Brief Analysis of Key Points of Hospital Building Electrical Equipment Management

2021 ◽  
Keyword(s):  
Electrical Equipment ◽  
Equipment Management ◽  
Key Points ◽  
Hospital Building

scholarly journals K-Means Clustering-Based Electrical Equipment Identification for Smart Building Application

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 27 ◽  
Author(s):  
Guiqing Zhang ◽  
Yong Li ◽  
Xiaoping Deng
Keyword(s):  
Euclidean Distance ◽  
Electrical Equipment ◽  
Equipment Management ◽  
Electrical Characteristics ◽  
Cluster Centroid ◽  
Distance Threshold ◽  
Smart Building ◽  
Load Characteristics ◽  
Biot System ◽  
Identification Model

With the development and popular application of Building Internet of Things (BIoT) systems, numerous types of equipment are connected, and a large volume of equipment data is collected. For convenient equipment management, the equipment should be identified and labeled. Traditionally, this process is performed manually, which not only is time consuming but also causes unavoidable omissions. In this paper, we propose a k-means clustering-based electrical equipment identification toward smart building application that can automatically identify the unknown equipment connected to BIoT systems. First, load characteristics are analyzed and electrical features for equipment identification are extracted from the collected data. Second, k-means clustering is used twice to construct the identification model. Preliminary clustering adopts traditional k-means algorithm to the total harmonic current distortion data and separates equipment data into two to three clusters on the basis of their electrical characteristics. Later clustering uses an improved k-means algorithm, which weighs Euclidean distance and uses the elbow method to determine the number of clusters and analyze the results of preliminary clustering. Then, the equipment identification model is constructed by selecting the cluster centroid vector and distance threshold. Finally, identification results are obtained online on the basis of the model outputs by using the newly collected data. Successful applications to BIoT system verify the validity of the proposed identification method.

Mechanical & Electrical Equipment Management Mode Control Computer-Based Network (Wireless)

2013 ◽  
Vol 321-324 ◽  
pp. 2191-2194
Author(s):  
Jin Rong Wang
Keyword(s):  
Objective Function ◽  
Contact Area ◽  
Electrical Equipment ◽  
Feasible Region ◽  
Equipment Management ◽  
Forming Simulation ◽  
Automatic Mode ◽  
Control Computer ◽  
Computer Based ◽  
The Right

The right management managing direction is an important prerequisite for generating qualified parts, an important step before the management forming simulation is to determine the reasonable direction of the managing. Manually adjust parts in order to overcome rely on experience, the drawbacks to the managing direction, the direction of the managing tool and forming the contact area of the sheet as the goal of automatic determination mode. Objective function of the direction of the managing for the variable contact area in the managing direction of the feasible region, the use of heritage modes to optimize the objective function of the contact area and, ultimately feasible within the contact area corresponding to the managing direction, that is the best managing direction. The measured results show that the direction of the managing based on genetic mode, the automatic mode can fast and accurate to obtain the optimal direction of managing.

Intelligent Control System Design for Electrical Equipment Management

2016 ◽  
Vol 851 ◽  
pp. 521-525
Author(s):  
Yu Qiao Meng
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Signal Transmission ◽  
Electrical Equipment ◽  
Control System Design ◽  
Equipment Management ◽  
Intelligent Control System ◽  
Network Controller ◽  
Data Signal ◽  
Terminal Equipment

The article design a kind of intelligent control system with the function that can synthesize data, signal transmission and power control at the same time that it includes six components such as terminal equipment, network, controller, circuit, intelligent socket, electrical equipment. This article introduces the three detailed system implementation case with electrical equipment management mode of supply, working mode and working state of intelligent control is realized through the design of the system and improve the applicability of the system.

Autism and Neurodiversity: Addressing Concerns and Offering Implications for the School-Based Speech-Language Pathologist

2020 ◽  
pp. 1-7
Author(s):  
Laura S. DeThorne ◽  
Kelly Searsmith
Keyword(s):  
Service Provision ◽  
Individualized Education Program ◽  
Institutional Constraints ◽  
Individualized Education ◽  
Existing Structures ◽  
Eligibility Determination ◽  
Speech Language Pathologist ◽  
School Based ◽  
Key Points ◽  
C Center

Purpose The purpose of this article is to address some common concerns associated with the neurodiversity paradigm and to offer related implications for service provision to school-age autistic students. In particular, we highlight the need to (a) view first-person autistic perspectives as an integral component of evidence-based practice, (b) use the individualized education plan as a means to actively address environmental contributions to communicative competence, and (c) center intervention around respect for autistic sociality and self-expression. We support these points with cross-disciplinary scholarship and writings from autistic individuals. Conclusions We recognize that school-based speech-language pathologists are bound by institutional constraints, such as eligibility determination and Individualized Education Program processes that are not inherently consistent with the neurodiversity paradigm. Consequently, we offer examples for implementing the neurodiversity paradigm while working within these existing structures. In sum, this article addresses key points of tension related to the neurodiversity paradigm in a way that we hope will directly translate into improved service provision for autistic students. Supplemental Material https://doi.org/10.23641/asha.13345727

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