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Risk-based water quality decision-making under small data using Bayesian network

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Journal of Central South University 2012年第11期19卷 3215-3224页

作者：张庆庆许月萍田烨张徐杰 Institute of Hydrology and Water Resources Civil EngineeringZhejiang University

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small ***,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small *** all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are *** final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

关键词： water quality risk pollution reduction decisions Bayesian network conditional linear Gaussian Model small data

A LoRa-based protocol for connecting IoT edge computing nodes to provide small-data-based services

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Digital Communications and Networks 2022年第3期8卷 257-266页

作者： Kiyoshy Nakamura Pietro Manzoni Alessandro Redondi Edoardo Longo Marco Zennaro Juan-Carlos Cano Carlos T.Calafate Universitat Politécnica de Valéncia ValenciaSpain Abdus Salam International Centre for Theoretical Physics(ICTP) TriesteItaly Politecnico di Milano MilanItaly

data is becoming increasingly *** regularly interact with a variety of structured data,ranging from SQLite databases on the phone to personal sensors and open government ***“digital traces left by individuals through these interactions”are sometimes referred to as“small data”.Examples of“small data”include driving records,biometric measurements,search histories,weather forecasts and usage *** this paper,we present a flexible protocol called LoRaCTP,which is based on LoRa technology that allows data“chunks”to be transferred over large distances with very low energy *** provides all the mechanisms necessary to make LoRa transfer reliable by introducing a lightweight connection setup and allowing the ideal sending of an as-long-as necessary data *** designed this protocol as communication support for small-data edge-based IoT solutions,given its stability,low power usage,and the possibility to cover long *** evaluated our protocol using various data content sizes and communication distances to demonstrate its performance and reliability.

关键词： small data Edge computing LoRa IoT

Knowledge-reused transfer learning for molecular and materials science

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Journal of Energy Chemistry 2024年第11期98卷 149-168页

作者： An Chen Zhilong Wang Karl Luigi Loza Vidaurre Yanqiang Han Simin Ye Kehao Tao Shiwei Wang Jing Gao Jinjin Li National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.

关键词： Machine learning Transfer learning small data Molecule Material science

Notes on Global Existence for the Nonlinear Schrdinger Equation Involves Derivative

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Acta Mathematica Sinica,English Series 2014年第10期30卷 1735-1747页

作者： Shi Yang ZHENG School of Mathematics and Systems Science Beihang University

In this paper, we consider the scattering for the nonlinear SchrSdinger equation with small, smooth, and localized data. In particular, we prove that the solution of the quadratic nonlinear SchrSdinger equation with nonlinear term ｜u｜2 involving some derivatives in two dimension exists globally and scatters. It is worth to note that there exist blow-up solutions of these equations without derivatives. Moreover, for radial data, we prove that for the equation with p-order nonlinearity with derivatives, the similar results hold for P≥ =2d＋3/2d-1 and d ≥ 2, which is lower than the Strauss exponents.

关键词： SchrSdinger equation global well-posedness scatter small data quadratic

大数据与小数据结合：信息时代城市研究方法探讨

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地理 2017年第4期

作者：秦萧甄峰

信息技术的快速发展引起了城市研究领域的“大数据”热潮，并带来了传统城市研究方法的变革。但是，其自身存在的诸多缺陷使得学者不得不重新考虑传统小数据的应用角色。但是，传统小数据并没有失去其应用价值，相反，以城市与居民行为... 详细信息

信息技术的快速发展引起了城市研究领域的“大数据”热潮，并带来了传统城市研究方法的变革。但是，其自身存在的诸多缺陷使得学者不得不重新考虑传统小数据的应用角色。但是，传统小数据并没有失去其应用价值，相反，以城市与居民行为活动关系研究为主体的信息时代的城市研究必需充分结合大数据与小数据，并探讨适宜的方法论与方法框架，从而应对日益复杂的城市问题和居民需求。提出“物质空间与活动空间结合”、“相关关系与因果关系结合”、“宏观分析与微观挖掘结合”的3个方法论，并在此基础上构建了“大样本空间发展评价+空间差异与联系发现+小样本影响因素探究”、“小样本模型构建+影响因素发现+大样本验证及挖掘”、“微观活动分析+活动空间界定+影响因素探究”3种方法框架，且分析了这些框架的具体应用，以期为未来的城市研究提供思路和方法借鉴。

关键词：大数据小数据 EDCF MFVE AADSF 城市研究信息时代 big data small data EDCF MFVE AADSF urban studies information era

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从小数据到大数据