Kompetenzzentrum Künstliche Intelligenz
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In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
We consider in details the dual models for the Goldstone mesons (pions) scattering in the presence of the explicit chiral symmetry breaking caused by non-zero current quark mass. New method of incorporation of the quark masses into the dual model is suggested. In contrast to the previously considered in the literature methods, the dual amplitude obtained by this method is consistent with all low-energy theorems following from the Effective Chiral Lagrangian (EChL) to the O(p^4) order and simultaneously it does not contain states with negative width. The resonance spectrum of the model and its implications for the fourth and sixth order EChL in large N_c limit are discussed. We argue that the possible relations between large N_c QCD and some underlying string theory can be revealed by studying interactions of hadrons at low-energies.
We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the "myopic" approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions.
We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created automatically from the XML-structured data. The interconnection between XML-failure trees is handled as object oriented instances of Bayesian sub-networks within master-network covering the entire process and monitoring its overall condition, output quality and equipment effectiveness. We implement sequential on-line adaptivity of models' parameters to reflect changes in process operation. The system learning can be supervised by user feedback on the actual root cause. The general RCA methodology is applied to plate cutting in a hot rolling mill.
We propose a methodology for Root Cause Analysis (RCA), allowing fast and flexible decision support for operators, maintenance staff and process engineers in pulp and paper industry. RCA can identify non-obvious process problems and is therefore a powerful complement to normal automatic control. The general methodology is applied to a continuous digester.
In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia.
We consider a critical composite superconformal string model to desribe hadronic interactions. We present a new approach of introducing hadronic quantum numbers in the scattering amplitudes. The physical states carry the quantum numbers and form a common system of eigenfunctions of the operators in this string model. We give explicit constructions of the quantum number operators.
We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand.
Applications of object-oriented Bayesian networks for causal analysis of process disturbances
(2003)
We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-measurable disturbances as a decision advice complement to the distributed control system (DCS). The approach includes prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. The methodology has been applied on a screening process with a pressure-flow network in a Pulp Mil.
