• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of KIISE
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• v.42 no.8
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• pp.1057-1064
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• 2015

The tactical network is system based on wireless networking technologies that ties together surveillance reconnaissance systems, precision strike systems and command and control systems. Several alternative paths exist in the network because it is connected as a grid to improve its survivability. In addition, the network topology changes frequently as forces and combatants change their network access points while conducting operations. However, most Internet routing standards have been designed for use in stable backbone networks. Therefore, tactical networks may exhibit a deterioration in performance when these standards are implemented. In this paper, we propose Priority based Multi-Path routing with Local Optimization(PMPLO) for a tactical backbone network. The PMPLO separately manages the global and local metrics. The global metric propagates to other routers through the use of a routing protocol, and it is used for a multi-path configuration that is guaranteed to be loop free. The local metric reflects the link utilization that is used to find an alternate path when congestion occurs, and it is managed internally only within each router. It also produces traffic that has a high priority privilege when choosing the optimal path. Finally, we conducted a simulation to verify that the PMPLO can effectively distribute the user traffic among available routers.

• Korean Journal of Construction Engineering and Management
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• v.18 no.2
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• pp.58-70
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• 2017

The planning for material lifting operations is one of the key processes in high-rise building construction. Several previous studies have used rough calculations by referring to existing practices or establishing a target value for lifting cycle time or operating rate. Therefore, the purpose of this study is to propose a material lifting process optimization method for reducing the lifting cycle time and increasing the operating rate. In this study, we improve the cyclic operation network (CYCLONE) modeling method that considers the duration and zone information of each work task. This method can be used to hand over work tasks to another crew group in the work process. According to this methodology, this study optimizes the material lifting process, performs a sensitivity analysis, and evaluates the field applicability of the proposed material lifting process optimization method. Therefore, the optimized process was then applied to a high-rise building construction site. The lifting work process time and operating rate for the simulated as - is lifting process data, optimized process data, and field application result data were compared for each lifting height. From this comparison, the effectiveness of the optimization methodology was confirmed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.4
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• pp.350-358
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• 2009

This paper describes a result of the guidance and control for re-entry vehicle during TAEM phase. TAEM phase (Terminal Aerial Energy Management phase) has many conditions, such as density, velocity, and so on. Under these conditions, we have optimized trajectory and other states for guidance in TAEM phase. The optimized states consist of 7 variables, down-range, cross range, altitude, velocity, flight path angle, vehicle's azimuth and flight range. We obtained the optimized reference trajectory by DIDO tool, and used feedback linearization with neural network for control re-entry vehicle. By back propagation algorithm, vehicle dynamics is approximated to real one. New command can be decided using the approximated dynamics, delayed command input and plant output, NARMA-L2. The result by this control law shows a good performance of tracking onto the reference trajectory.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.158-167
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• 2017

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.522-530
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• 2002

This paper introduces a neural network (NN) -based nonlinear predictor for the LP (Linear Prediction) residual. To evaluate the effectiveness of the NN-based nonlinear predictor for LP-residual, we first compared the average prediction gain of the linear long-term predictor with that of the NN-based nonlinear long-term predictor. Then, the effects on the quantization noise of the nonlinear prediction residuals were investigated for the NN-based nonlinear predictor A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Experimental results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.1
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• pp.165-172
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• 1988

An optimal network design method using continuous form of design variables is considered. Modified Hooke-and-Jeeves algorithm has been implemented in order to solve nonlinear progamming problem which is approximately equivalent to the real network design problem (NDP) with system. efficiency criteria(i. e. travel time and costs) and construction cost as objective function. Various forms of construction cost function, locations of initial solution, and dimension of initial step size of link improvement are taken into account to show the validity of this approach. The results obtained are quite promising in terms of the numbers of evaluations in solving NDP, and the speed of convergence. Finally, some techniques in choosing efficient intial solution, initial step size and approximation are given.

• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.417-430
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• 2007

The selection of ground stations is one of the essential process of IGS (International GNSS Service) products. High quality GPS data should be collected from the globally distributed ground stations. In this study, we investigated an effect of ground station network selection on GPS satellite ephemeris. The GPS satellite ephemeris obtained from the twelve ground station networks were analyzed to investigate the effect of selection of ground stations. For data quality check, the observations, the number of cycle slips, and multipath of pseudoranges for L1 and L2 were considered. The ideal network defined by Taylor-Karman structure and SOD (Second Order Design) were used to obtain the optimal ground station network.