A Data-Driven Adaptive Sampling Method Based on Edge Computing
نویسندگان
چکیده
منابع مشابه
A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes
The applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity (KS) for identifying the location of damage and estimating the level of damage severity. An impr...
متن کاملAdaptive cluster sampling with a data driven stopping rule
The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size could be easily out ...
متن کاملA Sampling-based Scheduling Method for Distributed Computing
In this paper, we propose a new solution for dynamic task scheduling in distributed environment. We argue that a function is existed in the items: execution time, the size of data and the algorithm, therefore we can deduce the execution time of a data mining task from the corresponding the size of data and algorithm. We adopt the sampling method for process the tasks scheduling in distributed d...
متن کاملTable-driven Adaptive Importance Sampling
Monte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene. In this paper, we present Table-driven Adaptive Importance Sampling (TAIS), a sam...
متن کاملAN ADAPTIVE IMPORTANCE SAMPLING-BASED ALGORITHM USING THE FIRST-ORDER METHOD FOR STRUCTURAL RELIABILITY
Monte Carlo simulation (MCS) is a useful tool for computation of probability of failure in reliability analysis. However, the large number of samples, often required for acceptable accuracy, makes it time-consuming. Importance sampling is a method on the basis of MCS which has been proposed to reduce the computational time of MCS. In this paper, a new adaptive importance sampling-based algorith...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2020
ISSN: 1424-8220
DOI: 10.3390/s20082174