Fuzzy Synthetic Assessment of Air Traffic Controllers Situational Awareness Based on Short-term Memory Measurement

2012 ◽  
Author(s):  
Yongxin Liu ◽  
Jian Zhang ◽  
Wanle Wang ◽  
Debin Zhao
Keyword(s):  
Situational Awareness ◽  
Short Term Memory ◽  
Air Traffic ◽  
Short Term ◽  
Term Memory ◽  
Air Traffic Controllers ◽  
Fuzzy Synthetic Assessment ◽  
Synthetic Assessment

scholarly journals Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mahdi Yousefzadeh Aghdam ◽  
Seyed Reza Kamel Tabbakh ◽  
Seyed Javad Mahdavi Chabok ◽  
Maryam Kheyrabadi
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Short Term Memory ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Data Mining Algorithms ◽  
Long Short Term Memory

AbstractNowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

scholarly journals Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

2021 ◽  
Author(s):  
Mahdi Yousefzadeh Aghdam ◽  
Seyed Reza Kamel ◽  
Seyed Javad Mahdavi Chabok ◽  
maryam khairabadi
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Short Term Memory ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Learning Machine ◽  
Deep Learning Model

Abstract Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

scholarly journals Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

2020 ◽  
Author(s):  
Mahdi Yousefzadeh Aghdam ◽  
Seyed Reza Kamel ◽  
Seyed Javad Mahdavi Chabok ◽  
maryam khairabadi
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Short Term Memory ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Learning Machine ◽  
Deep Learning Model

Abstract Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Working, Declarative, and Procedural Memory in Children With Developmental Language Disorder

2020 ◽  
Vol 63 (12) ◽  
pp. 4162-4178
Author(s):  
Emily Jackson ◽  
Suze Leitão ◽  
Mary Claessen ◽  
Mark Boyes
Keyword(s):  
Working Memory ◽  
Short Term Memory ◽  
Language Disorder ◽  
Declarative Memory ◽  
Memory Systems ◽  
Procedural Memory ◽  
Typically Developing ◽  
Short Term ◽  
Term Memory ◽  
Visual Spatial

Purpose Previous research into the working, declarative, and procedural memory systems in children with developmental language disorder (DLD) has yielded inconsistent results. The purpose of this research was to profile these memory systems in children with DLD and their typically developing peers. Method One hundred four 5- to 8-year-old children participated in the study. Fifty had DLD, and 54 were typically developing. Aspects of the working memory system (verbal short-term memory, verbal working memory, and visual–spatial short-term memory) were assessed using a nonword repetition test and subtests from the Working Memory Test Battery for Children. Verbal and visual–spatial declarative memory were measured using the Children's Memory Scale, and an audiovisual serial reaction time task was used to evaluate procedural memory. Results The children with DLD demonstrated significant impairments in verbal short-term and working memory, visual–spatial short-term memory, verbal declarative memory, and procedural memory. However, verbal declarative memory and procedural memory were no longer impaired after controlling for working memory and nonverbal IQ. Declarative memory for visual–spatial information was unimpaired. Conclusions These findings indicate that children with DLD have deficits in the working memory system. While verbal declarative memory and procedural memory also appear to be impaired, these deficits could largely be accounted for by working memory skills. The results have implications for our understanding of the cognitive processes underlying language impairment in the DLD population; however, further investigation of the relationships between the memory systems is required using tasks that measure learning over long-term intervals. Supplemental Material https://doi.org/10.23641/asha.13250180

Comparison of Auditory, Language, Memory, and Attention Abilities in Children With and Without Listening Difficulties

2020 ◽  
Vol 29 (4) ◽  
pp. 710-727
Author(s):  
Beula M. Magimairaj ◽  
Naveen K. Nagaraj ◽  
Alexander V. Sergeev ◽  
Natalie J. Benafield
Keyword(s):  
Auditory Processing ◽  
Short Term Memory ◽  
Group Differences ◽  
Long Term Memory ◽  
Short Term ◽  
Significant Group ◽  
Term Memory ◽  
Memory And Attention ◽  
Speed And Accuracy

Objectives School-age children with and without parent-reported listening difficulties (LiD) were compared on auditory processing, language, memory, and attention abilities. The objective was to extend what is known so far in the literature about children with LiD by using multiple measures and selective novel measures across the above areas. Design Twenty-six children who were reported by their parents as having LiD and 26 age-matched typically developing children completed clinical tests of auditory processing and multiple measures of language, attention, and memory. All children had normal-range pure-tone hearing thresholds bilaterally. Group differences were examined. Results In addition to significantly poorer speech-perception-in-noise scores, children with LiD had reduced speed and accuracy of word retrieval from long-term memory, poorer short-term memory, sentence recall, and inferencing ability. Statistically significant group differences were of moderate effect size; however, standard test scores of children with LiD were not clinically poor. No statistically significant group differences were observed in attention, working memory capacity, vocabulary, and nonverbal IQ. Conclusions Mild signal-to-noise ratio loss, as reflected by the group mean of children with LiD, supported the children's functional listening problems. In addition, children's relative weakness in select areas of language performance, short-term memory, and long-term memory lexical retrieval speed and accuracy added to previous research on evidence-based areas that need to be evaluated in children with LiD who almost always have heterogenous profiles. Importantly, the functional difficulties faced by children with LiD in relation to their test results indicated, to some extent, that commonly used assessments may not be adequately capturing the children's listening challenges. Supplemental Material https://doi.org/10.23641/asha.12808607

The Link Between Verbal Short-Term Memory and Anomia Treatment Gains

2019 ◽  
Vol 28 (3) ◽  
pp. 1039-1052
Author(s):  
Reva M. Zimmerman ◽  
JoAnn P. Silkes ◽  
Diane L. Kendall ◽  
Irene Minkina
Keyword(s):  
Short Term Memory ◽  
Controlled Trial ◽  
Treatment Success ◽  
Linear Regression Models ◽  
Language Performance ◽  
Short Term ◽  
Term Memory ◽  
Treatment Gains ◽  
Post Hoc

Purpose A significant relationship between verbal short-term memory (STM) and language performance in people with aphasia has been found across studies. However, very few studies have examined the predictive value of verbal STM in treatment outcomes. This study aims to determine if verbal STM can be used as a predictor of treatment success. Method Retrospective data from 25 people with aphasia in a larger randomized controlled trial of phonomotor treatment were analyzed. Digit and word spans from immediately pretreatment were run in multiple linear regression models to determine whether they predict magnitude of change from pre- to posttreatment and follow-up naming accuracy. Pretreatment, immediately posttreatment, and 3 months posttreatment digit and word span scores were compared to determine if they changed following a novel treatment approach. Results Verbal STM, as measured by digit and word spans, did not predict magnitude of change in naming accuracy from pre- to posttreatment nor from pretreatment to 3 months posttreatment. Furthermore, digit and word spans did not change from pre- to posttreatment or from pretreatment to 3 months posttreatment in the overall analysis. A post hoc analysis revealed that only the less impaired group showed significant changes in word span scores from pretreatment to 3 months posttreatment. Discussion The results suggest that digit and word spans do not predict treatment gains. In a less severe subsample of participants, digit and word span scores can change following phonomotor treatment; however, the overall results suggest that span scores may not change significantly. The implications of these findings are discussed within the broader purview of theoretical and empirical associations between aphasic language and verbal STM processing.

Symptomvalidierungstests in der neuropsychologischen Diagnostik: eine Analogstudie

2004 ◽  
Vol 15 (2) ◽  
pp. 81-90 ◽  
Author(s):  
Thomas Merten ◽  
Matthias Henry ◽  
Robin Hilsabeck
Keyword(s):  
Short Term Memory ◽  
Memory Test ◽  
Short Term ◽  
Term Memory ◽  
Word Memory Test ◽  
Word Memory ◽  
Complex Figure ◽  
Trail Making ◽  
Figure Test ◽  
Word Completion

Zusammenfassung: In der neuropsychologischen Diagnostik, mehr noch aber in der Begutachtung gewinnen Symptomvalidierungstests (SVT) zur Untersuchung der Leistungsmotivation zunehmend an Bedeutung. In einer Analogstudie wurde die Güte zweier international bekannter Verfahren (Word Memory Test; Amsterdam Short Term Memory Test) sowie einer Neuentwicklung (Word Completion Memory Test) untersucht. Zusätzlich wurden Leistungstests eingesetzt: der Trail Making Test (TMT), der Complex Figure Test sowie die Standard Progressive Matrices (SPM). Eine Gruppe von 10 experimentellen Simulanten wurde spezifisch auf die Vortäuschung von Gedächtnisstörungen vorbereitet, während eine Kontrollgruppe (n = 10) optimale Testanstrengung zeigen sollte. Alle SVT führten im Gegensatz zu den Simulationsmarkern des TMT und der SPM zu einer ausgezeichneten Klassifikationsgüte (95-100 %). Die neuropsychologischen Leistungsmaße wiesen zwar signifikante Gruppenunterschiede aus, zeigten aber auch eine nicht unbedeutende Überlappung der Verteilungen. Mehr Studien sind notwendig, um den SVT in den deutschsprachigen Ländern den Platz zu sichern, den sie international aktuell in der klinisch-neuropsychologischen Forschung und Praxis einnehmen.

Dynamic Visual Noise Affects Visual Short-Term Memory for Surface Color, but not Spatial Location

2010 ◽  
Vol 57 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Kevin Dent
Keyword(s):  
Short Term Memory ◽  
Spatial Location ◽  
Visual Noise ◽  
Dynamic Visual Noise ◽  
Short Term ◽  
Surface Color ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Experimental Tool ◽  
Spatial Locations

In two experiments participants retained a single color or a set of four spatial locations in memory. During a 5 s retention interval participants viewed either flickering dynamic visual noise or a static matrix pattern. In Experiment 1 memory was assessed using a recognition procedure, in which participants indicated if a particular test stimulus matched the memorized stimulus or not. In Experiment 2 participants attempted to either reproduce the locations or they picked the color from a whole range of possibilities. Both experiments revealed effects of dynamic visual noise (DVN) on memory for colors but not for locations. The implications of the results for theories of working memory and the methodological prospects for DVN as an experimental tool are discussed.

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