The paper presents algorithm development results for adaptive data processing of the troposphere parameters remote sensing. The algorithm is implemented in the LabVIEW graphical programming environment and processes d...
详细信息
ISBN:
(纸本)9781538607770
The paper presents algorithm development results for adaptive data processing of the troposphere parameters remote sensing. The algorithm is implemented in the LabVIEW graphical programming environment and processes data of the ground-based water vapor radiometer (WVR) in a quasi-real time scale. To identify trends and forecast changes in the time series of WVR data during intense precipitation, it is proposed to use the method of Singular Spectrum Analysis - "Caterpillar"-SSA". To determine the anomalous values of the initial time series data, the fuzzy method is used. The results of the comparison of the troposphere integral water vapor content obtained with the help of WVR and global navigation satellite systems are given.
Despite software industries' successful utilization of Service-Oriented Computing (SOC) to streamline software development, machine learning (ML) development has yet to fully integrate these practices. This dispar...
详细信息
ISBN:
(纸本)9783031457272;9783031457289
Despite software industries' successful utilization of Service-Oriented Computing (SOC) to streamline software development, machine learning (ML) development has yet to fully integrate these practices. This disparity can be attributed to multiple factors, such as the unique challenges inherent to ML development and the absence of a unified framework for incorporating services into this process. In this paper, we shed light on the disparities between services-oriented computing and machine learning development. We propose "Everything as a Module" (XaaM), a framework designed to encapsulate every ML artifacts including models, code, data, and configurations as individual modules, to bridge this gap. We propose a set of additional steps that need to be taken to empower machine learning development using services-oriented computing via an architecture that facilitates efficient management and orchestration of complex ML systems. By leveraging the best practices of services-oriented computing, we believe that machine learning development can achieve a higher level of maturity, improve the efficiency of the development process, and ultimately, facilitate the more effective creation of machine learning applications.
Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to levera...
详细信息
ISBN:
(纸本)9781509026753
Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to leverage cloud infrastructure for scalable solutions and real-time services. Thus large volumes of heterogeneous and high frequency sensor data are fed into IoT cloud services for real-time actionable insight, which raises great challenges of performance and adaptability on cloud solutions. In this paper, we propose a streaming based processing infrastructure for high throughput and low latency IoT real-time analytics services. A dataadaptive mechanism is also introduced for heterogeneous data stream integration, interpreting and processing with application logics, as well as context stream. We implemented the proposed mechanisms with spark streaming, and deployed real time IoT analytics service in cloud. Experiment results show that the service has a good scalability and high throughput for IoT data analytics.
Google Trends data has gained increasing popularity in the applications of behavioral finance,decision science and risk *** of Google's wide range of use,the Trends statistics provide significant information about...
详细信息
Google Trends data has gained increasing popularity in the applications of behavioral finance,decision science and risk *** of Google's wide range of use,the Trends statistics provide significant information about the investor sentiment and intention,which can be used as decisive factors for corporate and risk management ***,an anomaly,a significant increase or decrease,in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends *** through time,the baseline noise power shows a gradual change an adaptive threshold method is required to track and learn the baseline noise for a correct *** this end,we introduce an online method to classify meaningful deviations in Google Trends *** extensive experiments,we demonstrate that our method can successfully classify various anomalies for plenty of different data.
暂无评论