In the process of using line structured light for measurement, it is an essential step to quickly and accurately extract the center position from the line structured light stripe to improve the accuracy of the line st...
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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 unsignaliz...
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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.
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 th...
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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...
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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.
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 t...
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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), speec...
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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 neces...
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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...
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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.
作者：
Yan, JingZhou, ShihuaDalian University
Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China
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 fu...
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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 k...
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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
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