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Finding the Different Patterns in Buildings Data Using Bag of Words Representation with Clustering

Usman Habib, Gerhard Zucker
2015 2015 13th International Conference on Frontiers of Information Technology (FIT)  
The K Means clustering is used to automatically identify the ON (operational) cycles of the chiller.  ...  Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller.  ...  Q6a_m3h HT cycle Flow (water) reading Q12_m3h MT cycle Flow (water) reading Q7_m3h LT cycle Flow (water) reading T_HTre HT cycle temperature on return side.  ... 
doi:10.1109/fit.2015.60 dblp:conf/fit/HabibZ15 fatcat:j3jbcuozzrhc3kn5yraqfeukii

Complex building's energy system operation patterns analysis using bag of words representation with hierarchical clustering

Usman Habib, Khizar Hayat, Gerhard Zucker
2016 Complex Adaptive Systems Modeling  
Methods: The use of k-means clustering is being proposed to automatically identify the On (operational) cycles of a system operating with a duty cycle.  ...  This paper proposes a method to support analysis of energy systems and validates it using operational data from a cold water chiller.  ...  (water) reading Q12_m3h MT cycle Flow (water) reading Q7_m3h LT cycle Flow (water) reading T _HTre HT cycle temperature on return side T _HTsu HT cycle temperature on supply side T _MTre  ... 
doi:10.1186/s40294-016-0020-0 fatcat:jpwsw67vobfqtdan3k2i6soh7u

Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

Marco Maggipinto, Elena Pesavento, Fabio Altinier, Giuliano Zambonin, Alessandro Beghi, Gian Antonio Susto
2019 Energies  
In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance  ...  this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based  ...  For example, in our case, the load weight (from 1 kg to 8 kg) is an important discriminator that highly impacts the amount of water absorbed by the laundry and imposes a hierarchical structure to our problem  ... 
doi:10.3390/en12214080 fatcat:hrlufhej7jacjdlkndoszfnp4i

Index [chapter]

2021 Data Science Applied to Sustainability Analysis  
, 8, 177 Support vector machines (SVM), 8-9, 46, 212-213 life cycle assessment (LCA), 7 Soil and Water Conservation Stations), 44 Synthetic Aperture Radar on European Remote Sensing satellites (SAR/ERS  ...  cycle, 21 Plant available water-holding capacity (PAWC), 63-64 Poverty, global levels of, 4-5, 5f Power conversion efficiency (PCE), 133-134 Predictive Ocean and Atmospheric Model for Australia  ... 
doi:10.1016/b978-0-12-817976-5.00036-x fatcat:wqtjjdxujracfi5lpfrkryjjj4

Methodology of physiography zoning using machine learning: A case study of the Black Sea

Denis Krivoguz
2020 Russian Journal of Earth Science  
Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area.  ...  Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.  ...  Non-hierarchical -optimize a certain objective function. 1. Graph theory algorithms; 2. EM algorithm; 3. -means algorithm ( -means clustering); 4. fuzzy algorithms.  ... 
doi:10.2205/2020es000707 fatcat:xluhdffnbrb73fc3jlnuqya6ry

Papers Title

2021 2021 26th International Computer Conference, Computer Society of Iran (CSICC)  
Fuzzy Classification DPSA: A Brief Review for Design Pattern Selection Approaches Water Cycle Algorithm-Based Control for Optimal Consensus Problem A Graph-based Semantical Extractive Text Summarization  ...  Algorithm A Neuro-Fuzzy Classifier Based on Evolutionary Algorithms Fuzzy Optimal Control Approach in Low-Thrust Orbit Transfer Problem A Novel Wireless Network-on-Chip Architecture for Multicore Systems  ... 
doi:10.1109/csicc52343.2021.9420588 fatcat:pjym2xz7cfa2vi6tlk3aclxy34

Learning the distance metric in a personal ontology

Hui Yang, Jamie Callan
2008 Proceeding of the 2nd international workshop on Ontologies and nformation systems for the semantic web - ONISW '08  
Based on that, this paper presents a supervised hierarchical clustering framework to incorporate personal preferences for distance metric learning in personal ontology construction.  ...  In this framework, periodic manual guidance provides training data for learning a distance metric and the learned metric is used during automatic activities to further construct the ontology.  ...  Algorithm 2.4 gives the pseudo-codes for the supervised hierarchical clustering algorithm.  ... 
doi:10.1145/1458484.1458488 dblp:conf/cikm/YangC08a fatcat:kpiugoh7k5dlxlhwzzyhtmir5e

IGW/DL: A Digital Library for Teaching and Learning Hydrogeology and Groundwater Modeling

Chunmiao Zheng, Rui Ma
2010 Ground Water  
modeling • Algorithmic visualizations Vol. 48, No. 3-GROUND WATER-May-June 2010 NGWA.org  ...  Finally, the two categories listed in the library index, "Hierarchical modeling" and "Algorithmic visualizations" are not yet supported.  ... 
doi:10.1111/j.1745-6584.2010.00693.x fatcat:yum65466kbbmxipkmewd4k2hai

Water pipe condition assessment: a hierarchical beta process approach for sparse incident data

Zhidong Li, Bang Zhang, Yang Wang, Fang Chen, Ronnie Taib, Vicky Whiffin, Yi Wang
2013 Machine Learning  
The main aims of this work are three-fold: (1) For sparse incident data, develop an efficient approximate inference algorithm based on hierarchical beta process. (2) Apply the hierarchical beta process  ...  based method to water pipe condition assessment. (3) Interpret the outcomes in financial terms usable by the water utilities.  ...  Acknowledgements This study is a joint work of National ICT Australia and Sydney Water Corporation.  ... 
doi:10.1007/s10994-013-5386-z fatcat:ziuqhnl7izdk7mj4bhft7f6b7a

Data-mining-based system for prediction of water chemistry faults

A. Kusiak, S. Shah
2006 IEEE transactions on industrial electronics (1982. Print)  
The system functions include data preprocessing, learning, prediction, alarm generation, and display. A hierarchical decision-making algorithm for fault prediction has been developed.  ...  The alarm system was applied for prediction and avoidance of water chemistry faults (WCFs) at two commercial power plants.  ...  Data-mining algorithms produced easy-to-interpret multiple rule sets, which were employed by the hierarchical decisionmaking algorithm to predict faults.  ... 
doi:10.1109/tie.2006.870706 fatcat:zl5yc6pya5dqhcpmuscyeetnli

Dam Burst: A region-merging-based image segmentation method [article]

Rui Tang, Wenlong Song, Xiaoping Guan, Huibin Ge, Deke Kong
2020 arXiv   pre-print
To avoid over segmentation, multiple thresholds of criteria are adopted in region merging process to produce hierarchical segmentation results.  ...  Until now, all single level segmentation algorithms except CNN-based ones lead to over segmentation. And CNN-based segmentation algorithms have their own problems.  ...  ., Ltd for their continuous trust and support for non-deep-learning computer vision algorithms, thank Prof.  ... 
arXiv:2003.04797v1 fatcat:bw6wucgirzcktaotsz4gyeh3bm

Unsupervised Space-Time Clustering using Persistent Homology [article]

Umar Islambekov, Yulia Gel
2019 arXiv   pre-print
We evaluate the performance of our algorithm on synthetic data and compare it to other well-known clustering algorithms such as K-means, hierarchical clustering and DBSCAN.  ...  We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.  ...  The algorithms based on hierarchy build a hierarchical relationships among data points to perform clustering [27] .  ... 
arXiv:1910.11525v1 fatcat:vtgehn6ayfgilbtspknis3hqj4

Hierarchical Cluster Analysis by R language for Pattern Recognition in the Bathymetric Data Frame: a Case Study of the Mariana Trench, Pacific Ocean

Polina Lemenkova
2018 Figshare  
Then the dendrogram was sorted using machine learning algorithm.  ...  The hierarchical dendrogram method was selected to test the data, due to its flexibility: this type of the machine learning is used specially for unlabelled data, i.e. without defined categories or groups  ... 
doi:10.6084/m9.figshare.7531550.v2 fatcat:5tmoiw543rdk3kqd7tzm3scqty

Hierarchical Cluster Analysis by R Language for Pattern Recognition in the Bathymetric Data Frame: a Case Study of the Mariana Trench, Pacific Ocean

Polina Lemenkova
2019 Figshare  
Then the dendrogram was sorted using machine learning algorithm.  ...  The hierarchical dendrogram method was selected to test the data, due to its flexibility: this type of the machine learning is used specially for unlabelled data, i.e. without defined categories or groups  ... 
doi:10.6084/m9.figshare.7358072.v3 fatcat:pqjat72hwndz7fsxypzdbn5u7u

Wide range operation of a power unit via feedforward fuzzy control [thermal power plants]

R. Garduno-Ramirez, K.Y. Lee
2000 IEEE transactions on energy conversion  
A two-level hierarchical control scheme for wide-range operation of fossil fuel power units is presented.  ...  This approach provides off-line learning capability to the control system.  ...  Figs. 10-15 show the system performance during a simple cycle. Fig. 10 demonstrates that load tracking is very good.  ... 
doi:10.1109/60.900503 fatcat:n2pfatjd5bfaxabhzw3ubvocyy
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