• Steel and Composite Structures
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• v.47 no.3
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• pp.375-382
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• 2023

The H-shaped steel beam is popular due to its ease of manufacturing and connection to the column. This profile, which is used as a shallow beam, needs the high weak-axis bending stiffness and torsional stiffness to meet the overall stability. Achieving the local beam flange stability, bearing capacity, bending stiffness, and torsional requirements need a great thickness and width of the beam flange, which causes, which will cause more uneconomical structural design. So, the box-section beam is the ideal alternative. However, the current design specifications do not have design rules for the bolt-and-welded connection of the box-section beam and box-section column. The paper proposes a novel bolt-and-welded connection of the box-section beams and box-section columns based on a high-rise structural design scheme. Three connection models, BASE, WBF, and RBS, are analyzed under cyclic loading in ABAQUS software. The failure modes, hysteresis response, bearing capacity, ductility, plastic rotation angle, energy dissipation, and stiffness degradation of all models are determined and compared. Compared with the other two models, the model WBF exhibited excellent seismic performance, ductility, and plastic rotation ability. Finally, model WBF was chosen as the connection scheme used in the project design.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on 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 analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.198-207
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• 2023

Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2E
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• pp.27-31
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• 2000

Evaluating log-likelihood of Gaussian mixture density is major computational burden for the keyword spotting system using continuous HMM. In this paper, we employ the bucket box intersection (BBI) algorithm to reduce the computational complexity of keyword spotting. We make some modification in implementing BBI algorithm in order to increase the discrimination ability among the keyword models. According to our keyword spotting experiments, the modified BBI algorithm reduces 50% of log-likelihood computations without performance degradation, while the original BBI algorithm under the same condition reduces only 30% of log-likelihood computations.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Journal of KIBIM
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• v.8 no.4
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• pp.13-22
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• 2018

Future performance prediction of bridges is challenging task for structural engineers. Well-organized information from design, construction and operation stages is essential for the assessment of structures. Digital twin model is a new concept to realize more reliable data platform for management of infrastructures. Damage history including degradation of material, cracking, corrosion, etc. needs to be accumulated in the digital model. The digital model is linked to the analysis model for the assessment of structural performance considering changed mechanical properties of structural components. In this paper, initial definition digital twin model of a PSC-I girder bridge is proposed.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.4
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• pp.16-24
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• 2013

The analysis of performance and internal flow according to various numerical models for interior ballistics has been conducted. The initial flow has been mainly affected by the drag model of propellants and their drag degradation reduces oscillations of differential pressure between the breech and the shot base. Models of Nusselt number haven't influenced the major performance of interior ballistics. The negative differential pressure isn't generated in the case without the heat transfer of propellants.

• Steel and Composite Structures
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• v.19 no.3
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• pp.547-567
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• 2015

The paper makes the experimental and finite-element-analysis investigation on the seismic behavior of the rebar-penetrated joint between gangue concrete filled steel tubular column and reinforced gangue concrete beam under low cyclic reversed loading. Two specimens are designed and conducted for the experiment to study the seismic behavior of the rebar-penetrated joint under cyclic loading. Then, finite element analysis models of the rebar-penetrated joint are developed using ABAQUS 6.10 to serve as the complement of the experiment and further analyze the seismic behavior of the rebar-penetrated joint. Finite element analysis models are also verified by the experimental results. Finally, the hysteretic performance, the bearing capacity, the strength degradation, the rigidity degradation, the ductility and the energy dissipation of the rebar-penetrated joint are evaluated in detail to investigate the seismic behavior of the rebar-penetrated joint through experimental results and finite element analysis results. The research demonstrates that the rebar-penetrated joint between gangue concrete filled steel tubular column and reinforced gangue concrete beam, with full and spindle-shaped load-displacement hysteretic curves, shows generally the high ductility and the outstanding energy-dissipation capacity. As a result, the rebar-penetrated joint exhibits the excellent seismic performance and meets the earthquake-resistant requirements of the codes in China. The research provides some references and suggestions for the application of the rebar-penetrated joint in the projects.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.290-305
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• 2022

Purpose: Currently, port facility informatization technology is focused on the planning and design phases, so the necessity of research and technology development on the port facility maintenance system based on life cycle-level information is emphasized. Method: Based on the maintenance history data of port facilities and facility operation information, from the perspective of the life cycle of port facilities, the system is configured to enable maintenance decisions for port facilities through analysis of aging patterns, performance degradation prediction models, and risk analysis and proposed a method of expressing information. Result: A function was developed to simultaneously display the SOC performance evaluation and the comprehensive performance evaluation developed in this study, so that mid-to long-term maintenance and reinforcement and facility expansion can be applied and comparatively judged. Conclusion: The integrated port performance system developed in this study induces and supports the risk minimization of port facility management by proactively promoting appropriate repair and reinforcement measures through historical and operational information on port facilities.