Machine Learning Tools in Machinery Faults Diagnosis: A Review

2014 ◽  
Vol 575 ◽  
pp. 833-836
Author(s):  
K.H. Hui ◽  
Lim Meng Hee ◽  
M. Salman Leong
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Tools ◽  
Faults Diagnosis ◽  
Automatic Machinery

Machinery faults can be detected by various signal processing tools; however, they require human expertise to achieve maximum success. Machine learning tools can help to achieve automatic machinery-faults diagnosis. This paper provides a brief review of the most common machine learning tools.

scholarly journals Signal Processing and Analysis of a Person on the Basis of Time, Age and Gender

2020 ◽  
Vol 9 (5) ◽  
pp. 1135-1139
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Constant Time ◽  
Age Group ◽  
Learning Tools ◽  
Age And Gender ◽  
Human Speech ◽  
And Gender ◽  
Two Parameters ◽  
Google Search

Nowadays, signal processing is very common in machine learning. Tools like MATLAB and PRAAT made this really easy. Best example is google search using voice, this can be mostly beneficial for illiterate people and children. I have collected the sample of speech of 160 people of different age group, different genders, and at different and constant time as databases and then these databases are analysed.. Each database consists of samples of human speech uttering the same first 10 alphabets of hindi varnamala .I have used two parameters i.e. formants and intensity for analysing the results of database.I have also characterized it into the form of constant time and variant time. Our aim is to identify the voices of a person on the basis of age and gender through different paremeters like frequency and formants

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

scholarly journals Organizing workers and machine learning tools for a less oppressive workplace

2021 ◽  
Vol 59 ◽  
pp. 102353
Author(s):  
Amber Grace Young ◽  
Ann Majchrzak ◽  
Gerald C. Kane
Keyword(s):  
Machine Learning ◽  
Learning Tools

scholarly journals An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hayssam Dahrouj ◽  
Rawan Alghamdi ◽  
Hibatallah Alwazani ◽  
Sarah Bahanshal ◽  
Alaa Alameer Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Optimization Problems
Sign in / Sign up

Export Citation Format

Share Document