• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.87-92
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• 2000

In excavation of tunnels especially located in shallow depth, it is not rare to meet geological change in excavation progress worse than expected in the initial design stage. This paper present a case study on the re-design of excavation and support system of a shallow tunnel under construction where it meets the unexpected bad geological condition during excavation. The detailed geological investigation shows that the rock mass is heavily weathered and fractured with RMR value less than 20. Considering this geological condition, the design concept is focused on the reinforcement of the ground preceding the excavation of tunnel. Two design patterns, LW-grouting & forepoling with pilot tunnelling method and the steel pipe reinforced grouting method, are suggested. Numerical analysis by FLAC shows that these two patterns give the tunnel and roof ground stable in excavation process while the original design causes severe failure zone around the tunnel and floor heaving. In point of the mechanical stability and the degree of construction, the steel pipe reinforced grouting technique proved to be good for the reinforcement of heavily fractured rock mass in tunnelling. This assessment and design process would be a guide in the construction of tunnels in heavily weathered and fractured rock mass situation.

• Tunnel and Underground Space
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• v.17 no.4
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• pp.255-265
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• 2007

As the mechanism and effect range of subsidence are altered according to the various conditions (the ground condition, the earth pressure, the geometric condition of underground cavity and the structure load), the analysis and prediction of subsidence in abandoned mining area are very difficult. Also, as the geological characteristics and the mining methods are differed in each mines, the application of the pre-existing reinforcements without improvement has a lot of difficulties and limits. In this study, the various underground investigation such as long-depth core drilling, seismic tomography and BIPS (borehole image processing system) were performed, the distribution of underground cavity and coal seam and rock relaxation condition were analyzed. And we predicted the type of subsidence and estimated the subsidence by theories of mining subsidence. With these results, we analyzed the mechanism of subsidence occurrence in the research object area. Finally, we improved existing methods which were applied to the abandoned mining area and also we established the rational reinforcement for the ground and structure foundation against each subsidence cause.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3745-3751
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• 2011

In this study flexural strength of damaged concrete beams reinforced by CFS is analysed. Nonlinear section analysis is used to include stress status of tension bars and compressive concrete under loads acting on the original member at the time of strengthening. Calculated flexural strength is compared with Sin-Hong formula which is frequently used in CFS reinforcement design. Nonlinear analysis with variation of the number of strengthening CFS, the ratio of tensile reinforcement, the ratio of section dimension shows that the flexural strength of CFS reinforced beams much depends on reinforcing stage. From the result of this analysis, the flexural strength of CFS reinforced concrete beam is reduced according to the magnitude of pre-loaded service loads.

• Journal of Korean Academy of Nursing
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• v.34 no.3
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• pp.440-448
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• 2004

Purpose: This study was to evaluate the effects of a physical activity reinforcement program on exercise compliance, depression, and anxiety in continuous ambulatory peritoneal dialysis(CAPD) patients. Method: A nonequivalent control group with a pre-post test was designed. Data collection was done from December, 2002 to June, 2003 at a hoapital. The degree of depression and anxiety of the patients was assessed by the score of SCL-90-R, and exercise compliance was measured by exercise period, frequency, time and intensity. The experimental group was composed of 19 participants who were educated based on an exercise education protocol and carried out walking exercises two to four times a week after hearing verbal persuasion biweekly through the telephone or a face-to-face interview for 12 weeks, while 17 participants in control group received no intervention. Result: 1. The experimental group showed significant improvement in self-efficacy of exercise compliance (U=79.00, p=.01), exercise period ($x^2$=20.84, p=.00), exercise frequency ($x^2$=9.03, p=.0l), exercise time ($x^2$=9.03, p=.0l) and exercise intensity ($x^2$=11.09, p=.00) compared to those of the control group. 2. The experimental group showed a lower depression score (U=84.50, p=.01) than the results of the control group. 3. However, there were no changes in anxiety level compared to the control group. Conclusion: The physical activity reinforcement program was found to have an effect on exercise compliance and the depression score of CAPD patients. The results provided evidence for the importance of physical activity and verbal persuasion in CAPD patients.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.29-35
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• 2020

In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.67-76
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• 2020

This paper reports an experimental study to evaluate the flexural behavior of concrete beams reinforced using Fe based shape memory alloy (Fe-SMA) bars. For the experiment, a concrete beam of 200mm×300mm×2,200mm was produced, and a 4% pre-strained Fe-SMA bar was used as a tensile reinforcement. As experimental variables, type of tensile reinforcement (SD400, Fe-SMA), reinforcement ratio (0.2, 0.39, 0.59, 0.78), activation of Fe-SMA (activation, non-activation), and joint method of Fe-SMA bar (Continuous, welding, coupler) were considered. The electric resistance heating method was used to activate the Fe-SMA bar, and a current of 5A/㎟ was supplied until the specimen reached 160℃. After the upward displacement of the specimen due to the camber effect was stabilized, a three-point flexural loading experiment was performed using an actuator of 2,000 kN capacity. As a result of the experiment, it was found that the upward displacement occurred due to the camber effect as the Fe-SMA bar was activated. The specimen that activated the Fe-SMA bar had an initial crack at a higher load than the specimen that did not activate it. However, as with general prestressed concrete, the effect of the prestress by Fe-SMA activation on the ultimate state of the beam was insignificant.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.99-110
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• 2023

In this study, in order to strengthen the AI utilization competency of pre-service secondary science teachers, a project activity in which pre-service teachers directly create an 'AI-based molecular structure customized learning support tool' by using Google's teachable machine was developed and applied. To this end, the program developed for 26 third-grade pre-service teachers enrolled in the Department of Chemistry Education at H University in Chungcheongbuk-do was applied for 14 sessions during extracurricular activities. Then, the perceptions of 'understanding how AI works', 'efficacy of using AI in science classes', and 'plans to utilize AI in science classes' were investigated. As a result of the study, it was found that the program developed in this study was effective in helping pre-service teachers understand the operating principle of AI technology for machine learning at a basic level and learning how to use it. In addition, the program developed in this study was found to be effective in increasing the efficacy of pre-service teachers for the use of AI in science classes. And it was also found that pre-service teachers recognized the aspect of using AI technology as a new teaching·learning strategy and tool that can help students understand science concepts. Accordingly, it was found that the program developed in this study had a positive impact on pre-service teachers' AI utilization competency reinforcement and perception improvement at the basic level. Implications of this were discussed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.2
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• pp.11-21
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• 2000

Tunnelling has been created as a great extent in view of less land space available because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities. In tunnelling, it is often faced that measures are obliged to be taken without confirmation for such abnormality as diverged movement of surrounding rock mass, growing crack of shotcrete and yielding of rockbolts. In this case, it is usually said that the judgments of experienced engineers for the selection of measure are importance and allowed us to get over the situations in many construction sites. But decrease of such experienced engineers need us to develop the new system to assist the selection of measures for the abnormality without any experiences of similar tunnelling sites. In this study, After a lot of tunnelling reinforcement methods were surveyed and the detail application were studied, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.