• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.6
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• pp.569-576
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• 2011

This paper proposes a new dead time compensation method for a PWM inverter. Recently, PWM inverters are extensively used for industry applications, such as ac motor drives, distributed grid-connected systems and a static synchronous compensator (STATCOM). Nonlinear characteristics of the switch and the inverter dead time cause a current distortion and deterioration of power quality. The dominant harmonics in the output current are the $5^{th}$ and $7^{th}$ harmonics in the stationary frame, and the $6^{th}$ harmonics in the synchronous rotating frame. In this paper, a resonant controller which compensates the $6^{th}$ harmonics in the synchronous rotating frame is proposed. This method does not require any off-line experimental measurements, additional hardware and complicated mathematical computations. Furthermore, the proposed method is easy to implement and does not cause any stability problem.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.59-73
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• 2007

Although there exist many middlewares such as Havi, Jini, LonWorks, UPnP, and SLP, new middlewares specialized for diverse information appliances are expected to appear continuously as home networks evolve. In this paper, we examine an integrated architecture for supporting interoperability among heterogeneous middlewares under home network, we also propose and implement a scenario-based user-oriented integrated architecture for efficient home automation which is different from existing methods. HOMI(Homenetwork Middleware for Interoperability) architecture proposed in this paper provides interfaces that assist users with designing and modifying desirable scenarios using a script interpreter language HOMIL(HOMI Language). Different from an existing integrated middleware architecture, HOMI improves efficiency and convenience of interoperation between heterogeneous appliances for home automation allowing users to design and organize scenarios through these interfaces. HOMI classifies interoperation services into time contort, synchronization context, and asynchronization context and helps to execute next services considering contexts when a specific event occurs. Applying modified scenarios immediately to home network environment, HOMI provides users with seamless services without installing new applications, updating the server, or rebooting in order to adopt new scenarios. Lastly, distribution agents into several devices, we solved the overhead problem occurred in a centralized architecture for integrated middleware.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.85-97
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• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• Journal of Animal Science and Technology
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• v.48 no.1
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• pp.29-38
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• 2006

The objective of this study was to estimate genetic variances of growth curve parameters in Hanwoo cows. The data used in this study were records from 1,083 Hanwoo cows raised at Hanwoo Experiment Station, National Livestock Research Institute(NLRI). First evaluation model(Model I) fit year-season of birth and age of dam as fixed effects and second model(Model II) added age at the final weight as a linear covariate to Model I. Heritability estimates of A, b and k from Gompertz model were 0.22, 0.11 and 0.07 using modelⅠ and 0.28, 0.11 and 0.12 using modelⅡ. Those from Von Bertalanffy model were 0.22, 0.11 and 0.07 using modelⅠ, 0.28, 0.11 and 0.12 using modelⅡ. Heritability estimates of A, b and k from Logistic model were 0.14, 0.07 and 0.05 using modelⅠ, 0.18, 0.07 and 0.12 using modelⅡ. Heritability estimates of A from Gompertz model were higher than those from Von Bertalanffy model or Logistic model in both model Ⅰand model Ⅱ. Heritability estimates of b from Logistic model were higher than those from Gompertz model or Von Bertalanffy model in both modelⅠand model Ⅱ. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight, 12 month weight, 18 month weight, 24 month weight, 36 month weight were after linear age adjustment 0.27, 0.11, 0.19, 0.14, 0.16, 0.23, 0.52 and 0.32, respectively. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight and 24 month weight fit by Gompertz model were larger than those estimated from linearly adjusted data. Heritability estimates of 12 month weight, 18 month weight and 36 month weight fit by Von Bertalanffy model were larger than those estimated from linearly adjusted data. In the multitrait analyses for parameters from Gompertz model, genetic and phenotypic correlations between A and k parameters were －0.47 and －0.67 using modelⅠand －0.56 and －0.63 using model Ⅱ. Those between the A and b parameters were 0.69 and 0.34 using modelⅠand 0.72 and 0.37 using model Ⅱ. Those between the b and k parameters were －0.26 and 0.01 using modelⅠand －0.30 and 0.01 using model Ⅱ. In the multitrait analyses for parameters from Von Bertalanffy model, genetic and phenotypic correlations between A and k parameters were －0.49 and －0.67 suing model Ⅰ and －0.57 and －0.70 using modelⅡ. Those between the A and b parameters were 0.61 and 0.33 using modelⅠ and 0.60 and 0.30 using model Ⅱ. Those between the b and k parameters were －0.20 and 0.02 using modelⅠ and 0.16 and 0.00 using modelⅡ. In the multitrait analyses for parameters from Logistic model, genetic and phenotypic correlations between A and k parameters were －0.43 and －0.67 using model Ⅰ and －0.50 and －0.63 using modelⅡ. Those between the A and b parameters were 0.47 and 0.22 using modelⅠ and 0.38 and 0.24 using modelⅡ. Those between the b and k parameters were －0.09 and 0.02 using model Ⅰ and －0.02 and 0.13 using model Ⅱ.