• Journal of Sensor Science and Technology
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• v.22 no.5
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• pp.364-368
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• 2013

Vibrational energy harvester based on piezoelectricity has been expected to be the dominant energy harvesting technology due to the advantages of high conversion efficiency, light weight and small size, night operation, etc. Its commercialization is just around the corner but the integration with power management electronics should be solved in advance. In this paper, therefore, fully-integrated design environment for piezoelectric energy harvesting systems is presented to assist co-design with the power management electronics. The proposed design environment is capable of analyzing the energy harvester including the package-induced damping effects and simulating the device and its power management electronics simultaneously. When the developed design environment was applied to the fabricated device, the simulated resonant frequency matched well with the experimental result with a difference of 2.97% only. Also, the complex transient response was completed in short simulation time of 3,001 seconds including the displacement distribution over the device geometry. Furthermore, a full-bridge power management circuit was modeled and simulated with the energy harvester simultaneously. Therefore the proposed, fully-integrated design environment is accurate and fast enough for the contribution on successful commercialization of piezoelectric energy harvester.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.1-6
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• 2014

With increasing number of citizen using subway stations everyday, safety, health and comfort of passengers and occupants became an important social issue. Considering the fact that various physical variables and pollutants are related to indoor air quality (IAQ) which may cause health problem, IAQ need to be closely monitored and controlled in multiple locations in subway stations. This study is a continuation of the previous studies and delay induced in wireless-wired network is experimentally evaluated and the risk involved is assessed. In doing that, a key parameter is identified to be the network delay in different network media. Application of information-theoretic measure to assess the risk in network delay is then discussed. The idea is based on the general principles of engineering design and their applications to quantification of uncertainty in network delay. Experimental results show that more risk is involved in wireless data communication. Efficient and fast conversion of transmission data in both LonWorks/IP server and ZL converter is also noted.

• Journal of Power Electronics
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• v.10 no.4
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• pp.342-350
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• 2010

Randomizing the switching frequency (RSF) to reduce the electromagnetic interference (EMI) of switching power converters is a well-known technique that has been previously discussed. The randomized pulse position (RPP) technique, in which the switching frequency is kept fixed while the pulse position (the delay from the starting of the switching cycle to the turn-on instant within the cycle) is randomized, has been previously addressed in the literature for the same purpose. This paper presents a double-hybrid technique (DHB) for EMI reduction in dc-dc switching regulators. The proposed technique employed both the RSF and the RPP techniques. To effectively spread the conducted-noise frequency spectrum and at the same time attain a satisfactory output voltage quality, two parameters (switching frequency and pulse position) were randomized, and a third parameter (the duty ratio) was controlled by a digital compensator. Implementation was achieved using field programmable gate array (FPGA) technology, which is increasingly being adopted in industrial electronic applications. To evaluate the contribution of the proposed DHB technique, investigations were carried out for each basic PWM, RPP, RSF, and DHB technique. Then a comparison was made of the performances achieved. The experimentally investigated features include the effect of each technique on the common-mode, differential-mode, and total conducted-noise characteristics, and their influence on the converter’s output ripple voltage.

• Nuclear Engineering and Technology
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• v.39 no.1
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• pp.51-62
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• 2007

To efficiently design safety-critical systems such as nuclear power plants, with the requirement of high reliability, methodologies allowing for rigorous interactions between the synthesis and analysis processes have been proposed. This paper attempts to develop a reliability-centered design framework through an interactive process between Axiomatic Design (AD) and Fault Tree Analysis (FTA). Integrating AD and FTA into a single framework appears to be a viable solution, as they compliment each other with their unique advantages. AD provides a systematic synthesis tool while FTA is commonly used as a safety analysis tool. These methodologies build a design process that is less subjective, and they enable designers to develop insights that lead to solutions with improved reliability. Due to the nature of the two methodologies, the information involved in each process is complementary: a success tree versus a fault tree. Thus, at each step a system using AD is synthesized, and its reliability is then quantified using the FT derived from the AD synthesis process. The converted FT provides an opportunity to examine the completeness of the outcome from the synthesis process. This study presents an example of the design of a Containment Heat Removal System (CHRS). A case study illustrates the process of designing the CHRS with an interactive design framework focusing on the conversion of the AD process to FTA.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.306-309
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• 2005

This parer describes the tracking control simulation of 6-DOF shaking table with a bell crank structure, which converts the direction of reciprocating movements. For the Joint coordinate-based control which uses lengths of each actuator, the trajectory conversion process inverse kinematics is performed. Applying the Newton-Euler approach, the dynamic equation of the shaking table is derived. To cope with nonlinear problems, time-delay control(TDC) is considered, which has been noted for its exceptional robustness to parameter uncertainties and disturbance, in addition to steady-state accuracy and computational efficiency. If the nominal model is equal to the real system, joint coordinate-based control can be very efficient. However, manufacturing tolerances installation errors and link offsets contaminate the nominal values of the kinematic parameters used in the kinematic model of the shaking table. To compensate differences between the nominal model and the real system. the joint coordinate-based control using acceleration feedback in the Cartesian coordinate space is proposed.

• Journal of Advanced Navigation Technology
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• v.17 no.3
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• pp.271-278
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• 2013

In this paper, ECEF to ECI coordinate transformation algorithm which uses EOP parameters in GPS civil navigation message is introduced, and ECEF to ECI coordinate transformation simulation results were analyzed. The ECEF to ECI coordinate transformation includes GPS to UTC, and UTC to other types of time conversions and EOP data processing algorithms. The ECEF to ECI coordinate conversion algorithm was certified using real LEO satellite position, velocity GPS data, and EOP data which offered by the Earth Orientation Center.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1844-1854
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• 2018

Isolated power systems (IPS) are often characterized by a weak grid due to small power grids. The grid side voltage is no longer equivalent to an ideal voltage source of an infinitely big power grid. The conversion control of new energy sources, parameter perturbations as well as the load itself can easily cause the system voltage to oscillate or to become unstable. To solve this problem, increasing the energy-storage power sources is usually used to improve the reliability of a system. In order to provide support for the voltage, the energy-storage power source inverter needs an method to control the voltage source. Therefore, this paper has proposed the active damping control of a voltage source inverter (VSI) based on virtual compensation. By simplifying the VSI double closed-loop control, two feedback compensation channels have been constructed to reduce the VSI output impedance without changing the characteristics of the voltage gain of a system. This improvement allows systems to operate stably in a larger range. A frequency-domain analysis, and simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.

• Smart Structures and Systems
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• v.19 no.1
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• pp.67-90
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• 2017

The interdisciplinary research area of small scale energy harvesting has attracted tremendous interests in the past decades, with a goal of ultimately realizing self-powered electronic systems. Among the various available ambient energy sources which can be converted into electricity, wind energy is a most promising and ubiquitous source in both outdoor and indoor environments. Significant research outcomes have been produced on small scale wind energy harvesting in the literature, mostly based on piezoelectric conversion. Especially, modeling methods of wind energy harvesting techniques plays a greatly important role in accurate performance evaluations as well as efficient parameter optimizations. The purpose of this paper is to present a guideline on the modeling methods of small-scale wind energy harvesters. The mechanisms and characteristics of different types of aeroelastic instabilities are presented first, including the vortex-induced vibration, galloping, flutter, wake galloping and turbulence-induced vibration. Next, the modeling methods are reviewed in detail, which are classified into three categories: the mathematical modeling method, the equivalent circuit modeling method, and the computational fluid dynamics (CFD) method. This paper aims to provide useful guidance to researchers from various disciplines when they want to develop and model a multi-way coupled wind piezoelectric energy harvester.

• Atmosphere
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• v.29 no.2
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• pp.227-239
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• 2019

This paper reviews various bulk-type cloud microphysics parameterizations (BCMPs). BCMP, predicting the moments of size distribution of hydrometeors, parameterizes the grid-resolved cloud and precipitation processes in atmospheric models. The generalized gamma distribution is mainly applied to represent the hydrometeors size distribution in BCMPs. BCMP can be divided in three different methods such as single-moment, double-moment, and triple-moment approaches depending on the number of prognostic variables. Single-moment approach only predicts the hydrometeors mixing ratio. Double-moment approach predicts not only the hydrometeors mixing ratio but also the hydrometeors number concentration. Triple-moment approach predicts the dispersion parameter of hydrometeors size distribution through the prognostic reflectivity, together with the number concentrations and mixing ratios of hydrometeors. Triple-moment approach is the most time expensive method because it has the most number of prognostic variables. However, this approach can allow more flexibility in representing hydrometeors size distribution relative to single-moment and double-moment approaches. At the early stage of the development of BMCPs, warm rain processes were only included. Ice-phase categories such as cloud ice, snow, graupel, and hail were included in BCMPs with prescribed properties for densities and sedimentation velocities of ice-phase hydrometeors since 1980s. Recently, to avoid fixed properties for ice-phase hydrometeors and ad-hoc category conversion, the new approach was proposed in which rimed ice and deposition ice mixing ratios are predicted with total ice number concentration and volume.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.