Motion planning problems are characterized by huge search spaces and complex obstacle structures with no concise mathematical expression. The fixed-wing airplane application considered in this thesis adds differential constraints and point-wise bounds, i. e. an infinite number of equality and inequality constraints. An optimal trajectory planning approach is presented, based on the randomized Rapidly-exploring Random Trees framework (RRT*). The local planner relies on differential flatness of the equations of motion to obtain tree branch candidates that automatically satisfy the differential constraints. Flat output trajectories, in this case equivalent to the airplane's flight path, are designed using Bézier curves. Segment feasibility in terms of point-wise inequality constraints is tested by an indicator integral, which is evaluated alongside the segment cost functional. Although the RRT* guarantees optimality in the limit of infinite planning time, it is argued by intuition and experimentation that convergence is not approached at a practically useful rate. Therefore, the randomized planner is augmented by a deterministic variational optimization technique. To this end, the optimal planning task is formulated as a semi-infinite optimization problem, using the intermediate result of the RRT(*) as an initial guess. The proposed optimization algorithm follows the feasible flavor of the primal-dual interior point paradigm. Discretization of functional (infinite) constraints is deferred to the linear subproblems, where it is realized implicitly by numeric quadrature. An inherent numerical ill-conditioning of the method is circumvented by a reduction-like approach, which tracks active constraint locations by introducing new problem variables. Obstacle avoidance is achieved by extending the line search procedure and dynamically adding obstacle-awareness constraints to the problem formulation. Experimental evaluation confirms that the hybrid approach is practically feasible and does indeed outperform RRT*'s built-in optimization mechanism, but the computational burden is still significant. / Bewegungsplanungsaufgaben sind typischerweise gekennzeichnet durch umfangreiche Suchräume, deren vollständige Exploration nicht praktikabel ist, sowie durch unstrukturierte Hindernisse, für die nur selten eine geschlossene mathematische Beschreibung existiert. Bei der in dieser Arbeit betrachteten Anwendung auf Flächenflugzeuge kommen differentielle Randbedingungen und beschränkte Systemgrößen erschwerend hinzu. Der vorgestellte Ansatz zur optimalen Trajektorienplanung basiert auf dem Rapidly-exploring Random Trees-Algorithmus (RRT*), welcher die Suchraumkomplexität durch Randomisierung beherrschbar macht. Der spezifische Beitrag ist eine Realisierung des lokalen Planers zur Generierung der Äste des Suchbaums. Dieser erfordert ein flaches Bewegungsmodell, sodass differentielle Randbedingungen automatisch erfüllt sind. Die Trajektorien des flachen Ausgangs, welche im betrachteten Beispiel der Flugbahn entsprechen, werden mittels Bézier-Kurven entworfen. Die Einhaltung der Ungleichungsnebenbedingungen wird durch ein Indikator-Integral überprüft, welches sich mit wenig Zusatzaufwand parallel zum Kostenfunktional berechnen lässt. Zwar konvergiert der RRT*-Algorithmus (im probabilistischen Sinne) zu einer optimalen Lösung, jedoch ist die Konvergenzrate aus praktischer Sicht unbrauchbar langsam. Es ist daher naheliegend, den Planer durch ein gradientenbasiertes lokales Optimierungsverfahren mit besseren Konvergenzeigenschaften zu unterstützen. Hierzu wird die aktuelle Zwischenlösung des Planers als Initialschätzung für ein kompatibles semi-infinites Optimierungsproblem verwendet. Der vorgeschlagene Optimierungsalgorithmus erweitert das verbreitete innere-Punkte-Konzept (primal dual interior point method) auf semi-infinite Probleme. Eine explizite Diskretisierung der funktionalen Ungleichungsnebenbedingungen ist nicht erforderlich, denn diese erfolgt implizit durch eine numerische Integralauswertung im Rahmen der linearen Teilprobleme. Da die Methode an Stellen aktiver Nebenbedingungen nicht wohldefiniert ist, kommt zusätzlich eine Variante des Reduktions-Ansatzes zum Einsatz, bei welcher der Vektor der Optimierungsvariablen um die (endliche) Menge der aktiven Indizes erweitert wird. Weiterhin wurde eine Kollisionsvermeidung integriert, die in den Teilschritt der Liniensuche eingreift und die Problemformulierung dynamisch um Randbedingungen zur lokalen Berücksichtigung von Hindernissen erweitert. Experimentelle Untersuchungen bestätigen, dass die Ergebnisse des hybriden Ansatzes aus RRT(*) und numerischem Optimierungsverfahren der klassischen RRT*-basierten Trajektorienoptimierung überlegen sind. Der erforderliche Rechenaufwand ist zwar beträchtlich, aber unter realistischen Bedingungen praktisch beherrschbar.

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This thesis addresses key challenges in intersection traffic monitoring using overhead fisheye cameras, focusing on object detection, localization, vehicle maneuver classification, and traffic violation detection. A data augmentation technique was developed to improve the performance of deep learning-based object detection algorithms for fisheye images. By fine-tuning these models, significant improvements in Average Precision (AP) were achieved for vehicle and pedestrian detection, effectively addressing object orientation and size variability. A novel calibration method was introduced to mitigate the effects of road surface elevation changes on object localization. This method accurately translates image coordinates into geographical coordinates by incorporating 3D road characteristics. The proposed localization algorithm, validated through field tests, demonstrated high accuracy in localizing both cars and pedestrians. Furthermore, Kalman filtering techniques were integrated to enhance object tracking, providing precise localization even in complex environments like sloped streets. In addition, a self-learning vehicle maneuver classification and counting algorithm was developed, capable of recognizing various vehicle movements such as turns and U-turns. The algorithm’s performance was validated in real-world scenarios, where it successfully classified and counted vehicle maneuvers at multiple intersections. Moreover, a traffic violation detection system was designed on top of the maneuver classification algorithm to identify common infractions like box-blocking and illegal turns at intersections. The outcomes of this research contribute to a comprehensive system that enhances traffic monitoring, safety enforcement, and operational efficiency at intersections, offering practical solutions to modern traffic management challenges. / Thesis / Doctor of Philosophy (PhD) / This thesis explores methods for improving intersection traffic monitoring using overhead fisheye cameras. It enhances vehicle and pedestrian detection with a refined object detection model that improves accuracy by effectively handling object orientation and size variations. A new calibration technique has been developed to address road surface elevation changes, improving the precision of object localization. The thesis also presents a novel object localization approach that combines Kalman filtering with camera altitude correction, enabling accurate object localization in complex environments like sloped streets. Additionally, a new vehicle counting algorithm is designed to handle fisheye imagery and traffic management challenges. This system has proven effective in real-world tests, accurately classifying the vehicle maneuvers used to detect traffic violations such as illegal turns and box-blocking with an impressive precision rate. The proposed methods significantly enhance real-time traffic monitoring and enforcement, contributing to safer and more efficient intersections.

<p dir="ltr">Since the 2000s, there has been a growing interest in qualitative research in the field of Japanese language education, including life story research (e.g., Miyo, 2015). One of the purposes of life story research in Japanese language education is to pay attention to, listen to, and describe the voices of people to whom Japanese language education has so far paid little attention (Miyo, 2014, 2015a). Many studies have listened to the voices of Japanese language learners living in Japan and described their experiences (e.g., Miyo, 2009; Sato, 2015; Maruyama & Ozawa, 2018). However, many studies have not paid particular attention to the region where the learners are staying. One gets the impression that the place could be anywhere as long as the learners are in Japan. Further, cross-cultural adaptation research often focuses on international cross-cultural contact. However, in recent years, it has been pointed out that cross-cultural contact also involves regional differences within a single country (Gui et al., 2012; Berry, 2016). In other words, foreigners staying in a host country experience not only intercultural contact between their own country and the host country but also intercultural contact arising from regional differences within the host country, making the adaptation process complex.</p><p dir="ltr">This narrative case study focused on one American learner of Japanese, Kevin (pseudonym), and his experience on Futaba Island (pseudonym) and analyzed his process of cross-cultural adaptation. Data was collected from his diary and semi-structured interviews with him over six months. The collected data were then graphically represented using the method of Trajectory Equifinality Modeling (TEM) (Sato et al., 2009; Sato et al., 2014). The data obtained in this study showed that Kevin’s back-and-forth between Futaba Island and the mainland impeded and facilitated his cross-cultural adaptation in different ways. Unlike people in large cities on the mainland, people on Futaba Island stare at Kevin and treat him as a special guest, which made him aware of cultural barriers, leading him to construct an identity as an outsider and feel, “I will never fully integrate into Japanese society.” Furthermore, when he traveled to the mainland, he experienced reverse culture shock by encountering many American tourists that he cannot see on Futaba Island. Seeing American tourists behaving incompatibly with Japanese cultural norms made Kevin realize that he was more integrated into Japanese society than he had thought. All of this suggests that when considering the cross-cultural adaptation of foreigners staying in Japan, it is essential to take a place-based perspective on where they are in Japan and where they have been during their stay. Furthermore, just because a person is staying in the host country does not mean that cross-cultural adaptation is influenced by factors that are exclusive to the host country. While staying in the host country, one’s cross-cultural adaptation may be influenced by people from or by events in their home country. It is necessary to take into account home country-related factors as well.</p>

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper presents the dynamic RF link estimation result for telemetry system of KSLV (Korea Space Launch Vehicle)-I. In particular, it utilizes the parameters of the instantaneous vehicle antenna gain pattern in three dimensions, the improvement by polarization diversity combiner at the ground receiver, and the free space propagation loss. The structural transformation and discontinuity of ground plane after the separation events of nose fairing, stage, and spacecraft, are also included in this analysis. As a consequence, the prediction of link variation has been performed in accordance with ARDP (Antenna Radiation Distribution Plot) and look angle trace of vehicle. In addition, the optimum position of onboard antennas has been investigated to provide better RF link margin in the nominal trajectory.

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The terminal phase telemetry data acquisition has always been a challenging task especially for long and medium range test launches. The task becomes more complicated if the article under test describes a very low altitude cruise terminal phase trajectory. Generally, for long and medium range missions test fired into sea the terminal phase data is acquired by deploying instrumented ships in the vicinity of impact point but beyond the safety corridor. But for long range missions with low altitude cruise terminal phase trajectory and wide safety corridor this conventional approach will not work out because of limitation of LOS from the ship platforms. Hence, for such applications an air borne telemetry receiving system is also to be added to the down range instrumentation network. This paper describes a typical and cost effective air borne system realized utilizing the Commercial Off The Shelf (COTS) products and technology. This paper also addresses as to how the signal scattering problems are tackled in the design implementation.

Today, the air transport industry has become an essential element of global society by its great contributions to the wide exchanges of cultures/people and to the rapid growth in the world economy. However, on the other hand, the adverse impacts on the environment caused by air transport, such as air pollution, noise and climate change, are drawing, increasingly, growing public concern. In order to address the steady growth in air-travel demand in the next decades through an environmentally-friendly way and realise the ACARE 2020 environmental goals, The Clean Sky programme has been launched by European Union over the period 2008 – 2013. The project research, described in this thesis and sponsored by the Clean Sky programme, aims at evaluating the feasibility of reducing the environmental impact of commercial aviation through the introduction of changes in the aircraft operational rules and procedures, as well as the application of the new-generation propfan (open rotor) engine, based on flight trajectory multidisciplinary optimisation and analysis of commercial aircraft. In order to accomplish the above research objectives, a complete methodology to achieve and realise optimum flight trajectories has been initially proposed. Then, 12 component-level models which function as simulating different disciplines, such as aircraft performance, engine performance, engine gaseous emission, and flight noise, have been developed or selected/adopted. Further, nine system-level integration and optimisation models were built. These system-level models simulate flights from Amsterdam Schiphol airport in the Netherlands to Munich airport in Germany flown by different types of aircraft through different flight phases with different optimisation objectives. Finally, detailed investigations into the flight trajectory optimisations were performed, extensive optimisation results were achieved and corresponding description, analysis and comparisons were provided. The main contributions of this work to knowledge broadly comprise the following: 1) the further development regarding the methodology of flight trajectory multidisciplinary optimisation; 2) previous work on aircraft trajectory optimisation has often considered fixed objectives over the complete flight trajectory. This research focused on representative flight phases of a flight mission with different optimisation objectives, namely, noise impact and fuel burn during the departure phase; fuel burn and flight time during en route phase; and noise impact and NOx emission during the arrival phase; 3) this research has extended the current flight trajectory optimisations to turboprop and propfan equipped aircraft. As a result, a relative complete 2D flight trajectory multidisciplinary optimisation spectrum, spanned by primary commercial aircraft types, primary flight phases and primary optimisation objectives of interest, has been built. Although encouraging progress have been achieved, this project research, as with any other research activity, is also only ‘on the way’ rather than coming to the ‘end’ point. There are still many aspects which can be improved further and there is still much new research and exploration which can be investigated further. All these have also been suggested in this thesis.

Continuous Descent Arrival (CDA) is a procedure where aircraft descend, at or near idle thrust, from their cruise altitude to their Final Approach Fix without leveling off. By eliminating inefficient leveling off at low altitude, CDA provides benefits such as fuel savings, flight time savings, and the significant noise reduction near airports, but the usage of CDAs has been limited in low traffic condition due to difficulty in the separation management. For the successful CDA without degradation of the runway throughput, air traffic controllers should know the performance bound of the CDA trajectory and control the time of arrival for each aircraft, which is interpreted as Required Time of Arrival (RTA) from the aircraft standpoint. This thesis proposes a novel trajectory optimization methodology to meet RTA constraint. The CDA trajectory optimization problem in the flight management system is modeled as a path constrained optimal control problem of switched dynamical system. A sequential method that performs mode sequence estimation and parameter optimization, sequentially, is proposed to solve this problem. By analyzing the relaxed optimal solution with simplified dynamics, a computationally efficient algorithm to find the optimal switching structure is proposed and applied for the mode sequence estimation. This thesis also proposes a performance-bound analysis methodology using optimal control techniques to help controllers make a feasible schedule for CDA operations at a meter fix. The feasible time range analysis for a wide variety of aircraft is performed by using the proposed methodology. Based on the analysis result, a single flight time strategy is proposed for the application of CDA in high traffic conditions. The simulation with real traffic data has been shown that the single flight time strategy, combined with the proposed fixed RTA trajectory optimization, guarantees the conflict free CDA operation.

One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots. Observed teammate action sequences can be learned to perform trajectory recognition which can be used to determine their current task. Previously, we have applied behavior histograms, hidden Markov models (HMMs), and conditional random fields (CRFs) to perform trajectory recognition as an approach to task monitoring in the absence of commu- nication. To demonstrate trajectory recognition of various autonomous vehicles, we used trajectory-based techniques for model generation and trajectory discrimination in experiments using actual data. In addition to recognition of trajectories, we in- troduced strategies, based on the honeybee’s waggle dance, in which cooperating autonomous teammates could leverage recognition during periods of communication loss. While the recognition methods were able to discriminate between the standard trajectories performed in a typical survey mission, there were inaccuracies and delays in identifying new trajectories after a transition had occurred. Inaccuracies in recog- nition lead to inefficiencies as cooperating teammates acted on incorrect data. We then introduce the Trajectory Adaptation for Recognition (TAR) framework which seeks to directly address difficulties in recognizing the trajectories of autonomous vehicles by modifying the trajectories they follow to perform them. Optimization techniques are used to modify the trajectories to increase the accuracy of recognition while also improving task objectives and maintaining vehicle dynamics. Experiments are performed which demonstrate that using trajectories optimized in this manner lead to improved recognition accuracy.