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A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using Shannon Entropy Method

A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using ...

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2020 International Conference on Innovative Solutions in Hydropower and Environmental and Civil engineering

作者： Zhang, Qi Yu, Yi Zhang, Weimin College of Meteorology and Oceanography National University of Defense Technology Changsha410073 China College of Computer Science National University of Defense Technology Changsha410073 China Key Laboratory of Software Engineering for Complex Systems Changsha410073 China

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables' (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS's spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast. © Published under licence by IOP Publishing Ltd.

关键词： Weather forecasting

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Zhu, Kaiwei Kong, Shihan Wu, Fang Sun, Jinan Yu, Junzhi College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China SPIC Nuclear Energy Co. Ltd. China Peking University National Engineering Research Center for Software Engineering Beijing100871 China

ISBN: (纸本)9798350372601

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world. © 2024 IEEE.

关键词： Target tracking

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Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification 27th

Learning from the Negativity: Deep Negative Correlation Meta...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030678319

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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An Empirical Study on the Influence of Social Interactions for the Acceptance of Answers in Stack Overflow

An Empirical Study on the Influence of Social Interactions f...

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Asia-Pacific Conference on software engineering

作者： Zhang Zhang Xinjun Mao Yao Lu Shangwen Wang Jinyu Lu Key Laboratory of Software Engineering for Complex Systems Changsha China National University of Defense Technology Changsha China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

In knowledge-sharing communities like Stack Overflow (SO), users can post questions, give answers and choose one answer as an accepted answer. The accepted answers will be important references for users when they encounter similar questions. Essentially, posting questions and giving answers is an interactive process occurring among community users, and choosing accepted answers is actually a decision-making process involving multiple factors. Previous works examined the impact on this decision process from the user, question and answer viewpoints. Social interactions between the questioners and answerers, although being popular according to our pre-analysis, have never been considered as a factor that can influence the decisions. To fill this gap, this paper first proposes a comprehensive answer acceptance model that integrates the answer features established by social interactions as well as information of users, questions and answers. We then divide social interactions into two stages and propose a method to calculate the relationship between the questioner and the answerer by analyzing these social interactions. Finally, we investigate the influence of social interactions for the acceptance of answers by performing logistic regression analysis. The results reveal several findings: (1) social-based features explain 16.6 % of the variance explained together, indicating that social interactions have significant and important effects on the acceptance of answers; (2) social interactions that occur after the answer is posted are more influential than these occur before the answer is posted. Based on the findings, we further conduct an online study of 132 SO users, and the respondents report that social interactions have a greater impact on the acceptance of answers than other judgments of answers such as upvotes, downvotes and not accepting answers.

关键词： Analytical models Decision making Transforms Predictive models Regression analysis Logistics software engineering

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Complementary Learning System Based Intrinsic Reward in Reinforcement Learning

Complementary Learning System Based Intrinsic Reward in Rein...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zijian Gao Kele Xu Hongda Jia Tianjiao Wan Bo Ding Dawei Feng Xinjun Mao Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learning systems utilizing short-term and long-term memories. Inspired by the fact that humans evaluate curiosity by comparing current observations with historical information, we propose a novel intrinsic reward, namely CLS-IR, which aims to address the problems caused by sparse extrinsic rewards. Specifically, we train a self-supervised predictive model with short-term and long-term memories via exponential moving averages. We employ the information gain between the two memories as the intrinsic reward, which does not incur additional training costs but leads to better exploration. To investigate the effectiveness of CLS-IR, we conduct extensive experimental evaluations; the results demonstrate that CLS-IR can achieve state-of-the-art performance on Atari games and DeepMind Control Suite.

关键词： Learning systems Training Deep learning Costs Reinforcement learning Games Signal processing

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Improving the Performance of Lattice Boltzmann Method with Pipelined Algorithm on A Heterogeneous Multi-zone Processor 23rd

Improving the Performance of Lattice Boltzmann Method with...

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23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Zhang, Qingyang Xu, Lei Chen, Rongliang Chen, Lin Chen, Xinhai Wang, Qinglin Liu, Jie Yang, Bo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410000 China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031299261

Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especially on recent multi-core or many-core platforms. This paper develops a parallel software framework for 3D LBM simulation on a heterogeneous multi-zone processor, MT-3000. An improved pipelined algorithm named Pencil-H is proposed, which can not only fully exploit the advantages of each component of MT-3000 but also overlap the time of calculation and communication. Moreover, an architecture-aware multi-level parallelization algorithm is developed to fully utilize the computational performance of MT-3000. A benchmark test is performed to verify the reliability and test the performance of the LBM code. Experimental results show that the optimized code achieves a 32.02 × speedup compared with using 16 CPU cores and achieves a performance of 286.03MLUPS which reaches 72.3% of the theoretical peak performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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Progressive Diversifying Policy for Multi-Agent Reinforcement Learning

Progressive Diversifying Policy for Multi-Agent Reinforcemen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shaoqi Sun Yuanzhao Zhai Kele Xu Dawei Feng Bo Ding National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

Multi-Agent Reinforcement Learning (MARL) has recently achieved promising performance in many collaborative decision making tasks. However, one of the main bottleneck challenges for MARL is the sparsity of the team reward, which can lead to the homogenization of agents’ behaviors. To address these issues, we propose a Progressive Diversifying Policy (PDP) algorithm in this paper. Specifically, we propose to actively amplify the diversity between agents’ policies during learning and exploit diversity as an additional intrinsic reward for MARL. Furthermore, we propose a progressive diversity boosting policy to find a better team policy. Leveraging the aforementioned improvements, our method can handle sparse team rewards and alleviate the homogeneous behaviors of agents. We conduct experiments on widely-used MARL environments and the results show that PDP can provide state-of-the-art performance while maintaining a competitive convergence speed.

关键词： Decision making Collaboration Signal processing algorithms Reinforcement learning Diversity methods Signal processing Boosting

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Diversifying Message Aggregation in Multi-Agent Communication Via Normalized Tensor Nuclear Norm Regularization

Diversifying Message Aggregation in Multi-Agent Communicatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuanzhao Zhai Kele Xu Bo Ding Dawei Feng Zijian Gao Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

The use of graph attention networks (GAT) in communication-enhanced multi-agent reinforcement learning (Comm-MARL) has become prevalent. While successful, GAT can lead to homogeneity in the strategies of message aggregation, which can severely limit multi-agent coordination. To address this challenge, we study the adjacency tensor of the communication graph. Then we define a new nuclear tensor rank and its convex surrogate, the normalized tensor nuclear norm to measure the homogeneity of message aggregation. Leveraging the norm, we further propose a plug-and-play regularizer on the adjacency tensor, named Normalized Tensor Nuclear Norm Regularization (NTNNR), to actively enrich the diversity of message aggregation during the training stage. NTNNR is agnostic to specific Comm-MARL algorithms and can be flexibly integrated with different graph-attention methods. Empirical results demonstrate that aggregating messages using NTNNR-enhanced GAT can improve the efficiency of the training and achieve higher asymptotic performance than existing message aggregation methods.

关键词： Training Tensors Signal processing algorithms Reinforcement learning Signal processing Acoustics Speech processing

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Formal Verification Based Synthesis for Behavior Trees 9th

Formal Verification Based Synthesis for Behavior Trees

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9th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2023

作者： Hong, Weijiang Chen, Zhenbang Li, Minglong Li, Yuhan Huang, Peishan Wang, Ji College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9789819986637

Behavior trees (BTs) have been extensively applied in the area of both computer games and robotics, as the control architectures. However, the construction of BTs is labor-expensive, time-consuming, and even impossible as the complexity of task increases. In this work, we propose a formal verification based synthesis method to automatically construct BTs whose behaviors satisfy the given Linear Temporal Logic (LTL) specifications. Our method first explores candidate BTs by a grammar-based Monte Carlo Tree Search (MCTS), then the explored BTs are transformed into Communicating Sequential Processes (CSP) models. After that, we invoke the verifier to check the models’ correctness w.r.t. specifications, and provide feedback based on the verification result for guiding the search process. The application of our method on several representative robotic missions indicates its promising. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavior Trees CSP MCTS Synthesis

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Gathering GitHub OSS Requirements from Q&A Community: an Empirical Study

Gathering GitHub OSS Requirements from Q&A Community: an Emp...

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IEEE International Conference on engineering of complex Computer systems

作者： Hao Huang Yao Lu Xinjun Mao Key Laboratory of Software Engineering for Complex Systems College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728185583

ISBN: (纸本)9781728185590

Cross-community collaboration can exploit the expertise and knowledges of crowds in different communities. Recently increasing users in open source software (OSS) community like GitHub attempt to gather software requirements from question and answer (Q&A) communities such as Stack Overflow (SO). In order to investigate this emerging cross-community collaboration phenomenon, the paper presents an exploratory study on cross-community requirements gathering of OSS projects in GitHub. We manually sample 3266 practice cases and quantitatively analyze the popularity of the phenomenon, the characteristics of the gathered requirements, and cross-community collaboration behaviors of users. Some important findings are obtained: more than half of the requirements gathered from SO are enhancements and the majority of the gathered requirements are non-functional requirements. In addition, OSS developers can directly obtain related solutions and contributions of the gathered requirements from SO in the gathering process.

关键词： Collaboration Manuals Tools software Time factors Open source software software development management

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