Developing a hybrid data-fitting procedure and a case study for patient service time

2021 ◽  
pp. 1-11
Author(s):  
Ping-Shun Chen ◽  
Hsiu-Wen Chen ◽  
Rex Aurelius C. Robielos ◽  
Wen-Yu Chen ◽  
John Howell B. De Pedro ◽  
...  
Keyword(s):  
Service Time ◽  
Data Fitting ◽  
Fitting Procedure ◽  
Hybrid Data ◽  
Patient Service

scholarly journals Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

2020 ◽  
Vol 53 (2) ◽  
pp. 11692-11697
Author(s):  
M. Hotvedt ◽  
B. Grimstad ◽  
L. Imsland
Keyword(s):  
Data Driven ◽  
Flow Meter ◽  
Hybrid Data

scholarly journals Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

2020 ◽  
Vol 5 (4) ◽  
pp. 29
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Gas Turbine ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Flame Tube ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Data Driven Modeling ◽  
Engine Power

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

Analysis of Tugboat Activities using AIS Data for the Tianjin Port

2020 ◽  
Vol 2674 (5) ◽  
pp. 498-509
Author(s):  
Shukai Chen ◽  
Feng Wang ◽  
Xiaoyang Wei ◽  
Zhijia Tan ◽  
Hua Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Analysis ◽  
Service Time ◽  
Automatic Identification ◽  
Identification System ◽  
Crucial Importance ◽  
Two Stage ◽  
Temporal And Spatial ◽  
Shed Light

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

scholarly journals Simulation Based Approach for Improving Outpatient Clinic Operations

2020 ◽  
Vol 39 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Muhammad Ahmed Kalwer ◽  
Sonia Irshad Mari ◽  
Muhammad Saad Memon ◽  
Anweruddin Tanwari ◽  
Ali Arsalan Siddiqui
Keyword(s):  
Data Collection ◽  
Service Time ◽  
Discrete Event ◽  
Service Rate ◽  
Optimum Number ◽  
Service Channel ◽  
Event Simulation ◽  
Number Of Patients ◽  
Stop Watch ◽  
Patient Service

The aim of this study is to suggest the optimum number and schedule of doctors at the OPD (Out-Patient Department) of Gastrology of a hospital in Pakistan. In order to achieve this aim, the discrete event simulation model is developed to minimize waiting time of patients. Data is collected for one week from the OPD; Data collection variables are arrival and service rate of patients, their salaries/income, patient‘s OPD fee, doctor’s charges/patient, service time of patients at each of service channel i.e. reception, triage and doctors’ cabin. Stop watch is used for recording the service time of patients. Input analyzer is used to reveal the distribution of the data. Rockwell arena software version 14.5 is used to model and simulate the queuing system of the outpatient department. Scenario analysis is conducted in four scenarios; in each of the scenario doctors were assumed to be seated for one additional hour. During the period of data collection, it is observed that most of the patients are coming with an appointment of doctors therefore, it is not justified to suggest the hiring of new doctor; especially when patients are coming for the particular doctor; therefore, already available doctors are suggested to be seated longer in the OPD; that is the way to serve the maximum number of patients in the virtual queue of patients that has been kept waiting for having an appointment and for their turn to see the doctor.

scholarly journals Comparison of data-fitting models for schistosomiasis: a case study in Xingzi, China

2013 ◽  
Vol 8 (1) ◽  
pp. 125 ◽  
Author(s):  
Yi Hu ◽  
Cheng-Long Xiong ◽  
Zhi-Jie Zhang ◽  
Robert Bergquist ◽  
Zeng-Liang Wang ◽  
...  
Keyword(s):  
Data Fitting

Service Level Agreements for Smart Healthcare in Cloud

2020 ◽  
pp. 1-14
Author(s):  
Mridul Paul ◽  
Ajanta Das
Keyword(s):  
Cloud Service ◽  
Service Level ◽  
Service Level Agreements ◽  
Functional Requirements ◽  
Service Architecture ◽  
Smart Healthcare ◽  
Study Implementation ◽  
Guaranteed Service ◽  
Patient Service

With the advancement of Cloud computing, the adoption of cloud service in various industries is fast increasing. This is evident in the healthcare domain where the adoption is on the rise recently. However, the research contribution in this domain has been limited to certain functions. While cloud can increase availability, reachability of services, it is critical to design the healthcare service before provisioning. Besides, it is important to formulate Service Level Agreements (SLAs) to ensure that consumers can get guaranteed service from the service provider. The objective of this paper is to design the cloud based smart services for patient diagnostics. This research specifically defines service architecture for patients, physicians and diagnostic centers. In order to measure the proposed services, metrics of each SLA parameter is described with its functional and non-functional requirements. This paper also explains a case study implementation of a basic patient service using Google App Engine.

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