臺灣博碩士論文加值系統

航空器於飛行過程中，「任務圓滿、安全第」一是相當重要的事。航空器經不起重大事故的發生，由於飛安事件與事故以人為因素所佔的比例極高，因此減少人為疏失是一件非常重要的課題，飛行員的狀況警覺是人為疏失中不可漠視的要件。救援直升機在極短的時間內接獲多重的不確定性、複雜性導致了飛行員的心智工作負荷影響其狀況警覺，因而使飛行員狀況警覺降低，產生飛行決策錯誤。為了解決這個問題，在現今仍未有任何實體機實證的情況下，本研究大膽地採用了實證的方式，運用實際飛行即時探測飛行員的狀況警覺，這是一項概念作法。基於此概念，本研究提出現況評估法、狀況警覺評量技術；此外也包含了工作量指標評量法。我們以內政部空中勤務總隊的UH-1H 傳統手動型直升機飛行員實施試驗。為了驗證其有效性，所有的測試數據均來自檢定機師利用飛行員生日檢定時實施。在實驗中，客觀量測是以現況評估法執行；主觀量測是以狀況警覺評量技術與工作負荷指標評量法作為衡量標準。
本研究以實體機實際飛行期間，量測心智工作負荷與狀況警覺之關係。獲取飛行員狀況警覺量測值後，以飛行員等級、年齡、飛行時數(經驗)、狀況警覺的類別、心智工作負荷的類別等為變數，再以電腦軟體對彼此間之數據實施相關分析、變異數分析、t 檢定、回歸分析。本實驗結果說明了空中勤務總隊的飛行員如何提升其狀況警覺之說服性，達到滿意適合的結果，有效找尋出人為疏失的最大因子。

The process of flying an aircraft requires the pilot to complete tasks at every moment. Safety is the highest priority while completing these tasks. Aircraft can’t afford the occurrence of major accidents. A high proportion of aircraft accidents and incidents are caused by an accumulation of human factors. Get rid of human factors, reduce human errors is also another very important topic. The situation awareness (SA) of the pilot is a vital element that can’t be ignored in human error. Rescue helicopter pilots must manage multiple uncertainties in a very short period of time; the complexity of a flight mission imposes a heavy mental workload (MWL) on the pilot. A heavy MWL decreases the pilot’s SA, possibly resulting in flight decision errors. Currently, the literature contains little empirical evidence regarding real-world helicopter flights. In order to solve this problem, a novel empirical approach is adopted in this study, using real-time probe to measure the awareness of the pilot operating a real aircraft in flight. Because both subjective and objective assessments of the pilot’s SA during rescue flights are urgent, Situation Present Assessment Method (SPAM); Situation Awareness Rating Technique (SART); NASA-Task Load Index (NASA-TLX) are considered in this study. The participants were National Airborne Service Corps (NASC) pilots who were accustomed to flying UH-1H helicopters with traditional manual controls. Checking pilots verified the effectiveness of the measurements. In our experiments, the objective assessments were based on the execution of SPAM; the subjective assessments for standard criteria were based on SART and NASA-TLX.
In this research, real-world helicopter flights was measured to examine the relationship between MWL and SA. After obtaining the pilot’s SA assessment, we used computer software to calculate the data based on the pilots’ positions, the pilots’ ages, the pilots’ flight-hours (experience), the type of awareness, and the type of MWL; these data were consider with relative analysis, variance analysis, t - test, and regression analysis. The experimental results explain how NASC pilots can optimize SA and avoid the most dangerous factors of human error.

誌 謝
摘 要
Abstract
Contents
List of Figures
List of Tables
Chapter 1 Introduction
1.1 Background: Situation Awareness
1.2 Motivation
1.3 Purpose
1.4 Research Methods
1.5 The Framework of this Study
Chapter 2 Literature Review
2.1 Review of Methods to Assess Situation Awareness
2.1.1 SA Requirements Analysis
2.1.2 Situation Awareness Global Assessment Technique (SAGAT)
2.1.3 Situation Awareness Rating Technique (SART)
2.1.4 Situation Present Assessment Method (SPAM)
2.1.5 Situation Awareness Behavioral Rating Scale (SABARS)
2.1.6 Propositional Networks
2.2 Mental Workload Method Literature Review
2.2.1 Primary and Secondary Task Performance Measures
2.2.2 Physiological Measures
2.2.3 NASA-Task Load Index (NASA-TLX)
2.3 Related Literature Review
2.3.1 Predicting Perceived Situation Awareness of Low Altitude Aircraft in Terminal Airspace Using Probe Questions
2.3.2 Assessing The Situation Awareness of Pilot Engaged in Self Spacing
2.3.3 Pilot and Controller Workload and Situation Awareness with Three Traffic Management Concept
2.3.4 The Impact Automation Assisted Aircraft Separation on Situation Awareness
Chapter 3 Methodology
3.1 The Methodology for Theme Study
3.1.1 Empirical Research on The Relationship Between Helicopter Pilots’ MWLs and SA
3.1.2 Empirical Research on the Relationship Between Helicopter The Perspective of Pilot’s Flight Level and The Observation of SA and MWL
3.1.3 Empirical Research Regarding Effects of Pilot Age and Flight Hours on Pilot MWL and SA
3.1.4 Real-Time Probe of SA In The Real World to Predict Non-Real-Time Technique
3.1.5 Utilizing Empirical Experiment to Evaluate Helicopter Pilots’ Crew Resource Management by SART Subjective Evaluation Method
3.2 Experiment Design
3.2.1 Participants
3.2.2 Instruments and Equipment
3.2.3 Material Design
3.2.3.1 SPAM Sheet Design
3.2.3.2 SART Sheet Design
3.2.3.3 NASA-TLX Sheet Design
3.2.4 Scenario Design
3.2.5 Flight Procedure Planning
3.2.5.1 Preparation Before Trial: Simultaneous SPAM and Checking Queries
3.2.5.2 Actual Implementation
Chapter 4 Result Analysis and Discussion
4.1 The Result Analysis and Discussion on The Relationship Between Empirical Study of SA and MWLs
4.2 The Result Analysis and Discussion on The Difference Between The Perspective of Pilot's Flight Level and The Observation of SA and MWL
4.2.1 ANOVA for Different Levels of Pilot Skills versus The Numbers of Correct Responses
4.2.2 ANOVA for Different Levels of Pilot Skills versus SA Responses Time
4.2.3 ANOVA for Different Levels of Pilot Experience versus MWL
4.3 The Analysis and Discussion of SA Results on Pilots' Age and Flight Hours for Their MWL
4.3.1 Correlation Analysis of Pilots
4.3.1.1 Pilots’ Age versus MWL
4.3.1.2 Pilots’ Flight Hours versus MWL
4.3.1.3 Pilots’ MWL versus SPAM
4.3.1.3.1 Pilots’ MWL versus SPAM Correct Responses
4.3.1.3.2 Pilots’ MWL versus SPAM Correct Latencies
4.3.1.4 Pilots’ MWL versus 3D-SART
4.3.1.5 Pilots’ Age versus SPAM
4.3.1.5.1 Pilots’ Age versus SPAM Correct Responses
4.3.1.5.2 Pilots’ Age versus SPAM Correct Response Latencies
4.3.1.6 Pilots’ Age versus 3D-SART
4.3.1.7 Pilots’ Flight Hours versus SPAM
4.3.1.7.1 Pilots’ Flight Hours versus SPAM Correct Responses
4.3.1.7.2 Pilots’ Flight Hours versus SPAM Correct Response Latencies
4.3.1.8 Pilots’ Flight Hours versus 3D-SART
4.3.2 ANOVA of Pilots
4.3.2.1 Pilots’ Age versus MWL
4.3.2.2 Pilots’ Age versus SPAM
4.3.2.2.1 Pilots’ Age versus SPAM Correct Responses
4.3.2.2.2 Pilots’ Age versus SPAM Correct Response Latencies
4.3.2.3 Pilots’ Age versus 3D-SART
4.3.2.4 Pilots’ Flight Hours versus MWL
4.3.2.5 Pilots’ Flight Hours versus SPAM
4.3.2.5.1 Pilots’ Flight Hours versus SPAM Correct Responses
4.3.2.5.2 Pilots’ Flight Hours versus SPAM Correct Latencies
4.3.2.6 Pilots’ Flight Hours versus 3D-SART
4.3.3 Regression Analysis of Pilots
4.4 The Result Data Analysis and Discussion Using Real-Time Probe SA to Predict Non-Real-Time Technique
4.4.1 Verify Correlation Analysis of SPAM
4.4.2 Regression Analysis of Both Methods
4.5 The Result Analysis and Discussion Using Non-Real-Time SART Assessment Technique to Assess Pilots’ Crew Resource Management
4.5.1 Administer SART for Pilots
4.5.2 Correlation Analysis and t-test of Pilots
4.6 General Discussion
Chapter 5 Conclusions and Future Studies
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