Design of Data Management Service Platform for Intelligent Electric Vehicle Charging Controller Multi-charger Model

The electric charging solutions for the residential market imply, in many situations, an increase in the contracted power in order to allow to perform an efficient charging cycle that starts when the charger is connected and ends when the VE battery is fully charged. However, the increase in contracted power is not always the best solution for faster and more efficient charging. With a focus on the residential market, the presented architecture is suitable for single-use and shared connection points, which are becoming common in apartment buildings without a closed garage, allowing for sharing the available electrical connections to the grid. The multi-charger architecture allows using one or several common charging points by applying a mesh network of intelligent chargers orchestrated by a residential gateway. Managing the generated data load involves enabling data flow between several independent data producers and consumers. The data stream ingestion system must be scalable, resilient, and extendable.info:eu-repo/semantics/publishedVersio

A Big Data Lake for Multilevel Streaming Analytics

Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely posed by the availability of streaming data at high velocity from various sources in multiple formats. The changes in data paradigm have led to the emergence of new data analytics and management architecture. This paper focuses on storing high volume, velocity and variety data in the raw formats in a data storage architecture called a data lake. First, we present our study on the limitations of traditional data warehouses in handling recent changes in data paradigms. We discuss and compare different open source and commercial platforms that can be used to develop a data lake. We then describe our end-to-end data lake design and implementation approach using the Hadoop Distributed File System (HDFS) on the Hadoop Data Platform (HDP). Finally, we present a real-world data lake development use case for data stream ingestion, staging, and multilevel streaming analytics which combines structured and unstructured data. This study can serve as a guide for individuals or organizations planning to implement a data lake solution for their use cases.Comment: 6 page

Development of a Framework for the Analysis and Assessment of Daily Airport Operations

Tremendous progress has been made over the last two decades towards modernizing the National Airspace System (NAS) by way of technological advancements, and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the NAS. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to inform better decision making so as to improve the efficiency and safety of airport operations. Efforts have been undertaken by stakeholders in the aviation industry to categorize airports as a means to facilitate the analysis of their operations. However, a comprehensive, repeatable, and robust approach for this very purpose is lacking. In addition, these efforts have not provided a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations. Furthermore, an efficient and secure framework for extracting, processing, and storing the data needed for the analysis and assessment of daily airport operations is needed, as the current process employed by FAA analysts is manual, time-consuming, and prone to human error. Consequently, this dissertation addresses these gaps through a set of methodologies that 1) leverage unsupervised Machine Learning algorithms to categorize daily airport operations, 2) leverage a supervised Machine Learning algorithm to determine the category that subsequent daily airport operations belong to, 3) facilitate the comparison of similar and different daily airport operations for the identification of trends and patterns, 4) enable stakeholders to analyze and assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations, and 5) develop a framework to facilitate the efficient and secure extraction, processing and storage of data needed for the analysis and assessment of daily airport operations. The developed framework automates the flow of data from extraction through storage, and enables users to track the flow of data in real time. It also facilitates data provenance by logging the history of all processes and is equipped with the capability to log errors and their causes, and to notify analysts via email whenever they occur. In addition, it has the capacity to automatically extract, process, and store the data needed for the analysis and assessment of the daily operations of all airports in the NAS. Indeed, this framework will be one of the first of its kind to be deployed into the FAA's Enterprise Information Management platform and will serve as a template for leveraging cloud-based services and technologies to improve operations in the NAS. Finally, this framework will enable FAA analysts to analyze and assess daily airport operations in an efficient manner to facilitate the identification of trends and patterns for better decision making, which will lead to improved airport operational performance.Ph.D

An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure

The railway industry plays a pivotal role in the socioeconomic landscape of many countries. However, its operation poses considerable challenges in terms of safety, environmental impact, and the intricacies of intertwined technical and social structures. Addressing these challenges necessitates the adoption of innovative approaches and advanced technologies. This doctoral research delves into the potential of the Internet of Things (IoT) as an enabler for railway infrastructure monitoring and predictive maintenance, aiming to enhance reliability, efficiency, and safety within the industry. Rooted in a pragmatic modelist philosophical stance, this thesis employs an exploratory sequential mixed-method approach incorporating qualitative and quantitative methodologies. The research process involves engaging with key stakeholders to gain insights into the challenges faced in railway maintenance and the opportunities presented by IoT implementation. Following this, an IoT system is developed, and a comprehensive value-creation framework is proposed for its effective implementation within the railway sector. The findings of this investigation underscore the transformative potential of IoT integration in railway infrastructure monitoring, yielding significant improvements in maintenance processes, safety, and operational efficiency. Furthermore, this doctoral research provides a foundation for future innovation and adaptation in the railway industry, contributing to its ongoing evolution and resilience in an ever-changing technological landscape

ARCS結合APPs融入會計學教學對學習動機及學習成效影響之研究

[[abstract]]本研究旨在探討 ARCS 結合 APPs 融入高職會計學，對進修部高職三年級學習動機與學習成效之表現。本研究採準實驗研究設計，進行每週四節課、為期六週的實驗教學活動。研究者以臺北市某高職進修部三年級二個班級（一班 為實驗組，另一班為對照組）作為研究對象，實驗組的學生人數為 27 人，對照組的學生人數為 32 人，共計 59 名學生。實驗組進行 ARCS 結合 APPs 融入會計學課程，對照組實施傳統講述法之會計學教學。研究工具為會計學學習動機量表及會計學學習成效測驗，以描述性統計分析、獨立樣本 t 檢定與成對樣本 t檢定分析進行量化分析，研究結論如下所述： 一、在會計學學習動機方面：分析結果實驗組顯著優於對照組，證實 ARCS 結合 APPs 融入教學，能提升學生的學習動機。 二、在會計學學習成效方面：分析結果實驗組顯著優於對照組，證實 ARCS 結合 APPs 融入教學，能提升學生的學習成效。 三、ARCS 結合 APPs 融入教學優於傳統講述法。 依據上述研究結論提出建議，以供教學設計、會計學教學及未來相關研究之參考。[[notice]]補正完