• Computers and Concrete
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• v.2 no.4
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• pp.267-291
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• 2005

To date, many studies have been conducted for the analysis and design of reinforced concrete members with disturbed regions. However, prestressed concrete deep beams have not been the subject of many investigations. This paper presents an evaluation of the behavior and strength of three pre-tensioned concrete deep beams failed by shear and bond slip of prestressing strands using a nonlinear strut-tie model approach. In this approach, effective prestressing forces represented by equivalent external loads are gradually introduced along strand's transfer length in the nearest strut-tie model joints, the friction at the interface of main diagonal shear cracks is modeled by the aggregate interlock struts along the direction of the cracks in strut-tie model, and an algorithm considering the effect of bond slip of prestressing strands in the strut-tie model analysis and design of pre-tensioned concrete members is implemented. Through the strut-tie model analysis of pre-tensioned concrete deep beams, the nonlinear strut-tie model approach proved to present effective solutions for predicting the essential aspects of the behavior and strength of pre-tensioned concrete deep beams. The nonlinear strut-tie model approach is capable of predicting the strength and failure modes of pre-tensioned concrete deep beams including the anchorage failure of prestressing strands and, accordingly, can be employed in the practical and precise design of pre-tensioned concrete deep beams.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.451-456
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• 2003

The behavior of concrete deep beams in shear is substantially influenced by beam size and shape, loading conditions, reinforcement details, and material properties. Therefore, it is not easy to predict the ultimate response of beams correctly and take into account all those factors in practical shear design. In this study, a grid softened strut-tie model approach for determining the shear strengths of various reinforced concrete deep beams is proposed. The validity of the approach is examined through the strength analysis of numerous reinforced concrete deep beams tested to failure. The approach can be further developed to improve the current deep beam design procedures by incorporating the actual shear resisting mechanisms of deep beams.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1994.06a
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• pp.191-198
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• 1994

A practical example of the axisymmetric deep drawing process is simulated by the elastic-plastic finite element analysis using updated Lagrangian approach considering the large deformation. An approach is suggested to solve the problem of the ductile fracture that may encounter during the deep drawing process. The result can be applied to the design of the die for the axisymmetric deep drawing.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.847-852
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• 2000

This paper presents an evaluation of the behavior and strength of two pre-tensioned concrete deep beams tested to failure with using the nonlinear strut-tie model approach. In the approach, the effective prestressing forces represented be equivalent external loads are gradually introduced along its transfer length in the nearest strut-tie model joints, the friction at the interface of main diagonal shear cracks is modeled by diagonal struts along the direction of the cracks in strut tie-model, and additional positioning of concrete ties a the place of steel ties is incorporated. Through the analysis of pre-tensioned concrete deep beams, the nonlinear strut-tie model approach proved to present effective solutions for prediction the essential aspects of the behavior and strength of pre-tensioned concrete deep beams.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.

• Structural Engineering and Mechanics
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• v.30 no.1
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• pp.77-97
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• 2008

In this paper, a strut-and-tie model approach has been proposed to directly calculate the amount of reinforcements in deep beams, and the force transfer mechanisms for this approach were investigated using linear finite element analysis. The proposed strut-and-tie model provides quite similar force transfer mechanisms to the results of linear finite element analysis for the 28 deep beams. The load-carrying capacities calculated from the proposed method are both accurate and conservative with little scatter or trends for the 214 deep beams. The deep beams have different concrete strengths including high-strength, various combinations of web reinforcements, and wide range of and a/d ratios. Good accuracy was also obtained using VecTor2, nonlinear finite element analysis tool based on the Modified Compression Field Theory. Since the proposed method provides a safe and reliable means for design of deep beams, this can serve to improve design provisions in future adjustments and development of design guidelines.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.