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The reduction made the time-frequency folding technique have a good parallel searching capability.
Data Resource Pool Definition 1: Data resource pool is the data space with parameter of L×M×N, as shown in Fig. 1.
The data elements of the resources pool are included in the data collection V: Figure 1.
Data Compression Data compression is an effective method to improve data processing speed and maintain the validity of data.
The data compression of VC and VP are done in the data compression module of resource pool.

Citation & Copyright (to be inserted by the publisher) Diagnosis and Reduction of Robot Arm Vibration for 12-inch Wafer Spin Scrubber Nohyu Kim 1 and Minsung Hong 2 1 School of Mechatronic Engineering, Korea University of Technology and Education Byungchun-myun, Chunan, Chungnam 330-860, Korea (nykim@kut.ac.kr) 2 Department of Mechanical Engineering, Ajou University Wonchun-Dong, Suwon, Kunggi 443-749, Korea Keywords: Spin Scrubber, Vibration reduction, Robot arm, Topology optimization Abstract.
Fig. 4 shows FFT data of a vibration signal obtained in the experiment, where the vibration modes including the bending and torsional modes are well separated and recognized.
Final optimal topology obtained from OPTISHAPE after the volume constraint of 60% reduction is represented in Fig. 7, where the top view of the optimal shape is different from the original model.
Diagnosis and experimental measurement of the vibrations for the current model and the optimal models improved by the topology optimization are also conducted to investigate the effect of the shape of the robot arm on the vibration reduction.
From the experimental results for two optimal models made of different materials, the optimal shape of the robot arm developed in this study is proved to be very effective to suppress the vibration by 30% reduction in RMS value.

Study Swaging Process for Reduction Swage Effect in Hard Disk Drive Joompondej Bamrungwongtaree1,a and Mongkol Mongkolwongrojn 2,b 1College of Data Storage Innovation, King Mongkut’s Institute of Technology Ladkrabang, Thailand 2Department of Mechanical Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand ajoompondej.bamrungwongtaree@seagate.com, bkmmongko@kmitl.ac.th Keywords: Ball Swaging, Finite Element, Head Stack Assembly, Spacer Key, Boss Tower Abstract.
Furthermore, the reduction of arm deformation and gap to arm cause increasing in HSA performance.
The new design of arm aperture is able to apply with thinner arm for reduction of the arm tip deformation and gap to arm.
Joompon: Effect of the Spacer Key to HSA, 3rd International Conference on Data Storage Technology, Bangkok, Thailand, 2010, pp.131-134 [8] B.
Joomponladej: A Development of modeling for Resonance Analysis, 4th International Conference on Data Storage Technology, Bangkok, Thailand, 2011, pp.108-111 [10] A.

Selective Catalytic Reduction(SCR) technology is one of the best after-treatment technologies that diesels control NOx emissions and satisfy increasingly strict emission regulations [2].
Off-line matching calibration analyzes the engine running state and reference index recorded in the test process, afterwards calibrates every MAP diagram and finally updates new data to controller.
Online matching calibration directly modifies the MAP diagram data in Flash or RAM[3] while the controller is running to make the engine run in real time according to the new parameters.
Otherwise, adjust control parameter of MAP data until oxynitride density does not exceed the prescribed limits by either ESC or ETC.
The adjustment of MAP diagram data control parameters adopts the method of adjusting correction coefficient.

The convergence of experimental data and calculations is shown.
Analysis of the `equation system (1) shows that for its solution apart from reference data some individual values are necessary from experiments carried out expressly.
In particular data for the mass of welding bath are presented at Fig. 1 and described by relationship (2): ▲ – UONI 13/45; ● – UONI 13/55; ■ – MR-3 Fig. 1.
Comparison of calculated and experimental data shows their concordance.
The disagreement between calculations and experimental data averages 15%, which is comparable with the experiment error and is generally accepted for such models [1].

The energy before the improvement is expressed as (3) where Epre is energy before the improvement (kWh/year) Ppre is power before the improvement dt is 15 minutes interval of data record period (min) r is date of data record (Monday – Sunday) n is the number of days of data record The level of energy consumption after the adjustment can be calculated as (4) where Epost is energy after the improvement (kWh/year) Ppost is power after the improvement dt is 15 minutes interval of data record period (min) r is date of data record (Monday – Sunday) n is the number of days of data record Research Methodology In the procedure, energy was measured before and after the improvement of voltage level at four electrical control panels which are lighting control panel, motor control panel, heating control panel, and chiller control panel respectively.
The data acquisition was uninterruptedly recorded every 15 minutes within 7 days by considering energy consumption in 24 hours [6] in an industry.
In spite of those dissimilarities, this paper shows results from the both techniques in order to be useful in selecting data as alternative sources for energy conservation.
Table 1 Voltage reduction ratio Solution 1 2 3 4 5 6 7 8 Tap Transformer (%) 0 2.5 5 7.5 10   Voltage Regulate (%) 0 1 2 3 4 5 6 7 Data Acquisition Before and after the Reduction of Voltage Level The four main parameters are that power, voltage, current, and power factor (PF) are measured and considered from the calculation in order to determine energy (kWh) each of the load types.
In this purpose, the data acquisition of all recorded data would be further analyzed according to the following information.

A standard generic car body known as Ahmed body is taken to set up simulation parameters in FLUENT by validating a test case against the experimental data available in literature.
A substantial reduction in drag force of 18.8% is achieved which can be attributed to lower frontal area and streamlined bodyworks design.
In this paper a 3D CFD study is performed on Ahmed body to validate simulation parameters based on reported experimental data [1].
The percentage of reduction in the drag coefficient is calculated to be 11.5%.
This leads to a compact and lightweight battery module with a weight reduction of 6 kgs or 10%.

The thermodynamic data of the compounds in the article are from the literature[11].
The required initial temperature of the reduction reaction can be decreased to avoid the unfavourable influence on the nitridation process.
So it is necessary to select appropriate additives for the reduction reaction.
The mechanism of the effects of additives on the carbothermal reduction nitriding to synthesize the vanadium nitride is still unclear.
Gokcen: Thermodynamic data for mineral technology, (Washington, DC: Bulletin 677, Bureau. of Mines, 1984) [12].

When reduction and reuse become difficult, recycling is desired.
Data Analysis Reliability Test The purpose of the reliability test is to prove the instrument is reliable.
Figure 1: Type of Projects According to Figure 1, the majority of respondent came from housing projects which contribute 47 percent of the data or 19 respondents.
Commercial projects came in second contributing 23 percent of the data or 9 respondents.
Analysis of Relationship between The 4R’ Techniques and Waste Produced on Sites In this analysis, the correlation method is used as a tool to analyze the data.

Then the concepts of synchronization edge, process, synchronization process, base process, process tree, data type and trace-set of types is given.
Process model and data model are two essential aspects in workflow management system.
(Data type).
The set of data types Dt is defined by the following syntax: A A is a set of atomic types, which cannot be decomposed in the type system.
Koehler, “The refined process structure tree,” DATA & KNOWLEDGE ENGINEERING, vol. 68, 2009, pp. 793-818