• Physical Therapy Korea
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• v.20 no.4
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• pp.55-64
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• 2013

The first purpose was to identify the plantar pressure distributions (peak pressure, pressure integral time, and contact area) during level walking, and stair ascent and descent in asymptomatic flexible flatfoot (AFF). The second purpose was to investigate whether peak pressure data during level walking could be used to predict peak pressure during stair walking by identifying correlations between the peak pressures of level walking and stair walking. Twenty young adult subjects (8 males and 12 females, age $21.0{\pm}1.7$ years) with AFF were recruited. A distance greater than 10 mm in a navicular drop test was defined as flexible flatfoot. Each subject performed at least 10 steps during level walking, and stair ascent and descent. The plantar pressure distribution was measured in nine foot regions using a pressure measurement system. A two-way repeated analysis of variance was conducted to examine the differences in the three dependent variables with two within-subject factors (activity type and foot region). Linear regression analysis was conducted to predict peak pressure during stair walking using the peak pressure in the metatarsal regions during level walking. Significant interaction effects were observed between activity type and foot region for peak pressure (F=9.508, p<.001), pressure time integral (F=5.912, p=.003), and contact area (F=15.510, p<.001). The regression equations predicting peak pressure during stair walking accounted for variance in the range of 25.7% and 65.8%. The findings indicate that plantar pressures in AFF were influenced by both activity type and foot region. Furthermore the findings suggest that peak pressure data during level walking could be used to predict the peak pressure data during stair walking. These data collected for AFF can be useful for evaluating gait patterns and for predicting pressure data of flexible flatfoot subjects who have difficulty performing activities such as stair walking. Further studies should investigate plantar pressure distribution during various functional activities in symptomatic flexible flatfoot, and consider other predictors for regression analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Journal of Korean Society of Forest Science
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• v.105 no.3
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• pp.351-359
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• 2016

This study was conducted to analyze the effect of climate change on the tree-ring growth of Pinus koraiensis in Korea. Annual tree-ring growth data of P. koraiensis collected by the $5^{th}$ National Forest Inventory were first organized to analyze yearly growth patterns of the species. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, five clusters were identified. Yearly growing degree days and standard precipitation index based on daily mean temperature and precipitation data from 1951 to 2010 were calculated by cluster. Using the information, yearly temperature effect index(TEI) and precipitation effect index(PEI) by cluster were estimated to analyze the effect of climatic conditions on the growth of the species. Tree-ring growth estimation equations by cluster were developed by using the product of yearly TEI and PEI as independent variable. The tree-ring growth estimation equations were applied to the climate change scenarios of RCP 4.5 and RCP 8.5 for predicting the changes in tree-ring growth by cluster of P. koraiensis from 2011 to 2100. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of P. koraiensis and for predicting changes in tree-ring growth patterns caused by climate change.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.3
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• pp.271-287
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• 2013

In this study, more convenient model(S-model) for predicting ground surface settlement is developed through comparing field monitoring data of the domestic subway applied shield TBM method with conventional equation & numerical analysis. Sample stations are chosen from whole of excavation section and lateral & vertical ground surface settlement characteristic with excavation are analysed. Based on analysis result, through the comparison with actual monitoring data, the model that is possible to compute maximum surface settlement and settlement influence area is suggested with assumption that lateral surface settlement forms are composed relaxed zone and elastic zone. In addition, vertical ground surface settlement patterns with excavation are similar to cubic-function and S-model with assumption that coefficients are function of tunnel diameter and depth is suggested. Consequently, the ground surface settlement patterns are significantly similar to actual monitoring data and numerical method result. Thus, as a result, when tunnels are excavated using sheild TBM through rather soft weathered soil & rock layer, prediction of ground surface settlement with excavation using convenient S-model is practicable.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.49-59
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• 2022

In this paper, we designed a new enzyme function prediction model PSCREM based on a study that compared and evaluated CNN and LSTM/GRU models, which are the most widely used deep learning models in the field of predicting functions and structures using protein sequences in 2020, under the same conditions. Sequence evolution information was used to preserve detailed patterns which would miss in CNN convolution, and the relationship information between amino acids with functional significance was extracted through overlapping RNNs. It was referenced to feature map production. The RNN family of algorithms used in small CNN-RNN models are LSTM algorithms and GRU algorithms, which are usually stacked two to three times over 100 units, but in this paper, small RNNs consisting of 10 and 20 units are overlapped. The model used the PSSM profile, which is transformed from protein sequence data. The experiment proved 86.4% the performance for the problem of predicting the main classes of enzyme number, and it was confirmed that the performance was 84.4% accurate up to the sub-sub classes of enzyme number. Thus, PSCREM better identifies unique patterns related to protein function through overlapped RNN, and Overlapped RNN is proposed as a novel methodology for protein function and structure prediction extraction.