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\"One of the first cohesive works on glass cockpit equipment (digital instrumentation being implemented in more aircraft), this book focuses on limiting in-flight issues and advancing the safe operation of highly automated aircraft\"-Provided by publisher.

This paper introduces the optical simulation software SPEOS based on the Monte Carlo algorithm to assist cockpit design in aircraft cockpit design. The purpose is to simulate and analyze different design schemes of aircraft cockpit instrument lighting in the design stage to determine whether the existing design scheme will have an adverse effect on human vision. This not only avoids the huge economic waste caused by the subsequent modification of the aircraft when problems are found in the manufacturing of sample sections or real aircraft cabin sections but also provides a more convenient and effective design and design inspection method for aircraft cockpit design engineers.

\"This book presents the Human Factors methodologies and applications thereof that can be utilized across the design, modeling and evaluation stages of the design lifecycle of new technologies entering future commercial aircraft. As advances are made to the architecture of commercial aircraft cockpits, Human Factors on the Flight Deck argues that it is vitally important that these new interfaces are safely incorporated and designed in a way that is usable to the pilot. Incorporation of Human Factors is essential to ensuring that engineering developments to avionic systems are integrated such that pilots can maintain safe interactions while gaining information of value. Case study examples of various technological advancements during their early conceptual stages are given throughout to highlight how the methods and processes can be applied across each stage. The text will be useful for professionals, graduate students, and academic researchers in the fields of aviation, human factors, and ergonomics\"-- Provided by publisher.

Taking an integrated, systems approach to dealing exclusively with the human performance issues encountered on the flight deck of the modern airliner, this book describes the inter-relationships between the various application areas of human factors, recognising that the human contribution to the operation of an airliner does not fall into neat pigeonholes. The relationship between areas such as pilot selection, training, flight deck design and safety management is continually emphasised within the book. It also affirms the upside of human factors in aviation - the positive contribution that it can make to the industry - and avoids placing undue emphasis on when the human component fails. The book is divided into four main parts. Part one describes the underpinning science base, with chapters on human information processing, workload, situation awareness, decision making, error and individual differences. Part two of the book looks at the human in the system, containing chapters on pilot selection, simulation and training, stress, fatigue and alcohol, and environmental stressors. Part three takes a closer look at the machine (the aircraft), beginning with an examination of flight deck display design, followed by chapters on aircraft control, flight deck automation, and HCI on the flight deck. Part four completes the volume with a consideration of safety management issues, both on the flight deck and across the airline; the final chapter in this section looks at human factors for incident and accident investigation. The book is written for professionals within the aviation industry, both on the flight deck and elsewhere, for post-graduate students and for researchers working in the area. Contents: Preface; A systems approach to human factors in aviation; Part 1 The Science Base: Human information processing; Workload; Situation awareness; Decision making; Error; Individual differences. Part 2 The (Hu)Man: Pilot selection; Training and simulation; Stress, fatigue and alcohol; Environmental stressors. Part 3 The Machine: Display design; Aircraft control; Automation; Human-computer interaction (HCI) on the flight deck. Part 4 The Management: Flight deck safety management: crew resource management and line operations safety audits; Airline safety management; Incident and accident investigation; Concluding thoughts: human factors in aviation as a route to increased operational efficiency; References; Index. Don Harris is Managing Director of HFI Solutions Ltd and Visiting Professor in the School of Aeronautics and Astronautics at Shanghai Jiao Tong University, China. Prior to founding the HFI Solutions Don was Professor of Aerospace Human Factors at Cranfield University. He has been involved in the design and certification of flight deck interfaces; worked in the safety assessment of helicopter operations for North Sea oil exploration and exploitation and was an accident investigator on call to the British Army Division of Army Aviation. Don is a Fellow of the Institute of Ergonomics and Human Factors and a Chartered Psychologist. He is also a member of the UK Human Factors National Technical Committee for Aerospace and Defence. In 2006 Don received the Royal Aeronautical Society Bronze award for work leading to advances in aerospace and in 2008 was part of the Human Factors Integration Defence Technology Centre team that received the UK Ergonomics Society President's Medal 'for significant contributions to original research, the development of methodology and the application of knowledge within the field of ergonomics'. Don is author or editor of several other Ashgate volumes, including 'Human Factors for Civil Flight Deck Design', 'Contemporary Issues in Human Factors and Aviation Safety' (with Helen Muir) and 'Modelling Command and Control' (with Neville Stanton and Chris Baber).

This paper discusses the method of expert assessment based on the probabilistic approach of the paired comparisons method. This mathematical apparatus is the basis for special software integrated into the universal stand for prototyping the cockpit, which allows for a systematic expert assessment at this stand according to pre-selected criteria with the control of the consistency of the answers. Using this method, the task of determining the feasibility of displaying layers of synthetic and enhanced vision on the flying frame is solved, but in general, the method allows evaluating any elements of the displays and controls.

Computational Fluids Dynamics (CFD) simulations are essential for optimizing the design of a cockpit’s internal environment, but the complex geometric models consume a significant amount of computational resources and time. Arbitrary simplification of geometric models may result in inaccurate calculations of physical fields. To address this issue, this study establishes a geometric model simplification strategy and successfully applies it to a cockpit. The implementation of the whole approach is divided into three steps, summarized in three methods, namely Sensitivity Analysis Method (SAM), Detail Suppression Method (DSM), and Evaluation Standards Method (ESM). Sensitivity analysis of the detailed features of the geometric model is performed using the adjoint method. The details of the geometric model are suppressed based on the principle of curvature continuity. After evaluation, the suppression degrees of detailed features with different sensitivity levels are obtained. The results demonstrate that this strategy can be employed to achieve precise simplification standards, thereby avoiding excessive deviations caused by arbitrary simplification and reducing the significant costs associated with trial-and-error simplification.

During a flight, pilots must rigorously monitor their flight instruments since it is one of the critical activities that contribute to update their situation awareness. The monitoring is cognitively demanding, but is necessary for timely intervention in the event of a parameter deviation. Many studies have shown that a large part of commercial aviation accidents involved poor cockpit monitoring from the crew. Research in eye-tracking has developed numerous metrics to examine visual strategies in fields such as art viewing, sports, chess, reading, aviation, and space. In this article, we propose to use both basic and advanced eye metrics to study visual information acquisition, gaze dispersion, and gaze patterning among novices and pilots. The experiment involved a group of sixteen certified professional pilots and a group of sixteen novice during a manual landing task scenario performed in a flight simulator. The two groups landed three times with different levels of difficulty (manipulated via a double task paradigm). Compared to novices, professional pilots had a higher perceptual efficiency (more numerous and shorter dwells), a better distribution of attention, an ambient mode of visual attention, and more complex and elaborate visual scanning patterns. We classified pilot’s profiles (novices—experts) by machine learning based on Cosine KNN (K-Nearest Neighbors) using transition matrices. Several eye metrics were also sensitive to the landing difficulty. Our results can benefit the aviation domain by helping to assess the monitoring performance of the crews, improve initial and recurrent training and ultimately reduce incidents, and accidents due to human error.

Cockpit noise, as a critical environmental factor affecting flight safety, may impair pilots’ cognitive functions, leading to a decreased operational performance and decision-making errors, thereby posing potential threats to aviation safety. In order to reveal the relationship between the cockpit noise sound pressure level and pilot physiological indicators, and provide a scientific basis for cockpit noise airworthiness standards, this experiment takes pilot trainees as the research subject. Based on the principle of multimodal data synchronization, a sound field reconstruction system is used to reconstruct the cockpit sound field. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) measurements are carried out in different sound pressure level noise operating environments. The results show that with the increase in the sound pressure level, the significant suppression of α-wave activity in the occipital and parietal regions suggests that the cortical resting state is lifted and visual attention is enhanced; the enhancement of the β-wave in the frontal regions reflects the enhancement of alertness and prefrontal executive control, and the suppression of θ-wave activity in the frontal and temporal regions may indicate that cognitive tuning is suppressed, which reflects the brain’s rapid adaptive response to external noise stimuli in a high-noise environment; noise exposure triggers sustained sympathetic nerve hyperactivity, which is manifested by a significant acceleration of the heart rate and a significant increase in the mean value of skin conductance when the noise sound pressure level exceeds 70 dB(A). The correlation analysis between physiological indicators shows that cockpit noise has a multi-system synergistic effect on human physiological indicators. The experimental results indicate that noise has a significant impact on EEG, ECG, and EDA indicators.

We developed a new tracer-aided hydrological model that disaggregates cockpit karst terrain into the two dominant landscape units of hillslopes and depressions (with fast and slow flow systems). The new model was calibrated by using high temporal resolution hydrometric and isotope data in the outflow of Chenqi catchment in Guizhou Province of south-western China. The model could track hourly water and isotope fluxes through each landscape unit and estimate the associated storage and water age dynamics. From the model results we inferred that the fast flow reservoir in the depression had the smallest water storage and the slow flow reservoir the largest, with the hillslope intermediate. The estimated mean ages of water draining the hillslope unit, and the fast and slow flow reservoirs during the study period, were 137, 326 and 493 days, respectively. Distinct seasonal variability in hydroclimatic conditions and associated water storage dynamics (captured by the model) were the main drivers of non-stationary hydrological connectivity between the hillslope and depression. During the dry season, slow flow in the depression contributes the largest proportion (78.4 %) of flow to the underground stream draining the catchment, resulting in weak hydrological connectivity between the hillslope and depression. During the wet period, with the resulting rapid increase in storage, the hillslope unit contributes the largest proportion (57.5 %) of flow to the underground stream due to the strong hydrological connectivity between the hillslope and depression. Meanwhile, the tracer-aided model can be used to identify the sources of uncertainty in the model results. Our analysis showed that the model uncertainty of the hydrological variables in the different units relies on their connectivity with the outlet when the calibration target uses only the outlet information. The model uncertainty was much lower for the “newer” water from the fast flow system in the depression and flow from the hillslope unit during the wet season and higher for “older” water from the slow flow system in the depression. This suggests that to constrain model parameters further, increased high-resolution hydrometric and tracer data on the internal dynamics of systems (e.g. groundwater responses during low flow periods) could be used in calibration.

Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation.