planets.<br />The well known groups of giant exoplanets, such as Hot Jupiters and Longer Period giants, are clearly identified in the<br />resulting UMAP 2D parameter space. For smaller planets, several groups were also visible but less separated. We also verified that the global structure is preserved, noticing, for example, the smaller planets (< 8R<sub><span class="st">⨁</span></sub>) are grouped together and well separated from the Hot Jupiters. Adding more samples<br />of well characterized small planets would certainly help their classification.</div>

scholarly journals Fault Detection in Multi-Core C&I Cable via Machine Learning Based Time-Frequency Domain Reflectometry

2019 ◽  
Vol 10 (1) ◽  
pp. 158 ◽  
Author(s):  
Chun-Kwon Lee ◽  
Seung Jin Chang
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Frequency Domain ◽  
Nuclear Power ◽  
Power Plants ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

The integrity and functionality of the control and instrumentation (C&I) cable systems are essential when it comes to ensuring the reliability and safety of system operations, especially in vehicles or power plants. Whenever a fault occurs in a multi-core cable, it not only affects signals of the individual faulty line but inflicts the rest through crosstalk and noise interference. Thus, it is imperative that cable diagnostic technologies are eligible of detecting the fault and further differentiating the faulty line to prevent the original fault from jeopardizing the entire system operation. We propose here a diagnostic method which detects the presence and the location of a fault, and further differentiates the faulty line within the multi-core C&I cables using a machine learning algorithm based on the time-frequency domain reflectometry results. Neural networks and the hierarchy clustering algorithm are used for fault detection and the identification of the faulty line. The proposed clustering algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&I cables for nuclear power plants. Hence, the proposed algorithm allows a fault in multi-core cables to be accurately detected and estimated when given the location and the reflection coefficient of a fault.

scholarly journals Design and Development of Machine Learning Algorithm for Forecasting Crime Rate

2019 ◽  
Vol 8 (11S) ◽  
pp. 1217-1222
Keyword(s):  
Machine Learning ◽  
Crime Rate ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Learning Approaches ◽  
Average Value ◽  
Data Mining Approach ◽  
Initial Stage ◽  
Euclidean Distance Function

Controlling crime is one of the necessary things for a peaceful life. Forecasting the crime helps in planning the strategies in this task. Modern data analysis techniques like classification and prediction may be utilized for this purpose. Classification is a data mining approach that allocates items in a group to target categories or classes. It also may be used to label a target item into any one of the classes identified.Among many available classification techniques, clustering is one of the unsupervised machine learning approaches that could be used for creating clusters as features to enhance classification models. There are various clustering algorithm available like K-mean clustering, Kernel K-mean algorithm etc.PCA algorithm is used to reduce the dimension of the huge amount of data used so that the data can be represented in smaller database with reduced noise in the dataset. In general, mode is a set of values which occurs frequently. Hence, instead of k-mean which is an average value, frequent values may produce better result.K-Mean algorithm creates clusters and groups data properly. But randomly assuming centroid for clusters in the initial stage leads to too much of computational cost. So, in this work, K mode Clustering algorithm was used for clustering asit replaces the Euclidean distance function with the simple matching dissimilarity measure. Once the clusters were formed, a new algorithm was used to forecast the crime rate or future values of the data in the cluster.The proposed approach was tested on crime dataset and found efficient in this domain while comparing with some existing approaches

APPLICATION OF ENSEMBLE ALGORITHM INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION

2017 ◽  
Vol 29 (06) ◽  
pp. 1750043 ◽  
Author(s):  
Cai-Jie Qin ◽  
Qiang Guan ◽  
Xin-Pei Wang
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Subjective Experience ◽  
Learning Algorithm ◽  
Low Cost ◽  
Detection Methods ◽  
Disease Detection ◽  
Ensemble Algorithm

Conventional coronary heart disease (CHD) detection methods are expensive, rely much on doctors’ subjective experience, and some of them have side effects. In order to obtain rapid, high-precision, low-cost, non-invasive detection results, several methods in machine learning were attempted for CHD detection in this paper. The paper adopted multiple evaluation criteria to measure features, combined with heuristic search strategy and seven common classification algorithms to verify the validity and the importance of feature selection (FS) in the Z-Alizadeh Sani CHD dataset. On this basis, a novelty algorithm integrating multiple FS methods into the ensemble algorithm (ensemble algorithm based on multiple feature selection, EA-MFS) was further proposed. The algorithm adopted Bagging approach to increase data diversity, used the aforementioned MFS methods for functional perturbation, employed major voting method to carry out the decision results, and performed selective integration in terms of the difference of base classifiers in the ensemble process. Compared with the single FS method, the EA-MFS algorithm could comprehensively describe the relationship of features, enhance the classification effect, and displayed better robustness. That meant the EA-MFS algorithm could reduce the dependence on dataset and strengthen the stability of the algorithm, all of which were of great significance for the clinical application of machine learning algorithm in coronary heart disease detection.

scholarly journals Contactless Sleep Monitoring Measurement Setup

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1083 ◽  
Author(s):  
Frederico Lima ◽  
Almothana Albukhari ◽  
Rui Zhu ◽  
Ulrich Mescheder
Keyword(s):  
Machine Learning ◽  
Strain Gauge ◽  
Learning Algorithm ◽  
Low Cost ◽  
Contactless Measurement ◽  
Machine Learning Algorithm ◽  
Sleep Monitoring ◽  
Unsupervised Machine Learning ◽  
Strain Gauge Sensor

In this work, a contactless measurement setup based on a low-cost weighing scale sensor is presented. The positioning of the sensor is a key point in our work with ballistocardiographic (BCG) measurements. This was demonstrated using a strain-gauge sensor placed at the head of the mattress to record the BCG signals, while an unsupervised machine learning algorithm was implemented to detect respiratory and cardiac cycles from patients laying in different postures. Comparison of BCG with electrocardiography (ECG) has shown the ability to detect, at least, 75% of every single heartbeat with the suggested setup and algorithm irrespective of patient’s postures.

scholarly journals Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

2019 ◽  
Vol 11 (22) ◽  
pp. 2596 ◽  
Author(s):  
Luca Zappa ◽  
Matthias Forkel ◽  
Angelika Xaver ◽  
Wouter Dorigo
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Learning Algorithm ◽  
Low Cost ◽  
Cost Effective ◽  
Training Data ◽  
Field Scale ◽  
Training Set

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

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