| 1. |
- Chen, DeJiu, Associate Professor, et al.
(författare)
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A Mechatronics-twin Framework based on Stewart Platform for Effective Exploration of Operational Behaviors of Prosthetic Sockets with Amputees
- 2022
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Ingår i: BIOSTEC - the 16th International Joint Conference on Biomedical Engineering Systems and Technologies. 2022. - : SCITEPRESS - Science and Technology Publications.
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Konferensbidrag (refereegranskat)abstract
- A Stewart platform is a six-degree-of-freedom parallel manipulator widely used as the motion base for flight simulators, antenna positioning systems, machine tool technology, etc. This work presents a novel mechatronics-twin framework that integrates such a manipulator with advanced biomechanical models and simulations for effective exploration of operational behaviors of prosthetic sockets with amputees. By means of the biomechanical models and simulations, the framework allows the users to first analyze the fundamental operational characteristics of individual amputees according to their specific body geometries, pelvis-femur structures, sizes of transfemoral sockets, etc. Such operational characteristics are then fed to one Stewart platform as the reference control signals for the generation of dynamic loads and behaviors of prosthetic sockets that are otherwise difficult to observe or realize with the real amputees. Experiments in form of integration testing show that the proposed control strategy is capable of generating expected dynamic operational conditions. Currently, the mechatronics-twin framework supports a wide range of biomechanical configurations and the quantification of the respective intra-socket load conditions for socket design optimization and anomaly detection.
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| 2. |
- Chen, DeJiu, Associate Professor, et al.
(författare)
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Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework
- 2022
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 12:3, s. 986-986
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
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| 4. |
- Nazem Tahmasebi, Kaveh, et al.
(författare)
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A Fault Injection Tool for Identifying Faulty Operations of Control Functions in Automated Driving Systems
- 2022
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Ingår i: New Advances in Dependability of Networks and Systems. - Cham : Springer Nature.
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Konferensbidrag (refereegranskat)abstract
- Along with an increasing usage of intelligent driver-assistance service in automotive vehicles, the guarantee of dependability and trustworthiness of the system functions for operation perception and control becomes particularly critical. To cope with the risks, an investigation for determining how such system functions would be affected by different operational conditions, including the deviations of expected inputs, software, and hardware anomalies, becomes necessary. This paper describes a tool that augments Simulink/MATLABTM with additional fault models for effective analysis of error behaviors of automated control systems. This constitutes a basis for simulating different fault scenarios, collecting the operational data, and thereby the design of embedded online functions for error detection and treatment. This approach is demonstrated by a case study with an automated emergency braking system.
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| 5. |
- Su, Peng, et al.
(författare)
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Enhancing Safety Assurance for Automated Driving Systems by Supporting Operation Simulation and Data Analysis
- 2023
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Ingår i: Proceeding of the 33rd European Safety and Reliability Conference. - : Research Publishing Services.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 6. |
- Tahmasebi, Kaveh Nazem, et al.
(författare)
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A Condition-Aware Stochastic Dynamic Control Strategy for Safe Automated Driving
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Condition-awareness regarding electrical and electronic components is not only significant for predictive maintenance of automotive vehicles but also plays a crucial role in ensuring the operational safety by supporting the detection of anomalies, faults, and degradations over lifetime. In this paper, we present a novel control strategy that combines stochastic dynamic control method with condition-awareness for safe automated driving. In particular, the effectiveness of condition-awareness is supported by two distinct condition-monitoring functions. The first function involves the monitoring of a vehicle's internal health condition using model-based approaches. The second function involves the monitoring of a vehicle's external surrounding conditions, using machine learning and artificial intelligence approaches. For the quantification of current conditions, the results from these monitoring functions are used to create system health indices, which are then utilized by a safety control function for dynamic behavior regulation. The design of this safety control function is based on a chance-constrained model predictive control model, combined with a control barrier function for ensuring safe operation. The novelty of the proposed method lies in a systematic integration of monitored external and internal conditions, estimated component degradation, and remaining useful life, with the controller's dynamic responsiveness. The efficacy of the proposed strategy is evaluated with adaptive cruise control in the presence of various sensory uncertainties.
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| 7. |
- Wang, Dekun, et al.
(författare)
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Integrated Control of Steering and Braking for Effective Collision Avoidance with Autonomous Emergency Braking in Automated Driving
- 2022
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Ingår i: Proceedings of the 30th Mediterranean Conference on Control and Automation (MED).
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Konferensbidrag (refereegranskat)abstract
- This paper presents a control approach allowing a tight integration of several driver assistance functions in automotive vehicles, including front steering, differential braking and autonomous emergency braking. The goal is to support a more effective collision avoidance with necessary emergency maneuvers during automated driving, while avoiding situations with a high risk of undesired vehicle instability due to separated steering and braking actions. The approach adopts a ModelPredictive Control (MPC) based methodology using a set of performance indexes calculated according to the actual time to collision, braking and steering capabilities. The effectiveness of the proposed methodology is evaluated in a simulation on MATLAB/Simulink.
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| 8. |
- Yu, Yan Feng, et al.
(författare)
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Robust Safety Control for Automated Driving Systems with Perception Uncertainties
- 2023
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Ingår i: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. - : Springer Nature. ; , s. 321-331
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Zhu, Zikai, et al.
(författare)
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Using a VAE-SOM architecture for anomaly detection of flexible sensors in limb prosthesis
- 2023
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Ingår i: Journal of Industrial Information Integration. - : Elsevier BV. - 2452-414X .- 2467-964X. ; 35
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Tidskriftsartikel (refereegranskat)abstract
- 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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