scholarly journals General Software System for Synthesis of Controllers for Robots and Robotized Technological Systems - A Case Study

1990 ◽  
Vol 23 (8) ◽  
pp. 181-190
Author(s):  
M.K. Vukobratović ◽  
D.M. Stokić
2019 ◽  
Vol 5 ◽  
Author(s):  
Matthew J. Miller ◽  
Karen M. Feigh

The construction of future technological systems in work domains that do not yet exist, known as the envisioned world problem, is an increasingly important topic for designers, particularly given the rapid rate of technological advancement in the modern era. This paper first discusses the theoretical underpinnings of using cognitive work analysis (CWA) for developing a decision support system (DSS) situated within the envisioned world problem and recasts the problem as pathway-dependent processes. Using this pathway-dependent framework, each stage of the envisioning process is described to reveal how human factors experts can link existing work domains to envisioned instances. Finally, a case study example of the envisioning process that incorporates CWA modelling is demonstrated as it pertains to the advancement of the human spaceflight domain. As a result, this paper provides a unified treatment of the envisioned world problem with an end-to-end example of one approach to designing future technologies for future work domains.


2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.


Author(s):  
Jihyun Lee ◽  
Sungwon Kang

For software testing, it is well known that the architecture of a software system can be utilized to enhance testability, fault detection and error locating. However, how much and what effects architecture-based software testing has on software testing have been rarely studied. Thus, this paper undertakes case study investigation of the effects of architecture-based software testing specifically with respect to fault detection and error locating. Through comparing the outcomes with the conventional testing approaches that are not based on test architectures, we confirm the effectiveness of architecture-based software testing with respect to fault detection and error locating. The case studies show that using test architecture can improve fault detection rate by 44.1%–88.5% and reduce error locating time by 3%–65.2%, compared to the conventional testing that does not rely on test architecture. With regard to error locating, the scope of relevant components or statements was narrowed by leveraging test architecture for approximately 77% of the detected faults. We also show that architecture-based testing could provide a means of defining an exact oracle or oracles with range values. This study shows by way of case studies the extent to which architecture-based software testing can facilitate detecting certain types of faults and locating the errors that cause such faults. In addition, we discuss the contributing factors of architecture-based software testing which enable such enhancement in fault detection and error locating.


2014 ◽  
Vol 20 (3) ◽  
Author(s):  
Jordan B. L. Smith ◽  
Isaac Schankler ◽  
Elaine Chew

Some important theories of music cognition, such as Lerdahl and Jackendoff’s (1983)A Generative Theory of Tonal Music, posit an archetypal listener with an ideal interpretation of musical structure, and many studies of the perception of structure focus on what different listeners have in common. However, previous experiments have revealed that listeners perceive musical structure differently, depending upon their music background and their familiarity with the piece. It is not known what other factors contribute to differences among listeners’ formal analyses, but understanding these factors may be essential to advancing our understanding of music perception.We present a case study of two listeners, with the goal of identifying the differences between their analyses, and explaining why these differences arose. These two listeners analyzed the structure of three performances, a set of improvised duets. The duets were performed by one of the listeners and Mimi (Multimodal Interaction for Musical Improvisation), a software system for human-machine improvisation. The ambiguous structure of the human-machine improvisations as well as the distinct perspectives of the listeners ensured a rich set of differences for the basis of our study.We compare the structural analyses and argue that most of the disagreements between them are attributable to the fact that the listeners paid attention to different musical features. Following the chain of causation backwards, we identify three more ultimate sources of disagreement: differences in the commitments made at the outset of a piece regarding what constitutes a fundamental structural unit, differences in the information each listener had about the performances, and differences in the analytical expectations of the listeners.


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