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Modular Fault Diagnosis in Fixed-Block Railway Signaling systems

IFAC-PapersOnLine 2016年第3期49卷 459-464页

作者： Durmus, Mustafa S. Ustoglu, lker Tsarev, Roman Y. Schwarz, Michael Electrical and Electronics Engineering Department Pamukkale University Denizli Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul Turkey Institute of Space and Information Technology Siberian Federal University Krasnoyarsk Russia Computer Architecture and System Programming Kassel University Kassel Germany

The diagnosis of possible faults in railway signaling systems is an important issue to provide safe travel and transportation in railways. Signaling system designers have to consider the possible faults which may occur in railway field components both on the requirements preparation phase and on the development phase of the signaling system software or namely, the interlocking system. Although the diagnosis of different unobservable faults is relatively hard, especially for large scale railway fields, this complexity can be overcome by using the Discrete Event system (DES) based modular diagnosis approach which is explained in this paper. The main advantage of using such modular approach for fault diagnosis in fixed-block signaling systems is the inspection of the diagnosability of the whole system with respect to its subsystems (railway field components). In this study, the diagnosability of the railway field equipment and the whole system is also explained with a case study. © 2016

关键词： Fault detection Discrete event simulation Failure analysis Railroad signal systems Railroads Signaling Development phase Field components Field equipment Fixed block signaling Interlocking systems Modular approach Railway signaling systems Signaling systems

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Intelligent Agent for Support of Decision Making on Civil Law Regulation of Contract for the Provision of in Vitro Fertilization 15

Intelligent Agent for Support of Decision Making on Civil La...

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15th IEEE International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020

作者： Hovorushchenko, Tetiana Herts, Alla Hnatchuk, Yelyzaveta Computer Engineering and System Programming Department Khmelnytskyi Ukraine Ivan Franko National University of Lviv Civil Law and Legal Proceedings Department Lviv Ukraine

ISBN: (纸本)9781728174433

Nowadays in Ukraine, there is a need for automation and removal of human from the processes of decision making on civil law regulation of contract for the provision of in vitro fertilization, which can significantly increase the correctness of the concluded contract in terms of civil law and medical law. So, the purpose of this study is the development of an intelligent agent for support of decision making on civil law regulation of contract for the provision of in vitro fertilization. The method of activity of intelligent agent for support of decision making on civil law regulation of contract for the provision of in vitro fertilization was first developed in this paper, which concludes that the content of the contract for the provision of in vitro fertilization is correct or incorrect, as well as a recommendation to conclude a contract (if its content is correct) or to revise the contract (if its content is incorrect). © 2020 IEEE.

关键词： Decision making

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Design of a Novel Safe Approach to Monitor Drivers While Operating Their Cars 7

Design of a Novel Safe Approach to Monitor Drivers While Ope...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Elhawawsha, Aseel Zeine, Carla Zeid Daou, Roy Abi Kalinowski, Dominik Khattar, Joseph Telawi, Samer Boercsoek, Josef University of Kassel Department of Computer Architecture and System Programming Kassel Germany Université la Sagesse Saculty of Engineering Mechatronics Eng. Department Beirut Lebanon

ISBN: (纸本)9798350325850

Road accidents have been known to be one of the leading death causes around the world for a long time. Thus, cars and all kinds of road vehicles form a huge source of danger, and they relate to multiple high risks. There are many factors that can contribute to road traffic accidents such as the health condition of the driver if a medical emergency event happened while driving, drowsiness, or mind-wandering. The proposed system can monitor the behavior of the driver while driving as well as his/her health status. Moreover, it analyzes the monitored data, alerts the driver in a timely manner, and assists him/her in adjusting the driving behavior. Different dangerous and risky situations which can be faced during driving can be captured and avoided by using such a system which leads at the end to increase road traffic safety and reduce vehicle collisions. Notifications are developed in cases of drowsiness, loss of focus, or other concerns that may emerge while driving. However, when the driver's ability to operate the vehicle safely is hampered by a health concern, the implemented system is only able at this stage to alarm the driver itself and the surrounding cars as a first level of alarms. © 2023 IEEE.

关键词： Roads and streets

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Improvement, application and verification of a new multivariate forecasting model for real industry related issues 31st

Improvement, application and verification of a new multivari...

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31st European Safety and Reliability Conference, ESREL 2021

作者： Krini, Abderrahim Börcsök, Josef Engineering of Remanufacturing and Quality Robert Bosch GmbH Schwäbisch Gmünd Germany Department of Computer Architecture and System Programming University Kassel Germany

ISBN: (纸本)9789811820168

With this work, a research contribution in the field of reliability theory has been made, with which a realistic prognosis of reliability parameters of technical systems can be carried out. The motivation to deal with this topic resulted from the realization that the prognosis quality of established prognosis models must be optimized. An early and realistic prognosis of reliability parameters contributes to the success of a concern, mainly through the early implementation of quality measures. The work focuses on the development of a new multivariate prognosis model, which uses multivariate stress parameters as reference variables. Its application enables the prediction of reliability parameters for electronic control units. The predicted reliability parameters can be specified as stress-dependent (bivariate/multivariate) or time-dependent variables. While univariate reference quantities usually use the time dwell time of a technical system, the prognosis model newly presented here can process multivariate reference quantities. During the time in the field, technical systems are not only exposed to different usage behavior, but also to other stresses and influences that make a not inconsiderable contribution to failure. The use of time in the field as a univariate reference variable does not allow for this differentiated consideration and does not take into account relevant information in the reliability analysis. All existing prediction models have in common that only univariate reference parameters can be processed. For a fully comprehensive reliability analysis, all stress variables that lead to a failure must be considered. This is not sufficiently possible with a simple univariate approach. With the new approaches, it is now possible for the first time to consider different stress variables, their changes and their effects on the technical system under investigation in a field data analysis. The presented approach for the multivariate prognosis model considers i

关键词： Forecasting

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Evaluating Length of a Shortest Adaptive Homing Sequence for Weakly Initialized FSMs

Evaluating Length of a Shortest Adaptive Homing Sequence for...

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2020 IEEE East-West Design and Test Symposium, EWDTS 2020

作者： Vinarskii, Evgenii Yevtushenko, Nina Lomonosov Moscow State University Department of Computer Science Moscow Russia Ivannikov Institute for System Programming of Ras Software Engineering Deparment Moscow Russia

ISBN: (数字)9781728198996

ISBN: (纸本)9781728198996

There are many research papers devoted to the state identification problem of finite state machines (FSMs) which are widely used for analysis of discrete event systems. A deterministic complete reduced FSM always has a homing sequence (HS) but a number of non-deterministic FSMs not necessarily have a preset HS;nevertheless, those FSMs can still have an adaptive HS. Moreover, adaptive HS can be shorter than the preset. If each state can be an initial state of a complete FSM, i.e., an FSM is non-initialized, then a shortest adaptive HS (if it exists) has polynomial length in the number of FSM states. We show that it is not the case for weakly-initialized FSMs: such FSMs can have a shortest adaptive HS of exponential length in the number of FSM states/transitions. However, the experimental evaluation shows that the non-polynomial upper bound for a shortest adaptive HS was never reached for randomly generated weakly-initialized two-input FSMs. © 2020 IEEE.

关键词： Adaptive homing sequence Non-deterministic FSM weakly initialized FSM

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Effects of varying diagnostic coverage on functional safety

Effects of varying diagnostic coverage on functional safety

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2014 International Symposium on Fundamentals of Electrical engineering, ISFEE 2014

作者： Üstoʇlu, Ilker Kaymakçi, Özgür T. Börcsök, Josef Department of Control and Automation Engineering Yildiz Technical University Esenler Istanbul34220 Turkey Department of Computer Architecture and System Programming University of Kassel Kassel34121 Germany

ISBN: (纸本)9781479968213

Diagnostics is one of the significant parameters of functional safety which concerns itself with revealing dangerous incidents that might impede the operation of safety systems. Diagnostic coverage factor (DC) is defined as a metric for diagnostics in that DC is one of the most important design parameters to measure the effectiveness of safety protection systems. This study addresses how diagnostics impacts proof test interval (TI) when the system is halted completely;a thorough maintenance and changes in parts are performed. Besides this, the relationship between average probability of failure on demand (PFDavg), DC, TI and mean time to failure (MTTF) are presented for 1oo2 and 1oo2D system architectures. © 2014 IEEE.

关键词： Safety factor

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Deep Learning in IoT: An LSTM Approach for NDVI Forecasting 3

Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Sherif, Khaled Azmy, Mohamed Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute Department of Communications Mansoura Egypt Delta Higher Institute Department of Civil Engineering Mansoura Egypt University of Tishreen Department of Statistics and Programming Faculty of Economics Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia

ISBN: (纸本)9798350323511

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series' sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes. © 2023 IEEE.

关键词： Forecasting

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Improvement and verification methods for components/systems in automobile applications in the field of reliability forecasting with respect to one and two dimensions of stress factors 30

Improvement and verification methods for components/systems ...

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30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020

ISBN: (纸本)9789811485930

The details of future failures of systems that are serially produced are important for the remanufacturing departments to frame an optimal strategy for the post series supply. The existing one-dimensional and two-dimensional reliability prediction models predict field failures as a function of time based on the stress which the components/systems experience in the field until failure. In day-to-day situations the failure forecasts depend on many external factors like registration delay, which is the delay between the production of a system and its registration in market, reporting delay, which is the delay between the field failure and its reporting time, the export of vehicles to other countries or permanent damage caused to the vehicles. The improvement procedures are integrated into the existing reliability forecast models to reduce the incompleteness of the warranty data due to the influence of external factors using MATLAB. The verification process is to compare the forecasted field failures by the existing models with the actual field failures. The prognosis is carried out based on different failures data sets. With the help of sales data from Original Equipment Manufacturers, the forecasted failures Year to Date are verified. A validation of the results is carried out to check the impact of the influencing factors. © ESREL2020-PSAM15 *** by Research Publishing, Singapore.

关键词： Forecasting

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Reliability Forecasting for Components/systems with respect to 1-, 2- And 3- Dimensions of Stress Factors 30

Reliability Forecasting for Components/Systems with respect ...

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30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020

作者： Krini, Abderrahim B rcs k, Josef Engineering of Remanufacturing and Quality Robert Bosch GmbH Schw bisch Gm nd Germany Department of Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (纸本)9789811485930

Remanufacturing, a popular term in the automotive industry, helps to reduce the need for raw materials, increase profitability, is an effective solution to the challenges of postal items and also benefits the environment. For the remanufacturing department, the reliability and failure rate of serial products is particularly important, and therefore the accurate prediction of failure rates is also important. It must be ensured that the product meets its reliability requirements. Existing one, two and three dimensional reliability prediction models predict field failures as a function of time, based on the stress experienced by the components/systems in the field until failure. The existing models have been extended to include the parameters that allow us to predict the reliability of refurbished systems. Improvements such as registration delays, reporting delays and the corrected candidate calculation, which deals with whether the product is still within the warranty period after the observation period, are present in the models and are applied to the systems. This reduces the incompleteness of warranty data due to the influence of external factors with MATLAB. The reliability of systems can be predicted with the lifetime prediction and further conclusions can be drawn for future development. In the verification process, the predicted field failures of the models are compared with the real field failures. ©ESREL2020-PSAM15 Organizers.

关键词： Failure rate

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