scholarly journals Bioelectrical pattern discrimination of Miconia plants by spectral analysis and machine learning

2020 ◽  
Author(s):  
Valéria M M Gimenez ◽  
Patrícia M Pauletti ◽  
Ana Carolina Sousa Silva ◽  
Ernane José Xavier Costa
Keyword(s):  
Machine Learning ◽  
Spectral Density ◽  
Frequency Domain ◽  
Pattern Discrimination ◽  
Oscillatory Behavior ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Experimental Botany ◽  
Power Spectral ◽  
Pattern Comparison

AbstractWe have conducted an in loco investigation into the species Miconia albicans (SW.) Triana and Miconia chamissois Naudin (Melastomataceae), distributed in different phytophysiognomies of three Cerrado fragments in the State of São Paulo, Brazil, to characterize their oscillatory bioelectrical signals and to find out whether these signals have distinct spectral density. The experiments provided a sample bank of bioelectrical amplitudes, which were analyzed in the time and frequency domain. On the basis of the power spectral density (PSD) and machine learning techniques, analyses in the frequency domain suggested that each species has a characteristic biological pattern. Comparison between the oscillatory behavior of the species clearly showed that they have bioelectrical features, that collecting data is feasible, that Miconia display a bioelectrical pattern, and that environmental factors influence this pattern. From the point of view of experimental Botany, new questions and concepts must be formulated to advance understanding of the interactions between the communicative nature of plants and the environment. The results of this on-site technique represent a new methodology to acquire non-invasive information that might be associated with physiological, chemical, and ecological aspects of plants.HighlightIn loco characterization of the bioelectrical signals of two Miconia species in the time and frequency domain suggests that the species have distinct biological patterns.

scholarly journals Vehicle response on kinematic excitation

2018 ◽  
Vol 211 ◽  
pp. 13001
Author(s):  
Veronika Valašková ◽  
Jozef Melcer
Keyword(s):  
Spectral Density ◽  
Frequency Domain ◽  
Power Spectral Density ◽  
Engineering Problem ◽  
Kinematic Excitation ◽  
The Road ◽  
Power Spectral ◽  
Road Profile ◽  
Vehicle Response ◽  
On The Road

The vehicle - roadway interaction is actual engineering problem solved on many workplaces in the world. At the present time preference is given to numerical and experimental approaches. Vehicle designers are interested in the vibration of the vehicle and the forces acting on the vehicle. Civil engineers are interested in the load acting on the road. Solution of the problem can be carried out in time or in frequency domain. Road unevenness is the main source of kinematic excitation of the vehicle and therefore the main source of dynamic forces acting both on the road and the vehicle. The offered article deals with one of the possibilities of numerical analysis of the vehicle response in frequency domain. It works with quarter model of the vehicle. For the selected computational model of the vehicle it quantifies the Frequency Response Functions (FRF) of both force and kinematic quantities. It considers the stochastic road profile. The Power Spectral Density (PSD) of the road profile is used as input value for the calculation of Power Spectral Density of the response. All calculations are carried out numerically in the environment of program system MATLAB. When we know the modules of FRF or the Power Response Factors (PRF) of vehicle model the calculation of vehicle response in frequency domain is fast and efficient.

scholarly journals Statistical characteristics of sources of vehicle kinematic excitation

2020 ◽  
Vol 313 ◽  
pp. 00011
Author(s):  
Jozef Melcer ◽  
Eva Merčiaková ◽  
Mária Kúdelčíková
Keyword(s):  
Probability Theory ◽  
Spectral Density ◽  
Surface Quality ◽  
Correlation Functions ◽  
Random Variable ◽  
Point Of View ◽  
Statistical Characteristics ◽  
Kinematic Excitation ◽  
Power Spectral ◽  
The Individual

The longitudinal and transverse road profiles represent the functions of a random variable from a mathematical point of view. It is appropriate to use methods of probability theory and mathematical statistics for their description. The unevenness of the runway surface is the main source of the vehicle's kinematic excitation. This paper describes the statistical properties of the mapped road profiles. It shows a way of categorizing road surface quality based on the power spectral density of unevenness. The interrelationships between the individual points of the profile and the profiles with one another are evaluated by correlation functions.

From machine learning to sustainable taxation: GPS traces of trucks circulating in Belgium

2020 ◽  
Author(s):  
Arnaud Adam ◽  
Isabelle Thomas
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Spatial Effects ◽  
Space And Time ◽  
Learning Techniques ◽  
Other Information ◽  
Gps Traces ◽  
The Individual ◽  
Taxation System

<p>Transport geography has always been characterized by a lack of accurate data, leading to surveys often based on samples that are spatially not representative. However, the current deluge of data collected through sensors promises to overpass this scarcity of data. We here consider one example: since April 1<sup>st</sup> 2016, a GPS tracker is mandatory within each truck circulating in Belgium for kilometre taxes. Every 30 seconds, this tracker collects the position of the truck (as well as some other information such as speed or direction), leading to an individual taxation of trucks. This contribution uses a one-week exhaustive database containing the totality of trucks circulating in Belgium, in order to understand transport fluxes within the country, as well as the spatial effects of the taxation on the circulation of trucks.</p><p>Machine learning techniques are applied on over 270 million of GPS points to detect stops of trucks, leading to transform GPS sequences into a complete Origin-Destination matrix. Using machine learning allows to accurately classify stops that are different in nature (leisure stop, (un-)loading areas, or congested roads). Based on this matrix, we firstly propose an overview of the daily traffic, as well as an evaluation of the number of stops made in every Belgian place. Secondly, GPS sequences and stops are combined, leading to characterise sub-trajectories of each truck (first/last miles and transit) by their fiscal debit. This individual characterisation, as well as its variation in space and time, are here discussed: is the individual taxation system always efficient in space and time?</p><p>This contribution helps to better understand the circulation of trucks in Belgium, the places where they stopped, as well as the importance of their locations in a fiscal point of view. What are the potential modifications of the trucks routes that would lead to a more sustainable kilometre taxation? This contribution illustrates that combining big-data and machine learning open new roads for accurately measuring and modelling transportation.</p>

scholarly journals Predictive Model Evaluation for PHM

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Chunsheng Yang ◽  
Yanni Zou ◽  
Jie Liu ◽  
Kyle R Mulligan
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Real World ◽  
Predictive Models ◽  
Model Evaluation ◽  
High Performance ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Real World Applications ◽  
Prognostic Application

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studies to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.

Non–human primate epidural ECoG analysis using explainable deep learning technology

2021 ◽  
Author(s):  
Hoseok Choi ◽  
Seokbeen Lim ◽  
Kyeongran Min ◽  
Kyoung-ha Ahn ◽  
Kyoung-Min Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Hand Movement ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Technology ◽  
3D Information

Abstract Objective: With the development in the field of neural networks, Explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach: We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results: The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance: As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

scholarly journals Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Zhao
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Heritage Sites ◽  
The Past ◽  
Sensing Technology ◽  
Learning Techniques ◽  
Cultural Relics

The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.

scholarly journals Machine Learning Techniques for Speech Recognition using the Magnititudes

2020 ◽  
Vol 8 (6) ◽  
pp. 1667-1671
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Data Processing ◽  
Supervised Learning ◽  
Current Trend ◽  
Large Datasets ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Supervised And Unsupervised Learning ◽  
Learning Techniques

Speech is the most proficient method of correspondence between people groups. Discourse acknowledgment is an interdisciplinary subfield of computational phonetics that creates approaches and advances that empowers the acknowledgment and interpretation of communicated in language into content by PCs. It is otherwise called programmed discourse acknowledgment (ASR), PC discourse acknowledgment or discourse to content (STT). It consolidates information and research in the etymology, software engineering, and electrical building fields. This, being the best methodology of correspondence, could likewise be a helpful interface to speak with machines. Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is that the data processing task of inferring a perform from labeled coaching information. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper offers a diagram of major innovative point of view and valuation for the fundamental advancement of speech recognitionand offers review method created in each phase of discourse acknowledgment utilizing supervised learning. The project will use ANN to recognize speeches using magnitudes with large datasets.

Indian Premier League Using Different Aspects of Machine Learning Algorithms

2021 ◽  
pp. 01-07
Author(s):  
Gande Akhila ◽  
◽  
◽  
◽  
Hemachandran K ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Test Dataset ◽  
Test Information ◽  
Premier League ◽  
Learning Techniques ◽  
Winning Percentage ◽  
Descriptive Models

The purpose of the present article is to highlight the outcomes of Indian premier league cricket match utilizing a managed taking in come nearer from a team-based point of view. The methodology consists of prescriptive and descriptive models. Descriptive model focuses mainly on two aspects they are, it describes data and statistics of the previous information. i.e., batting, balling or allrounder and It predicts past matches of IPL. Predictive model predicts ranking and winning percentage of the team. The two models show the measurements of winning level of the group Winner that the user has selected. This paper predicts the result through which technique match has highest result. The dataset consists of two groups that is the toss outcome, venue date, which tells about of the counterpart for all matches. Since the nature impact can't be expected in the game, 109 matches which were either finished by downpour or draw/tie, have been taken out from the dataset. The dataset is partitioned into two sections to be specific the test information and the train information.The readiness dataset contains the 70% of the information from our dataset and the test dataset contains 30% of the information from our dataset. There were all out of 3500 coordinates in getting ready dataset and 1500 matches. This paper has been researched earlier by different scholars like Pathak and Wadwa, Munir etl ,and many other scholars. This viewpoint discusses the application of INDIAN PREMIER LEAGUE Matches held in different states. Gives the score of batsman and bowler with the help of machine learning techniques. Focuses on predicted analysis which is predicted by applying with various AI strategies to the real outcome actual result and gives the percentage of predicted result.

THE EFFECTS ON SWAY ANGLE PERFORMANCE IN GANTRY CRANE SYSTEM BY USING PSD ANALYSIS

2015 ◽  
Vol 77 (21) ◽  
Author(s):  
Sharifah Yuslinda Syed Hussien ◽  
Rozaimi Ghazali ◽  
Hazriq Izzuan Jaafar ◽  
Chong Chee Soon
Keyword(s):  
Spectral Density ◽  
Frequency Domain ◽  
System Performance ◽  
Lagrange Equation ◽  
Comparative Assessment ◽  
Gantry Crane ◽  
Overhead Crane ◽  
Power Spectral ◽  
Payload Mass ◽  
Human Capability

Gantry Crane is also known as an overhead crane and widely used in industries, constructions or shipyards due to limited human capability to carry the various types of load. This system is developed to load and unload heavy materials from one place to another desired location. The problem is frequently occurs when the crane has to move the load at the required position while minimizing the sway angle of the oscillation. Thus, this research presents the investigation of the 2-D Gantry Crane System which focuses on the sway angle characteristics via Power Spectral Density (PSD) analysis. The mathematical dynamic model of the Gantry Crane System is developed using the Lagrange Equation. The system is simulated in MATLAB/Simulink environment and the results are presented in the form of time and frequency domain. A comparative assessment of the various payload mass and rope length of the system performance is presented and discussed.

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