5,959 research outputs found
Iterative Learning Control with Forgetting Factor for Urban Road Network
In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method
Automated Synthetic-to-Real Generalization
Models trained on synthetic images often face degraded generalization to real
data. As a convention, these models are often initialized with ImageNet
pre-trained representation. Yet the role of ImageNet knowledge is seldom
discussed despite common practices that leverage this knowledge to maintain the
generalization ability. An example is the careful hand-tuning of early stopping
and layer-wise learning rates, which is shown to improve synthetic-to-real
generalization but is also laborious and heuristic. In this work, we explicitly
encourage the synthetically trained model to maintain similar representations
with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize
(L2O)} strategy to automate the selection of layer-wise learning rates. We
demonstrate that the proposed framework can significantly improve the
synthetic-to-real generalization performance without seeing and training on
real data, while also benefiting downstream tasks such as domain adaptation.
Code is available at: https://github.com/NVlabs/ASG.Comment: Accepted to ICML 202
A Concept for Deployment and Evaluation of Unsupervised Domain Adaptation in Cognitive Perception Systems
Jüngste Entwicklungen im Bereich des tiefen Lernens ermöglichen Perzeptionssystemen
datengetrieben Wissen über einen vordefinierten Betriebsbereich,
eine sogenannte Domäne, zu gewinnen. Diese Verfahren des überwachten
Lernens werden durch das Aufkommen groß angelegter annotierter
Datensätze und immer leistungsfähigerer Prozessoren vorangetrieben und
zeigen unübertroffene Performanz bei Perzeptionsaufgaben in einer Vielzahl
von Anwendungsbereichen.Jedoch sind überwacht-trainierte neuronale Netze
durch die Menge an verfügbaren annotierten Daten limitiert und dies wiederum
findet in einem begrenzten Betriebsbereich Ausdruck. Dabei beruht
überwachtes Lernen stark auf manuell durchzuführender Datenannotation.
Insbesondere durch die ständig steigende Verfügbarkeit von nicht annotierten
großen Datenmengen ist der Gebrauch von unüberwachter Domänenanpassung
entscheidend. Verfahren zur unüberwachten Domänenanpassung sind
meist nicht geeignet, um eine notwendige Inbetriebnahme des neuronalen
Netzes in einer zusätzlichen Domäne zu gewährleisten. Darüber hinaus
sind vorhandene Metriken häufig unzureichend für eine auf die Anwendung
der domänenangepassten neuronalen Netzen ausgerichtete Validierung. Der
Hauptbeitrag der vorliegenden Dissertation besteht aus neuen Konzepten zur
unüberwachten Domänenanpassung. Basierend auf einer Kategorisierung
von Domänenübergängen und a priori verfügbaren Wissensrepräsentationen
durch ein überwacht-trainiertes neuronales Netz wird eine unüberwachte
Domänenanpassung auf nicht annotierten Daten ermöglicht. Um die kontinuierliche
Bereitstellung von neuronalen Netzen für die Anwendung in
der Perzeption zu adressieren, wurden neuartige Verfahren speziell für die
unüberwachte Erweiterung des Betriebsbereichs eines neuronalen Netzes
entwickelt. Beispielhafte Anwendungsfälle des Fahrzeugsehens zeigen, wie
die neuartigen Verfahren kombiniert mit neu entwickelten Metriken zur kontinuierlichen
Inbetriebnahme von neuronalen Netzen auf nicht annotierten
Daten beitragen. Außerdem werden die Implementierungen aller entwickelten
Verfahren und Algorithmen dargestellt und öffentlich zugänglich gemacht.
Insbesondere wurden die neuartigen Verfahren erfolgreich auf die unüberwachte
Domänenanpassung, ausgehend von der Tag- auf die Nachtobjekterkennung
im Bereich des Fahrzeugsehens angewendet
Automated Synthetic-to-Real Generalization
Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Yet the role of ImageNet knowledge is seldom discussed despite common practices that leverage this knowledge to maintain the generalization ability. An example is the careful hand-tuning of early stopping and layer-wise learning rates, which is shown to improve synthetic-to-real generalization but is also laborious and heuristic. In this work, we explicitly encourage the synthetically trained model to maintain similar representations with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize (L2O)} strategy to automate the selection of layer-wise learning rates. We demonstrate that the proposed framework can significantly improve the synthetic-to-real generalization performance without seeing and training on real data, while also benefiting downstream tasks such as domain adaptation. Code is available at: this https URL https://github.com/NVlabs/ASG
Multilane traffic density estimation with KDE and nonlinear LS and tracking with Scalar Kalman filtering
Tezin basılısı, İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.With increasing population, the determination of traffic density becomes very critical in managing the urban city roads for safer driving and low carbon emission. In this study, Kernel Density Estimation is utilized in order to estimate the traffic density more accurately when the speeds of the vehicles are available for a given region. For the proposed approach, as a first step, the probability density function of the speed data is modeled by Kernel Density Estimation. Then, the speed centers from the density function are modeled as clusters. The cumulative distribution function of the speed data is then determined by Kolmogorov-Smirnov Test, whose complexity is less when compared to the other techniques and whose robustness is high when outliers exist. Then, the mean values of clusters are estimated from the smoothed density function of the distribution function, followed by a peak detection algorithm. The estimation of variance values and kernel weights, on the other hand, are found by a nonlinear Least Square approach. As the estimation problem has linear and non-linear components, the nonlinear Least Square with separation of parameters approach is adopted, instead of dealing with a high complexity nonlinear equation. Finally, the tracking of former and latter estimations of a road is calculated by using Scalar Kalman Filtering with scalar state - scalar observation generality level. Simulations are carried out in order to assess theperformanceoftheproposedapproach. Forallexampledatasets, theminimummean square error of kernel weights is found to be less than 0.002 while error of mean values is found to be less than 0.261. The proposed approach was also applied to real data from sample road traffic, and the speed center and the variance was accurately estimated. By using the proposed approach, accurate traffic density estimation is realized, providing extra information to the municipalities for better planning of their cities.Declaration of Authorship ii
Abstract iii
Öz iv
Acknowledgments vi
List of Figures ix
List of Tables x
Abbreviations xi
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Methods to Find Probability Density Function and Cumulative Distribution Function . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
1.3 Traffic Density Estimation with Kernel Density Estimation . . . . . . . . 4
1.4 The Approaches for Determination of Key Parameters of Traffic Density Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . .5
1.5 Tracking between Estimated Data and New Data . . . . . . . . . . . . . . 6
1.6 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Literature Review 7
2.1 Methodologies Used for Estimation of Traffic Density . . . . . . . . . . . . 7
2.2 An Example Study of Traffic Density Estimation with KDE and CvM . . 9
2.3 Three Complementary Studies for Traffic Density Estimation and Tracking 9
2.4 Comparison of Three Different Nonlinear Estimation Techniques on the Same Problem . . . . . . . . . . . . . . . . . . . . . . . . .10
2.4.1 A Maximum Likelihood Approach for Estimating DS-CDMA Multipath Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Channel Estimation for the Uplink of a DS-CDMA System . . . . 12
2.4.3 A Robust Method for Estimating Multipath Channel Parameters in the Uplink of a DS-CDMA System. . . . . . . . . . . . . . .13
3 The Model 16
3.1 Finding Density Distribution with KDE . . . . . . . . . . . . . . . . . . . 16
3.2 Finding Empirical CDF with KS Test . . . . . . . . . . . . . . . . . . . . 18
3.3 Determination of Speed Centers via PDA . . . . . . . . . . . . . . . . . . 20
3.4 Estimation of Variance and Kernel Weights with Nonlinear LS Method . . 21
3.5 Tracking of Traffic Density Estimation with Scalar Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 Numerical Calculations for Traffic Density Estimation 26
4.1 An Example Traffic Scenario with Five Speed Centers . . . . . . . . . . . 26
4.2 The Estimation of A Real Time Data . . . . . . . . . . . . . . . . . . . . . 29
4.3 Traffic Density Estimation with Different Kernel Numbers . . . . . . . . . 29
5 Examples to Test Tracking Part of the Model 31
5.1 Tracking with the Change only in Mean Values . . . . . . . . . . . . . . . 32
5.2 Tracking with the Change only in Kernel Weights . . . . . . . . . . . . . . 35
5.3 Tracking with the Change in All Three Parameters . . . . . . . . . . . . . 36
6 Assesment 38
7 Conclusion 41
A Derivation of Newton-Raphson Method for the Estimation of Variance Values and Kernel Weights 43
Bibliography 4
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
ReFIoV: a novel reputation framework for information-centric vehicular applications
In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies
Mobility systems often suffer from a high price of anarchy due to the
uncontrolled behavior of selfish users. This may result in societal costs that
are significantly higher compared to what could be achieved by a centralized
system-optimal controller. Monetary tolling schemes can effectively align the
behavior of selfish users with the system-optimum. Yet, they inevitably
discriminate the population in terms of income. Artificial currencies were
recently presented as an effective alternative that can achieve the same
performance, whilst guaranteeing fairness among the population. However, those
studies were based on behavioral models that may differ from practical
implementations. This paper presents a data-driven approach to automatically
adapt artificial-currency tolls within repetitive-game settings. We first
consider a parallel-arc setting whereby users commute on a daily basis from a
unique origin to a unique destination, choosing a route in exchange of an
artificial-currency price or reward while accounting for the impact of the
choices of the other users on travel discomfort. Second, we devise a
model-based reinforcement learning controller that autonomously learns the
optimal pricing policy by interacting with the proposed framework considering
the closeness of the observed aggregate flows to a desired system-optimal
distribution as a reward function. Our numerical results show that the proposed
data-driven pricing scheme can effectively align the users' flows with the
system optimum, significantly reducing the societal costs with respect to the
uncontrolled flows (by about 15% and 25% depending on the scenario), and
respond to environmental changes in a robust and efficient manner
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