• Smart Structures and Systems
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• v.12 no.1
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• pp.1-22
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• 2013

Magnetic nanoparticle based bioseparation in microfluidics is a multiphysics phenomenon that involves interplay of various parameters. The ability to understand the dynamics of these parameters is a prerequisite for designing and developing more efficient magnetic cell/bio-particle separation systems. Therefore, in this work proof-of-concept experiments are combined with advanced numerical simulation to design and optimize the capturing process of magnetic nanoparticles responsible for efficient microfluidic bioseparation. A low cost generic microfluidic platform was developed using a novel micromolding method that can be done without a clean room techniques and at much lower cost and time. Parametric analysis using both experiments and theoretical predictions were performed. It was found that flow rate and magnetic field strength greatly influence the transport of magnetic nanoparticles in the microchannel and control the capturing efficiency. The results from mathematical model agree very well with experiments. The model further demonstrated that a 12% increase in capturing efficiency can be achieved by introducing of iron-grooved bar in the microfluidic setup that resulted in increase in magnetic field gradient. The numerical simulations were helpful in testing and optimizing key design parameters. Overall, this work demonstrated that a simple low cost experimental proof-of-concept setup can be synchronized with advanced numerical simulation not only to enhance the functional performance of magneto-fluidic capturing systems but also to efficiently design and develop microfluidic bioseparation systems for biomedical applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.534-540
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• 2014

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.753-758
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• 2005

This study attempts at building an integrated GIS computing framework for evaluating space-time accessibility of an individual with the approach of time geography model. The proposed method is based on the integration of mobile GIS and object-relational spatio-temporal database. Three components are central to our system: ( i ) mobile GIS application that transmits spatio-temporal trajectory data of an individual; ( ii ) spatio-temporal database server that incorporates the time geography model; and (iii) geovisualization client that provides time geographic queries to the spatio-temporal database. As for the mobile GIS application, spatio-temporal trajectory data collected by GPS-PDA client is automatically transmitted to the database server through mobile data management middleware. The spatio-temporal database server implemented by extending a generic DBMS provides spatio-temporal objects, functions and query languages. The geovisualization client illustrates 3D visual results of the queries about space-time path. space-time prism and space-time accessibility. This study shows a method of integrating mobile GIS and DBMS for time geography application, and presents an appropriate spatio-temporal data model for evaluating space-time accessibility of an individual.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.3
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• pp.329-338
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• 2000

CHILL language has been used for the software development of electronic switching and telecommunications system. ETRI CHILL-96 language is an extended CHILL language with the notions of object-orientation, concurrency, and generic type. In this paper, we discuss some design and implementation issues of ETRI CHILL-96 compiler. Renaming translation rules and function restructuring techniques are adapted for the purpose of preventing name conflict and producing debugging information. Those new extended features are preprocessed in the compilation and translated to extended CHILL intermediate codes. Such compilation technique enables ETRI CHILL-96 language to hold compatibility with software developed by CHILL language.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.283-292
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• 2020

Current sensors that use a Hall element and Hall IC to measure the magnetic fields generated in steel silicon core gaps do not distinguish between direct and alternating currents. Thus, they are primarily used to measure direct current (DC) in industrial equipment. Although such sensors can measure the DC when installed in expensive equipment, ascertaining problems becomes difficult if the equipment is set up in an unexposed space. The control box is only opened during scheduled maintenance or when anomalies occur. Therefore, in this paper, a method is proposed for facilitating the safety management and maintenance of equipment when necessary, instead of waiting for anomalies or scheduled maintenance. A Bluetooth 4.0 low-energy current-sensor system based on near-field communication is used, which compensates for the nonlinearity of the current-sensor output signal using a piecewise linear model. The sensor is controlled using its generic attribute profile. Sensor nodes and cell phones used to check the signals obtained from the sensor at 50-A input currents showed an accuracy of ±1%, exhibiting linearity in all communications within the range of 0 to 50 A, with a stable output voltage for each communication segment.

• Journal of the Korean Society for information Management
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• v.14 no.1
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• pp.77-105
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• 1997

The study confirmed that the methods for expanding headings and combined structures among the headings, subdivisions, qualifiers and inverted terms were diverse, and that the headings used for similar purposes do not have identical structural forms. It is also found that the standard symbols representing generic relationship in theasurus, which are introduced in the list of subject headings, do not show precisely the hiearchical relationship of phrase headings. MeSH in pre-coordinated system will be available for retrieval in the computer environment because it can make a link between headings and subdivisions in searching stage. The changes in retrieval circumstances and the seeking behaviors of the users demand that a new structure of subject headings be developed. It was proposed that the modification of the structure in the Hangul subject headings be essential.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1220-1225
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• 2010

This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.331-335
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• 2006

Theoretically, stable operations of an inlet are achieved at the design condition. However, at off-design conditions supersonic inlets often encounter the problem of aerodynamic instability, called inlet buzz. During inlet buzz, supersonic inlets exhibit considerable oscillation of the shock system in front of the inlet and corresponding large pressure fluctuations downstream. This phenomenon results in decrease of engine performance. An experimental and numerical study was conducted to investigate the phenomenon of supersonic inlet buzz on a generic, axisymmetric, external-compression inlet with a single-surface center-body. This study suggest that intermittent buzz exist and the frequency become to be large as increasing the back pressure.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1439-1461
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• 2015

Design criteria, modeling rules, and analysis principles of seismic isolation systems have already found place in important building codes and standards such as the Uniform Building Code and ASCE/SEI 7-05. Although real behaviors of isolation systems composed of high damping or lead rubber bearings are nonlinear, equivalent linear models can be obtained using effective stiffness and damping which makes use of linear seismic analysis methods for seismic-isolated buildings possible. However, equivalent linear modeling and analysis may lead to errors in seismic response terms of multi-story buildings and thus need to be assessed comprehensively. This study investigates the accuracy of equivalent linear modeling via numerical experiments conducted on generic five-story three dimensional seismic-isolated buildings. A wide range of nonlinear isolation systems with different characteristics and their equivalent linear counterparts are subjected to historical earthquakes and isolation system displacements, top floor accelerations, story drifts, base shears, and torsional base moments are compared. Relations between the accuracy of the estimates of peak structural responses from equivalent linear models and typical characteristics of nonlinear isolation systems including effective period, rigid-body mode period, effective viscous damping ratio, and post-yield to pre-yield stiffness ratio are established. Influence of biaxial interaction and plan eccentricity are also examined.