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Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 2011-2026页

作者： Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082 Wuxi Intelligent Control Research Institute(WICRI)of Hunan University Wuxi 214115China College of Intelligence Science and Technology Institute of Unmanned SystemsNational University of Defense TechnologyChangsha 410073China

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and *** paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and *** method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at *** algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization *** proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning *** intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic *** algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关键词： Autonomous driving decision-making reinforcement learning(RL) unsignalized intersection

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MFN: Explainable DNA triple helixes Stabilized design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xiaoru Sun, Lijun Xie, Lei Zheng, Yanfen Cao, Ben Wang, Bin Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy. © 2024 IEEE.

关键词： Crosstalk

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Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

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Big Data Mining and Analytics 2024年第3期7卷 781-793页

作者： Ruimin Ma Ruitao Xie Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Shenzhen Key Laboratory of Intelligent Bioinformatics and with Center for High Performance ComputingShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD *** the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack *** this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature *** ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared *** case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential ***,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.

关键词： Autism Spectrum Disorder(ASD)classification Contrastive Variational AutoEncoder(CVAE) transfer learning neuroanatomical interpretation

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A study of boundary conditions for pedestrian thorax impact test

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International Journal of Vehicle Systems Modelling and Testing 2024年第2期18卷 120-133页

作者： Long, Yongcheng Liu, Yu Xiao, Yu Ruan, Zhisheng Xiao, Zhi Zhang, Guanjun Huang, Jing State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing401122 China China Automotive Engineering Research Institute Co. Ltd. Chongqing401122 China Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha410082 China

Thorax injury is one of the main injuries during pedestrian impacts, and it is important to determine the boundary conditions for pedestrian thorax impactor tests. A series of virtual cases were built up to simulate the vehicle-pedestrian impact accidents. Then, the kinematic process was analysed during the vehicle-pedestrian impacts, and thorax impact parameters were extracted. Finally, the statistical characteristics of these impact parameters were obtained for thorax impactor tests. The results show that vehicle type affects the pedestrian impact kinematics and thorax impact parameters significantly. The range of thorax wrap-around distance is 928~1915 mm and 1018~1762 mm during sedan-pedestrian impact and SUV-pedestrian impact respectively. The average thorax impact speeds are about 5.8 m/s and 4.8 m/s in sedan-pedestrian impact and in SUV-pedestrian impact respectively. The average thorax impact velocity angles are 26.8° and 19.5° respectively. These results can provide the effective reference data for pedestrian thorax impactor tests. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Kinematics

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Speech Emotion Recognition using Channel Attention Mechanism 4

Speech Emotion Recognition using Channel Attention Mechanism

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Zhu, Ruifeng Sun, Caixia Wei, Xiaopeng Zhao, Lasheng Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798350347548

In order to improve the accuracy of speech emotion recognition, this paper proposes a speech emotion recognition method based on the channel attention mechanism. Firstly, Mel Frequency Ceptral Coefficient(MFCC), speech spectrograms and spectral envelopes are selected as the initial input features;then, multiple depth network models are used to extract feature maps from different angles in parallel;then, weights are assigned and fused to the feature maps output from each sub-depth network model by the channel attention mechanism;finally, the fused feature maps are used to predict emotion categories. The experimental results on CASIA, Emo-DB, and SAVEE emotion datasets show that the method achieves 88.3%, 85.1%, and 64.5% recognition accuracy, respectively, with better recognition performance compared to recent comparative literature models. © 2023 IEEE.

关键词： Emotion Recognition

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Leveraging Slot-Aware Enhancements for Improved Zero-Shot Dialogue State Tracking 6

Leveraging Slot-Aware Enhancements for Improved Zero-Shot Di...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Song, Tianhang Hu, Haolei Chen, Xinxin Zi, Kangli Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Task-oriented dialogue systems (TOD) aim to help users complete specific tasks through multiple rounds of dialogue, in which Dialogue State Tracking (DST) is a key component. The training of DST models typically necessitates the availability of a substantial corpus of dialogue data with turn-level annotations. However, the collection of such data is both costly and inefficient. Despite the impressive performance of systems based on advanced large language models (LLMs), their closed-source nature restricts the potential for local deployment. To address this challenge, this paper proposes a new zero-shot slot-aware enhanced model (SAE), which introduces a novel coded prompting framework to enhance the DST capability of small parameter models in new domains and task scenarios. In particular, we have devised a prompting framework that employs code to refine task instructions. Furthermore, we have proposed a slot-aware question generation mechanism that generates slot-aware questions based on contextual details in accordance with the dialogue context, thereby enhancing the model's perception and matching capabilities for slots. In order to verify the effectiveness of the model, we conducted experiments on the MultiWOZ dataset. The results show that the SAE model has achieved significant performance improvements in zero-shot DST tasks. © 2024 IEEE.

关键词： Zero-shot learning

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A copula-based reliability model for phased mission systems with dependent components (vol 39, pg 1533, 2023)

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第7期39卷 3179-3179页

作者： Li, X. Zhan, K. Xiong, X. Vu, T. Q. Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan China School of Media Design College of Technology and Design University of Economics Ho Chi Minh City Vietnam

Recently, researches on the reliability analysis of phased mission systems (PMSs) are very popular. And most of the existing research on PMSs assumed that all the components are statistically independent on each other, which is not meet the engineering reality. The system structure changes from phase to phase in the PMSs, so the dependency among components may also change in different phases. Meanwhile, the well-known phase dependency is also existing in the system reliability modeling. To model the varying dependency among components and phase dependency at the same time, a copula-based method is proposed for the reliability analysis of PMS with correlated components. Firstly, the system reliability of multi-state PMS with dependent components is evaluated by different copula functions. Then, multiple indicators are used to select the optimal copula functions for different phases. And an engineering case is illustrated to show the proposed copula-based method.

关键词： copula dependent components modular method phased mission system

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A Text Structure-based Extractive And Abstractive Summarization Method 7

A Text Structure-based Extractive And Abstractive Summarizat...

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7th International Conference on intelligent computing and Signal Processing, ICSP 2022

作者： Yan, Jing Zhou, Shihua Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China

ISBN: (纸本)9781665478571

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. The major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved. © 2022 IEEE.

关键词： Text processing

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Blockchain-Based Privacy-Preserving and Accountable Mobile Edge Outsourcing computing Framework for the Metaverse

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 711-724页

作者： Li, Chunpei Liu, Chen Liu, Peng Li, Xianxian Qiu, Wangjie Lei, Lei Jin, Yanli Guangxi Normal University Key Laboratory of Educational Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Zhongguancun Laboratory China Beihang University Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

Mobile Edge computing (MEC) enables Metaverse Terminal Devices (MTD) to perform complex tasks, including graphic rendering and physical simulation, by leveraging low-latency outsourced computing. However, existing research has not fully addressed the challenge of establishing an efficient outsourced computing service within an open and dynamic MEC environment that simultaneously ensures privacy and accountability. To address this, we proposes a blockchain-based privacy-preserving and accountable mobile edge outsourcing computing framework for the Metaverse, termed Meta-BMEOC. Specifically, we have designed an outsourcing computing protocol based on smart contracts and threshold secret sharing, enabling MTD to outsource tasks to multiple edge servers while preserving privacy. Furthermore, we have developed an off-chain smart contract protocol based on a Trusted Execution Environment. This protocol is designed to reduce the risk of malicious edge servers colluding to reconstruct the computational tasks of MTD, and it enables accountability for servers that return erroneous results. Additionally, we designed an incentive mechanism to resist malicious attacks and ensure system security and stability. Security analysis and experimental evaluation show that Meta-BMEOC not only ensures the privacy and accountability of outsourced computing but also provides outsourced computing services with lower computational latency. © 2017 IEEE.

关键词： Differential privacy

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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Tan, Shenglian Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

This paper proposes a surrogate-assisted evolutionary framework (called SELF) to solve expensive multitask optimization problems (ExMTOPs). SELF consists of two main phases: global knowledge transfer phase and local knowledge transfer phase. In the former, a multitask Gaussian process model (MTGP) is established by fusing previously evaluated solutions of multiple optimization tasks. MTGP can capture task-relevant information and the knowledge of landscapes. Then, differential evolution assisted with MTGP is proposed to preselect high-quality candidates. During the preselection, the knowledge of landscapes is transferred among multiple optimization tasks for locating promising regions quickly. In the latter, for each optimization task, Bayesian optimization is adopted to improve the quality of the best individual in the population. Moreover, the improved best individuals in the populations of multiple optimization tasks are adaptively transferred based on a transfer probability, which is computed through the task-relevant information provided by MTGP. By combining these two phases, SELF not only achieves the tradeoff between exploration and exploitation, but also utilizes the global and local knowledge transfer to improve the efficiency for solving ExMTOPs. We test SELF on seven benchmark test problems in the IEEE CEC2017 evolutionary multitask optimization competition. The results demonstrate that the performance of SELF is better than that of other seven advanced methods. In addition, we also apply SELF to deal with two real-world ExMTOPs. The designs provided by SELF exhibit the best performance among all the compared methods, verifying the potential of SELF in practical engineering applications. IEEE

关键词： Linear programming

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