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Platform development and gliding optimization of a robotic flying fish with morphing pectoral fins

BIOINSPIRATION & BIOMIMETICS 2023年第3期18卷 036010-036010页

作者： Chen, Di Wu, Zhengxing Dong, Huijie Meng, Yan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Syst Minist Educ Taiyuan 030024 Peoples R China

The aquatic-aerial robot with the free interface crossing can enhance adaptability in complex aquatic environments. However, its design is extremely challenging for the striking discrepancies in propulsion principles. The flying fish in nature exhibits remarkable multi-modal cross-domain locomotion capability, such as high-maneuvers swimming, agile water-air crossing, and long-distance gliding, providing extensive inspiration. In this paper, we present a unique aquatic-aerial robotic flying fish with powerful propulsion and a pair of morphing wing-like pectoral fins to realize cross-domain motion. Furthermore, to explore the gliding mechanism of flying fish, a dynamic model with a morphing structure of pectoral fins is established, and a double deep Q-network-based control strategy is proposed to optimize the gliding distance. Finally, experiments were conducted to analyze the locomotion of the robotic flying fish. The results suggest that the robotic flying fish can successfully perform the 'fish leaping and wing spreading' cross-domain locomotion with an exiting speed of 1.55 m s(-1) (5.9 body lengths per second, BL/s) and a crossing time of 0.233 s indicating its great potential in cross-domain. Simulation results have validated the effectiveness of the proposed control strategy and indicated that the dynamical adjustment of morphing pectoral fins contributes to improving the gliding distance. The maximum gliding distance has increased by 7.2%. This study will offer some significant insights into the system design and performance optimization of aquatic-aerial robots.

关键词： bioinspired robotic fish morphing pectoral fin aquatic-aerial crossing motion gliding motion optimization

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Multi-scale discriminative regions attention network for fine-grained vehicle classification

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 164-177页

作者： Rong, Wen-Zhong Han, Jin Cai, Ying-Hao Liu, Gen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by improving the ability of discriminative features extraction, but there is still room for further improvement in the localization accuracy of vehicle discriminative regions. Based on deep convolutional neural networks, we focus on finding a more efficient structure that pays more attention to the discriminative image regions to enhance the ability of fine-grained vehicle classification. We propose a novel Multi-Scale Discriminative Regions Attention Network (MS-DRAN), which extracts feature maps through ResNet-50 backbone network and generates multi-scale feature maps by a Feature Pyramid Network (FPN). Then, MS-DRAN generates discriminative regions attention maps on the multi-scale feature maps. The attention maps from shallow layer perform pixel-level multiplication with the feature maps from deeper layer. By this way, the network gradually extracts a more discriminating feature maps for classification. We also design a multi-task loss function to associate the classification results for each scale and optimize the network parameter in training. Lastly, we validate the MS-DRAN on Stanford Cars-196 dataset and CompCars dataset and achieves 94.3% and 98.1% in accuracy, respectively. ©2021 ISSN 2414-8105 (Online).

关键词： Classification (of information)

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Game Starts at GameStop: Characterizing the Collective Behaviors and Social Dynamics in the Short Squeeze Episode

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第1期9卷 45-58页

作者： Zheng, Xiaolong Tian, Hu Wan, Zhe Wang, Xiao Zeng, Daniel Dajun Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Beijing Normal Univ Belt & Rd Sch Beijing 100875 Peoples R China

In January 2021, the users of subreddit r/wallstreetbets (WSB) triggered an unprecedented short squeeze by driving up GameStop's stock price to an unimaginable high point. During the event, a large number of users participated in the discussion about GameStop and coordinated trading behavior on r/WSB to push the stock price higher. In this article, we investigate the characteristics of the collective behaviors and social dynamics from the evolutions of topological structure, discussed topics, and user sentiment polarity (SP) by constructing dynamic interaction networks, modeling the topic, and analyzing the user sentiment. We find that the topological structure of the interaction network evolves toward a more efficient direction, the discussed topics change more centralized, and the user sentiment tends to be more positive and divergent. And we reveal that part of GameStop's stock price is explained by the social media activity, popularity of the dominant topic, topic cohesiveness, SP of users, and sentiment divergence between interacted users on r/WSB. Our work quantitatively characterizes the interaction networks and user behavior during the GameStop short squeeze and provides an example to analyze the event which synchronously evolves in the physical space and cyberspace. It not only contributes to the analysis of social system behavior and structure but also provides valuable insights into the financial practice and policy decision-making.

关键词： Social networking (online) Dictionaries Stock markets Investment Games Analytical models Market research Dynamic interaction network financial market GameStop short squeeze social network analysis

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A Novel Method of Translation Memory to Improve Machine Translation 1

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Yue, Ping Niu, Yue Tao, Mo Zheng, Wei Li, Yinlin Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou Peoples R China Australian Natl Univ Canberra ACT 2601 Australia Wuhan 2nd Ship Design & Res Inst Sci & Technol Thermal Energy & Power Lab Wuhan 430205 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Numerous studies have provided evidence of the performance enhancement achieved in neural machine translation (NMT) through the utilization of translation memory (TM). Diverging from current approaches that rely on bilingual corpora as translation memories (TMs) and employ source-side similarity search for memory retrieval, our proposed framework adopts monolingual memories and incorporates a learnable memory retrieval mechanism that operates in a cross-lingual manner. The primary aspect of our framework is a cross-lingual in-memory retriever that enables translation memory. Tutilization using abundant monolingual data. This allows us to leverage the richness of monolingual resources effectively. Additionally, our framework facilitates the cooptimization of the memory retriever and the neural machine translation model to jointly work towards achieving the ultimate translation objective. The experimental results demonstrate that our model not only surpasses neural machine translation baselines that are augmented with bilingual translation memories but also exhibits superior performance. The ability of our model to leverage monolingual data enables its effectiveness in scenarios with limited resources and domain adaptation requirements. This showcases the versatility of our framework, making it well-suited for low-resource settings and facilitating domain-specific translations.

关键词： Translation memory Neural machine translation

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TAO-Based Data Governance in Intelligent Transportation systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on Intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

Intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

关键词：

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Sustainable Lifecycle management for Automotive Development via Multi-Dimensional Circular Design Framework

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第9期8卷 4151-4154页

作者： Zhang, Yongkang Chen, Jicheng Teng, Siyu Zhang, Hui Wang, Fei-Yue Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

With the continuous development of autonomous driving technology, intelligent vehicles represented by autonomous taxis have begun to participate in the transportation system. Improving the sustainability of intelligent vehicles can increase the useful life of vehicles and reduce the overall increase of vehicles in society. This reduction reduces emissions and energy consumption, thereby increasing environmental sustainability. However, most of the current intelligent vehicles are modified from existing vehicles. The initial design of these vehicle does not consider the requirements for autonomous driving, which can easily lead to a mismatch between the control algorithm and the mechanical structure. Moreover, current intelligent vehicles mainly focus on the development of autonomous driving algorithms and ignore sustainable design, which will lead to increased energy consumption and reduced lifespan. This letter proposes a multi-dimensional circular design (MDCD) framework to improve the sustainability of intelligent vehicles from sustainable lifecycle management perspective. The MDCD framework consists of six parts, which are intelligent design, sensing, control, supervision, diagnosis and recycle. This letter is one of the Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL).

关键词： Intelligent vehicles Sustainable development Recycling Intelligent control Energy consumption Roads Layout Sustainability intelligent vehicles vehicle lifecycle management energy efficiency circular design

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A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2077-2087页

作者： Sun, Yao Hu, Yunfeng Zhang, Hui Wang, Feiyue Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Jilin Univ Coll Commun Engn Changchun 130025 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Beihang Univ Ningbo Inst Technol NIT Ningbo 315323 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

A parallel supervision system is built in this paper in order to accurately estimate vehicleCO(2) emissions. Only on-board diagnostics (OBD)-independent information is used, making the model capable of making predictions based on future road gradients and planned speed trajectories. Based on the parallel theory, the actual traffic environment is considered the physical world, while the combined CO2 model (which consists of physical and data-driven models) is the core part of the artificial world. The physical model uses a cascaded structure with engine speeds and torques as intermediate variables, and the data-drivenmodel relies on a modified long short-term memory (LSTM) neural network. When the historical data is sufficient in size and diversity, the data-driven model is appropriate and achieves more accurate estimations;otherwise, the physical model is preferable because of its greater robustness. Based on this combined model, the supervision system can leverage both the learning ability and physics-based knowledge. Two real-world experimental case studies have been performed to validate this system. According to the research analysis, both the physical and data-driven models achieve sufficient accuracy. The physical model indicatesmore robustness even when some primary parameters (gear ratios) are unknown, which can be used as a supplement to the data-driven model. Moreover, the deterioration factor (DF) of vehicleCO(2) emissions is considered to simulate aged vehicles. This parallel supervision system can effectively address the gap between regulatory test cycles and real-world carbon emissions.

关键词： Combined CO2 estimation model deterioration factor OBD-independent parallel supervision system vehicle CO2 emissions

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Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-12页

作者： Yang, Lei Fan, Junfeng Liu, Yanhong Li, En Peng, Jinzhu Liang, Zize Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model.

关键词： Data augmentation deep convolutional network model object location samples updating semantic segmentation weld bead

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UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH

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MIS QUARTERLY 2022年第1期46卷 341-372页

作者： Xie, Jiaheng Liu, Xiao Zeng, Daniel Dajun Fang, Xiao Univ Delaware Dept Accounting & Management Informat Syst Lerner Coll Business & Econ Newark DE 19716 USA Arizona State Univ Dept Informat Syst WP Carey Sch Business Tempe AZ 85287 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Medication nonadherence (MNA) can lead to serious health ramifications and costs U.S. healthcare systems $290 billion annually. Understanding the reasons underlying patients' MNA is thus an urgent goal for researchers, practitioners, and the pharmaceuticalindustry in order to mitigate negative health and economic consequences. In recent years, patient engagement on social media sites has soared, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet these data remain untapped in existing MNA studies because of technical challenges such as long texts, decision-making based on negative sentiment, varied patient vocabulary, and the scarcity of relevant information. For this study, we developed a sentiment-enriched deep learning method (SEDEL) to address these challenges and extract reasons for MNA. We evaluated SEDEL using 53,180 reviews concerning180 drugs and achieved a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperformed state-of-the-art baseline models. We identified nine categories of MNA reasons, which were verified by domain experts. This study contributes to IS research by devising a novel deep-learning-based approach for reason mining and by providing direct implications for the health industry and for practitioners regarding the design of interventions

关键词： Sentiment-enriched deep learning reason mining social media analytics health risk analytics medication nonadherence

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A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward

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NEUROCOMPUTING 2021年 424卷 23-34页

作者： Liang, Mingming Wei, Qinglai Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper constructs a partial policy iteration adaptive dynamic programming (ADP) algorithm to solve the optimal control problem of nonlinear systems with discounted total reward. Compared with traditional policy iteration ADP algorithm, the approach updates the iterative control law only in a local region of the global system state space. With the benefit of this feature, the overall computational burden at each iteration for processing units can be significantly reduced. Hence, this feature enables our algorithm to be successfully executed on low-performance devices such as smartphones, smartwatches and the Internet of Things (IoT) objects. We provide the convergency analysis to show that the generated sequence of value functions is monotonically nonincreasing and can finally reach a local optimum. In addition, the corresponding local policy space is developed theoretically for the first time. Besides, when the sequence of the local system state spaces is chosen properly, we prove that the developed algorithm is capable of finding the global optimal performance index function for the nonlinear systems. Finally, we present a numerical simulation to demonstrate the effectiveness of the proposed algorithm. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Adaptive critic designs Adaptive dynamic programming Policy iteration Neural networks Neuro-dynamic programming Nonlinear systems Optimal control

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