• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.37-47
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• 2016

The practical constraints on the commercial FPGAs which contain dedicated wide function multiplexers in their slice structure are incorporated with one of the most advanced FPGA mapping algorithms based on the AIG (And-Inverter Graph), one of the best logic representations in academia. As the first step of the mapping process, cuts are enumerated as intermediate structures. And then, the cuts which can be mapped to the multiplexers are recognized. Without any increased complexity, the delay and area of multiplexers as well as LUTs are calculated after checking the requirements for the tree construction such as symmetry and depth limit against dynamically changing mapping of neighboring nodes. Besides, the root positions of multiplexer trees are identified from the RTL code, and annotated to the AIG as AOs (Auxiliary Outputs). A new AIG embedding the multiplexer tree structures which are intentionally synthesized by Shannon expansion at the AOs, is overlapped with the optimized AIG. The lossless synthesis technique which employs FRAIG (Functionally Reduced AIG) is applied to this approach. The proposed approach and techniques are validated by implementing and applying them to two RISC processor examples, which yielded 13~30% area reduction, and up to 32% delay reduction. The research will be extended to take into account the constraints on the dedicated hardware for carry chains.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.143-156
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• 2010

Faced with an increase of malicious threats from the Internet as well as local area networks, many companies are considering deploying a security system. To help a decision maker select a suitable security tool, this paper proposed a three-step integrated framework using linear fuzzy regression (LFR) and physical programming (PP). First, based on the experts' estimations on security criteria, analytic hierarchy process (AHP) and quality function deployment (QFD) are employed to specify an intermediate score for each criterion and the relationship among these criteria. Next, evaluation value of each criterion is computed by using LFR. Finally, a goal programming (GP) method is customized to obtain the most appropriate security tool for an organization, considering a tradeoff among the multi-objectives associated with quality, credibility and costs, utilizing the relative weights calculated by the physical programming weights (PPW) algorithm. A numerical example provided illustrates the advantages and contributions of this approach. Proposed approach is anticipated to help a decision maker select a suitable security tool by taking advantage of experts' experience, with noises eliminated, as well as the accuracy of mathematical optimization methods.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.35-44
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• 2014

This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.85-94
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• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.

• Journal of the Korea Computer Graphics Society
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• v.24 no.1
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• pp.9-16
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• 2018

In this paper, we introduce a visualization framework for cell image data obtained from optical diffraction tomography (ODT), including a method for representing cell morphology in 3D virtual environment and a color mapping protocol. Unlike commonly known volume data sets, such as CT images of human organ or industrial machinery, that have solid structural information, the cell image data have rather vague information with much morphological variations on the boundaries. Therefore, it is difficult to come up with consistent representation of cell structure for visualization results. To obtain desired visual representation of cellular structures, we propose an interactive visualization technique for the ODT data. In visualization of 3D shape of the cell, we adopt a volume rendering technique which is generally applied to volume data visualization and improve the quality of volume rendering result by using empty space jittering method. Furthermore, we provide a layer-based independent rendering method for multiple transfer functions to represent two or more cellular structures in unified render window. In the experiment, we examined effectiveness of proposed method by visualizing various type of the cell obtained from the microscope which can capture ODT image and fluorescence image together.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.94-104
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• 2010

The capacity of highway is restricted and traffic congestion is caused by increasing traffic demand. Also, greenhouse gases are increased by traffic congestion. CDM (Clean Development Mechanism) is an idea of interest to reduce greenhouse gases. However, CDM's cases applied in traffic field are rare. Thus, it is necessary that methodology to reduce greenhouse gas should be developed and applied to CDM. A methodology for identifying greenhouse gas emissions was developed in this paper. This methodology was developed on the basis of baseline methodology registered at UN. Travel time and speed in the conventional traffic condition and in the automated traffic condition are compared by BPR function. The calculated speed applied to emission factor equation and then $CO_2$ emissions was calculated. A simulation was executed to evaluate the validity of the developed methodology. In the result, advanced vehicle's $CO_2$ emissions are more than conventional vehicle's $CO_2$ emissions in the stable flow condition. However, advanced vehicle's $CO_2$ emissions are less than conventional vehicle's $CO_2$ emissions in the unstable flow condition. It is assure that capacity of highway is enhanced and efficiency of highway is improved by adopting advanced safety vehicle in the smart road.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.89-98
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• 2015

This paper provides a robust and non-fragile $H_{\infty}$ controller design algorithm for time-delayed systems with randomly occurring polytopic uncertainties and disturbances. First, we design time-delayed system considering randomly occurring uncertainties and disturbances. Next, The sufficient condition for the existence of robust and non-fragile $H_{\infty}$ controller is presented by LMI(linear matrix inequality) using Lyapunov stability analysis and $H_{\infty}$ performance measure. Since the obtained condition can be expressed as a PLMI(parameterized linear matrix inequality) by changes of variables and Schur complement, all solutions including controller gain, degrees of controller satisfying non-fragility, $H_{\infty}$ norm bound ${\gamma}$ can be calculated simultaneously. Finally, numerical examples are given to illustrate the performance and the effectiveness of the proposed robust and non-fragile $H_{\infty}$ controller compared with the deterministic uncertainty model even though there exists randomly occurring uncertainties, disturbances and time delays.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.12
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• pp.1339-1349
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• 2008

In this paper, a nonlinear relationship between an input complex envelope and an output complex envelope of m-th harmonic zone is theoretically analyzed, and AM/$AM_m$ and AM/$PM_m$ are defined. A scheme to extract these characteristics from measured in-phase and quadrature-phase data is suggested. The proposed analysis is verified with a fundamental-fundamental and fundamental-third harmonic measurements for a InGaP power amplifier(PA). Based on the harmonic-band nonlinear analysis and extraction scheme, a new technique to send a signal in m-th harmonic band with a harmonic signal Linearization Digital Predistortion(DPD) scheme is presented. A numerical analysis and a Look-Up Table(LUT) based DPD algorithms to linearize output signal on m-th harmonic zone are developed. For a 16- and a 64-QAM input signals, a DPD for third harmonic signal linearization is implemented, and output spectrum and signal constellation are measured. The wholly distorted signals are linearized, and thus the measured Error Vector Magnitudes (EVM) are 6.4 % and 6.5 % respectively. The results show that a proposed scheme linearizes a nonlinearly distorted harmonic band signals. The proposed nonlinear analysis and predistortion scheme can be applied to multiband transmitter in next generation software defined radio(SDR)/cognitive radio(CR) wireless system.

• Journal of the Korean earth science society
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• v.26 no.3
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• pp.268-275
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• 2005

One of the nonlinear statistical modelling, neural network method was applied to predict the Sea Surface Temperature Anomalies (SSTA) in the Nino regions, which represent El Nino indices. The data used as inputs in the training step of neural network model were the first seven empirical orthogonal functions in the tropical Pacific $(120^{\circ}\;E,\;20^{\circ}\;S-20^{\circ}\;N)$ obtained from the NCEP/NCAR reanalysis data. The period of 1951 to 1993 was adopted for the training of neural network model, and the period 1994 to 2003 for the forecasting validation. Forecasting results suggested that neural network models were resonable for SSTA forecasting until 9-month lead time. They also predicted greatly the development and decay of strong E1 Nino occurred in 1997-1998 years. Especially, Nino3 region appeared to be the best forecast region, while the forecast skills rapidly decreased since 9-month lead time. However, in the Nino1+2 region where they are relatively low by the influence of local effects, they did not decrease even after 9-month lead time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.