• Journal of Technologic Dentistry
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• v.42 no.2
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• pp.91-98
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• 2020

Purpose: This study is to compare the flexural strength and modulus by inserting a mesh and stick type fiberglass reinforcement into resin specimens. Methods: Wax specimens (length 64 mm, width 39 mm, thickness 5 mm) are prepared according to ISO 20795-1:2013. Mesh type and stick type glass fiber reinforcements were prepared. The prepared wax specimens were used plaster and flask for investment. The flask was separated and the wax was removed. The heat curing resin was injected into the flask, and then a mesh type and stick type fiberglass reinforcement were inserted. The prepared resin specimen was cut into three equal parts (length 64 mm, width 10 mm, thickness 3.3 mm). The mesh type glass fiber reinforcement (MT group) and the stick type glass fiber reinforcement (ST group) were classified into two groups. The prepared specimen was measured using a universal testing machine (UTM). The data were analyzed by Mann-Whitney U test, and the significance level was set to 0.05. Results: In the flexural strength, the ST group was higher than the MT group, and there was a significant difference between the two groups (p<0.05). In the flexural modulus, the ST group was higher than the MT group, and there was a significant difference between the two groups (p<0.05). Conclusion: The stick-type glass fiber inreased the flexural strength than the mesh-type glass fiber reinforcement.

• International Journal of Highway Engineering
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• v.17 no.3
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• pp.77-83
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• 2015

PURPOSES : In this study, a fracture-based finite element (FE) model is proposed to evaluate the fracture behavior of fiber-reinforced asphalt (FRA) concrete under various interface conditions. METHODS : A fracture-based FE model was developed to simulate a double-edge notched tension (DENT) test. A cohesive zone model (CZM) and linear viscoelastic model were implemented to model the fracture behavior and viscous behavior of the FRA concrete, respectively. Three models were developed to characterize the behavior of interfacial bonding between the fiber reinforcement and surrounding materials. In the first model, the fracture property of the asphalt concrete was modified to study the effect of fiber reinforcement. In the second model, spring elements were used to simulated the fiber reinforcement. In the third method, bar and spring elements, based on a nonlinear bond-slip model, were used to simulate the fiber reinforcement and interfacial bonding conditions. The performance of the FRA in resisting crack development under various interfacial conditions was evaluated. RESULTS : The elastic modulus of the fibers was not sensitive to the behavior of the FRA in the DENT test before crack initiation. After crack development, the fracture resistance of the FRA was found to have enhanced considerably as the elastic modulus of the fibers increased from 450 MPa to 900 MPa. When the adhesion between the fibers and asphalt concrete was sufficiently high, the fiber reinforcement was effective. It means that the interfacial bonding conditions affect the fracture resistance of the FRA significantly. CONCLUSIONS : The bar/spring element models were more effective in representing the local behavior of the fibers and interfacial bonding than the fracture energy approach. The reinforcement effect is more significant after crack initiation, as the fibers can be pulled out sufficiently. Both the elastic modulus of the fiber reinforcement and the interfacial bonding were significant in controlling crack development in the FRA.

• Journal of Korean Association for Spatial Structures
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• v.3 no.3 s.9
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• pp.29-37
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• 2003

• Journal of Oriental Neuropsychiatry
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• v.21 no.2
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• pp.87-101
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• 2010

Objectives : The aim of this experiment was to investigate the influences of Autonomic Nervous System and EEG by conducting Reinforcement-Reduction(補瀉) acupuncture stimulation to compare the changes in acupoints on the body before and after treatment of acupuncture at Xingjian(LR2) being referred as Fire-point(火穴) and Ququan(LR8) being referred as Water-point(水穴) of Yin Liver Meridian(足厥陰肝經). Methods : This study was carried out on 30 healthy female volunteers in their 20's. There were four tests conducted throughout and the period for each test was between 2 to 5days. HRV and EEG were measured for 5 minutes before acupuncture stimulation was conducted on LR2-Reinforcement, LR2-Reduction, LR8-Reinforcement and LR8-Reduction at random. During the 20 minutes of acupuncture treatment, same subjects were measured simultaneously to observe any significant changes in acupoints. Again, the same subjects were measured for 5 minutes after removing the acupuncture in order to perform a comparative analysis. Results : The measurement of HRV showed that LF, LFnorm and LF/HF ratio increased significantly(p<0.05) while HF, HF norm decreased significantly in case of LR2-Reinforcement & LR8-Reduction. Both LR2-Reduction and LR8-Reinforcement induced a significant increase in HFnorm. EEG measurement indicated low $\alpha$ wave decreased and high $\beta$ wave increased significantly at LR2-Reinforcement during post-acupuncture period compared with acupuncture stimulation period. Both LR2-Reduction and LR8-Reinforcement developed significantly low $\alpha$ wave and high $\alpha$ wave. High $\beta$ wave increased significantly at LR8-Reduction during the acupuncture stimulation in comparison with pre-acupuncture period. Conclusions : The manipulation of acupuncture stimulation at LR2-Reinforcement and LR8-Reduction enhanced the activity of sympathetic nerves and the state of arousal while that of para sympathetic nerves declined. On the other hand, LR2-Reduction and LR8-Reinforcement developed the levels of para sympathetic nerves and relaxation.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.380-383
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• 2003

Deep pile caps usually contain no transverse shear reinforcement and only small percentages of longitudinal reinforcement. The current design procedures including ACI 318-02 for the pile caps do not provide engineers with a clear understanding of the physical behavior of deep pile caps. In this study, the failure strengths of nine pile cap specimens tested to failure were evaluated using 3-dimensional strut-tie models. The analysis results obtained from the present study were compared with those obtained from several design methods, and the validity of the present method implementing 3-dimensional strut-tie models was examined.

• Structural Engineering and Mechanics
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• v.27 no.4
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• pp.393-408
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• 2007

This paper presents the findings of an experimental study to evaluate retrofit methods which address particular weaknesses that are often found in reinforced concrete structures, especially older structures, namely the lack of the required flexural and shear reinforcement within the columns and the lack of the required shear reinforcement within the joints. Thus, the use of a high-strength fiber jacket for cases of post-earthquake and pre-earthquake retrofitting of columns and beam-column joints was investigated experimentally. In this paper, the effectiveness of the two jacket styles was also compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.388-394
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• 2001

Reinforcement learning is a kind of unsupervised learning methods that an agent control rules from experiences acquired by interactions with environment. The eligibility is used to resolve the credit-assignment problem which is one of important problems in reinforcement learning, Conventional eligibilities such as the accumulating eligibility and the replacing eligibility are ineffective in use of rewards acquired in learning process, since on1y one executed action for a visited state is learned. In this paper, we propose a new eligibility, called the distributed eligibility, with which not only an executed action but also neighboring actions in a visited state are to be learned. The fuzzy Q-learning algorithm using the proposed eligibility is applied to a cart-pole balancing problem, which shows the superiority of the proposed method to conventional methods in terms of learning speed.

• Computers and Concrete
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• v.3 no.2_3
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• pp.79-90
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• 2006

This paper describes the results of an investigation into high mass-low velocity impact behaviour of reinforced concrete beams. Tests have been conducted on fifteen 2.7 m or 1.5 m span beams under drop-weight loads. A high-speed video camera has been used at rates of up to 4,500 frames per second in order to record the crack formation, propagation, particle spallation and scabbing. In some tests the strain in the reinforcement has been recorded using "Durham" strain gauged bars, a technique developed by Scott and Marchand (2000) in which the strain gauges are embedded in the bars, so that the strains in the reinforcement can be recorded without affecting the bond between the concrete and the reinforcement. The impact force acting on the beams has been measured using a load cell placed within the impactor. A high-speed data logging system has been used to record the impact load, strains, accelerations, etc., so that time histories can be obtained. This research has led to the development of computational techniques based on combined continuum/discontinuum methods (finite/discrete element methods) to permit the simulation of impact loaded reinforced concrete beams. The implementation has been within the software package ELFEN (2004). Beams, similar to those tested, have been analysed using ELFEN a good agreement has been obtained for both the load-time histories and the crack patterns.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.