Concept Generation Algorithms for Repository-Based Early Design

2006 ◽  
Author(s):  
Jayson Vucovich ◽  
Nikhil Bhardwaj ◽  
Hoi-Hei (Terence) Ho ◽  
Manjeshwar Ramakrishna ◽  
Mayur Thakur ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Design Research ◽  
Hidden Markov ◽  
Functional Model ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Design Knowledge ◽  
Concept Generation ◽  
Failure Data

Modern product and engineering design research explores methods for formally generating design concepts from stored knowledge. We discuss a design methodology which utilizes archived design knowledge gained from product dissection to aid novice designers in developing new product designs. In this design paradigm, new designs are developed as a model of the product’s intended functionality, rather than a model of actual, physical components. This paper formulates an algorithm to automatically generate a set of components to instantiate such a functional model using archived design knowledge, which maps components to the functions they can satisfy and provides precedents for which components can be connected. In order to avoid generating an exponential number of instantiations, component failure data is leveraged to develop a dynamic programming algorithm. In addition, a method which uses this information to train a Hidden Markov Model is also developed. This Hidden Markov Model is consulted to generate a set of instantiations with low failure rates while avoiding exponential runtime.

Feature Extraction for Bearing Prognostics Based on Continuous Hidden Markov Model

2014 ◽  
Vol 541-542 ◽  
pp. 1483-1486
Author(s):  
Jian She Kang ◽  
Xing Hui Zhang ◽  
Jin Song Zhao ◽  
Lei Xiao
Keyword(s):  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
High Reliability ◽  
Degradation Process ◽  
Health Condition ◽  
Diagnosis And Prognosis ◽  
Failure Data ◽  
Fault Diagnosis And Prognosis

Many research papers implemented fault diagnosis and prognosis when there are many history data. However, for some capital and high reliability equipment, it is very difficult to acquire some run-to-failure data. In this case, the fault diagnosis and prognosis become very hard. In order to address this issue, continuous hidden Markov model (CHMM) is used to track the degradation process in this paper. With the degradation, the log-likelihood which is the output of CHMM will decrease gradually. Therefore, this indicator can be used to evaluate the health condition of monitored equipment. Finally, bearing run-to-failure data sets are used to validate the model’s effectiveness

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.

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