Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Eine mikrosimulationsbasierte Methode zur Beurteilung der Leistungsfähigkeit von Shared Space

Shared space is a concept of urban street design which implies the creation of a level surface within the whole road reserve and is aimed at encouraging different road users to interact spontaneously and to negotiate priority with each other. To build successful shared spaces, traffic engineers can rely at present on specific guidelines as well as technical reports. Nevertheless, there is no method available to compute the performance of shared spaces in terms of Level Of Service (LOS). In order to address this gap, a new indicator of traffic quality for pedestrians is being developed. This measure of performance considers aspects of comfort related to the crossing, which pedestrians use to go from one side of the roadway to the other. During this movement, discomfort is generated by the necessity to solve the conflicts with vehicles. Therefore, factors which potentially influence comfort are mathematically formulated. Later, the performance indicator can be calibrated on the basis of the opinion of a group of respondents, who evaluated real-world crossing movements in video sequences. The effectiveness and usability of the developed indicator is demonstrated in an exemplary case study. A shared street in the district of Bergedorf, Hamburg (D) is selected and filmed. To reproduce the interaction of road users and the mechanism of space negotiation, an innovative modeling approach based on social force model (SFM) is proposed. The model is calibrated and implemented in a Java-based simulation tool. Alternative shared space scenarios, as well as conventional ones with space segregation, are simulated. The goal of this dissertation is to establish a method to evaluate the performances of shared spaces through traffic microsimulation. This method includes the data survey and acquisition, the definition of performance indicators, the development of a microsimulation approach, the calibration of the motion model on the basis of real-world data and finally the execution of simulations to collect the results. In addition, this work shows the necessity to employ a comfort-based indicator for pedestrian traffic quality in shared spaces. The benefits of this approach, with respect to conventional efficiency-based indicators as time delay, is properly shown in real-world situations and successively demonstrated by help of statistical methods.Shared Space ist ein Konzept der urbanen Straßengestaltung, das die Schaffung von niveaugleichen Zonen im gesamten Straßenquerschnitt beinhaltet, und darauf abzielt, die verschiedenen Verkehrsteilnehmer zu ermutigen, spontan zu interagieren und den Vorrang untereinander auszuhandeln. Um erfolgreiche Shared Spaces zu gestalten, können sich Ingenieure derzeit auf spezifische Richtlinien, sowie auf technische Berichte stützen. Dennoch gibt es keine Methode, um die Qualität des Shared Space im Hinblick auf den Level of Service (LOS) zu kalkulieren. Daher wird ein neuer Verkehrsqualitätsindikator für Fußgänger entwickelt. Diese Erfolgsmessgröße berücksichtigt Komfortaspekte hinsichtlich der von Fußgängern zur Querung der Straßen benutzten Übergänge. Während der Überquerung wird durch das Aushandeln des Vorrangs mit den Fahrzeugen ein Unbehagen erzeugt. Daher werden potentiell komfortbeeinflussende Faktoren mathematisch formuliert. Später kann der Leistungsindikator auf Basis der Ansicht einer Umfragegruppe, die reale Straßenüberquerungen in Videosequenzen auswertet, kalibriert werden. Die Effektivität und Tauglichkeit des entwickelten Indikators wird in einer exemplarischen Fallstudie im Hamburger Bezirk Bergedorf demonstriert. Hierzu wird der dortige Shared Space gefilmt. Um die Interaktion von Verkehrsteilnehmern und die Wirkungsweise der Verkehrsraumaushandlung nachzustellen, wird ein innovativer Modellierungsansatz, der auf dem sozialen Kräftemodell basiert, empfohlen. Das Modell wird in einem Java-basierten Simulationstool kalibriert und implementiert. Verschiedene Shared Space Arten und konventionelle Szenarien mit Raumtrennung werden simuliert. Das Ziel dieser Dissertation ist es, ein Verfahren zur Auswertung der Performances von Shared Spaces durch Verkehrsmikrosimulation zu entwickeln. Dieses Verfahren beinhaltet die Datenerhebung und –erfassung, die Definition der Leistungsindikatoren, die Entwicklung eines Mikrosimulationsansatzes und die Kalibrierung des Bewegungsmodells auf Basis realer Daten. Zudem werden Simulationen durchgeführt, um Ergebnisse zu sammeln. Des Weiteren zeigt diese Arbeit die Notwendigkeit, einen komfortbasierten Indikator für die Verkehrsqualität der Fußgänger in Shared Spaces zu verwenden. Die Vorteile dieses Ansatzes, gegenüber konventionellen, effizienzbasierten Indikatoren wie z.B. Zeitverzögerungen, werden entsprechend in praxistauglichen Situationen dargestellt und sukzessiv mittels statistischer Verfahren veranschaulicht

2023 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium

2023 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium

Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

NASA LaRC Workshop on Guidance, Navigation, Controls, and Dynamics for Atmospheric Flight, 1993

This publication is a collection of materials presented at a NASA workshop on guidance, navigation, controls, and dynamics (GNC&D) for atmospheric flight. The workshop was held at the NASA Langley Research Center on March 18-19, 1993. The workshop presentations describe the status of current research in the GNC&D area at Langley over a broad spectrum of research branches. The workshop was organized in eight sessions: overviews, general, controls, military aircraft, dynamics, guidance, systems, and a panel discussion. A highlight of the workshop was the panel discussion which addressed the following issue: 'Direction of guidance, navigation, and controls research to ensure U.S. competitiveness and leadership in aerospace technologies.

Relation of NEEDS to OSTA

The NEEDS program was examined, the interfaces between OSTA and NEEDS were identified, and the responsiveness of the NEEDS program to OSTA technological requirements were assessed. Existing and planned NEEDS elements are discussed