• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.272-287
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• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• Wind and Structures
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• v.26 no.4
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• pp.205-214
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• 2018

In this paper, the thermo-mechanical buckling characteristics of functionally graded (FG) size-dependent Timoshenko nanobeams subjected to an in-plane thermal loading are investigated by presenting a Navier type solution for the first time. Material properties of FG nanobeam are supposed to vary continuously along the thickness according to the power-law form and the material properties are assumed to be temperature-dependent. The small scale effect is taken into consideration based on nonlocal elasticity theory of Eringen. The nonlocal governing equations are derived based on Timoshenko beam theory through Hamilton's principle and they are solved applying analytical solution. According to the numerical results, it is revealed that the proposed modeling can provide accurate critical buckling temperature results of the FG nanobeams as compared to some cases in the literature. The detailed mathematical derivations are presented and numerical investigations are performed while the emphasis is placed on investigating the effect of the several parameters such as material distribution profile, small scale effects and aspect ratio on the critical buckling temperature of the FG nanobeams in detail. It is explicitly shown that the thermal buckling of a FG nanobeams is significantly influenced by these effects. Numerical results are presented to serve as benchmarks for future analyses of FG nanobeams.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.471-480
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• 2017

This paper investigates free vibration characteristics of a rotating functionally graded (FG) beam in hygro environments. In the present study, material properties of the FG beam vary continuously through thickness direction according to the power-law which approximates material properties of FG beam. The governing differential equations of motion are derived based on Euler-Bernoulli beam theory and using the Hamilton's principle which solved utilizing a semi-analytical technique called the Differential Transform Method (DTM). In order to verify the competency and accuracy of the current analysis, a comparative study with previous researches are performed and good agreement is observed. Influences of Several important parameters such as power-law exponent, hygro environment, rotational speed and slenderness ratio on natural frequencies are investigated and discussed in detail. It is concluded that these effects play significant role on dynamic behavior of rotating FG beam in the hygro environments. Numerical results are tabulated in several tables and figures that can be serving as benchmarks for future analyses of rotating FG beams in the hygro environments.

• Structural Engineering and Mechanics
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• v.64 no.1
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• pp.121-133
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• 2017

This paper proposes an analytical solution method for free vibration of curved functionally graded (FG) nonlocal beam supposed to different thermal loadings, by considering porosity distribution via nonlocal elasticity theory for the first time. Material properties of curved FG beam are assumed to be temperature-dependent. Thermo-mechanical properties of porous FG curved beam are supposed to vary through the thickness direction of beam and are assumed to be temperature-dependent. Since variation of pores along the thickness direction influences the mechanical and physical properties, porosity play a key role in the mechanical response of curved FG structures. The rule of power-law is modified to consider influence of porosity according to even distribution. The governing equations of curved FG porous nanobeam under temperature field are derived via the energy method based on Timoshenko beam theory. An analytical Navier solution procedure is used to achieve the natural frequencies of porous FG curved nanobeam supposed to thermal loadings with simply supported boundary condition. The results for simpler states are confirmed with known data in the literature. The effects of various parameters such as nonlocality, porosity volume fractions, type of temperature rising, gradient index, opening angle and aspect ratio of curved FG porous nanobeam on the natural frequency are successfully discussed. It is concluded that these parameters play key roles on the dynamic behavior of porous FG curved nanobeam. Presented numerical results can serve as benchmarks for future analyses of curve FG nanobeam with porosity phases.

• Korean Management Science Review
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• v.29 no.3
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• pp.121-134
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• 2012

In this study, we consider a component procurement planning problem where the procurement amounts of components are determined under assemble-to-order systems with demand uncertainty. In the problem, procurement amount of each component is decided before the demands of finished products are known and after the demands are identified the assembly amounts of the finished products are decided. In this study, the objective function of the problem is minimizing the total costs which are composed of purchase and inventory costs of the components and the backorder costs of the finished products. We assume that the uncertain demand information is given as multiple scenarios of the demands, and we propose procurement planning methods based on stochastic models which considering the multiple demand scenarios. To evaluate the performances of the proposed methods, computational experiments were carried out on the proposed methods as well as benchmarks including a method based on deterministic mathematical model and a heuristic. From the results of the computational tests, the superiorities of the proposed methods were shown.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.2
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• pp.137-144
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• 2001

In this paper, a state assignment algorithm was proposed to reduce power consumption in control-flow oriented finite state machines. The Markov chain model is used to reduce the switching activities, which closely relate with dynamic power dissipation in VLSI circuits. Based on the Markov probabilistic description model of finite state machines, the hamming distance between the codes of neighbor states was minimized. To express the switching activities, the cost function, which also accounts for the structure of a machine, is used. The proposed state assignment algorithm is tested with Logic Synthesis Benchmarks, and reduced the cost up to 57.42% compared to the Lakshmikant's algorithm.

• Journal of Families and Better Life
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• v.30 no.4
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• pp.133-146
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• 2012

Community building in Japan, called Machizukuri, has been in existence since the 1960s, and many successful cases are referred to by researchers and public service officials as benchmarks for creating livable cities and towns in Korea. The purpose of this study is to identify the building factors for a sustainable community and to suggest implications through the cases of machizukuri in Japan. Based on the literature review related to the research topic, on-site visit case study was used as the major research methodology. Four cases -Setagaya, Kawagoe, Motomachi, and Manazuru- were selected as the research subjects and interviews with the representatives of the community(resident) council and public officials were conducted during October 19-22, 2011. The project overview, purpose, and planning characteristics of each case were described for data analysis. The major findings are as follows. Although most of the machizukuri in Japan were administration-dependent at the beginning stage, the case projects in this study showed resident-independence (self-support) from the perspective of sustainability. The results showed that successful community building is an everlasting project that requires cooperation among personnels including residents, civic officials, and related council members.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.9-13
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• 2012

In order to overcome the complexity and power problems of superscalar processors, the multi-core architecture has been prevalent recently. Although the execution-driven simulation is wide spread, the trace-driven simulation has speed advantages over the execution-driven simulation. We present a methodology to simulate multi-core architecture using trace-driven simulator. Using SPEC 2000 benchmarks as input, the trace-driven simulation has been performed for the cores ranging from 2 to 16 extensively. As a result, the 16-core processor resulted in 4.1 IPC and 13.3 times speed up over single-core processor on the average.