scholarly journals Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Eugene K. Lee ◽  
Yosuke K. Kurokawa ◽  
Robin Tu ◽  
Steven C. George ◽  
Michelle Khine
Keyword(s):  
Machine Learning ◽  
Optical Flow ◽  
Cardioactive Drugs ◽  
Sensitive Method

scholarly journals A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanfei Xiang ◽  
Jianbing Ma ◽  
Xi Wu
Keyword(s):  
Machine Learning ◽  
Optical Flow ◽  
Weather Prediction ◽  
Radar Data ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Model Parameters ◽  
Real World Data ◽  
Flow Method ◽  
Optical Flow Method

Unpredicted precipitations, even mild, may cause severe economic losses to many businesses. Precipitation nowcasting is hence significant for people to make correct decisions timely. For traditional methods, such as numerical weather prediction (NWP), the accuracy is limited because the smaller scale of strong convective weather must be smaller than the minimum scale that the model can capture. And it often requires a supercomputer. Furthermore, the optical flow method has been proved to be available for precipitation nowcasting. However, it is difficult to determine the model parameters because the two steps of tracking and extrapolation are separate. In contrast, current machine learning applications are based on well-selected full datasets, ignoring the fact that real datasets quite often contain missing data requiring extra consideration. In this paper, we used a real Hubei dataset in which a few radar echo data are missing and proposed a proper mechanism to deal with the situation. Furthermore, we proposed a novel mechanism for radar reflectivity data with single altitudes or cumulative altitudes using machine learning techniques. From the experimental results, we conclude that our method can predict future precipitation with a high accuracy when a few data are missing, and it outperforms the traditional optical flow method. In addition, our model can be used for various types of radar data with a type-specific feature extraction, which makes the method more flexible and suitable for most situations.

scholarly journals Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory

2019 ◽  
Vol 10 ◽  
Author(s):  
Shogo Nagano ◽  
Shogo Moriyuki ◽  
Kazumasa Wakamori ◽  
Hiroshi Mineno ◽  
Hirokazu Fukuda
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Optical Flow ◽  
Flow Analysis ◽  
Leaf Movement ◽  
Growth Prediction ◽  
Plant Factory

scholarly journals Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3886
Author(s):  
Marek Florkowski
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Optical Flow ◽  
High Voltage ◽  
Partial Discharge ◽  
Electrical Power System ◽  
Deep Convolutional Neural Networks ◽  
High Voltage Insulation ◽  
Discharge Patterns

In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly detection algorithms can trigger early detection of the trend changes or the appearance of a new discharge form, and hence are suitable for PD monitoring applications. Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. The future monitoring systems should be equipped with trend evolution algorithms. In this context, two examples of insulation aging and application of PD-based monitoring are shown. The first one refers to deep convolutional neural networks used for classification of deterioration stages in high voltage insulation. The latter one demonstrates application of optical flow approach for motion detection in partial discharge images. The motivation for the research was the strive to machine-controlled pattern analysis, leading towards intelligent PD-based diagnostics.

scholarly journals MSDM: a machine learning model for precipitation nowcasting over east China using multi-source data

2020 ◽  
Author(s):  
Dawei Li ◽  
Yudi Liu ◽  
Chaohui Chen
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Optical Flow ◽  
Satellite Data ◽  
Lead Time ◽  
Rainfall Rate ◽  
Data Driven ◽  
East China ◽  
Source Data ◽  
Radar Echo

Abstract. East China is one of the most economically developed and most densely populated areas in the world. Due to its special geographical location and climate, East China is affected by different weather systems like monsoon, shear line, typhoon and extratropical cyclone, in the imminent future the rainfall rate affected by which is difficult to precisely predict. Traditional physics-based methods like Numerical Weather Prediction (NWP) tend to perform poorly for the nowcasting problem due to its spinup issue. Meanwhile, various meteorological stations are distributed here, generating a large amount of observation data every day, which has a great potential to be applied to data-driven methods. Thus, it is important to train a data-driven model from scratch that suitable to the specific weather situation of East China. We collect three kinds of data (radar, satellite, precipitation) in flood season from 2017 to 2018 of this area and preprocess them into ndarray (256 × 256) that cover East China with a domain of 12.8 × 12.8°.The Multi-Source Data Model (MSDM) which we developed combines the Optical flow, Random forest and Convolutional Neural Network (CNN). It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with a interval of 30 minutes as inputs and predicts radar echo intensity at a lead time of 30 minutes. To reduce the smoothing caused by convolution, we use Optical flow to predict satellite data in the following 120 minutes. The predicted radar echo from MSDM together with satellite data from Optical flow are recursively implemented in MSDM to achieve 120 minutes lead time. The predictions from MSDM are comparable to those of other baseline models with a high temporal resolution of 6 minutes. To solve the blurry image problems, we applied a modified SSIM as a loss function. Furthermore, we use Random forest with predicted radar and satellite data to estimate the rainfall rate, the results outperform those of the traditional Z-R relationship. The experiments confirm that machine learning with multi-source data provides more reasonable predictions and reveals a better non-linear relationship between radar echo and precipitation rate. Besides the algorithms will be developed, exploiting the potential of multi-source data will bring more improvements.

scholarly journals A Convection Nowcasting Method Based on Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Aifang Su ◽  
Han Li ◽  
Liman Cui ◽  
Yungang Chen
Keyword(s):  
Machine Learning ◽  
Flow Field ◽  
Optical Flow ◽  
Short Term Memory ◽  
Image Feature ◽  
Short Term ◽  
Term Memory ◽  
Optical Flow Field ◽  
Radar Echoes ◽  
Long Short Term Memory

In this study, a convection nowcasting method based on machine learning was proposed. First, the historical data were back-calculated using the pyramid optical flow method. Next, the generated optical flow field information of each pixel and the Red-Green-Blue (RGB) image information were input into the Convolutional Long Short-Term Memory (ConvLSTM) algorithm for training purposes. During the extrapolation process, dynamic characteristics such as the rotation, convergence, and divergence in the optical flow field were also used as predictors to form an optimal nowcasting model. The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. Based on learning through the above features, this algorithm can forecast the generation and dissipation trends of convective cells to some extent. The addition of the optical flow information can more accurately simulate nonlinear trends such as the rotation, or merging, or separation of radar echoes. The trajectories of radar echoes obtained through nowcasting are closer to their actual movements, which prolongs the valid forecasting period and improves forecast accuracy.

Machine Learning Based Surface Velocimetry

2021 ◽  
Author(s):  
Saber Ansari ◽  
Colin D. Rennie ◽  
Elizabeth C. Jamieson ◽  
Ousmane Seidou ◽  
Shawn P. Clark
Keyword(s):  
Machine Learning ◽  
Optical Flow ◽  
User Involvement ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
User Expertise ◽  
Infrastructure Design ◽  
New Instrumentation ◽  
Streamflow Data ◽  
The Impact

<p>Streamflow measurement is of great importance in hydrological research, water management and water infrastructure design. Traditional measurement methods typically employ intrusive techniques, and under certain conditions, obtaining accurate streamflow data with these techniques can be challenging because of safety concerns, especially in some critical circumstances, such as during flood flows. The advent of new instrumentation and technologies, and in particular advances in digital imagery, has led to the emergence of non-intrusive novel image-based technologies that can be used to estimate surface velocity, which in turn can be used to estimate streamflow. Image based technologies, most of which are based on correlation between consecutive images, have the potential for remote and on demand measurements and can provide data when the application of other traditional methods are not possible, reliable or safe. In this study, we present a novel machine learning based optical flow algorithm for streamflow surface velocimetry estimation. The developed algorithm is tested in different flow conditions and using drone and fixed photogrammetry. This method appears to outperform all the other available image-based surface velocimetry approaches (i.e. correlation based and classical optical flow methods). Moreover, this method requires the least user involvement for velocity estimation and thus reduces the impact or arbitrary choices linked to user expertise.</p>

scholarly journals MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data

2021 ◽  
Vol 14 (6) ◽  
pp. 4019-4034 ◽  
Author(s):  
Dawei Li ◽  
Yudi Liu ◽  
Chaohui Chen
Keyword(s):  
Machine Learning ◽  
Optical Flow ◽  
Satellite Data ◽  
Lead Time ◽  
Eastern China ◽  
Rainfall Rate ◽  
Machine Learning Algorithms ◽  
Physical Constraints ◽  
Flow Algorithm ◽  
Radar Echo

Abstract. Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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