A questioning based method to automatically acquire expert assembly diagnostic knowledge

In the domain of manual mechanical assembly, expert knowledge is an important means of supporting assembly planning that leads to fewer issues during actual assembly. Knowledge based systems can be used to provide assembly planners with expert knowledge as advice. However, acquisition of knowledge remains a difficult task to automate, while manual acquisition is tedious, time-consuming, and requires engagement of knowledge engineers with specialist knowledge to understand and translate expert knowledge. This paper describes the development, implementation and preliminary evaluation of a method that asks a series of questions to an expert, so as to automatically acquire necessary diagnostic and remedial knowledge as rules for use in a knowledge based system for advising assembly planners diagnose and resolve issues. The method, called a questioning procedure, organizes its questions around an assembly situation which it presents to the expert as the context, and adapts its questions based on the answers it receives from the expert. (C) 2014 Elsevier Ltd. All rights reserved.

Combining product information and process information to build virtual assembly situations for knowledge acquisition

Assembly is an important part of the product development process. To avoid potential issues during assembly in specialized domains such as aircraft assembly, expert knowledge to predict such issues is helpful. Knowledge based systems can act as virtual experts to provide assistance. Knowledge acquisition for such systems however, is a challenge, and this paper describes one part of an ongoing research to acquire knowledge through a dialog between an expert and a knowledge acquisition system. In particular this paper discusses the use of a situation model for assemblies to present experts with a virtual assembly and help them locate the specific context of the knowledge they provide to the system.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.