Real-time Operation Capability Research Articles

FPGA-Based Tactile Sensory Platform with Optical Fiber Data Link for Feedback Systems in Prosthetics

In this paper, we propose and validate a tactile sensory feedback system for prosthetic applications based on an optical communication link. The optical link features a low power and wide transmission bandwidth, which makes the feedback system suitable for a large number and variety of tactile sensors. The low-power transmission is derived from the employed UWB-based optical modulation technique. A system prototype, consisting of digital transmitter and receiver boards and acquisition circuits to interface 32 piezoelectric sensors, was implemented and experimentally tested. The system functionality was demonstrated by processing and transmitting data from the piezoelectric sensor at a 100 Mbps data rate through the optical link, measuring a communication energy consumption of 50 pJ/bit. The reported experimental results validate the functionality of the proposed sensory feedback system and demonstrate its real-time operation capabilities.

Speeding Up Semantic Instance Segmentation by Using Motion Information

Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to improve the accuracy of these methods rather than make them faster. This paper presents a novel solution to speed up the semantic instance segmentation task. The solution combines two state-of-the-art methods from semantic instance segmentation and optical flow fields. To reduce the inference time, the proposed framework (i) runs the inference on every 5th frame, and (ii) for the remaining four frames, it uses the motion map computed by optical flow to warp the instance segmentation output. Using this strategy, the execution time is strongly reduced while preserving the accuracy at state-of-the-art levels. We evaluate our solution on two datasets using available benchmarks. Then, we conclude on the results obtained, highlighting the accuracy of the solution and the real-time operation capability.

Fatigue damage diagnosis and prognosis of an aeronautical structure based on surrogate modelling and particle filter

A key issue affecting the performances of every human-conceived engineering system is its degradation, fatigue crack growth being one of the major structural deterioration phenomena. Fatigue crack growth is usually modelled as a stochastic process: uncertainty sources lie both in the item and in the physical degradation process variability. Fatigue crack growth deserves close attention, especially considering that condition-based maintenance methodologies are recently experiencing a major drive to increase their technology readiness level, requiring validated diagnostic and prognostic methodologies which should be capable of operating online and in real-time. In this regard, particle filters provide a consistent Bayesian framework, where the posterior distribution of the system degradation state is recursively approximated based on a time-growing stream of observations measuring the system response, enabling, in general, increasingly informed lifetime estimates. However, the real-time operation capability of such methods is hindered by their requirements in terms of computational power, which is mainly due to the complexity of the structural models they rely upon. Within this work, a comprehensive particle filter framework, able to deal with fatigue crack growth uncertainty sources while simultaneously addressing the computational burden issue, is proposed. The algorithm structure enables to simultaneously perform the diagnosis and prognosis of fatigue crack growth, while the adoption of the augmented state formulation allows to address scenarios where the degradation process of fatigue crack growth fails to meet the degradation model ruling the particle filter. Artificial neural networks–based surrogate modelling is adopted at different stages and embedded within the particle filter algorithm, relieving the computational burden associated with the evaluation of the trajectory likelihoods as well as enabling a fast estimation of the remaining useful life. Both simulated and experimental data sets regarding fatigue crack growth in an aluminium aeronautical panel are used for the algorithm testing, additionally proving the validity and effectiveness thereof by means of common prognostic performance metrics.

Damage diagnosis and prognosis in composite double cantilever beam coupons by particle filtering and surrogate modelling

Delamination is a failure mechanism which is intrinsic of laminated fibre-reinforced plastics and possibly one of the major concerns of laminated composite structures, since, under certain conditions, delaminations can grow up to an hazardous extent without visible traces. In order to keep pace with recent condition-based maintenance requirements, proper validated diagnostic and prognostic methods which should be capable of operating on-line and in real time are required. In this respect, particle filters provide a consistent Bayesian framework, where the posterior distribution of the system degradation status is recursively approximated based on a time-growing stream of observations measuring the system response. However, the real-time operation capability of such methods is hindered by their requirements in terms of analysis time, which is mainly due to the complexity of the models they rely upon. Within this work, a particle filter framework, able to deal with the inherent stochasticity of fatigue delamination growth – while simultaneously relieving the computational burden associated with the evaluation of the trajectory likelihoods – is provided, leveraging on surrogate modelling strategies. Simultaneous diagnosis and prognosis of a simulated carbon fibre-reinforced plastics double cantilever beam specimen subject to fatigue delamination growth are performed, based on the observation of the strain field pattern acquired at some specific locations. The posterior probability density function of the delamination extent during propagation is updated at each inspection time as well as the probability density function of the remaining useful life. Ultimately, the adoption of the augmented state formulation allows for the estimation and updating of the joint probability density function of the parameters driving the stochastic delamination propagation model. Results demonstrate the feasibility and potential of the proposed approach as a tool able to monitor the progressing delamination while simultaneously providing estimates about the remaining useful life of composite structures.

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

A primary challenge to the implementation of hybrid electric vehicles (HEVs) is the design of the energy management strategy for the vehicle. Most conventional strategies have been designed for passenger vehicles using rule-based or optimization-based control strategies that rely on navigation support; therefore, the optimal performance of heavy-duty HEVs that lack navigation support cannot be achieved using conventional strategies. In this study, we propose a nonlinear model predictive control (NMPC) for heavy-duty HEVs based on a random power prediction method. To obtain the models of multiple power sources, we analyzed the structure and powertrain of the vehicle using mathematical modeling methods. To account for the lack of navigation support, we used the data-driven prediction method by combining the grey model and Markov chain methods to obtain higher-accuracy ultra-short-term power prediction. Considering the predicted disturbance power, we established a multi-objective optimization function with explicit constraints to optimize fuel consumption, bus voltage, and battery state of charge. Under these constraints, a nonlinear programming problem based on the NMPC could be restricted to find an optimal numerical solution in real time. We validated the control strategy on a hardware-in-the-loop simulation platform and compared its results with those obtained using thermostat control, fuzzy, and dynamic programming approaches. The proposed control strategy achieved a considerably better all-round performance than rule-based control strategies; moreover, the results were considerably similar compared with those of offline global optimization strategies. Furthermore, the proposed method achieved excellent real-time operation capability, thereby providing a valuable reference for practical engineering applications.

An IC-based controllable stimulator for respiratory muscle stimulation investigations.

Functional Electrical Stimulation can be used to restore motor functions loss consecutive to spinal cord injury, such as respiratory deficiency due to paralysis of ventilatory muscles. This paper presents a fully configurable IC-centered stimulator designed to investigate muscle stimulation paradigms. It provides 8 current stimulation channels with high-voltage compliance and real-time operation capabilities, to enable a wide range of FES applications. The stimulator can be used in a standalone mode, or within a closed-loop setup. Primary in vivo results show successful drive of respiratory muscles stimulation using a computer-based dedicated controller.

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Abstract Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.

A rule based programming language for real-time applications

This paper presents a language based on production rules that integrates methods of procedural and heuristic programming, and has real-time operation capability. An interpreter was developed for executing programs written in this language under MSDOS environment. Several test programs were executed, and their results are stated from both formal aspects and execution time.