• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.113-125
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• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.1 s.71
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• pp.93-103
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• 2006

It is absolutely essential that safety assessment of the containment buildings during service life because containment buildings are last barrier to protect radioactive substance due to the accidents. Therefore, this study describes an enhanced degenerated shell finite element(FE) which has been developed for nonlinear FE analysis of reinforced concrete(RC) containment buildings with elasto-plastic material model. For the purpose of the material nonlinear analysis, Drucker-Prager failure criteria is adapted in compression region and material parameters which determine the shape of the failure envelop are derived from biaxial stress tests. Reissner-Mindlin(RM) assumptions are adopted to develop the degenerated shell FE so that transverse shear deformation effects is considered. However, it is found that there are serious defects such as locking phenomena in RM degenerated shell FE since the stiffness matrix has been overestimated in some situations. Therefore, shell formulation is provided in this paper with emphasis on the terms related to the stiffness matrix based on assumed strain method. Finally, the performance of the present shell element to analysis RC containment buildings is tested and demonstrated with several numerical examples. From the numerical tests, the present results show a good agreement with experimental data or other numerical results.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.326-335
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• 2002

Recently there have been a number of researches to provide code mobility to lazy functional language (LFL) programs by translating LFL programs to Java programs. These approaches are basically baled on architectural similarities between abstract machines of LFLs and Java. The abstract machines of LFLs and Java programming language, Spineless Tagless G-Machine(STGM) and Java Virtual Machine(JVM) respectively, share important common features such as built- in garbage collector and stack machine architecture. Thus, we can provide code mobility to LFLs by translating LFLs to Java utilizing these common features. In this paper, we propose a new translation scheme which fully utilizes architectural common features between STGM and JVM. By redefining STGM as an eval-apply evaluation model, we have defined a new translation scheme which utilizes Java Virtual Machine Stack for function evaluation and totally eliminates stack simulation which causes array manipulation overhead in Java. Benchmark program translated to Java programs by our translation scheme run faster on JDK 1.3 than those translated by the previous schemes.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.335-347
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• 2024

Open-vocabulary 3D point cloud instance segmentation (OV-3DIS) is a challenging visual task to segment a 3D scene point cloud into object instances of both base and novel classes. In this paper, we propose a novel model Open3DME for OV-3DIS to address important design issues and overcome limitations of the existing approaches. First, in order to improve the quality of class-agnostic 3D masks, our model makes use of T3DIS, an advanced Transformer-based 3D point cloud instance segmentation model, as mask proposal module. Second, in order to obtain semantically text-aligned visual features of each point cloud segment, our model extracts both 2D and 3D features from the point cloud and the corresponding multi-view RGB images by using pretrained CLIP and OpenSeg encoders respectively. Last, to effectively make use of both 2D and 3D visual features of each point cloud segment during label assignment, our model adopts a unique feature ensemble method. To validate our model, we conducted both quantitative and qualitative experiments on ScanNet-V2 benchmark dataset, demonstrating significant performance gains.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.400-425
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• 2018

This study presents the research results and current status of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to simulate the coupled hydro-mechanical behavior of fault, including slip or reactivation, induced by water injection. The first research step of Task B is a benchmark simulation which is designed for the modelling teams to familiarize themselves with the problem and to set up their own codes to reproduce the hydro-mechanical coupling between the fault hydraulic transmissivity and the mechanically-induced displacement. We reproduced the coupled hydro-mechanical process of fault slip using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. A methodology to formulate the hydro-mechanical coupling relations of two different hydraulic aperture models and link the solid element of TOUGH2 and the interface element of FLAC3D was suggested. In addition, we developed a coupling module to update the changes in geometric features (mesh) and hydrological properties of fault caused by water injection at every calculation step for TOUGH-FLAC simulator. Then, the transient responses of the fault, including elastic deformation, reactivation, progressive evolutions of pathway, pressure distribution and water injection rate, to stepwise pressurization were examined during the simulations. The results of the simulations suggest that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOLVAEX-2019 Task B and validated using the field data from fault activation experiments in a further study.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.209-218
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• 2012

Recently, the Field Programmable Stateful Logic Array (FPSLA) was proposed as one of the most promising system integration technologies which will extend the life of the Moore's law. This work is the first proposal of the FPSLA design automation flow, and the approaches to logic synthesis, synchronization, physical mapping, and automatic placement of the FPSLA designs. The synchronization at each gate for pipelining determines the x-coordinates of cells, and reduces the placement to 1-dimensional problems. The objective function and its gradients for the non-linear optimization of the net length and placement density have been remodeled for the reduced global placement problem. Also, a recursive algorithm has been proposed to legalize the placement by relaxing the density overflow of bipartite bin groups in a top-down hierarchical fashion. The proposed model and algorithm are implemented, and validated by applying them to the ACM/SIGDA benchmark designs. The output state of a gate in an FPSLA needs to be duplicated so that each fanout gate can be connected to a dedicated copy. This property has been taken into account by merging the duplicated nets into a hyperedge, and then, splitting the hyperedge into edges as the optimization progresses. This yields additional 18.4% of the cell count reduction in the most dense logic stage. The practicality of the FPSLA can be further enhanced primarily by incorporating into the logic synthesis the constraint to avoid the concentrated fains of gates on some logic stages. In addition, an efficient algorithm needs to be devised for the routing problem which is based on a complicated graph. The graph models the nanowire crossbar which is trimmed to be embedded into the FPSLA fabric, and therefore, asymmetric. These CAD tools can be used to evaluate the fabric efficiency during the architecture enhancement as well as automate the design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.69-78
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• 2011

In this study, an optimization method using multi-objective genetic algorithm(MOGA) has been proposed to develop a fuzzy control algorithm that can effectively control a smart tuned mass damper(TMD). A 76-story benchmark building subjected to wind load was selected as an example structure. The smart TMD consists of 100kN MR damper and the natural period of the smart TMD was tuned to the first mode natural period of the example structure. Damping force of MR damper is controlled to reduce the wind-induced responses of the example structure by a fuzzy logic controller. Two input variables of the fuzzy logic controller are the acceleration of 75th floor and the displacement of the smart TMD and the output variable is the command voltage sent to MR damper. Multi-objective genetic algorithm(NSGA-II) was used for optimization of the fuzzy logic controller and the acceleration of 75th story and the displacement of the smart TMD were used as objective function. After optimization, a series of fuzzy logic controllers which could appropriately reduce both wind responses of the building and smart TMD were obtained. Based on numerical results, it has been shown that the control performance of the smart TMD is much better than that of the passive TMD and it is even better than that of the sample active TMD in some cases.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.719-729
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• 2000

Prefetching aims at reducing memory latency by fetching, in advance, data that are likely to be requested by the processor in a near future. The effectiveness of prefetching is determined by how accurate the prediction on the needed instructions and data is. Most previous studies on prefetching were limited to proposing a particular prefetch scheme and its performance evaluation, paying little attention to theoretical aspects of prefetching. This paper focuses on the theoretical aspects of instruction prefetching. For this purpose, we propose a clairvoyant prefetch model that makes use of perfect history information. Based on this theoretical model, we analyzed upper limits on the prefetch prediction accuracies of the SPEC benchmarks. The results show that the prefetch prediction accuracy is very high when there is no cache. However, as the size of the instruction cache increases, the prefetch prediction accuracy drops drastically. For example, in the case of the spice benchmark, the prefetch prediction accuracy drops from 53% to 39% when the cache size increases from 2Kbyte to 16Kbyte (assuming 16byte block size). These results indicate that as the cache size increases, most localities are captured by the cache and that instruction prefetching based on the information extracted from the references that missed in the cache suffers from prediction inaccuracies