| 1. |
- Chen, Yizhi, 1995-, et al.
(författare)
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Online Image Sensor Fault Detection for Autonomous Vehicles
- 2022
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 120-127
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Konferensbidrag (refereegranskat)abstract
- Automated driving vehicles have shown glorious potential in the near future market due to the high safety and convenience for drivers and passengers. Image sensors' reliability attract many researchers' interests as many image sensors are used in autonomous vehicles. We propose an online image sensor fault detection method based on comparing the historical variances of normal pixels and defective pixels to detect faults. For fault pixels without uncertainty, with a detecting window of more than 30 frames, we get 100% accuracy and 100% recall on realistic continuous traffic pictures from the KITTI data set. We also explore the influence of fault pixel values' uncertainty from 0% to 25% and study different fixed thresholds and a dynamic threshold for judgments. Strict threshold, which is 0.1, has a high accuracy (99.16%) but has a low recall (34.46%) for 15% uncertainty. Loose threshold, which is 0.3, has a relatively high recall (83.78%) but mistakes too many normal pixels with 18.17% accuracy for 15% uncertainty. Our dynamic threshold balances the accuracy and recall. It gets 100% accuracy and 58.69% recall for 5% uncertainty and 78.38% accuracy and 55.39% recall for 15% uncertainty. Based on the detected damage pixel rate, we develop a health score for evaluating the image sensor system intuitively. It can also be helpful for making decision about replacing cameras.
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| 2. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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A Low-Complexity and High-Performance Energy Management Strategy of a Hybrid Electric Vehicle by Model Approximation
- 2022
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Ingår i: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). - Mexico City, Mexico : IEEE. ; , s. 455-462
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Konferensbidrag (refereegranskat)abstract
- The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures.
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| 3. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin’s minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and then has higher fuel efficiency than a non-adaptive dynamic programming (DP)controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
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| 4. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; , s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
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| 5. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Fuel Minimization of a Hybrid Electric Racing Carby Quasi-Pontryagin’s Minimum Principle
- 2021
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Ingår i: IEEE Transactions on Vehicular Technology. - : IEEE. - 0018-9545 .- 1939-9359.
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Tidskriftsartikel (refereegranskat)abstract
- This paper improves the fuel efficiency of a student-made parallel hybrid electric racing car whose internal combustion engine (ICE) either operates with peak efficiency or is turned off. The control to the ICE thus becomes a binary problem. Owing to the very limited computation resource onboard, the energy management strategy (EMS) for this car must have small time and space complexities. A computationally efficient controller that combines the advantages of dynamic programming (DP) and Pontryagins minimum principle (PMP) is developed to run on a low-cost microprocessor. DP is employed offline to calculate the optimal speed trajectory, which is used as the reference for the online PMP to determine the real-time ICE on/off status and the electric motor (EM) torques. The normal PMP derives the optimal costate trajectory through solving partial differential equations. The proposed quasi-PMP (Q-PMP) method finds the costate from the value function obtained by DP. The fuel efficiency and computational complexity of the proposed controller are compared against several state of art methods through both model-in-the-loop (MIL) and processor-in-the-loop (PIL) simulations. The new method reaches similar fuel efficiency as the explicit DP, but requires less than 1% onboard flash memory. The performance of the Q-PMP controller is compared between binary-controlled and continuously controlled engines. It achieves roughly 12% higher fuel efficiency for the binary engine with only approximately 1/3 CPU utilization.
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| 6. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Optimal and Adaptive Engine Switch Control for a Parallel Hybrid Electric Vehicle Using a Computationally Efficient Actor-Critic Method
- 2023
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Ingår i: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 416-423
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Konferensbidrag (refereegranskat)abstract
- Energy management strategies (EMSs) are crucial to the fuel economy of hybrid electric vehicles (HEVs). However, due to the lack of efficient solving approaches, most of existing EMSs mainly focus on the optimal torque split between the internal combustion engine (ICE) and the electric motor but neglect improper ICE on/off switches, and thus usually suffer degraded fuel economy and even unacceptable drivability in practice. To tackle this issue, this paper presents a novel EMS that uses an efficient actor-critic (AC) method to regulate ICE switches with limited computation resources. While common AC methods use complex neural networks (NNs) with arbitrary initialization, the proposed AC uses piecewise cubic polynomials whose parameters are initialized based on optimized solutions of dynamic programming (DP). By this means, the AC can quickly converge with high computation efficiency. The testing results from processor-in-the-loop (PIL) simulations showcase that, compared with a rule-based EMS with tabular value functions, the proposed EMS can greatly improve the equivalent fuel economy by eliminating improper ICE switches after only several iterations of adaptive learning and dramatically save onboard memory space owing to the concise AC structure.
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| 7. |
- Lu, Zhonghai, et al.
(författare)
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Wearable pressure sensing for lower limb amputees
- 2022
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Ingår i: BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665469173 ; , s. 105-109, s. 105-109
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Konferensbidrag (refereegranskat)abstract
- Pressure sensing in prosthetic sockets is valuable as it provides quantified data to assist prosthetists in designing comfortable sockets for amputees. We present a wearable pressure sensing system for lower limb amputees. The full system consists of three essential elements from sensing scheme (wearable sensors, sensor calibration and deployment), electronic measurement system (embedded hardware and software), to time-series database and visualization. The full system has been successfully applied in clinical trials to effectively collect pressure data in real-time.
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| 8. |
- Zhu, Wenyao, et al.
(författare)
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Evaluation of Time Series Clustering on Embedded Sensor Platform
- 2021
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Ingår i: 2021 24TH EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD 2021). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 187-191
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Konferensbidrag (refereegranskat)abstract
- Clustering is one of the major problems in studying the time series data, while solving this problem on the embedded platform is almost absent because of the limitation of computational resources on the edge. In this paper, two typical clustering algorithms, K-means and Self-Organizing Map (SOM), together with Euclidean distance measurement and dynamic time warping (DTW) are studied to verify their feasibility on an embedded sensor platform. For the given datasets, the models are trained on a computer and moved to an ESP32 microprocessor for inference. It is found that the SOM achieves similar accuracy compared with K-means, while its inference process takes a longer time. The experiment results show that a sample with 300 data points can be clustered into 12 clusters within 40 ms by SOM with the DTW model, while the fastest model can run at around 2 ms using K-means with Euclidean distance model. In other words, it can process the data collected from 40 sensors per second in 680 ms. The clustering function can be scheduled with the real-time data acquisition and transmission tasks. The performance gathered supports that it is feasible to deploy the time series clustering model on the embedded sensor platform.
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| 9. |
- Zhu, Wenyao, et al.
(författare)
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Redundancy Reduction for Sensor Deployment in Prosthetic Socket : A Case Study
- 2022
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 22:9, s. 3103-
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Tidskriftsartikel (refereegranskat)abstract
- The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson-Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting.
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