• Journal of Nutrition and Health
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• v.31 no.3
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• pp.354-362
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• 1998

The purposes of this study were to develop standardized indices of staffing needs in each school, foodservice system through work sampling methodology . Conventional school foodservices were classified into 5 groups depending on size of meals served. Commissary school foodservices were also classified into 5 groups by cluster analysis using number of meals served, number of satellite schools, and time for transportation of food. Work measurement through work sampling methodology was conducted in 15 conventional and 21 commissary foodservices during 3 consecutive days from September to October in 1995. Statistical data analysis was completed using the SAS programs for descriptive analysis, cluster analysis, and simple linear regression. The results were as follows : Average points of leveling factors of conventional and commissary foodservices were 1.066 and 1.061 , respectively. Mean labor hours per work force was 328 minutes and 366 minutes in conventional and commissary foodservice , respectively. Standardized work time was calculated using leveling factor, ILO allowance rate (175) , and observational work time. The model for standardized indices of staffing needs was developed based on simple linear regression in each school foodservice system. In conventional school foodservice systems(for 100-1,900 meals per day) standardized staffing needs=3.2497 +0.005267$\times$number of meals served (F=273.1, R-square 0.9750, p<0.001). In commissary school foodservice systems (for 200-1,600 meals per day ) Standardized staffing needs=3.393384 +0.0063$\times$number of meals served (F=30.78, R-square 0.6580, p<0.001).

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.536-542
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• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.171-182
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• 2015

The general wavelet transform has profitable property in non-stationary signal analysis specially. The tunable Q-factor wavelet transform is a fully-discrete wavelet transform for which the Q-factor Q and the asymptotic redundancy r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The transform is based on a real valued scaling factor and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its over-sampling rate, with modest over-sampling rates being sufficient for the analysis/synthesis functions to be well localized. This paper describes filter design of 2D discrete-time wavelet transform for which the Q-factor is easily specified. With the advantage of this transform, perfect reconstruction filter design and implementation for performance improvement are focused in this paper. Hence, the 2D transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. Therefore, application for performance improvement in multimedia communication field was evaluated.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.352-358
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• 2012

A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawback is its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoring system in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since it is important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Down sampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increase compression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electrocardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photoplethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared to other previous reports.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.451-459
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• 2017

There are many researches showing that when a process change has occurred, variable sampling intervals (VSI) control chart is better than the fixed sampling interval (FSI) control chart in terms of reducing the required time to signal. When the process engineers use VSI control procedure, frequent switching between different sampling intervals can be a complicating factor. However, average number of samples to signal (ANSS), which is the amount of required samples to signal, and average time to signal (ATS) do not provide any control statistics about switching performances of VSI charts. In this study, we evaluate numerical switching performances of multivariate VSI EWMA chart including average number of switches to signal (ANSW) and average switching rate (ASWR). In addition, numerical study has been carried out to examine how to improve the performance of considered chart with accumulate-combine approach under several different smoothing constant and sample size. In conclusion, process engineers, who want to manage the correlation coefficients of related quality variables, are recommended to make sample size as large and smoothing constant as small as possible under permission of process conditions.

• Journal of IKEEE
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• v.15 no.4
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• pp.281-286
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• 2011

DVB-T2 is a next generation DTV transmission standard, which supports various channel bandwidths. Since the symbol rate of the DVB-T2 signal is changed according to its bandwidth, the received signal needs to be sampled at a different frequency. The sampling frequency should be also adjusted depending on the sampling frequency offset due to the frequency error in the local oscillator. The sampling frequency can be locked into the symbol frequency by resampling the sampled data by ADC running at a fixed frequency. In this paper a resampler architecture for DVB-T2 is proposed. And the simulation results reveal the spectral characteristics of each subcarriers used in DVB-T2 system.

• Korean Journal of Optics and Photonics
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• v.11 no.5
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• pp.347-352
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• 2000

We have stabIhzed the laser frequency of a commercial dIode laser using a ULE (Ultra-low expansion material) reference cavIty and the cunent modulation charactenstics of the dIode itself The liuewIdth of the frce running laser was about a 1 MHz (rms) for a sampling time of 1 s and the drift rate was 300 kHz/s. Vvhen the laser is locked to oue sIde of the transmission signal from the ULE reference cavity, the lincwidth was reduced to 46 kHz (nus) for a sampling time longer than 10 ms. The root Allan variance was less than 2 kHz for a sampling time langeI than10 ms. 10 ms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.160-161
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• 2010

In this paper, trademark image retrieval system is proposed by using color information and shape information. We use the image for a color information by dividing into the area and extracting the area color distribution histogram. We use for the shape information by preprocessing of the boundary extraction, the centroid extraction, angular sampling etc. and calculating of the sum of the distance from the centroid to the boundary, the standard deviation, and the rate of long axis to short axis. In particular, centroid by using the angular sampling can extract feature and reduce the processing time. Users can perform searchs using the color and shape information, and also the two methods by mixing can be used by weighting.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.