@article{RoehrlIglesiasRozasWeidl2008, author = {R{\"o}hrl, Norbert and Iglesias-Rozas, Jos{\´e} R. and Weidl, Galia}, title = {A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools}, series = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, volume = {2008}, journal = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, number = {30/1}, pages = {33 -- 8}, year = {2008}, abstract = {To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80\% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84\% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas.}, subject = {Cytologie}, language = {en} } @article{WeidlRoehrlRozas2007, author = {Weidl, Galia and R{\"o}hrl, Norbert and Rozas, Javier}, title = {Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features}, series = {Histology and Histopathology}, volume = {2007}, journal = {Histology and Histopathology}, number = {22/9}, doi = {10.14670/HH-22.947}, pages = {947 -- 962}, year = {2007}, abstract = {This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading.}, subject = {Histologie}, language = {en} } @incollection{RoehrlIglesiasRozasWeidl2007, author = {R{\"o}hrl, Norbert and Iglesias-Rozas, Jos{\´e} R. and Weidl, Galia}, title = {Computer Assisted Classification of Brain Tumors}, series = {Data Analysis, Machine Learning and Applications}, booktitle = {Data Analysis, Machine Learning and Applications}, publisher = {Springer}, doi = {10.1007/978-3-540-78246-9_7}, pages = {55 -- 60}, year = {2007}, abstract = {The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.}, subject = {Hirntumor}, language = {en} } @article{WeidlRodeHorchetal.2005, author = {Weidl, Galia and Rode, Manfred and Horch, Alexander and Shaw, Christopher and Vollmer, Andreas}, title = {Automated root cause analysis of faults and disturbances in rolling mills}, series = {Stahl und Eisen}, volume = {2005}, journal = {Stahl und Eisen}, number = {125/8}, pages = {29 -- 34}, year = {2005}, abstract = {The developed methodology for Root Cause Analysis (RCA) demonstrates a decision support tool evaluating the process state based on both qualitative and quantitative information. The presented RCA system uses the available data to extract the most probable root causes and proposes an action sequence. The learning ability of the system allows its sequential on-line adaptation to reflect changes in process operation.}, subject = {Walzwerk}, language = {de} } @article{WeidlMadsenIsraelson2005, author = {Weidl, Galia and Madsen, Anders L. and Israelson, S.}, title = {Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes}, series = {Computers \& Chemical Engineering}, volume = {2005}, journal = {Computers \& Chemical Engineering}, number = {29/9}, doi = {10.1016/j.compchemeng.2005.05.005}, pages = {1996 -- 2009}, year = {2005}, abstract = {The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users' actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice.}, subject = {Prozess{\"u}berwachung}, language = {en} } @inproceedings{Weidl2004, author = {Weidl, Galia}, title = {ADAPTIVE RISK ASSESSMENT IN COMPLEX LARGE SCALE PROCESSES WITH REDUCED COMPUTATIONAL COMPLEXITY}, series = {9th International Conference on Industrial Engineering Theory, Applications \& Practice, November 27-30, 2004}, booktitle = {9th International Conference on Industrial Engineering Theory, Applications \& Practice, November 27-30, 2004}, year = {2004}, abstract = {We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on the effect variables by utilizing causal probabilistic modeling. The process is modeled by Hidden Markov Models (HMM), and object oriented dynamic Bayesian networks (OOBNs). Various modeling techniques and assumptions have been used to reduce the computational complexity and ensure fast inference. The methodology is applied in a case study.}, subject = {Prozessmodell}, language = {en} } @inproceedings{WeidlMadsenDahlquist2003, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Applications of object-oriented Bayesian networks for causal analysis of process disturbances}, series = {SIMS'2003 (44th International Conference of the Scandinavian Simulation Society)}, booktitle = {SIMS'2003 (44th International Conference of the Scandinavian Simulation Society)}, year = {2003}, abstract = {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.}, subject = {Prozessmodell}, language = {en} } @inproceedings{WeidlMadsenDahlquist2003, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Object Oriented Bayesian Networks for Industrial Process Operation}, series = {Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003}, booktitle = {Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003}, year = {2003}, abstract = {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.}, subject = {Prozesssteuerung}, language = {en} } @inproceedings{WeidlVollmarDahlquist2003, author = {Weidl, Galia and Vollmar, Gerhard and Dahlquist, Erik}, title = {Adaptive Root Cause Analysis under uncertainties in industrial process operation}, series = {The Foundations of Computer Aided Process Operations (FOCAPO 2003)}, booktitle = {The Foundations of Computer Aided Process Operations (FOCAPO 2003)}, year = {2003}, abstract = {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.}, subject = {Prozesssteuerung}, language = {en} } @article{WeidlMadsenDahlquist2002, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions}, series = {Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications}, volume = {2002}, journal = {Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications}, number = {87}, pages = {221 -- 230}, year = {2002}, abstract = {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.}, subject = {Prozesssteuerung}, language = {en} } @inproceedings{WeidlDahlquist2002, author = {Weidl, Galia and Dahlquist, Erik}, title = {ROOT CAUSE ANALYSIS FOR PULP AND PAPER APPLICATIONS}, series = {Proceedings of the 10th Control Systems Conference, Stockholm, Sweden, June 3-5, 2002}, booktitle = {Proceedings of the 10th Control Systems Conference, Stockholm, Sweden, June 3-5, 2002}, pages = {343 -- 347}, year = {2002}, abstract = {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.}, subject = {Prozessanalyse}, language = {en} } @article{RaiynWeidl2024, author = {Raiyn, Jamal and Weidl, Galia}, title = {Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events}, series = {Smart Cities}, volume = {7}, journal = {Smart Cities}, number = {1}, doi = {https://doi.org/10.3390/ smartcities7010018}, pages = {460 -- 474}, year = {2024}, abstract = {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}, subject = {Autonomes Fahrzeug}, language = {en} } @article{DahlquistLindbergKarlssonetal.2001, author = {Dahlquist, Erik and Lindberg, Thomas and Karlsson, Christer Per and Weidl, Galia and Bigaran, Carlo and Davey, Austin}, title = {Integrated Process Control, Fault Diagnostics, Process Optimization and Production Planning - Industrial IT}, series = {IFAC Proceedings Volumes}, volume = {2001}, journal = {IFAC Proceedings Volumes}, number = {34/27}, doi = {10.1016/S1474-6670(17)33567-X}, pages = {47 -- 55}, year = {2001}, abstract = {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.}, subject = {Prozesssteuerung}, language = {en} } @article{PolyakovWeidl1997, author = {Polyakov, Maxim V. and Weidl, Galia}, title = {Chiral expansion in the dual (string) model of the Goldstone meson scattering}, series = {High Energy Physics - Phenomenology}, volume = {1997}, journal = {High Energy Physics - Phenomenology}, number = {0/0}, doi = {10.48550/arXiv.hep-ph/9612486}, pages = {1 -- 13}, year = {1997}, abstract = {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.}, subject = {Stringtheorie}, language = {en} }