• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.51-55
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• 2017

This paper aims to maximize the energy harvesting rate and channel capacity in RF-energy harvesting networks (RF-EHNs) under the constraints of maximum transmit power and minimum quality of service (QoS) in terms of rate capacity for each user. We study a multi-user RF-EHN with frequency division multiple access (FDMA) in a Rayleigh channel. An access point (AP) simultaneously transmitting wireless information and power in the RF-EHN serves a subset of active users which have a power-splitting antenna. To gauge the network performance, we define energy efficiency (EE) and propose an optimization solution for maximizing EE with Lagrangian dual decomposition theory. In simulation results, we confirm that the EE is effectively maximized by the proposed solution with satisfying the given constraints.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1689-1696
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• 2017

In this paper, the voltage measurement-based coordinated Voltage/VAR control (VMCVVC) algorithm for conservation voltage reduction(CVR) is proposed. The proposed algorithm has the purpose of enhancing the CVR effect through coordinated control of the voltage control devices such as the distributed energy resources and the load tap changer(LTC) transformers. It calculates the references of the voltage control devices such that the bus voltages are maintained at as close to the lower operation limit as possible. For this purpose, firstly, the distribution system is divided into LTC transformer control zones through topological search. Secondly, the reactive power references of the reactive power control devices are determined such that the voltage profile of the section is flattened. Finally, the tap references of the LTC transformers are calculated to lower the voltage profile. The effectiveness of the proposed algorithm is demonstrated through case studies using IEEE test network.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.274.2-274.2
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• 2013

We described a simple and efficient fabrication method for generating microfluidic channels with a circular-cross sectional geometry by exploiting the reflow phenomenon of a thick positive photoresist. Initial rectangular shaped positive photoresist micropatterns on a silicon wafer, which were fabricated by a conventional photolithography process, were converted into a half-circular shape by tuning the temperature to around $105^{\circ}C$. Through optimization of the reflow conditions, we could obtain a perfect circular micropattern of the positive photoresist, and control the diameter in a range from 100 to 400 ${\mu}m$. The resultant convex half-circular photoresist was used as a template for fabricating a concave polydimethylsiloxane (PDMS) through a replica molding process, and a circular PDMS microchannel was produced by bonding two half-circular PDMS layers. A variety of channel dimensions and patterns can be easily prepared, including straight, S-curve, X-, Y-, and T-shapes to mimic an in vivo vascular network. To inform an endothelial cell layer, we cultured primary human umbilical vein endothelial cells (HUVECs) inside circular PDMS microchannels, and demonstrated successful cell adhesion, proliferation, and alignment along the channel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.649-657
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• 2008

This paper describes the optimized output performances such as output power and the third order intermodulation in GaN high power transistor packages which consist of chip die, chip capacitors, and wire bonding. The optimized output power according to wire bonding techniques, and third order intermodulation performances according to wire bonding and bias conditions are discussed. In addition, it is shown through the nonlinear simulation that how the output performances are sensitive to the inductance values which are realized by wire bonding for matching network in the limited package area.

• Smart Structures and Systems
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• v.7 no.5
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• pp.417-432
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• 2011

The present paper is devoted to vibration canceling and shape control of piezoelastic slender beams. Taking into account the presence of electric networks, an extended electromechanically coupled Bernoulli-Euler beam theory for passive piezoelectric composite structures is shortly introduced in the first part of our contribution. The second part of the paper deals with the concept of passive shape control of beams using shaped piezoelectric layers and tuned inductive networks. It is shown that an impedance matching and a shaping condition must be fulfilled in order to perfectly cancel vibrations due to an arbitrary harmonic load for a specific frequency. As a main result of the present paper, the correctness of the theory of passive shape control is demonstrated for a harmonically excited piezoelelastic cantilever by a finite element calculation based on one-dimensional Bernoulli-Euler beam elements, as well as by the commercial finite element code of ANSYS using three-dimensional solid elements. Finally, an outlook for the practical importance of the passive shape control concept is given: It is shown that harmonic vibrations of a beam with properly shaped layers according to the presented passive shape control theory, which are attached to an resistor-inductive circuit (RL-circuit), can be significantly reduced over a large frequency range compared to a beam with uniformly distributed piezoelectric layers.

• Smart Structures and Systems
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• v.5 no.1
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• pp.43-54
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• 2009

This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• Computers and Concrete
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• v.20 no.6
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3204-3220
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• 2021

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.