• Journal of KIISE
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• v.42 no.2
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• pp.227-234
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• 2015

This paper proposes a modified MI-SVM algorithm by considering data distribution. The previous MI-SVM algorithm seeks the margin by considering the "most positive" instance in a positive bag. Positive instances included in positive bags are located in a similar area in a feature space. In order to reflect this characteristic of positive instances, the proposed method selects the "most positive" instance by calculating the distance between each instance in the bag and a pivot point that is the intersection point of all positive instances. This paper suggests two ways to select the "most positive" pivot point in the training data. First, the algorithm seeks the "most positive" pivot point along the current predicted parameter, and then selects the nearest instance in the bag as a representative from the pivot point. Second, the algorithm finds the "most positive" pivot point by using a Diverse Density framework. Our experiments on 12 benchmark multi-instance data sets show that the proposed method results in higher performance than the previous MI-SVM algorithm.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1019-1032
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• 2013

The accurate prediction of future mortality is an important issue due to recent rapid increases in life expectancy. An accurate estimation and prediction of mortality is important to future welfare policies. The optimal selection of a mortality model is important to estimate and predict mortality; however, the period of time series data used is also an important issue. It is essential to understand that the time series data for mortality is short in Korea and the data before 1982 is incomplete. This paper divides the time series of Korean mortality into two sets to compare the parameter estimates of the LC model and LC model with a cohort effect by the period of data used. A modeling and prediction of the mortality index and cohort effect index as well as the evaluation of future life expectancy is conducted. Finally, some suggestions are proposed for the future prediction of mortality.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.529-535
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• 2014

In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.10
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• pp.941-946
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• 2010

This study aims to investigate the effects of soybean biodiesel fuel on exhaust emissions with regards to two combustion modes: conventional combustion(existence of PM-NOx trade-off behavior) and low temperature combustion(LTC) in a 1.7 L common rail direct injection diesel engine. As compared to conventional combustion, LTC was achieved by adopting a heavier exhaust gas recirculation and strategic injection parameter optimization. Two sets of fuels, i.e. ultra low sulfur diesel(ULSD) and 20% volumetric blends of soybean biodiesel with ULSD(B20) were used. Regardless of the fuel type, in LTC the simultaneous reduction of PM and NOx was observed and both levels were significantly lower than in case of conventional combustion. Under the given engine operating condition in the case of conventional combustion, B20 produced less PM and more NOx than ULSD. In the case of LTC combustion, B20 produced more PM and NOx than ULSD.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.3
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• pp.65-72
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• 2011

Extensive computer simulations to account for the randomness in the process of seismic demand estimation have been a serious obstacle to the adoption of probabilistic performance assessments for the decision of applying seismic intervention schemes. In this study, a method for rapid fragility assessments based on a response database and the fragility contour method are presented. By the comparison of response contours in different formats, it is shown that representing maximum responses in ductility demand is better for the investigation of the effect of structural parameter changes on seismic demands than representations in absolute values. The presented fragility contour enables designers to practically investigate the probabilistic performance level of every possible retrofit option in a convenient manner using visualized data sets. This example demonstrates the extreme efficiency of the proposed approach in performing fragility assessments and successful application to the seismic retrofit strategies based on limit state probabilities.

• Journal of the Korean Society of Propulsion Engineers
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• v.5 no.1
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• pp.69-75
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• 2001

Linear GPA(Gas Path Analysis) and non-linear GPA programs for performance diagnostics of a turboprop engine were developed, and a study for selection of optimal measurement variables was performed. Simultaneous faults in the compressor, the compressor turbine and the power turbine, which occur damage of the engine, were assumed. The non-linear GPA analysis was carried out with an iterative method, where the performance degradation rate of independent parameters was divided into same intervals. It was compared with the result by the Newton-Raphson method for observing the effect of an iterative method. According to the analysis result, it was found that performance of non-linear GPA can be influenced on the type of the iterative method. For showing effects of the number of measurement variables both the linear and non-linear GPAs were analyzed with 10, 8 and 6 measurement sets, respectively. RMS error between them were compared each other. It was realized that the more measurement parameters are used, and the more accurate result may be obtained. However much better result can be obtained with measurement parameters selected properly Moreover, RMS error by using non-linear GPA was less than that by using linear GPA.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.184-197
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• 2015

This paper builds on a recent method, chain routing with even energy consumption (CREEC), for designing a wireless sensor network with chain topology and for scheduling the communication to ensure even average energy consumption in the network. In here a new suboptimal design is proposed and compared with the CREEC design. The chain topology in CREEC is reconfigured after each group of n converge-casts with the goal of making the energy consumption along the new paths between the nodes in the chain as even as possible. The new method described in this paper designs a single near-optimal Hamiltonian circuit, used to obtain multiple chains having only the terminal nodes different at different converge-casts. The advantage of the new scheme is that for the whole life of the network most of the communication takes place between same pairs of nodes, therefore keeping topology reconfigurations at a minimum. The optimal scheduling of the communication between the network and base station in order to maximize network lifetime, given the chosen minimum length circuit, becomes a simple linear programming problem which needs to be solved only once, at the initialization stage. The maximum lifetime obtained when using any combination of chains is shown to be upper bounded by the solution of a suitable linear programming problem. The upper bounds show that the proposed method provides near-optimal solutions for several wireless sensor network parameter sets.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.439-452
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• 2007

We study on the privacy preserving data mining, PPDM for short, by using randomization. The theoretical PPDM based on the secure multi-party computation techniques is not practical for its computational inefficiency. So we concentrate on a practical PPDM, especially randomization technique. We survey various privacy measures and study on the privacy preserving mining of association rules by using randomization. We propose a new randomization operator, binomial selector, for privacy preserving technique of association rule mining. A binomial selector is a special case of a select-a-size operator by Evfimievski et al.[3]. Moreover we present some simulation results of detecting an appropriate parameter for a binomial selector. The randomization by a so-called cut-and-paste method in [3] is not efficient and has high variances on recovered support values for large item-sets. Our randomization by a binomial selector make up for this defects of cut-and-paste method.

• The Journal of Engineering Geology
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• v.16 no.2 s.48
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• pp.171-178
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• 2006

The seismological observation of Korea began in 1905, and has been run with continuous earthquake network of observation, expanding to the advanced country, but still has some problems in accuracy and speed for report. There are many problems to announce the early warning system for earthquakes and tsunami in the East Sea because most events in the East Sea occur outside the seismic network. Therefore multi-waveform data conversion and composition from the surrounding countries such as Korea, Japan and Far East Russia are requested in order to improve more accurate determination of the earthquake parameters. We used FESNET(Far East Seismic Network) technology to analyze the May 29 and June 1 Earthquakes, and the March 20, 2005 Fukuoka Earthquake in this research, using the data sets of KMA, Japan(JMA/MIED) and IRIS stations. It was found out that use of FESNET resulted in more better outputs than that of a single network, either KMA or JMA stations.