Intelligent Management of Measurement Units Equivalences in Food Databases

2018 ◽  
pp. 296-306
Author(s):  
Beatriz Sevilla-Villanueva ◽  
Karina Gibert ◽  
Miquel Sànchez-Marrè
Keyword(s):  
Measurement Units ◽  
Intelligent Management

Intelligent management systems in operations: a review

1998 ◽  
Vol 49 (7) ◽  
pp. 682-699 ◽  
Author(s):  
N C Proudlove ◽  
S Vaderá ◽  
K A H Kobbacy
Keyword(s):  
Management Systems ◽  
Intelligent Management

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Estimação de Fontes Harmônicas em Sistemas de Potência

2020 ◽  
Vol 20 ◽  
pp. 11
Author(s):  
Arthur Mesquita ◽  
Marcelo Lima ◽  
Thales Wulfert ◽  
Igor Delgado ◽  
Leandro Manso ◽  
...  
Keyword(s):  
Phasor Measurement Units ◽  
Phasor Measurement ◽  
Measurement Units

Este artigo apresenta uma metodologia para a estimação de fontes de correntes harmônicas em sistemas elétricos de potência a partir do uso da Análise de Componentes Independentes (ICA). Através dos fasores de tensão harmônica complexa obtidos por PMUs (Phasor Measurement Units), os perfis harmônicos podem ser observados e considerados para a estimação das fontes. Um algoritmo baseado em um filtro de média móvel é responsável por dissociar as variações lentas e rápidas dos perfis harmônicos que são empregados à ICA para a estimação das fontes de corrente, rastreando-as ao longo do dia. Simulações computacionais no sistema IEEE 14 Barras são realizadas no intuito de validar a metodologia proposta.

Steady-state Security Assessment Based on K-Means Clustering Algorithm and Phasor Measurement Units

2020 ◽  
Vol 13 (4) ◽  
pp. 559-570
Author(s):  
Bassam A. Hemade ◽  
Hamed A. Ibrahim ◽  
Hossam E.A. Talaat
Keyword(s):  
Steady State ◽  
Clustering Algorithm ◽  
System Security ◽  
Phasor Measurement Units ◽  
Operating Conditions ◽  
Security Assessment ◽  
State Security ◽  
Phasor Measurement ◽  
Measurement Units ◽  
Silhouette Analysis

Background: The security assessment plays a crucial role in the operation of the modern interconnected power system network. Methods: Hence, this paper addresses the application of k-means clustering algorithm equipped with Principal Component Analysis (PCA) and silhouette analysis for the classification of system security states. The proposed technique works on three principal axes; the first stage involves contingency quantification based on developed insecurity indices, the second stage includes dataset preparation to enhance the overall performance of the proposed method using PCA and silhouette analysis, and finally the application of the clustering algorithm over data. Results: The proposed composite insecurity index uses available synchronized measurements from Phasor Measurement Units (PMUs) to assess the development of cascading outages. Considering different operational scenarios and multiple levels of contingencies (up to N-3), Fast Decoupled Power Flow (FDPF) have been used for contingency replications. The developed technique applied to IEEE 14-bus and 57-bus standard test system for steady-state security evaluation. Conclusion: The obtained results ensure the robustness and effectiveness of the established procedure in the assessment of the system security irrespective of the network size or operating conditions.

Hybrid Combination of GIS,GPS,WSN and GPRS Technology in Modern Digital Agriculture Application

2010 ◽  
Vol 108-111 ◽  
pp. 1158-1163 ◽  
Author(s):  
Peng Cheng Nie ◽  
Di Wu ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Yong He
Keyword(s):  
Large Scale ◽  
Field Application ◽  
Wireless Sensor ◽  
Information Collection ◽  
Scale Field ◽  
Large Scale Field ◽  
Field Information ◽  
Intelligent Management ◽  
Digital Agriculture ◽  
And Control

In order to improve the information management of the modern digital agriculture, combined several modern digital agriculture technologies, namely wireless sensor network (WSN), global positioning system (GPS), geographic information system (GIS) and general packet radio service (GPRS), and applied them to the information collection and intelligent control process of the modern digital agriculture. Combining the advantage of the local multi-channel information collection and the low-power wireless transmission of WSN, the stable and low cost long-distance communication and data transmission ability of GPRS, the high-precision positioning technology of the DGPS positioning and the large-scale field information layer-management technology of GIS, such a hybrid technology combination is applied to the large-scale field information and intelligent management. In this study, wireless sensor network routing nodes are disposed in the sub-area of field. These nodes have GPS receiver modules and the electric control mechanism, and are relative positioned by GPS. They can real-time monitor the field information and control the equipment for the field application. When the GPS position information and other collected field information are measured, the information can be remotely transmitted to PC by GPRS. Then PC can upload the information to the GIS management software. All the field information can be classified into different layers in GIS and shown on the GIS map based on their GPS position. Moreover, we have developed remote control software based on GIS. It can send the control commands through GPRS to the nodes which have control modules; and then we can real-time manage and control the field application. In conclusion, the unattended automatic wireless intelligent technology for the field information collection and control can effectively utilize hardware resources, improve the field information intelligent management and reduce the information and intelligent cost.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

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