Ukuran Gejala Data Belum Dikelompokan (Studi Kasus : Jumlah Warga Yang Meninggal Akibat Terjangkit Virus Covid-19 di Kota Depok Per-oktober 2020)

2020 ◽  
Author(s):  
Adellina Sylvira Azis ◽  
M.Alfarisi Farabbi ◽  
Dian Kristianto Tatarang ◽  
Aziiz firmansyach
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Geometric Mean ◽  
Measurement Data ◽  
Microsoft Excel ◽  
Sample Data ◽  
Harmonic Average ◽  
Symptom Measurement ◽  
The Right ◽  
Calculated Average

The statistic is a method developed for analyzing, analyzing, and compiling sample data to get the right data. Also, observation is needed to get accurate and concrete data. Various kinds of methods can be used to obtain the data, one of which is the Symptom Symptoms Data Center is the symptom data which is divided into two, namely the symptom center symptom data grouped and the data center symptom grouped. This journal will explain in detail the size of Symptoms in unclassified data centers Symptom Measurement of Unclassified Data Centers or also Symptom Size Single grouped data centers are data that are not arranged in a frequency distribution, so there are no category intervals and category midpoints. Symptom measurement data centers have not been grouped namely the calculated average (mean), measuring / geometric mean, harmonic average, tertiary average, median, mode, and fractile (quartile, decile, percentile). Measurement can use Microsoft Excel and SPSS applications

Ukuran Gejala Pusat Data Belum Dikelompokan(Kebakaran di Provinsi-Provinsi Tertentu di Indonesia Tahun 2015-2016)

2020 ◽  
Author(s):  
Riska Nurhapsari Santoso ◽  
Yudis Satrio Utomo ◽  
Yuliani Luturmasse
Keyword(s):  
Statistical Analysis ◽  
Frequency Distribution ◽  
Data Center ◽  
Grouped Data ◽  
Sample Data ◽  
Calculated Average

Abstract - Statistics is a framework of theories and methods that have been developed to collect, analyze, and write sample data in order to obtain useful conclusions. Statistics is the science of ways of collecting, classifying, analyzing, and searching for information related to the collection of data that investigations and conclusions based on evidence in the form of figures.Based on the results of the study can be concluded as follows: the size of the symptoms of the data center has not been grouped is the data compiled into the frequency distribution so that it does not have class intervals and midpoints of the class. Symptom Size Un-Grouped Data Center The size of the data center included in the statistical analysis is the calculated average (mean), median, mode, and fractil (quartile, decile, percentile)

Exergy Analysis of Data Center Thermal Management Systems

2003 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Exergy Analysis ◽  
Thermal Control ◽  
Management Systems ◽  
World Systems ◽  
Sample Data ◽  
Potential Applications ◽  
Temperature Ranges

Data centers today contain more computing and networking equipment than ever before. As a result, a higher amount of cooling is required to maintain facilities within operable temperature ranges. Increasing amounts of resources are spent to achieve thermal control, and tremendous potential benefit lies in the optimization of the cooling process. This paper describes a study performed on data center thermal management systems using the thermodynamic concept of exergy. Specifically, an exergy analysis has been performed on sample data centers in an attempt to identify local and overall inefficiencies within thermal management systems. The development of a model using finite volume analysis has been described, and potential applications to real-world systems have been illustrated. Preliminary results suggest that such an exergy-based analysis can be a useful tool in the design and enhancement of thermal management systems.

A Model Transformation Approache of Complex Systems - Case Study in Design Evolutions of Data Centers

2014 ◽  
Vol 513-517 ◽  
pp. 1208-1214
Author(s):  
Montri Wiboonrat
Keyword(s):  
Data Center ◽  
Model Transformation ◽  
Capital Investment ◽  
Data Centers ◽  
Design Data ◽  
It Managers ◽  
Legacy Data ◽  
The Right ◽  
Depth Interviews

Unpredictable transaction requirements of IT business lead to miss design the right size of data center. Over design data center contributed to surplus capital investment and lifetime operations. Legacy data centers designed before the 2nd millenniums over design capacity more than 60% of actual load. The research objectives are created a model transformation approach from legacy data center to mobile and modular data center M2DC and proposed multivariate optimization for the right sizing of data center as business needs by using case study. The research method is investigation and assessment through 21 sample data centers and in-depth interviews with IT managers (32) and data center consultants (8). The fact findings have shown the standardized modular of M2DC force requirements to fit in the building boxes and expansion as needs.

scholarly journals Cold LOGIK and RDHX Solution for Data Center Energy Optimization

2018 ◽  
Vol 7 (3.4) ◽  
pp. 113
Author(s):  
T Suresh ◽  
Dr A. Murugan
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Heat Dissipation ◽  
Cooling System ◽  
Cooling Power ◽  
Operational Cost ◽  
Power Cost ◽  
The Right

In all types of data center, keeping the right temperature with less cost and energy is one of important objective as energy saving is crucial in increased data driven industry. Energy saving is global focus for all industry. In Information technology, more than 60% of energy is utilized in data centers as it needs to be up and running. As per Avocent data center issue study, across globe more than 54% of data centers are in redesigning process to improve their efficiency and reduce operational cost and energy consumption. Data center managers and operators major challenge was how to maintain the temperature of servers with less power and energy. When the densities of data center energy nearing 5 kilowatts (kW) per cabinet, organizations are trying to find a way to manage the heat through latest technologies. Power usage per square can be reduced by incorporating liquid-cooling devices instead of increasing airflow volume. This is especially important in a data center with a typical under-floor cooling system. This research paper uses Rear-Door Heat eXchangers (RDHx) and cool logic solutions to reduce energy consumption. It gives result of implementation of Cold Logik and RDHx solution to Data center and proves that how it saves energy and power. Data center has optimized space, cooling, power and operational cost by implementing RDHx technology. This will enable to add more servers without increasing the space and reduce cooling and power cost. It also saves Data center space from heat dissipation from servers.  

Measured-Temperature Interpolation and Visualization for Data Centers

2013 ◽  
Author(s):  
Xuanhang (Simon) Zhang ◽  
Christopher M. Healey ◽  
Zachary R. Sheffer ◽  
James W. VanGilder
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Three Dimensional ◽  
Measurement Data ◽  
Temperature Sensors ◽  
Sensor Data ◽  
Temperature Fields ◽  
Measured Temperature ◽  
Local Cooling ◽  
Maintenance Requirements

The growing demand for data center facilities has made intelligently managed data center operations necessary. For temperature measurement and thermal management, a common practice is to install a limited number of temperature sensors evenly distributed throughout the room. However, data center operators rarely fully equip facilities with temperature sensors due to their cost, complexity, and maintenance requirements, creating vacancies in the data center temperature and cooling picture. The local nature of sensor data can also be misinterpreted and misused. Without novel methods to interpret and visualize temperatures obtained by prediction or measurement, data center operators cannot easily identify urgent local cooling issues or quickly examine the temperature at other location. This paper presents methods to predict a full three-dimensional temperature field in data centers from a limited number of measurement points. Several different statistical interpolating schemes are discussed. We also validate the interpolated temperature fields against benchmark data from Computation Fluid Dynamics (CFD) and show good agreement.

DMSA-scintigraphy in paediatrics: Is the evaluation of the geometric mean necessary for the calculation of the differential renal function?

2001 ◽  
Vol 40 (04) ◽  
pp. 107-110 ◽  
Author(s):  
B. Roßmüller ◽  
S. Alalp ◽  
S. Fischer ◽  
S. Dresel ◽  
K. Hahn ◽  
...  
Keyword(s):  
Renal Function ◽  
Geometric Mean ◽  
Region Of Interest ◽  
Posterior View ◽  
Renal Abnormality ◽  
Anterior View ◽  
Differential Renal Function ◽  
The Mean ◽  
The Right ◽  
To Receive

SummaryFor assessment of differential renal function (PF) by means of static renal scintigraphy with Tc-99m-dimer-captosuccinic acid (DMSA) the calculation of the geometric mean of counts from the anterior and posterior view is recommended. Aim of this retrospective study was to find out, if the anterior view is necessary to receive an accurate differential renal function by calculating the geometric mean compared to calculating PF using the counts of the posterior view only. Methods: 164 DMSA-scans of 151 children (86 f, 65 m) aged 16 d to 16 a (4.7 ± 3.9 a) were reviewed. The scans were performed using a dual head gamma camera (Picker Prism 2000 XP, low energy ultra high resolution collimator, matrix 256 x 256,300 kcts/view, Zoom: 1.6-2.0). Background corrected values from both kidneys anterior and posterior were obtained. Using region of interest technique PF was calculated using the counts of the dorsal view and compared with the calculated geometric mean [SQR(Ctsdors x Ctsventr]. Results: The differential function of the right kidney was significantly less when compared to the calculation of the geometric mean (p<0.01). The mean difference between the PFgeom and the PFdors was 1.5 ± 1.4%. A difference > 5% (5.0-9.5%) was obtained in only 6/164 scans (3.7%). Three of 6 patients presented with an underestimated PFdors due to dystopic kidneys on the left side in 2 patients and on the right side in one patient. The other 3 patients with a difference >5% did not show any renal abnormality. Conclusion: The calculation of the PF from the posterior view only will give an underestimated value of the right kidney compared to the calculation of the geometric mean. This effect is not relevant for the calculation of the differntial renal function in orthotopic kidneys, so that in these cases the anterior view is not necesssary. However, geometric mean calculation to obtain reliable values for differential renal function should be applied in cases with an obvious anatomical abnormality.

Data Center Equipment Reliability Concerns—Contamination Issues, Standards Actions, and Case Studies

2013 ◽  
Author(s):  
Chris Muller ◽  
Chuck Arent ◽  
Henry Yu
Keyword(s):  
Case Studies ◽  
Data Center ◽  
Data Centers ◽  
Electronic Equipment ◽  
Circuit Board ◽  
Lead Free ◽  
Reliable Operation ◽  
Contamination Assessment ◽  
Equipment Reliability ◽  
Equipment Failures

Abstract Lead-free manufacturing regulations, reduction in circuit board feature sizes and the miniaturization of components to improve hardware performance have combined to make data center IT equipment more prone to attack by corrosive contaminants. Manufacturers are under pressure to control contamination in the data center environment and maintaining acceptable limits is now critical to the continued reliable operation of datacom and IT equipment. This paper will discuss ongoing reliability issues with electronic equipment in data centers and will present updates on ongoing contamination concerns, standards activities, and case studies from several different locations illustrating the successful application of contamination assessment, control, and monitoring programs to eliminate electronic equipment failures.

scholarly journals Is This a Geolibrary? A Case of the Idaho Geospatial Data Center

2017 ◽  
Vol 19 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Maria Anna Jankowska ◽  
Piotr Jankowski
Keyword(s):  
Digital Library ◽  
Future Development ◽  
Data Center ◽  
Data Centers ◽  
Public Domain ◽  
The State ◽  
Geospatial Data ◽  
Design And Implementation ◽  
Geographic Data

The article presents the Idaho Geospatial Data Center (IGDC), a digital library of public-domain geographic data for the state of Idaho. The design and implementation of IGDC are introduced as part of the larger context of a geolibrary model. The article presents methodology and tools used to build IGDC with the focus on a geolibrary map browser. The use of IGDC is evaluated from the perspective of accessa and demand for geographic data. Finally, the article offers recommendations for future development of geospatial data centers.

Perimeter Cooling Unit and Localized Row-Based Cooling Unit Transient Air Flow Effects Modeling and Characterization in Data Centers

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Bahgat G. Sammakia ◽  
Russell Tipton ◽  
Mark Seymour
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Cooling Systems ◽  
Dynamic Thermal Management ◽  
Hybrid Cooling ◽  
Cooling Unit ◽  
Cooling Air

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

Numerical Study to Analyze the Effects of Hot/Cold Aisle Configurations on Heat Transfer Phenomenon in a Data Center

2013 ◽  
Author(s):  
Kamran Nazir ◽  
Naveed Durrani ◽  
Imran Akhtar ◽  
M. Saif Ullah Khalid
Keyword(s):  
Heat Transfer ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Numerical Study ◽  
High Energy ◽  
Research Area ◽  
Transfer Phenomenon ◽  
Heat Transfer Phenomenon ◽  
Active Research

Due to high energy demands of data centers and the energy crisis throughout the world, efficient heat transfer in a data center is an active research area. Until now major emphasis lies upon study of air flow rate and temperature profiles for different rack configurations and tile layouts. In current work, we consider different hot aisle (HA) and cold aisle (CA) configurations to study heat transfer phenomenon inside a data center. In raised floor data centers when rows of racks are parallel to each other, in a conventional cooling system, there are equal number of hot and cold aisles for odd number of rows of racks. For even number of rows of racks, whatever configuration of hot/cold aisles is adopted, number of cold aisles is either one greater or one less than number of hot aisles i.e. two cases are possible case A: n(CA) = n(HA) + 1 and case B: n(CA) = n(HA) − 1 where n(CA), n(HA) denotes number of cold and hot aisles respectively. We perform numerical simulations for two (case1) and four (case 2) racks data center. The assumption of constant pressure below plenum reduces the problem domain to above plenum area only. In order to see which configuration provides higher heat transfer across servers, we measure heat transfer across servers on the basis of temperature differences across racks, and in order to validate them, we find mass flow rates on rack outlet. On the basis of results obtained, we conclude that for even numbered rows of rack data center, using more cold aisles than hot aisles provide higher heat transfer across servers. These results provide guidance on the design and layout of a data center.

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