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Data quantity governance for machine learning in materials science

National science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of engineering & technology 2024.

关键词： Cost functions

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Test Suite Generation Based on Context-Adapted Structural Coverage for Testing DNN 24

Test Suite Generation Based on Context-Adapted Structural Co...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kong, Wei Du, Qianjin Cao, Huayang Li, Hu Wang, Tong Tian, Jianwen Kuang, Xiaohui National Key Laboratory of Science and Technology on Information System Security Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9788995004395

Deep learning (DL) introduces a novel data-driven programming paradigm, where the system logic is constructed through data training. However, this approach poses challenges in terms of system analysis and defect detection. To address this issue, recent efforts have focused on testing deep learning systems using intuitive criteria known as Neuron Coverage (NC) and its variants. These criteria measure the activation proportion of neurons in a neural network. Unfortunately, recent DL applications have largely overlooked the importance of context during testing. To address this gap, this paper first incorporates the context of the DL pipeline deployed before test execution, such as medical diagnosis and Android Malware detection. Next, we formulate structural coverage criteria to guide test suite generation based on the properties of DL pipeline in different contexts. Furthermore, we proposed a coverage-guided search algorithm to efficiently generate test suites. Experimental results highlight the effectiveness of our approach in uncovering numerous erroneous behaviors in contexts such as medical image diagnosis and Android malware detection. This approach significantly enhances the robustness of DL models by considering the structural aspects of the DL pipeline. Copyright 2023 KICS.

关键词： Deep learning

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A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

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Intelligent and Converged Networks 2024年第4期5卷 317-335页

作者： Chao Wang Yiyun Shi Sumin Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

Currently,the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning *** identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult,ultimately preventing the achievement of the most optimal *** paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive *** reasoning aims to enhance performance in scenarios with poor system robustness,complex tasks,and diverse goals.A deep learning model is constructed,trained to extract scene features,and combined with expert knowledge,then transformed into a quantum annealable *** final strategy is obtained using a D-wave quantum computer with quantum tunneling effect,which helps in finding optimal solutions by jumping out of local suboptimal *** on 400000 real data,four algorithms are compared:minimum-cost flow,sequential markov decision process,hot-dot strategy,and driver-prefer *** average total revenue increases by about 10%and vehicle utilization by about 15%in various *** summary,the proposed architecture effectively solves the e-hailing reposition problem,offering new directions for robust artificial intelligence in big data decision problems.

关键词： intuitive reasoning reinforcement learning quantum annealing

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Robust twin-field quantum key distribution through sending or not sending

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National science Review 2023年第4期10卷 76-85页

作者： Cong Jiang Zong-Wen Yu Xiao-Long Hu Xiang-Bin Wang Jinan Institute of Quantum Technology State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Data Communication Science and Technology Research Institute School of Physics State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Frontier Science Center for Quantum Information

The sending-or-not-sending(SNS) protocol is one of the most major variants of the twin-field(TF)quantum key distribution(QKD) protocol and has been realized in a 511-km field fiber, the farthest field experiment to date. In practice, however, all decoy-state methods have unavoidable source errors, and the source errors may be non-random, which compromises the security condition of the existing TF-QKD protocols. In this study, we present a general approach for efficiently calculating the SNS protocol's secure key rate with source errors, by establishing the equivalent protocols through virtual atenuation and the tagged model. This makes the first result for TF QKD in practice where source intensity cannot be controlled exactly. Our method can be combined with the two-way classical communication method such as active odd-parity pairing to further improve the key rate. The numerical results show that if the intensity error is within a few percent, the key rate and secure distance only decrease marginally. The key rate of the recent SNS experiment in the 511-km field fiber is still positive using our method presented here, even if there is a ±9.5% intensity fluctuation. This shows that the SNS protocol is robust against source errors.

关键词： quantum information quantum key distribution sending or not sending twin field source error

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Integrating vegetation phenological characteristics and polarization features with object-oriented techniques for grassland type identification

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地球空间信息科学学报（英文版） 2024年第3期27卷 794-810页

作者： Bin Sun Pengyao Qin Changlong Li Zhihai Gao Alan Grainger Xiaosong Li Yan Wang Wei Yue Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijingChina Key Laboratory of Forestry Remote Sensing and Information System NFGABeijingChina School of Information Technology and Engineering Guangzhou College of CommerceGuangzhouChina School of Geography University of LeedsLeedsUK Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina Shandong Geographical Institute of Land Spatial Data and Remote Sensing Technology JinanChina

Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed *** limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland *** address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study *** integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland *** results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grass-land types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland *** overall accuracy reaches 82.61％,with a kappa coefficient of *** to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94％and 0.19,***,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38％.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to

关键词： Grassland types vegetation phenological characteristics polarization feature integrated active and passive remote sensing object-oriented classification

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Cooperative Local Path Planning for Multi-unmanned Vehicles Formation Using the Enhanced Artificial Potential Field 3rd

Cooperative Local Path Planning for Multi-unmanned Vehicles...

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhang, Xin Duan, Xuting Zhou, Jianshan Zhang, Long Opening Project of Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport PRC Beijing China School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

ISBN: (纸本)9789819711024

An enhanced approach utilizing APF(artificial potential field) method is introduced in this paper. By adopting this approach, the challenge of local minima that may occur when dealing with local path planning for unmanned vehicles is tackled. When the unmanned vehicle approaches an obstacle, a virtual attractive potential field is implemented, utilizing the deviation in the heading angle of the vehicle to control the steering angle. Consequently, the vehicle can avoid obstacles, resolving the issue of local minima and enabling its application in path planning for multi-unmanned vehicles. The ROS with Gazebo simulation platform is used to verify the enhanced algorithm’s effectiveness in multi-unmanned vehicle path planning in an unknown environment. © Beijing HIWING Scientific and Technological information Institute 2024.

关键词： Motion planning

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A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

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International Journal of Digital Earth 2023年第1期16卷 272-288页

作者： Chunmei He Jia Sun Yuwen Chen Lunche Wang Shuo Shi Feng Qiu Shaoqiang Wang Torbern Tagesson Hubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China Department of Physical Geography and Ecosystem Science Lund UniversityLundSweden State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China International Institute for Earth System Science Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingPeople’s Republic of China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to ***,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their *** study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and *** sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT *** VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent *** ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl *** with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield *** new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

关键词： Leaf carotenoids content leaf chlorophyll content PROSPECT model ratio of Car to Chl vegetation index

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Global output feedback adaptive stabilization for systems with long uncertain input delay

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science China(information sciences) 2023年第1期66卷 326-327页

作者： Zhuping WANG Jingcheng LIU Chao HUANG Hao ZHANG Huaicheng YAN School of Electronics and Information Engineering Tongji University Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology

Dear editor,The uncertain input delay is frequently encountered in engineering control systems. Adaptation is indispensable when the uncertain input delay is significant. In existing delayadaptive controllers [1–6], the actuator state must be measured to achieve global stability. Recently, a logic-based switching delay-adaptive state-feedback controller [7] was proposed to realize global stability without measuring the actuator state.

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Underwater images enhancement using contrast limited adaptive parameter settings histogram equalization

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Multimedia Tools and Applications 2024年 1-15页

作者： Chen, Yahui Liang, Yitao College of Information Science and Engineering Henan University of Technology Henan Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan Zhengzhou China

CLAHE is widely used in underwater image processing because of its excellent performance in contrast enhancement. The selection of the clip point formula is the core problem of the CLAHE methods, and the selection of suitable clipping value has become the focus of some extended methods. In this paper, an automatic CLAHE underwater image enhancement algorithm is proposed. The method determines the clipping value according to the high-order moment dynamic features of each block of the underwater image. By quantifying the dynamic features of each block in the image more precisely, and then adding it to the clipping value formula, the contrast and details of the underwater image can be effectively enhanced. In order to effectively improve the saturation and brightness of underwater images, this paper chooses a more accurate and intuitive HSV model. Experimental results show that our methods enhance the contrast subjectively, while suppressing the amplification of noise very well, and also increase the saturation of underwater images. In objective metrics, our method obtains the best values in underwater quality assessment (UIQM), SSIM, and PSNR. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Underwater imaging

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