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22 results on '"Tang, Bangbei"'

1. Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning

Author

Chen, Hao, Guo, Gang, Tang, Bangbei, Hu, Guo, Tang, Xiaolin, and Liu, Teng

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence
Abstract

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce time consumption and guarantee control performance., Comment: 28 pages, 14 figures

Published
2020

2. Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle

Author

Guo, Xiaowei, Liu, Teng, Tang, Bangbei, Tang, Xiaolin, Zhang, Jinwei, Tan, Wenhao, and Jin, Shufeng

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to address the defect of DRL in tedious training time. First, an optimization control modeling of a hybrid tracked vehicle is built, wherein the elaborate powertrain components are introduced. Then, a bi-level control framework is constructed to derive the energy management strategies (EMSs). The upper-level is applying the particular deep deterministic policy gradient (DDPG) algorithms for EMS training at different speed intervals. The lower-level is employing the TL method to transform the pre-trained neural networks for a novel driving cycle. Finally, a series of experiments are executed to prove the effectiveness of the presented control framework. The optimality and adaptability of the formulated EMS are illuminated. The founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance., Comment: 11 pages, 11 figures

Published
2020

5. Autonomic Responses Associated with Olfactory Preferences of Fragrance Consumers: Skin Conductance, Respiration, and Heart Rate.

Author

Tang, Bangbei, Zhu, Mingxin, Wu, Yingzhang, Guo, Gang, Hu, Zhian, and Ding, Yongfeng

Subjects
*OLFACTORY perception, *DATA acquisition systems, *CONSUMER preferences, *HEART beat, *WEARABLE technology, *RESPIRATION
Abstract

Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals. In response to this gap, a synchronous data acquisition system was established using the ErgoLAB multi-channel physiology instrument and olfactory experience tester. Thirty-three participants were recruited for the olfactory preference experiments, and three types of autonomic response data (skin conductance, respiration, and heart rate) were collected. The results of both individual and overall analyses indicated that olfactory preferences can lead to changes in skin conductance (SC), respiration (RESP), and heart rate (HR). The trends of change in both RESP and HR showed significant differences (with the HR being more easily distinguishable), while the SC did not exhibit significant differences across different olfactory perception preferences. Additionally, gender differences did not result in significant variations. Therefore, HR is more suitable for evaluating olfactory perception preferences, followed by RESP, while SC shows the least effect. Moreover, a logistic regression model with a high accuracy (84.1%) in predicting olfactory perception preferences was developed using the changes in the RESP and HR features. This study has significant implications for advancing the assessment of consumer olfactory preferences. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Research on railway vehicle modal parameter identification method based on drop impact load.

Author

He, Xiaolong, Duan, Zhengyong, Wu, Yangjun, Tang, Dayong, Peng, Shuai, and Tang, Bangbei

Subjects
RAILROAD trains, PARAMETER identification, IMPACT loads, MATHEMATICAL models
Abstract

This paper investigates the modal parameter identification technique utilizing a drop impact load. A 12-DOF mathematical model of a Railway Vehicle System (RWVS) was constructed, followed by theoretical calculations, simulation analysis, and tests with four different drop impact loads. The responses to these loads were inspected through FFT, and the modal frequencies of the RWVS were determined by the Peak Picking (P-P) method. The simulation demonstrated that four types of drop impact loads can activate the bounce, pitch, and roll modes of the RWVS. The theoretical calculation and simulation analysis revealed that the maximum error of modal frequency identification is no greater than 6.5%. The experimental results verified that the drop impact excitation method can be used to identify the modal parameters of a complex railway vehicle system, which is highly beneficial for the design and verification of the vehicle structure. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Driver's Hand-Foot Coordination and Global-Regional Brain Functional Connectivity Under Fatigue: Via Graph Theory and Explainable Artificial Intelligence

Author

Wu, Yingzhang, Li, Wenbo, Zhang, Jie, Tang, Bangbei, Xiang, Jinlin, Li, Shen, and Guo, Gang

Abstract

Driving fatigue impairs the driver's hand-foot coordination, a significant factor in traffic accidents. However, the functional brain connectivity underlying driver coordination remains unclear. In this study, we conducted a monotonous long-distance driving experiment (approximately 2 hours) with thirty participants, who rated their fatigue level every 6 minutes using the Karolinska sleepiness scale (KSS). We then categorized the ratings into three fatigue levels: alertness or slight fatigue (AF), moderate fatigue (MF), and severe fatigue (SF). Emergency avoidance scenarios occurred randomly during the experiment, requiring the driver to brake and steer simultaneously to prevent collisions. The multi-band brain functional connectivity network and the critical connections were utilized for global-regional brain functional connectivity analysis and explainable artificial intelligence (XAI) analyses. We found that inter-region connectivity between cognitive and motor regions in the frontal-central and frontal-parietal regions decreased as the driver's fatigue increased to MF. Simultaneously, intra-region connectivity within the central region decreased, impairing intra-motor region connectivity. These diminished the driver's coordination ability. However, profound fatigue triggered an inter-regional and intra-regional reorganization of the brain functional network, reflecting anti-fatigue characteristics but insufficient to enhance driving performance. The XAI results indicated that the beta-band brain functional connection network dataset achieved the highest accuracy (0.941) on the Resnet18 model and revealed fatigue differences in the cognitive and motor regions of the dataset. Our study demonstrated that the driver's hand-foot coordination had distinct brain functional connectivity characteristics under different fatigue states, which could inform the design of neural network detection algorithms for driving fatigue.

Published
2024
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19. A study on evaluation methods of human computer interact & user experience testing towards smart phones-vehicle interconnect products

Author

Li Lin Li Lin, Mingqing Tang Mingqing Tang, Gang Guo, Bangbei Tang Bangbei Tang, and Wenbo Li Wenbo Li

Subjects
Engineering, Interconnection, Index system, Smart phone, User experience design, business.industry, Human–computer interaction, Evaluation methods, Automotive industry, business, Motion tracking system, Psychological scale
Abstract

To enhance the smart phones-automotive interconnect products experience pleasure, due to the fact that there is a few method to test and evaluate Human-Computer Interaction and user experience of the smart phone-automotive interconnect products, An evaluation method based on the user behavior / psychology on smart phone-automotive interconnect products was proposed, a series index system of evaluation and testing human computer interactive & user experience is constructed. Comprehensive evaluation method of a smart phone - automotive interconnect products' interation and user experience is build. A total of 20 target users was recruited, where the finger motion tracking system, facial expression analysis system and psychological scale were applied to analyze the user experience data of two smart phone-automotive interconnect products, as well as to examine experiment data through the proposed method, it is concluded that the experimental results verify the feasibility of the evaluation method.

Published
2016

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