9 results on '"Yin, Guodong"'
Search Results
2. CapCAM: A Multilevel Capacitive Content Addressable Memory for High-Accuracy and High-Scalability Search and Compute Applications.
- Author
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Ma, Xiaoyang, Zhong, Hongtao, Xiu, Nuo, Chen, Yiming, Yin, Guodong, Narayanan, Vijaykrishnan, Liu, Yongpan, Ni, Kai, Yang, Huazhong, and Li, Xueqing
- Subjects
STATIC random access memory ,ASSOCIATIVE storage ,FIELD-effect transistors ,TECHNOLOGICAL innovations ,ELECTRIC potential - Abstract
As one type of associative memory, content-addressable memory (CAM) has become a critical component in several applications, including caches, routers, and pattern matching. Compared with the conventional CAM that could only deliver a “matched or not-matched” result, emerging multilevel CAM (ML-CAM) is capable of delivering “the degree of match” with multilevel distance calculation. This feature has been desired in applications that need beyond-Boolean matching results. However, existing ML-CAM designs are limited by the bit-cell device discharging current mismatch and vulnerability to the timing of sensing operations for distance calculation. This inherent constraint makes it difficult to further improve the accuracy and scalability toward higher accuracy and higher dimension matching. In this work, we propose CapCAM, a multilevel Capacitive Content Addressable Memory. It could be implemented based on either static random-access memory (SRAM) or emerging technologies, e.g., the ferroelectric field-effect transistor (FeFET). CapCAM could provide linear and stable voltage drop scaled by the match degree and need no strict timing for result sensing, which embraces the high-accuracy and high-scalability search. The inherent enabler of CapCAM is the charge-domain computing mechanism. This article will present the basic concept, operating mechanisms, detailed circuit designs, and circuit-level simulations of CapCAM. Besides, we apply CapCAM to few-shot learning applications and compare CapCAM with the current-domain TCAM designs. Results show 99.2% accuracy for a five-way five-shot classification task with our proposed CapCAM design while considering 1-fF capacitors, 20-domain FeFETs, and 256 columns. In contrast, the prior work based on discharging dynamics requires strict timing controls and suffers from accuracy degradation under the same configuration, which demonstrates CapCAM’s capability of low-power, accurate, and scalable multilevel CAM (ML-CAM) computing. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
3. Driver’s Individual Risk Perception-Based Trajectory Planning: A Human-Like Method.
- Author
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Yan, Yongjun, Wang, Jinxiang, Zhang, Kuoran, Liu, Yulong, Liu, Yahui, and Yin, Guodong
- Abstract
Lane-changing is a critical issue for autonomous vehicles (AVs), especially in complex environments. In addition, different drivers have different handling preferences. How to provide personalized maneuvers for individual drivers to increase their trust is another issue for AVs. Therefore, a framework of human-like path planning is proposed in this paper, considering driver characteristics of visual-preview, subjective risk perception, and degree of aggressiveness. In the decision making module, a model is built to select the most suitable merging spot, with respect to safety factors and the driver’s degree of aggressiveness. And a novel environmental potential field (PF) suitable for arbitrary road structures is designed to describe the driver’s individual risk perception. In the trajectory planning module, a model predictive control (MPC) based path planner is designed according to the decisions in coincidence with the driver’s individual intentions of collision avoidance. Simulation results have demonstrated that the proposed path planner can provide with personalized trajectories for different combinations of driver preferences and steering characteristics, in scenarios of curved roads with different risks of collision. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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4. Stochastic Stable Control of Vehicular Platoon Time-Delay System Subject to Random Switching Topologies and Disturbances.
- Author
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Xu, Liwei, Jin, Xianjian, Wang, Yan, Liu, Ying, Zhuang, Weichao, and Yin, Guodong
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ELECTRIC vehicles ,AIR resistance ,MARKOVIAN jump linear systems ,SYSTEMS theory ,STABILITY theory ,HYPERSONIC planes - Abstract
This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. First, a random vehicle platoon system composed entirely of several pure electric vehicles is built. The random variation of data transmission link among the platoon in a natural traffic environment is considered and molded by the Markov chain combined with the directed graph method. The influence of delays and discrete data in wireless communication, road slope, and air resistance on the vehicle platoon is also considered by introducing the external interferences and equivalent information delays. Additionally, to ensure the vehicle platoon’s inner-vehicle stability, the variable-gain distributed controller is proposed based on the Markovian jumping system stability theory and $ H_\infty$ control. Finally, the $ \mathcal {L}_{2}$ stochastic string stability is defined to attenuate perturbations as they propagate through the platoon. Simulation studies about a vehicle platoon under four communication topologies random switching with two different control methods are provided to verify the theoretical result. It is shown that, compared to traditional platoon robust control, it is possible to achieve the vehicle platoon’s stability even if in the continuous mutation of unstable topologies by using the proposed control approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter.
- Author
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Wang, Yan, Geng, Keke, Xu, Liwei, Ren, Yaping, Dong, Haoxuan, and Yin, Guodong
- Subjects
KALMAN filtering ,FUZZY systems ,TIRES ,REFERENCE values ,HEURISTIC algorithms - Abstract
The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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6. Path Planning on Large Curvature Roads Using Driver-Vehicle-Road System Based on the Kinematic Vehicle Model.
- Author
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Wang, Jinxiang, Yan, Yongjun, Zhang, Kuoran, Chen, Yimin, Cao, Mingcong, and Yin, Guodong
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VEHICLE models ,CURVATURE ,AUTONOMOUS vehicles ,TRAFFIC safety ,ENVIRONMENTAL security ,ROADS - Abstract
Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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7. Geometry-Based Cooperative Localization for Connected Vehicle Subject to Temporary Loss of GNSS Signals.
- Author
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Wang, Fa'an, Xu, Liwei, Zhuang, Weichao, Yin, Guodong, Pi, Dawei, Liang, Jinhao, Liu, Ying, and Lu, Yanbo
- Abstract
Onboard GNSS signal loss is a challenging issue for automated vehicles during driving in urban areas due to the effects of tall and dense buildings, flyovers, tunnels, and vegetation. To improve the vehicle localization accuracy when GNSS signals temporarily lost, this paper proposes a Geometry-based cooperative localization method (GCL) for the Internet of Vehicles. The vehicle position is estimated using mathematical geometry information, including vehicle dynamics and road shapes, which could reduce the environment-aware position constraint of cooperative localization. The relocation approach is enabled by communicating with neighboring vehicles to attenuate GNSS signal loss on localization-based services. The efficiency and scalability of GCL are evaluated in different driving conditions. The results showed that GCL achieved better localization performance than the state-of-the-art techniques in shorter and longer GNSS signal loss situations. The experimental results demonstrated the capabilities and effectiveness of the proposed algorithm to handle typical GNSS signal loss driving scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
8. Ensemble Learning Based Brain–Computer Interface System for Ground Vehicle Control.
- Author
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Zhuang, Jiayu, Geng, Keke, and Yin, Guodong
- Subjects
BRAIN-computer interfaces ,CONVOLUTIONAL neural networks ,MOTOR imagery (Cognition) ,EVOKED potentials (Electrophysiology) ,AUTOMOBILE driving simulators ,SIGNAL-to-noise ratio ,PROPORTIONAL navigation - Abstract
This article establishes a novel electroencephalograph (EEG)-based brain–computer interface (BCI) system for ground vehicle control with potential application of mobility assistance to the disabled. To enable an intuitive motor imagery (MI) paradigm of “left,” “right,” “push,” and “pull,” a driving simulator based EEG data recording and automatic labeling platform is built for dataset making. In the preprocessing stage, a wavelet and canonical correlation analysis (CCA) combined method is used for artifact removal and improving signal-to-noise ratio. An ensemble learning based training and testing framework is proposed for MI EEG data classification. The average classification accuracy of proposed framework is about 91.75%. This approach essentially takes advantage of the common spatial pattern (CSP) with ability of extracting the feature of event-related potentials and the convolutional neural networks (CNNs) with powerful capacity of feature learning and classification. To convert the classification results of EEG data segments into motion control signals of ground vehicle, shared control strategy is used to realize the control command of “left-steering,” “right-steering,” “acceleration,” and “stop” considering collision avoidance with obstacles detected by a single-line LIDAR. The online experimental results on a model vehicle platform validate the significant performance of the established BCI system and reveal the application potential of BCI on the vehicle control and automation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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9. Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing With Ferroelectric FETs.
- Author
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Yin, Guodong, Cai, Yi, Wu, Juejian, Duan, Zhengyang, Zhu, Zhenhua, Liu, Yongpan, Wang, Yu, Yang, Huazhong, and Li, Xueqing
- Abstract
Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM shows the opportunity for higher energy efficiency and resistance to device variations. However, the area occupation and standby leakage power of existing SRAM-based charge-domain CiM (CD-CiM) are high. This brief proposes the first concept and analysis of CD-CiM using nonvolatile memory (NVM) devices. The design implementation and performance evaluation are based on a proposed 2-transistor-1-capacitor (2T1C) CiM macro using ferroelectric field-effect-transistors (FeFETs), which is free from leakage power and much denser than the SRAM solution. With the supply voltage between 0.45V and 0.90V, operating frequency between 100MHz to 1.0GHz, binary neural network application simulations show over 47%, 60%, and 64% energy consumption reduction from existing SRAM-based CD-CiM, SRAM-based current-domain CiM, and RRAM-based current-domain CiM, respectively. For classifications in MNIST and CIFAR-10 data sets, the proposed FeFET-based CD-CiM achieves an accuracy over 95% and 80%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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