• Steel and Composite Structures
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• v.47 no.2
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• pp.147-165
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• 2023

The paper contrasts conventional seismic design with a design that incorporates buckling-restrained bracing in three-dimensional reinforced concrete buildings (BRBs). The suboptimal structures may be found using the multi-objective chimp optimization algorithm (MEN-ChOA). Given the constraints and dimensions, ChOA suffers from a slow convergence rate and tends to become stuck in local minima. Therefore, the ChOA is improved by niching and evolutionary operators to overcome the aforementioned problems. In addition, a new technique is presented to compute seismic and dead loads that include all of a structure's parts in an algorithm for three-dimensional frame design rather than only using structural elements. The performance of the constructed multi-objective model is evaluated using 12 standard multi-objective benchmarks proposed in IEEE congress on evolutionary computation. Second, MEN-ChOA is employed in constructing several reinforced concrete structures by the Mexico City building code. The variety of Pareto optimum fronts of these criteria enables a thorough performance examination of the MEN-ChOA. The results also reveal that BRB frames with comparable structural performance to conventional moment-resistant reinforced concrete framed buildings are more cost-effective when reinforced concrete building height rises. Structural performance and building cost may improve by using a nature-inspired strategy based on MEN-ChOA in structural design work.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• Journal of Korean Neurosurgical Society
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• v.30 no.sup1
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• pp.55-60
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• 2001

Objective : The purpose of the present study was to examine neurologic changes, fusion rate and degree of kyphosis from the surgical results of those patients who underwent multi-level anterior interbody fusion and internal fixation. Methods : Among 63 cases of the patients who received multi-level anterior interbody fusion and internal fixation in 5 years between 1995 to 1999 at the neurosurgery department, we performed a retrospective study in 52 cases that could be followed up with dynamic view imaging ; the results were compared and analyzed. The analysis was based on the results of history taking, physical findings and radiologic findings, and Odom criteria were used to classify those cases with neurologic changes. Results : Among those 52 cases in whom the follow-up was possible for at least a year and dynamic view imaging was possible, bone fusion was seen in 93% of the trauma cases and 95% in the non-trauma cases and overall bone fusion was observed in 94% of the cases. Bone fusion was seen in 93% of the autobone cases, 95% of the allobone cases, and 94% of the Mesh cases. Radiologic changes were observed by comparing the lateral view after surgery ; kyphosis was seen in 53% of the autobone cases, in 70% of the allobone cases, and in 35% of Mesh cases ; in 45% and 60% of the non-trauma cases and trauma cases, respectively ; and in 55% of the 2 level fusion cases and 46% of the 3 level fusion cases. Neurologic changes classified according to Odom criteria showed excellent result in 48% of all the cases, good in 23%, fair in 4%, and poor in 25%. Conclusion : Even those cases with multi-level fusion, a high fusion rate could be obtained by performing anterior interbody fusion and internal fixation ; those cases with kyphosis were related more with the presence or absence of posterior compartment injury rather than the fusion level ; and those trauma cases showed not much difference in the fusion rate compared with non-trauma cases but had a high possibility of kyphosis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.10-24
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• 2014

In-vehicle warning information systems(IWIS) is an effective countermeasure for preventing traffic crashes. It provides drivers with warnng messages about upcoming hazards to draw proper evasive maneuvering. This study developed a methodology for evaluating the effectiveness of IWIS based on an integrated index to identify driver's responsive behavior. The proposed index consists of characteristics of longitudinal and lateral behavior of vehicle maneuverings. Also, a method to assign mixed-weights in the context of multi-criteria decision making framework was adopted to develop the evaluation method. It is expected that the outcome of this study is useful in designing more effective in-vehicle warning information systems.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.311-317
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• 2015

It is important to analyze and offer specialized start-ups effect factors for collegians in order to systematically and successfully support technology-centered start-ups of collegians, whose social experience is insufficient. However, consistently changing start ups environmental analysis is difficult, which can be a problem, because technology-centered start-ups factor analysis is carried out depending on statistical package. This paper proposes an ATSA system that demonstrates effect factors on technology-centered start-ups decision making as a hierarchial structure by using AHP, inputs the effect factors to judgment matrix after pre-processing, calculates standardized values and weights, verifies consistency, and draws priorities through weights integration. It was confirmed that the ATSA system can efficiently support decision making for technology-centered start-ups by quantitatively analyzing qualitative factors through experiments by applying multi-criteria decision making to the analyses of start-ups founders' internal and external factors and various start-ups environments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.7
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• pp.727-734
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• 2014

This paper describes multi-scale based ductile fracture simulation using finite element (FE) damage analysis. The maximum and crack initiation loads of cracked components were predicted using proposed virtual testing method. To apply the local approach criteria for ductile fracture, stress-modified fracture strain model was adopted as the damage criteria with modified calibration technique that only requires tensile and fracture toughness test data. Element-size-dependent critical damage model is also introduced to apply the proposed ductile fracture simulation to large-scale components. The results of the simulation were compared with those of the tests on SA333 Gr. 6 full-scale pipes at $288^{\circ}C$, performed by the Battelle Memorial Institute.

• Journal of Korea Water Resources Association
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• v.41 no.4
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• pp.365-377
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• 2008

In this study, PROMETHEE(Preference Ranking Organization METHod for Enrichment Evaluations) which is one of the multi criteria decision making methods is applied to estimate the relative inundation risk of the urban subcatchment. For this purpose, several factors which have an effect on the inundation risk are selected and used to perform PROMETHEE. Those are elevation average, slope average, density of conduit, population and sediment yields per unit area of each subcatchment. Based on them, PROMETHEE is performed and the relative inundation risk for each subcatchment is estimated. For the validation of the suggested method, the results from the suggested method are compared with the historical inundation records occured on 1998 and the relative inundation risk estimated by the method considering sediment yields per unit area only. From the comparison, it is found that the suggested method may generate better results to estimate the relative inundation risk of each subcatchment than the method considering sediment yields per unit area only. Also, it can be applied to establish a rehabilitation order of subcatchments for mitigating the inundation risk.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Korean Journal of Human Ecology
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• v.20 no.2
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• pp.391-400
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• 2011

Internet shopping malls for fashion products(e.g., apparel, cosmetics and accessory) may become a major player with a promising future because of its tremendous growth in e-commerce. In addition, the fashion market has been segmented by various types of shopping malls on the internet. For many types of internet shopping malls, literatures give us numerous types, such as general mall, specialty mall, open-market, mall-in-mall, department-mall, brand-mall, and a specialized category mall, etc. Although each mall specializes in different activities, a unified categorization with managerially meaningful implications has not been made. This paper aims to explore criteria of internet shopping malls based on previous research related to shopping mall types for fashion products. The results found that internet shopping malls for fashion products were classified based on physical space, openness of the mall, number of companies, method of profit, specialization of products, number of product categories, and brand products dealt with. Internet shopping mall for fashion products was classified into online malls versus online malls versus offline mall, open mall versus closed mall, single mall versus multi mall, retail-trade mall versus syndicated mall, general mall vs specialize mall, one-product category mall versus multi-product category mall, and brand mall versus non-brand mall. These findings could offer an important contribution in research and practice, and an insight into developing appropriate strategies for effective fashion shopping mall management related products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.