• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.473-493
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• 2019

5G vehicular communication is one of key enablers in next generation intelligent transportation system (ITS), that require ultra-reliable and low latency communication (URLLC). To meet this requirement, a new hybrid vehicular network structure which supports both centralized network structure and distributed structure is proposed in this paper. Based on the proposed network structure, a new vehicular network utility model considering the latency and reliability in vehicular networks is developed based on Euclidean norm theory. Building on the Pareto improvement theory in economics, a vehicular network uplink optimization algorithm is proposed to optimize the uplink utility of vehicles on the roads. Simulation results show that the proposed scheme can significantly improve the uplink vehicular network utility in vehicular networks to meet the URLLC requirements.

• Advances in Computational Design
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• v.8 no.2
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• pp.179-190
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• 2023

In this paper, building information modelling (BIM) associated to the principle of significant items emerged at quantities and costs in the optimization of productivity related to the rehabilitation of the building where proposed and discussed. A quantitative and qualitative study related to the field of application based on some parameters such as pathology diagnosis, projects documents and bills of quantities were used for model development at the preliminary stage of this work. The study identified 14 quantities significant items specified to cost value based on the use of the 80/20 Pareto rule, through the integration of building information modelling (BIM) in the optimisation of labour productivity for rehabilitation of buildings. The results of this study reveal the reliability and the improvement of labour productivity using building information modelling process integrating quantities and cost significant items.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.5
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• pp.1043-1053
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• 1994

This study suggests a practical but rational approach for the development of reliability-based LRFD criteria for transmission towers under wind and ice loadings in Korea. Based on available statistical data on wind speed and icing on transmission lines in Korea, the design wind and ice loads are obtained by Monte Carlo Simulations. In the study, the AFOSM reliability method and an Importance Sampling Technique are used for the element and system reliability evaluation of actual transmission towers. Based on the selected target reliabilities, a set of load and resistance factors for the LRFD criteria are calibrated using the AFOSM and the code optimization technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• Ocean and Polar Research
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• v.36 no.4
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• pp.487-494
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• 2014

Deep-seabed test miner operated by a self-propelled mining system moving on soft soil is an essential device to secure floating and towing performances. The performances of the tracked vehicle are seriously influenced by noise factors such as the shear strength of the seafloor, bottom current, seafloor slope, speed of tracked vehicle, reaction forces of flexible hose, steering ratio, etc. Due to uncertainties related to noise factors, the design of a deep-sea manganese nodules test miner that satisfies target reliabilities is difficult. Therefore, reliability-based design optimization (RBDO) is required to guarantee system reliability under circumstances where uncertainties related to noise factors prevail. Among noise factors, the bottom current, a bimodal distribution, is censored due to the observation limit of measurement devices. Therefore, estimated distribution of the bottom current is inaccurate without considering these characteristics and the result of RBDO cannot be guaranteed. In this paper, we define censored data as unknown values over the limit of observation. If this data is estimated by using Akaike information criterion (AIC) that cannot consider the characteristics of censored data, the distribution of estimated data cannot guarantee accurate reliability. Therefore, censored AIC that can consider the characteristics of data is used to estimate accurate distribution of the bottom current. Finally, RBDO, under circumstances where uncertainties related to noise factors combined censored data are present, is performed on the mobility of a deep-sea manganese nodules test miner.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.111-127
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• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• International Journal of Reliability and Applications
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• v.17 no.1
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• pp.85-105
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• 2016

In this paper accelerated life testing is incorporated in quality control technique of acceptance sampling plan to induce early failures in high reliability products.Stress under accelerated condition can be applied in constant-stress, step-stress and progressive-stress or combination of such loadings. A ramp-stress results when stress is increased linearly (from zero) with time. In this paper optimum failure-censored ramp-stress accelerated life test sampling plan for log-logistic distribution has been formulated with cost considerations. The log-logistic distribution has been found appropriate for insulating materials. The optimal plans consist in finding optimum sample size, sample proportion allocated to each stress, and stress rate factor such that producer's and consumer's interests are safeguarded. Variance optimality criterion is used when expected cost per lot is not taken into consideration, and bilevel programming approach is used in cost optimization problems. The methods developed have been illustrated using some numerical examples, and sensitivity analyses carried out in the context of ramp-stress ALTSP based on variable SSP for proportion nonconforming.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.461-472
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• 2013

In this paper, we deal with a simulation-based reliability and maintainability design problem of a warship and want to determine the optimal values of MTBF and MTTR of all units and ALDT of the warship. The system availability and life cycle cost are used as optimization criteria and estimated by simulation. A hybrid genetic algorithm with a heuristic method is proposed to find near-optimal solutions and numerical examples are also studied to investigate the effect of model parameters to the optimal solutions and compare with a general genetic algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.984-991
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• 2015

This paper presents the methodology for optimal design of power grid for offshore wind power plant (OWPP) and optimum location of offshore substation. The proposed optimization process is based on a genetic algorithm, where the objective cost model is composed of investment, power loss, repair, and reliability cost using the net present value during the whole OWPP life cycle. A probability wind power output is modeled to reflect the characteristics of a wind power plant that produces electricity through wind and to calculate the reliability cost called expected energy not supplied. The main objective is to find the minimum cost for grid connection topology by submarine cables which cannot cross each other. Cable crossing was set as a constraint in the optimization algorithm of grid topology of the wind power plant. On the basis of this method, a case study is conducted to validate the model by simulating a 100-MW OWF.