Pattern recognition and classification of HVAC rule-based faults in commercial buildings

2016 ◽  
Author(s):  
Bradford Littooy ◽  
Sophie Loire ◽  
Michael Georgescu ◽  
Igor Mezic
Keyword(s):  
Pattern Recognition ◽  
Commercial Buildings ◽  
Rule Based

On Pattern Recognition in Rule-Based Topology Modification

2008 ◽  
Author(s):  
Thomas Kormeier ◽  
Stephan Rudolph
Keyword(s):  
Pattern Recognition ◽  
Composite Structures ◽  
Sound Design ◽  
Design Environment ◽  
Major Drawback ◽  
Rule Based ◽  
Design Proposal ◽  
Stress Patterns ◽  
Topology Modification

Classical topology optimization aims at achieving a problem suited material distribution in a structure by identification of lightly loaded areas and local element-wise reduction of stiffness. The resulting topologic layout often contains small substructures which are complicated to manufacture, hence requiring an additional manual smoothing during the structural interpretation phase. One major drawback of this approach is that the results still have to be interpreted by an engineer and consequently be translated into a feasible structure. In order to gain a first conceptual yet topologically sound design proposal for composite structures, this paper presents an alternate method for an explicit, pattern based topology modification approach combined with numerical simulation of tape-laying technology. It is assumed that certain patterns exist in stress fields that are extractable by pattern recognition algorithms known from image processing. In the case that prototypical structural reinforcements for such stress patterns can be defined, an automatic topology modification algorithm with the goal of increasing the stiffness is feasible. The classification of these stress patterns is achieved by using dimensionless features matching the stress patterns with their appropriate reinforcements. When integrated into a rule-based conceptual design environment, this explicit topology modification offers the potential to generate simple and easily manufacturable topological reinforcement proposals in an automated structural design loop.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Erratum: Text compression as rule based pattern recognition

1993 ◽  
Vol 29 (24) ◽  
pp. 2155
Author(s):  
H.U. Khan ◽  
J. Ahmad ◽  
A. Mahmood ◽  
H.A. Fatmi
Keyword(s):  
Pattern Recognition ◽  
Text Compression ◽  
Rule Based

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

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