Search results for: virtual fabric

Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

Abstract:

Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

Procedia PDF Downloads 249

Authors: Pablo Reche

Abstract:

Since 1980’s aquaculture has become the biggest economic activity in southern Chile, being Salmo salar and Oncorhynchus mykiss the main finfish species. High fish density makes both species prone to contract diseases, what drives the industry to big losses, affecting greatly the local economy. Three are the most concerning infective agents, the infectious salmon anemia virus (ISAv), the bacteria Piscirickettsia salmonis and the copepod Caligus rogercresseyi. To regulate the industry the government arranged the salmon farms within management areas named as barrios, which coordinate the fallowing periods and antibiotics treatments of their salmon farms. In turn, barrios are gathered into larger management areas, named as macrozonas whose purpose is to minimize the risk of disease transmission between them and to enclose the outbreaks within their boundaries. However, disease outbreaks still happen and transmission to neighbor sites enlarges the initial event. Salmon disease agents are mostly transported passively by local currents. Thus, to understand how transmission occurs it must be firstly studied the physical environment. In Chile, salmon farming takes place in the inner seas of the southernmost regions of western Patagonia, between 41.5ºS-55ºS. This coastal marine system is characterised by western winds, latitudinally modulated by the position of the South-Eats Pacific high-pressure centre, high precipitation rates and freshwater inflows from the numerous glaciers (including the largest ice cap out of Antarctic and Greenland). All of these forcings meet in a complex bathymetry and coastline system - deep fjords, shallow sills, narrow straits, channels, archipelagos, inlets, and isolated inner seas- driving an estuarine circulation (fast outflows westwards on surface and slow deeper inflows eastwards). Such a complex system is modelled on the numerical model MIKE3, upon whose 3D current fields particle-track-biological models (one for each infective agent) are decoupled. Each agent biology is parameterized by functions for maturation and mortality (reproduction not included). Such parameterizations are depending upon environmental factors, like temperature and salinity, so their lifespan will depend upon the environmental conditions those virtual agents encounter on their way while passively transported. CLIC (Connectivity-Langrangian–IFOP-Chile) is a service platform that supports the graphical visualization of the connectivity matrices calculated from the particle trajectories files resultant of the particle-track-biological models. On CLIC users can select, from a high-resolution grid (~1km), the areas the connectivity will be calculated between them. These areas can be barrios and macrozonas. Users also can select what nodes of these areas are allowed to release and scatter particles from, depth and frequency of the initial particle release, climatic scenario (winter/summer) and type of particle (ISAv, Piscirickettsia salmonis, Caligus rogercresseyi plus an option for lifeless particles). Results include probabilities downstream (where the particles go) and upstream (where the particles come from), particle age and vertical distribution, all of them aiming to understand how currently connectivity works to eventually propose a minimum risk zonation for aquaculture purpose. Preliminary results in Chiloe inner sea shows that the risk depends not only upon dynamic conditions but upon barrios location with respect to their neighbors.

Keywords: aquaculture zonation, Caligus rogercresseyi, Chilean Patagonia, coastal oceanography, connectivity, infectious salmon anemia virus, Piscirickettsia salmonis

Procedia PDF Downloads 152