Predicting Pilot Error in Nextgen: Pilot Performance Modeling and Validation Efforts

We review 25 articles presenting 5 general classes of computational models to predict pilot error. This more targeted review is placed within the context of the broader review of computational models of pilot cognition and performance, including such aspects as models of situation awareness or pilot-automation interaction. Particular emphasis is placed on the degree of validation of such models against empirical pilot data, and the relevance of the modeling and validation efforts to Next Gen technology and procedures

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed

A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model

Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech

International audienceWe have applied two state-of-the-art speech synthesis techniques (unit selection and HMM-based synthesis) to the synthesis of emotional speech. A series of carefully designed perceptual tests to evaluate speech quality, emotion identification rates and emotional strength were used for the six emotions which we recorded -, , ,, , . For the HMM-based method, we evaluated spectral and source components separately and identified which components contribute to which emotion.Our analysis shows that, although the HMM method produces significantly better neutral speech, the two methods produce emotional speech of similar quality, except for emotions having context-dependent prosodic patterns. Whilst synthetic speech produced using the unit selection method has better emotional strength scores than the HMM-based method, the HMM-based method has the ability to manipulate the emotional strength. For emotions that are characterized by both spectral and prosodic components, synthetic speech using unit selection methods was more accurately identified by listeners. For emotions mainly characterized by prosodic components, HMM-based synthetic speech was more accurately identified. This finding differs from previous results regarding listener judgements of speaker similarity for neutral speech. We conclude that unit selection methods require improvements to prosodic modeling and that HMM-based methods require improvements to spectral modeling for emotional speech. Certain emotions cannot be reproduced well by either method

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function

Natural Sounding Standard Malay Speech Synthesis Based On UTMK EBMT Architecture System

In this research work, we make natural sounding speech synthesis as the main goal. This goal was chosen following the type of demanded speech synthesis application systems by the industrial market; the limited-domain speech synthesis application systems. The limited-domain speech synthesis application system has restricted number of vocabularies (less flexible) but requires a highly natural sounding of speech synthesis. Based on the evolution of speech synthesis technique, one can conclude that using natural speech units without applying any signal processing is the technique to produce the most natural sounding of synthetic speech

Analysis of Speaker Adaptation Algorithms for HMM-based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm

In this paper we analyze the effects of several factors and configuration choices encountered during training and model construction when we want to obtain better and more stable adaptation in HMM-based speech synthesis. We then propose a new adaptation algorithm called constrained structural maximum a posteriori linear regression (CSMAPLR) whose derivation is based on the knowledge obtained in this analysis and on the results of comparing several conventional adaptation algorithms. Here we investigate six major aspects of the speaker adaptation: initial models transform functions, estimation criteria, and sensitivity of several linear regression adaptation algorithms algorithms. Analyzing the effect of the initial model, we compare speaker-dependent models, gender-independent models, and the simultaneous use of the gender-dependent models to single use of the gender-dependent models. Analyzing the effect of the transform functions, we compare the transform function for only mean vectors with that for mean vectors and covariance matrices. Analyzing the effect of the estimation criteria, we compare the ML criterion with a robust estimation criterion called structural MAP. We evaluate the sensitivity of several thresholds for the piecewise linear regression algorithms and take up methods combining MAP adaptation with the linear regression algorithms. We incorporate these adaptation algorithms into our speech synthesis system and present several subjective and objective evaluation results showing the utility and effectiveness of these algorithms in speaker adaptation for HMM-based speech synthesis