• Earthquakes and Structures
• /
• v.5 no.1
• /
• pp.83-109
• /
• 2013

A simple damper optimization method is proposed to find optimal damper allocation for shear buildings under both target added damping ratio and interstorey drift ratio (IDR). The damping coefficients of added dampers are considered as design variables. The cost, which is defined as the sum of damping coefficient of added dampers, is minimized under a target added damping ratio and the upper and the lower constraint of the design variables. In the first stage of proposed algorithm, Simulated Annealing, Nelder Mead and Differential Evolution numerical algorithms are used to solve the proposed optimization problem. The candidate optimal design obtained in the first stage is tested in terms of the IDRs using linear time history analyses for a design earthquake in the second stage. If all IDRs are below the allowable level, iteration of the algorithm is stopped; otherwise, the iteration continues increasing the target damping ratio. By this way, a structural response IDR is also taken into consideration using a snap-back test. In this study, the effects of the selection of upper limit for added dampers, the storey mass distribution and the storey stiffness distribution are all investigated in terms of damper distributions, cost function, added damping ratio and IDRs for 6-storey shear building models. The results of the proposed method are compared with two existing methods in the literature. Optimal designs are also compared with uniform designs according to both IDRs and added damping ratios. The numerical results show that the proposed damper optimization method is easy to apply and is efficient to find optimal damper distribution for a target damping ratio and allowable IDR value.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.67-79
• /
• 2021

Lessons Learned(LL) is a military term defined as all activities that promote future development by finding problems and need improvement in education and reality in the field of warfare development. In this paper, we focus on presenting actual examples and applying AI analysis inference techniques to solve revealed problems in promoting LL activities, such as long-term analysis, budget problems, and necessary expertise. AI legal advice services using cognitive computing-related technologies that have already been practical and in use, were judged to be the best examples to solve the problems of LL. This paper presents intelligent LL inference techniques, which utilize AI. To this end, we want to explore theoretical backgrounds such as LL analysis definitions and examples, evolution of AI into Machine Learning, cognitive computing, and apply it to new technologies in the defense sector using the newly proposed L²-OODA ensemble algorithm to contribute to implementing existing power improvement and optimization.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.31 no.6
• /
• pp.258-263
• /
• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.421-436
• /
• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of the Korea Society for Simulation
• /
• v.21 no.4
• /
• pp.65-73
• /
• 2012

Due to the development of technology related to a weapon system and the info-communication, the battle system of a warship has to manage many kinds of human intervention tactics according to the complicated battlefield environment. Therefore, many kinds of studies about M&S(Modeling & Simulation) have been carried out recently. The previous M&S system based on an agent, however, has simply used non-flexible(or fixed) tactics. In this paper, we propose an agent modeling methodology which has reinforcement learning function for spontaneous(active) reaction and generation evolution learning Function using Genetic Algorithm for more proper reaction for warship battle. We experiment with virtual 1:1 warship combat simulation on the west sea so as to test validity of our proposed methodology. We consequently show the possibility of both reinforcement and evolution learning in a warship battle.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.37 no.5
• /
• pp.29-44
• /
• 2000

Adaptation of operator probabilities is one of the most important and promising issues in evolutionary computation areas. This is because the setting of appropriate probabilities is not only very tedious and difficult but very important to the performance improvement of genetic algorithms. Many researchers have introduced their algorithms for setting or adapting operator probabilities. Experimental results in most previous works, however, have not been satisfiable. Moreover, Tuson have insisted that “the adaptation is not necessarily a good thing” in his papers［$^1$$^2$］. In this paper, we propose a self-tuning scheme for adapting operator probabilities in genetic algorithms. Our scheme was extensively tested on four function optimization problems and one combinational problem； and compared to simple genetic algorithms with constant probabilities and adaptive genetic algorithm proposed by Srinivas et al［$^3$］. Experimental results showed that our scheme was superior to the others. Our scheme compared with previous works has three advantages： less computational efforts, co-evolution without additional operations for evolution of probabilities, and no need of additional parameters.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.3A
• /
• pp.205-214
• /
• 2011

In 3rd generation project partnership long term evolution, wireless multicast techniques which send the same data to multiple users under single frequency networks have attracted much attention. In the multicast system, the transmission mode needs to be selected for efficient data transfer while satisfying the multicast coverage requirement. To achieve this, users' channel state information (CSI) should be available at the transmitter. However, it requires too much uplink feedback resource if all the users are allowed to transmit their CSI at all the time. To solve this problem, in this paper, the multicast coverage prediction is suggested. In the proposed algorithm, each user measures its transition probabilities between the success and the fail state of the decoding. Then, it periodically transmits its CSI to the basestation. Using these feedbacks, the basestation can predict the multicast coverage. From the simulation results, we demonstrate that the proposed scheme can predict the multicast system coverage.

• Journal of Korea Society of Industrial Information Systems
• /
• v.15 no.4
• /
• pp.7-15
• /
• 2010

This study proposes a rad o resource management scheme to maximize the performance of the LTE(Long Term Evolution) uplink, using SC-FDMA(Single Carrier-Frequency Division Multiple Access). Rather than the single-cell SC-FDMA system the existing studies are mainly concerning, this study focuses on multi-cell system which needs considering the interaction among cells. Radio resource management is divided into two phases, planning and operation phases. The former is for the master eNB(e-NodeB) to allocate RBs(radio bearer) to eNB, the latter for eNB to assign RBs to the mobiles in the cell. For each phase, an optimization model and greedy algorithm are proposed. Optimization models aim to maximize the system performance while satisfying the constraints for both QoS and RB continuity. The greedy algorithms, like generic ones, move from a solution to a neighboring one having the best objective value among neighboring ones. From the numerous numerical experiments, the performance and characteristics of the algorithms are analyzed. This study is expected to play a volunteering role in radio resource management for the multi-cell SC-FDMA system.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.38.2-38.2
• /
• 2020

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1 kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. Inside the simulation box. we zoom-in on a high-resolution cuboid region with a volume of 1049 × 114 × 114 Mpc3. The subgrid physics chosen to model galaxy formation includes radiative heating/cooling, reionization, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OpenMP version of the RAMSES code, specifically targeted for modern many-core many thread parallel architectures. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder to analyse the large outputs of HR5. The simulation successfully reproduces many observations, such as the cosmic star formation history, connectivity of galaxy distribution and stellar mass functions. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.3
• /
• pp.253-261
• /
• 2004

Game theory is mathematical analysis developed to study involved in making decisions. In 1928, Von Neumann proved that every two-person, zero-sum game with finitely many pure strategies for each player is deterministic. As well, in the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) introduced by Maynard Smith. Keeping pace with these game theoretical studies, the first computer simulation of co-evolution was tried out by Hillis in 1991. Moreover, Kauffman proposed NK model to analyze co-evolutionary dynamics between different species. He showed how co-evolutionary phenomenon reaches static states and that these states are Nash equilibrium or ESS introduced in game theory. Since the studies about co-evolutionary phenomenon were started, however many other researchers have developed co-evolutionary algorithms, in this paper we propose Game theory based Co-Evolutionary Algorithm (GCEA) and confirm that this algorithm can be a solution of evolutionary problems by searching the ESS.To evaluate newly designed GCEA approach, we solve several test Multi-objective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by co-evolutionary algorithm and analyze optimization performance of GCEA by comparing experimental results using GCEA with the results using other evolutionary optimization algorithms.