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  • 1.
    Chen, DeJiu
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Ottikkutti, Suranjan
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Vartholomeos, Panagiotis
    Tahmasebi, Kaveh Nazem
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Karamousadakis, Michalis
    Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, p. 986-986Article in journal (Refereed)
    Abstract [en]

    Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous. 

  • 2.
    Kang, Shuting
    et al.
    University of Chinese Academy of Sciences, Beijing, China; Institute of Software Chinese Academy of Sciences, Beijing, China.
    Guo, Heng
    University of Chinese Academy of Sciences, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; .
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Zhang, Lijun
    Institute of Software Chinese Academy of Sciences, Beijing, China.
    Liu, Guangzhen
    Institute of Software Chinese Academy of Sciences, Beijing, China.
    Xue, Yunzhi
    Institute of Software Chinese Academy of Sciences, Beijing, China.
    Wu, Yanjun
    Institute of Software Chinese Academy of Sciences, Beijing, China.
    ECSAS: Exploring critical scenarios from action sequence in autonomous driving2023In: Proceeding of 2023 IEEE 32nd Asian Test Symposium (ATS), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Rare critical scenarios are crucial to verify the performance of autonomous driving in different situations. Critical scenario generation requires the ability of sampling critical combinations from an infinite parameter space in the logical scenario. Existing solutions aim to explore the correlation of action parameters in the initial scenario rather than action sequences. How to model action sequences so that one can further consider the effects of different action parameters is the bottleneck of the problem. In this paper, we solve the problem by proposing the ECSAS framework. Specifically, we first propose a description language, BTScenario, allowing us to model action sequences of scenarios. We then use reinforcement learning to search for combinations of critical action parameters. Several optimizations are proposed to increase efficiency, including action mask and replay buffer. Experimental results show that our model with strong collision ability and effectively outperforms the existing methods on various nontrivial scenarios.

  • 3.
    Khound, Parthib
    et al.
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Mohammed, Omar
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Gronwald, Frank
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Performance Index Modeling from Fault Injection Analysis for an Autonomous Lane-Keeping System2023In: Proceeding of the 33rd European Safety and Reliability Conference, Research Publishing Services , 2023Conference paper (Refereed)
    Abstract [en]

    A faulty sensor data could not only undermine the stability but also drastically compromise the safety of autonomoussystems. The reliability of the functional operation can be significantly enhanced, if any monitoring modules canevaluate the risk on the system for a particular fault in a sensor. Based on the estimated risk, the system can thenexecute the necessary safety operation. To develop a risk evaluating algorithm, the relation between the faults and theeffects should be known. Therefore, to establish such cause-and-effect relationship, this paper presents a performanceindexing method that quantifies the effects caused by given fault types with different intensities. Here, the consideredsystem is a lane keeping robot and the only sensor used for the functional operation is a red, green, and blue (RGB)camera. The lane keeping algorithm is modeled using a supervised artificial intelligence (AI) learning method. Toquantify the effects with performance indices (PIs), different faults are injected to the RBG camera. For an injectedfault type, the system’s PI is evaluated from the AI algorithm’s (open-loop) outcome and the lane keeping (closedloop) outcome. The lane keeping/closed-loop outcome is quantified from the trajectory data computed using thestrapdown inertial navigation algorithm with the measurement data from a 6D inertial measurement unit (IMU).

  • 4.
    Mohammed, Omar
    et al.
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Khound, Parthib
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Gronwald, Frank
    Chair of Reliability of Technical Systems and Electrical Measurement, University of Siegen, 57076 Siegen, Germany.
    Multilevel Artificial Intelligence Classification of Faulty Image Data for Enhancing Sensor Reliability2023In: Proceeding of the 33rd European Safety and Reliability Conference, Research Publishing Services , 2023Conference paper (Refereed)
    Abstract [en]

    A multi-stage classification algorithm is proposed to predict the fault type and its associated intensity level of acamera input frame to enhance the reliability of a camera-based system. A fault injecting tool is used to generate thedataset required for the training. The model architecture mainly comprises three convolutions neural network (CNN)layers and three fully connected layers. The model achieves 93.8% accuracy for predicting a fault type. For the faultintensity prediction the accuracy significantly varies for each fault type but for some faults, the model achieves avery good prediction accuracy. However, for some other faults the accuracy can be remarkably low. The primaryreason for this gap is that the intensity levels of all considered faults can be described in a sufficiently quantitativeway, i.e., there is no sufficient metric available so far. 

  • 5.
    Su, Peng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Using Fault Injection for the Training of Functions to Detect Soft Errors of DNNs in Automotive Vehicles2022In: New Advances in Dependability of Networks and Systems / [ed] Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J., Springer, 2022, Vol. 484, p. 308-318Conference paper (Refereed)
    Abstract [en]

    Advanced functions based on Deep Neural Networks (DNN) have been widely used in automotive vehicles for the perception of operational conditions. To be able to fully exploit the potential benefits of higher levels of automated driving, the trustworthiness of such functions has to be properly ensured. This remains a challenging task for the industry as traditional approaches to system verification and validation, fault-tolerance design, become insufficient, due to the fact that many of these functions are inherently contextual and probabilistic in operation and failure. This paper presents a data centric approach to the fault characterization and data generation for the training of monitoring functions to detect soft errors of DNN functions during operation. In particular, a Fault Injection (FI) method has been developed to systematically inject both layer- and neuron-wise faults into the neural networks, including bit-flip, stuck-at, etc. The impacts of injected faults are then quantified via a probabilistic criterion based on Kullback-Leibler Divergence. We demonstrate the proposed approach based on the tests with an Alexnet.

  • 6.
    Su, Peng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Fan, Tianyu
    KTH.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Scheduling Resource to Deploy Monitors in Automated Driving Systems2023In: Dependable Computer Systems and Networks: Proceedings of the 18th International Conference on Dependability of Computer Systems DepCoS-RELCOMEX, Springer Nature , 2023, p. 285-294Conference paper (Refereed)
    Abstract [en]

    Deep Neural Networks (DNN) constitute an important technology for operational perception in Automated Driving Systems (ADS). However, the trustworthiness of such DNN is one concern in the system engineering and quality management. Therefore, it is critical to monitor conditions and ensure the safety of the implementations for this advanced technology. One solution is to use Conditional Monitors (CM) to detect possible faults. However, such monitors challenge resource (e.g., data and memory) management of limited memory space in the ADS hardware. This paper proposes a resource scheme for deploying a monitor in ADS by integrating dynamic memory scheduling with Responsibility-Sensitive Safety (RSS). We use the car-following system as a case study to evaluate our scheme. YOLOv5 and KITTI datasets simulate a perception module where various monitors detect faults. We measure the time cost of conventional scheduling pipelines and our method. Compared with the conventional method, our scheme reduces 43.7% of execution time per cycle.

  • 7.
    Su, Peng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Kang, ShuTing
    University of Chinese Academy of Sciences, China; Institute of Software Chinese Academy of Sciences, China.
    Tahmasebi, Kaveh Nazem
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Enhancing Safety Assurance for Automated Driving Systems by Supporting Operation Simulation and Data Analysis2023In: Proceeding of the 33rd European Safety and Reliability Conference, Research Publishing Services , 2023Conference paper (Refereed)
    Abstract [en]

    Automated Driving Systems (ADS) employ various techniques for operation perception, task planning and vehicle control. For driving on public roads, it is critical to guarantee the operational safety of such systems by attaining Minimal Risk Condition (MRC) despite unexpected environmental disruptions, human errors, functional faults and security attacks. This paper proposes a methodology to automatically identify potentially highly critical operational conditions by leveraging the design-time information in terms of vehicle architecture models and environment models. To identify the critical operating conditions, these design-time models are combined systematically with a variety of faults models for revealing the system behaviours in the presence of anomalies. The contributions of this paper are summarized as follows: 1) The design of a method for extracting related internal and external operational conditions from different system models. 2) The design of software services for identifying critical parameters and synthesizing operational data with fault injection. 3) The design for supporting operation simulation and data analysis.

  • 8.
    Su, Peng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Lu, Zhonghai
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Combining Self-Organizing Map with Reinforcement Learning for Multivariate Time Series Anomaly Detection2023In: Proceedings 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023Conference paper (Refereed)
    Abstract [en]

    Anomaly detection plays a critical role in condition monitors to support the trustworthiness of Cyber-Physical Systems (CPS). Detecting multivariate anomalous data in such systems is challenging due to the lack of a complete comprehension of anomalous behaviors and features. This paper proposes a framework to address time series multivariate anomaly detection problems by combining the Self-Organizing Map (SOM) with Deep Reinforcement Learning (DRL). By clustering the multivariate data, SOM creates an environment to enable the DRL agents interacting with the collected system  operational data in terms of a tabular dataset. In this environment, Markov chains reveal the likely anomalous features to support the DRL agent exploring and exploiting the state-action space to maximize anomaly detection performance. We use a time series dataset, Skoltech Anomaly Benchmark (SKAB), to evaluate our framework. Compared with the best results by some currently applied methods, our framework improves the F1 score by 9%, from 0.67 to 0.73. 

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  • 9.
    Su, Peng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Warg, Fredrik
    RISE Research Institutes of Sweden, Faculty of Dependable, Transport Systems Department, Borås, Sweden.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    A Simulation-Aided Approach to Safety Analysis of Learning-Enabled Components in Automated Driving Systems2023In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6152-6157Conference paper (Refereed)
    Abstract [en]

    Artificial Intelligence (AI) techniques through Learning-Enabled Components (LEC) are widely employed in Automated Driving Systems (ADS) to support operation perception and other driving tasks relating to planning and control. Therefore, the risk management plays a critical role in assuring the operational safety of ADS. However, the probabilistic and nondeterministic nature of LEC challenges the safety analysis. Especially, the impacts of their functional faults and incompatible external conditions are often difficult to identify. To address this issue, this article presents a simulation-aided approach as follows: 1) A simulation-aided operational data generation service with the operational parameters extracted from the corresponding system models and specifications; 2) A Fault Injection (FI) service aimed at high-dimensional sensor data to evaluate the robustness and residual risks of LEC. 3) A Variational Bayesian (VB) method for encoding the collected operational data and supporting an effective estimation of the likelihood of operational conditions. As a case study, the paper presents the results of one experiment, where the behaviour of an Autonomous Emergency Braking (AEB) system is simulated under various weather conditions based on the CARLA driving simulator. A set of fault types of cameras, including solid occlusion, water drop, salt and pepper, are modelled and injected into the perception module of the AEB system in different weather conditions. The results indicate that our framework enables to identify the critical faults under various operational conditions. To approximate the critical faults in undefined weather, we also propose Variational Autoencoder (VAE) to encode the pixel-level data and estimate the likelihood.

  • 10.
    Yu, Yan Feng
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Tahmasebi, Kaveh Nazem
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Robust Safety Control for Automated Driving Systems with Perception Uncertainties2023In: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis: Proceedings The 16th European Workshop on Advanced Control and Diagnosis (ACD 2022), Springer Nature , 2023, Vol. 467, p. 321-331Conference paper (Other academic)
    Abstract [en]

    Safety assurance and trustworthiness guarantees represent some of the most challenging problems in the development of next generation automated driving and driving assistance systems. A systematic approach, with measures ranging from development-time modeling and simulation support to operation-time mechanisms for situation-awareness and adaptation, becomes necessary for tackling the problems. This paper presents a novel approach to safety control for automated driving under the condition of uncertain perception due to emergent properties in the environment or sensor faults. Based on the theory of optimal control, the safety controller is built upon a control barrier function and a model predictive control function. The effectiveness of the proposed strategy is evaluated on a simulation scenario created in the open-source autonomous driving simulator CARLA.

  • 11.
    Zhu, Zikai
    et al.
    School of Information Science and Engineering, Fudan University, Shanghai, China.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design.
    Sean, Zhong
    Department of Computer and Information Science, Linköping University, Linköping, Sweden.
    Huang, Jiayu
    School of Information Science and Engineering, Fudan University, Shanghai, China.
    Ottikkutti, Suranjan
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design.
    Tahmasebi, Kaveh Nazem
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design.
    Zou, Zhuo
    School of Information Science and Engineering, Fudan University, Shanghai, China.
    Zheng, Li-rong
    School of Information Science and Engineering, Fudan University, Shanghai, China.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design.
    Using a VAE-SOM architecture for anomaly detection of flexible sensors in limb prosthesis2023In: Journal of Industrial Information Integration, ISSN 2452-414X, Vol. 35, article id 100490Article in journal (Refereed)
    Abstract [en]

    Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture is validated via two public datasets and achieves the best F1 scores. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on real-life productions.

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