• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.109-116
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• 2021

Since RDF (Resource Description Framework) triples are modeled as graph, we cannot directly adopt existing solutions in relational databases and XML technology. In order to store, index, and query Linked Data more efficiently, we propose a convergence indexing method combined R*-tree and K-dimensional trees. This method uses a hybrid storage system based on HDD (Hard Disk Drive) and SSD (Solid State Drive) devices, and a separated filter and refinement index structure to filter unnecessary data and further refine the immediate result. We perform performance comparisons based on three standard join retrieval algorithms. The experimental results demonstrate that our method has achieved remarkable performance compared to other existing methods such as Quad and Darq.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.222-229
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• 2003

In this paper, we address a layout design problem, PTN[2], for determining an appropriate 2-class-based dedicated storage layout in a class of unit load storage systems. Our strong conjecture is that PTNI2] is NP-hard. Restricting PTN[2], we provide three solvable cases of PTN[2] in which an optimal solution to the solvable cases is one of the partitions based on the PAI(product activity index)-nonincreasing ordering. However, we show with a counterexample that a solution based on the PAI-non increasing ordering does not always give an optimal solution to PTN[2]. Utilizing the derived properties, we construct an effective heuristic algorithm for solving PTN[2] based on a PAI-non increasing ordering with performance ratio bound. Our algorithm with O($n^2$) is effective in the sense that it guarantees a better class-based storage layout than a randomized storage layout in terms of the expected single command travel time.

• Transactions on Electrical and Electronic Materials
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• v.17 no.3
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• pp.134-138
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• 2016

The fullerene solar cells are becoming a feasible choice due to the advanced developments in donor materials and improved fabrication techniques of devices. Recently, sufficient optimization and improvements in the processing techniques like incorporation of solvent vapor annealing (SVA) with additives in solvents has become a major cause of prominent improvements in the performance of organic solar cell-based devices . On the other hand, the challenge of reduced open circuit voltage (V_oc) remains. This study presents an approach for significant performance improvement of overall device based on organic small molecular solar cells (SMSCs) by following a two step technique that comprises thermal annealing (TA) and SVA (abbreviated as SVA+TA). In case of exclusive use of SVA, reduction in V_oc can be eliminated in an effective way. The characteristics of charge carriers can be determined by the measurement of transient photo-voltage (TPV) and transient photo-current (TPC) that determines the scope for improvement in the performance of device by two step annealing. The recovery of reduced V_oc is linked with the necessary change in the dynamics of charge that lead to increased overall performance of device. Moreover, SVA and TA complement each other; therefore, two step annealing technique is an appropriate way to simultaneously improve the parameters such as V_oc, fill factor (FF), short circuit current density (J_sc) and PCE of small molecular solar cells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.542-558
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• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• Structural Engineering and Mechanics
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• v.55 no.1
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• pp.229-244
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• 2015

H-infinity norm relates to the maximum in the frequency response function and H-infinity control method focuses on the case that the vibration is excited at the fundamental frequency, while 2-norm relates to the output energy of systems with the input of pulses or white noises and 2-norm control method weighs the overall vibration performance of systems. The trade-off between the performance in frequency-domain and that in time-domain may be achieved by integrating two indices in the mixed vibration control method. Based on the linear fractional state space representation in the modal space for a piezoelectric flexible structure with uncertain modal parameters and un-modeled residual high-frequency modes, a mixed dynamic output feedback control design method is proposed to suppress the structural vibration. Using the linear matrix inequality (LMI) technique, the initial populations are generated by the designing of robust control laws with different H-infinity performance indices before the robust 2-norm performance index of the closed-loop system is included in the fitness function of optimization. A flexible beam structure with a piezoelectric sensor and a piezoelectric actuator are used as the subject for numerical studies. Compared with the velocity feedback control method, the numerical simulation results show the effectiveness of the proposed method.

• Earthquakes and Structures
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• v.8 no.1
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• pp.57-76
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• 2015

The Performance-based Earthquake Engineering (PBEE) concept implies the definition of multiple target performance levels of damage which are expected to be achieved (or not exceeded), when the structure is subjected to earthquake ground motion of specified intensity. These levels are associates to different return period (RP) of earthquakes and structural behaviors quantified with adopted factors or indexes of control. In this work an 8-level PBEE study is carried out, finding different curves for control index or Engineering Demand Parameters (EDP) of levels that assess the structural behavior. The results and the curves for each index of control allow to deduce the structural behavior at an a priori unspecified RP. A general methodology is proposed that takes into account a possible optimization process in the PBEE field. Finally, an application to 8-level seismic performance assessment to structure in a Spanish seismic zone permits deducing that its behavior is deficient for high seismic levels (RP > 475 years). The application of the methodology to a low-to-moderate seismic zone case proves to be a good tool of structural seismic design, applying a more sophisticated although simple PBEE formulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.134-139
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• 2009

Various modules are generally combined with one another in order to perform rotorcraft preliminary design and its optimization. At the stage of the preliminary design, analysis fidelity is less important than the rapid assessment of a design is. Most of the previous researchers attempted to implement sophisticated applications in order to increase the fidelity of analysis, but the present paper focuses on a rapid assessment while keeping the similar level of fidelity. Each small-sized module will be controlled by an externally-operated global optimization module. Results from each module are automatically handled from one discipline to another which reduces the amount of computational effort and time greatly when compared with manual execution. Automatically handled process decreases computational cycle and time by factor of approximately two. Previous researchers and the rotorcraft industries developed their own integrated analysis for rotorcraft design task, such as HESCOMP, VASCOMP, and RWSIZE. When a specific mission profile is given to these programs, those will estimate the aircraft size, performance, rotor performance, component weight, and other aspects. Such results can become good sources for the supplemental analysis in terms of stability, handling qualities, and cost. If the results do not satisfy the stability criteria or other constraints, additional sizing processes may be used to re-evaluate rotorcraft size based on the result from stability analysis. Trade-off study can be conducted by connecting disciplines, and it is an important advantage in a preliminary design study. In this paper among the existing rotorcraft design programs, an adequate program is selected for a baseline of the design framework, and modularization strategy will be applied and further improvements for each module be pursued.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.751-756
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• 2003

Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.