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- 저자 : CHI-JUI WU (검색결과 4 건)
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Determination of the quality of stripe-marked and cracked eggs during storage
Yu Chi Liu,Ter Hsin Chen,Ying Chen Wu,Fa Jui Tan 아세아·태평양축산학회 2017 Animal Bioscience Vol.30 No.7
Objective: Stripe marks, which occasionally occur on the shell, do not cause breakage to the shell and shell membranes of eggs. This study investigated the quality of intact eggs (IEs), minor stripe-marked eggs (MEs), severe stripe-marked eggs (SEs), and cracked eggs (CEs) during 3-week storage at 25°C. Methods: Shell eggs were collected the day after being laid and were washed. Among them, eggs without any visual cracks or stripe marks on the shells were evaluated as IEs by the plant employees using candling in a darkened egg storage room; the remaining eggs exhibited some eggshell defects. At day 3, the eggs were further categorized into IEs, MEs, SEs, CEs, and broken eggs (BEs) on the basis of the description given. Except BEs, which were discarded, the remaining eggs were stored at 25°C (approximate relative humidity 50%) and then analyzed. Results: Stripe marks were observed primarily within the first 3 days after washing. At day 3, CEs had significantly (p<0.05) lower Haugh unit values, but all eggs had grades AA or A, according to the United States Department of Agriculture standard. As storage time increased, differences in egg quality between groups were more obvious. IEs had the highest eggshell breaking strength. During storage, the total plate counts and pathogens, namely Escherichia coli, Campylobacter spp., Staphylococcus aureus, and Salmonella spp., were not detectable in the internal content of IEs and SEs. Conclusion: In conclusion, cracks degraded egg quality severely and minor stripe marks only slightly influenced the egg quality.
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Combining ICA and SVR in Times Series Predication
Wensheng Dai,Jui-Yu Wu,Chi-Jie Lu 인하대학교 정석물류통상연구원 2009 인하대학교 정석물류통상연구원 학술대회 Vol.2009 No.10
In this paper, a time series prediction approach by combing independent component analysis (ICA) and support vector regression (SVR)is proposed ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signal without knowing any prior knowledge of the mixing mechanism. SVR is and artificial intelligence forecasting technique and has been widely applied in time series prediction problems. The proposed approach First uers ICA to the forecasting variables for generating the independent components(ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise. The SVR then uses the denoised forecasting variables to build the forecausting model. in order to evaluaate the performance of the proposed approach the TAIEX(Taiwan Stock Exchange Capitalization weighted Steock index) closing cash index is usde as the illusrtative example. Experimental results show that the proposed model outperforms the SVR model with mon ?filtered forecasting variables and random walk model
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Extensive Power Factor Calculation Method Using FPGA Considering Power Quality Disturbance
SHU-CHEN WANG,CHI-JUI WU 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
The power quality is more and more serious, because a lot of information technology equipments and power electric converters were used in recent years. Harmonic and load unbalance can affect the measurement result of power factor of utility. This paper used six formulas for power factor measurement, and they will be different according to the characteristics of load. The technology of semiconductor was more and more maturely. Design of SOC (System On a Chip) is a trend to achieve the strong and small volume in the future. The Fast Fourier Transform (FFT) is used to analyze the six formula of power factor. The simulation system was modeled in Hardware Description Language (VHDL) and some novel IP (intellectual property) cores, such as CORDIC core and FFT core by the way of Bottom-Up. The measurement data were employed to calculate active, reactive and apparent power. The average error rate of our design is about 0.05%.
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Analysis of Power System QV Curve by Fuzzy Modeling
SHU-CHEN WANG,CHI-JUI WU,PEI-HWA HUANG 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Voltage instability of power system comes from increasing load rapidly, and causes bus voltage to drop. When voltage is out of control and it can be voltage collapse. This paper presents the fuzzy modeling approach to the description QV curve of power system for analysis of voltage stability. The QV curve can identify voltage stability limit, and it determine robustness of power system. The fuzzy system model is basically a collection of fuzzy IF-THEN rules that are combined via fuzzy reasoning for describing the features of a system under study. The method of fuzzy modeling has been proven to be well-suited for modeling nonlinear industrial processes described by input-output data. In view of the nonlinear characteristic of the QV curve, the method of fuzzy modeling is employed for representing the curve. Based on the Sugeno-type fuzzy model, various models with different numbers of modeling rules are used to describing the QV curve. It is found that such fuzzy model offers both quantitative and qualitative descriptions for the QV curve.
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