The nearest neighbor gradient system. A rigorous model for a version of the minimal entropy production principle

1990 ◽  
Vol 59 (3-4) ◽  
pp. 827-843
Author(s):  
Michael G. M�rmann
Keyword(s):  
Entropy Production ◽  
Nearest Neighbor ◽  
Gradient System ◽  
System A ◽  
Rigorous Model ◽  
Minimal Entropy Production ◽  
Nearest Neighbor Gradient System

scholarly journals K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

2021 ◽  
Vol 9 (6) ◽  
pp. 2650-2657
Author(s):  
Mohd Hatta Jopri ◽  
Mohd Ruddin Ab Ghani ◽  
Abdul Rahim Abdullah ◽  
Mustafa Manap ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Single Point ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
System A ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Source

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

On Master Operators with Extremal Entropy Production

1978 ◽  
Vol 33 (10) ◽  
pp. 1122-1132
Author(s):  
O. Seeberg
Keyword(s):  
Entropy Production ◽  
Maximal Entropy ◽  
Extremal Points ◽  
Minimal Entropy Production

Abstract The master operators B which cause the entropy production dH/dt = - k-1 dS/dt to become extremal for fixed statistical operators W are constructed and discussed. There are boundaries of the set B of master operators, B = {B | Σ B2vu = b} for which the problem is solvable yielding minimal entropy production, while no solution exists in the set B without any constraints. Operators with maximal entropy production must be extremal points of B.

Central Station-Based Demand Prediction for Determining Target Inventory in a Bike-Sharing System

2020 ◽  
Author(s):  
Jianbin Huang ◽  
Heli Sun ◽  
He Li ◽  
Longji Huang ◽  
Ao Li ◽  
...  
Keyword(s):  
New York ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Queuing Model ◽  
Demand Prediction ◽  
Existing Problems ◽  
System A ◽  
Bike Sharing ◽  
Central Station

Abstract Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems.

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