The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004. FAA is required to license or permit commercial space launches, but to allow the space tourism industry to develop, the act prohibited FAA from regulating crew and spaceflight participant safety before 2012—a moratorium that was later extended but will now expire on September 30, 2015. Since October 2014, there have been three mishaps involving FAA licensed or permitted launches.

美聯邦航空廳(Federal Aviation Administration)의 航空機 製作檢査 制度의 現況

This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In particular we are interested in optimal paths, i.e., we want to minimize the total flight time. We propose two different formulations of the multiaircraft conflict avoidance problem as a mixed-integer linear program: in the first case only velocity changes are admissible maneuvers, in the second one only heading angle changes are allowed. Due to the linear formulation of the two problems, solutions may be obtained quickly with standard optimization software, allowing our approach to be implemented in real time.

Conflict resolution problems for air traffic management systems solved with mixed integer programming

Online Water Monitoring System Based on ZigBee and GPRS

Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Deep Learning (DL). DL is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, DL is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Levenberg-Marquart algorithm is applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results. In order to study effect of stack denoising autoencoder and LM algorithm on the model structure, two other structures are also designed. First structure is based on autoencoder and LM algorithm (SAE-LM), and the second structure is based on denoising autoencoder only (SDA). To investigate the three models, we apply the proposed model on U.S flight dataset that it is imbalanced dataset. In order to create balance dataset, undersampling method are used. We measured precision, accuracy, sensitivity, recall and F-measure of the three models on two cases. Accuracy of the proposed prediction model analyzed and compared to previous prediction method. results of three models on both imbalanced and balanced datasets shows that precision, accuracy, sensitivity, recall and F-measure of SDA-LM model with imbalanced and balanced dataset is improvement than SAE-LM and SDA models. The results also show that accuracy of the proposed model in forecasting flight delay on imbalanced and balanced dataset respectively has greater than previous model called RNN.

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Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

With increasing attention paid to the application of agent-based modeling in decision-making issues, a large number of related studies have been published in management science and operational research areas. This study adopts multiple methods, including bibliometric mapping, text mining, and qualitative analysis, to comprehensively review relevant research to explore knowledge frontiers and evolution in decision-making research based on agent-based modeling (DM-ABM). This review is based on 1190 relevant journal articles from the Web of Science Core Collection dataset and 37,167 collectively cited references of the articles. The top ten most-cited studies that constitute the intellectual milestones of DM-ABM were identified. Keywords and research topics develop rapidly; recent research have paid most attention to the keywords “model,” “system,” and “simulation” and topics “learning,” “contracts,” “protocols,” and “self-learning.” The top 24 references with the strongest citation bursts were displayed to show that the area was increasingly active from 2001 to 2010. Transition points were mapped to reveal the top five studies with the highest betweenness centrality, which considerably influences knowledge evolution. Then, the top three clusters are identified as the frontier areas and analyzed by text mining, including intelligent agents, model validation, and collaborative decision making. Finally, the most recent research in this field is investigated, and four future research directions are proposed: the advanced intelligence of agents, approach to reality, group decision making, innovative modeling methodologies and diversified applications. 

Mapping the knowledge frontiers and evolution of decision making based on agent-based modeling

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the US economy On‐time operati

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

The Chesapeake Bay is the largest estuary in the United States, where its watershed is home to more than 3,600 species of plants and animals and more than 16.6 million people. However one of its major issues is water pollution. Good water quality is vital for the health of all these plants, animals and people. In order to act upon this problem and restore the water, there is the need to monitor the water quality. There are currently several organizations and agencies monitoring different parts of the bay and working to restore the bay. [1]This project analyzes innovative ways to improve water quality monitoring in the West and Rhode rivers. The water in these rivers is currently monitored by a River Keeper. There is the need for an improved system with higher frequency of data input, higher accuracy of higher quality sensors, and a wider range of parameters being monitored.

Designing an automated water quality monitoring system for West and Rhode Rivers

Efficient team decision making has become vital in organizations, dominated by the complex system of systems (SoS) Inefficient decisions can result in significant extra cost and reduction in the overall performance within the organizations Therefore, SoS success requires an adaptable design that can proactively evaluate and modify decisions in a dynamic process Improved systems engineering (SE) processes and tools are needed during the systems’ development lifecycle to make decisions, and rapidly evaluate and assess multiple design alternatives to effectively select the architectural design strategies that yield the highest mission performance This research aims to increase the knowledge of SoS engineering by developing an agent-based model of team collaboration in an operational SoS The model evaluates and selects the decision(s) for SoS comprised of collaborative teams that result in higher mission performance The proposed agent-based model was applied to test the team decision-making improvements within the SoS structure of the air transportation domain The experimental results indicated significant improvements in decision coordination, decision workload, and overall mission performance in the air transportation domain This research contributes to the agent-based modeling of the SoS teams and can assist with evaluating team decisions and improving decision making as an essential element in the field of SE

An SoS Framework for Improved Collaborative Decision Making

INTRODUCTION: In-flight medical emergencies (IFMEs) average 1 of every 604 flights and are expected to increase as the population ages and air travel increases. Flight diversions, or the rerouting of a flight to an alternate destination, occur in 2 to 13% of IFME cases, but may or may not be necessary as determined after the fact. Estimating the effect of IFME diversions compared to nonmedical diversions can be expected to improve our understanding of their impact and allow for more appropriate decision making during IFMEs.METHODS: The current study matched multiple disparate datasets, including medical data, flight plan and track data, passenger statistics, and financial data. Chi-squared analysis and independent samples t-tests compared diversion delays and costs metrics between flights diverted for medical vs. nonmedical reasons. Data were restricted to domestic flights between 1/1/2018 and 6/30/2019.RESULTS: Over 70% of diverted flights recover (continue on to their intended destination after diverting); however, flights diverted due to IFMEs recover more often and more quickly than do flights diverted for nonmedical reasons. IFME diversions introduce less delay overall and cost less in terms of direct operating costs and passenger value of time (averaging around 38,000) than do flights diverted for nonmedical reasons.DISCUSSION: Flights diverted due to IFMEs appear to have less impact overall than do flights diverted for nonmedical reasons. However, the lack of information related to costs for nonrecovered flights and the decision factors involved during nonmedical diversions hinders our ability to offer further insights.Lewis BA, Gawron VJ, Esmaeilzadeh E, Mayer RH, Moreno-Hines F, Nerwich N, Alves PM. Data-driven estimation of the impact of diversions due to in-flight medical emergencies on flight delay and aircraft operating costs. Aerosp Med Hum Perform. 2021; 92(2):99105.

Ehsan Esmaeilzadeh

Papers

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

Designing an automated water quality monitoring system for West and Rhode Rivers

An SoS Framework for Improved Collaborative Decision Making

Data-Driven Estimation of the Impact of Diversions Due to In-Flight Medical Emergencies on Flight Delay and Aircraft Operating Costs.

An Agent-Based Model for Improved System of Systems Decision making in Air Transportation