• Journal of the Korean Geotechnical Society
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• v.19 no.4
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• pp.133-144
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• 2003

The resonant column testing is a laboratory testing method to determine the shear modulus and material damping factor of soils. The method has been widely used for many applications and its importance has increased. Since the first use of the testing method in 1960's, the low-technology electronic devices fir testing and data acquisition have limited the measurement only to the amplitude of the linear spectrum. The limitations of the testing method are also attributed to the assumption of linear-elastic material in the theory of the resonant column testing and also to the incomplete understanding of the dynamic behaviour of the resonant column testing device. Recently, Joh et al. proposed a theory to overcome the limitations of the resonant column testing by deriving the equation of motion and providing its solution for the resonant column testing device. This study proposed the improved data reduction and analysis method for the resonant column testing, thanks to the advanced data acquisition system and the new theoretical solution for the resonant column testing system. For the verification of the proposed data reduction and analysis method, the numerical simulation of the resonant column testing was performed by the finite element analysis. Also, a series of resonant column testing were performed fir Joomunjin sand, which verified the feasibility of the proposed method and revealed the limitations of the conventional data reduction and analysis method.

• Steel and Composite Structures
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• v.48 no.2
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• pp.207-233
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• 2023

Steel-concrete composite box girder bridges are widely used in the construction of highway and railway bridges both domestically and abroad due to their advantages of being light weight and having a large spanning ability and very large torsional rigidity. Composite box girder bridges exhibit the effects of shear lag, restrained torsion, distortion and interface bidirectional slip under various loads during operation. As one of the most commonly used calculation tools in bridge engineering analysis, one-dimensional models offer the advantages of high calculation efficiency and strong stability. Currently, research on the one-dimensional model of composite beams mainly focuses on simulating interface longitudinal slip and the shear lag effect. There are relatively few studies on the one-dimensional model which can consider the effects of restrained torsion, distortion and interface transverse slip. Additionally, there are few studies on vehicle-bridge integrated systems where a one-dimensional model is used as a tool that only considers the calculations of natural frequency, mode and moving load conditions to study the dynamic response of composite beams. Some scholars have established a dynamic analysis model of a coupled composite beam bridge-train system, but where the composite beam is only simulated using a Euler beam or Timoshenko beam. As a result, it is impossible to comprehensively consider multiple complex force effects, such as shear lag, restrained torsion, distortion and interface bidirectional slip of composite beams. In this paper, a 27 DOF vehicle rigid body model is used to simulate train operation. A two-node 26 DOF finite beam element with composed box beams considering the effects of shear lag, restrained torsion, distortion and interface bidirectional slip is proposed. The dynamic analysis model of the coupled composite box girder bridge-train system is constructed based on the wheel-rail contact relationship of vertical close-fitting and lateral linear creeping slip. Furthermore, the accuracy of the dynamic analysis model is verified via the measured dynamic response data of a practical composite box girder bridge. Finally, the dynamic analysis model is applied in order to study the influence of various mechanical effects on the dynamic performance of the vehicle-bridge system.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Tribology and Lubricants
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• v.36 no.5
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• pp.279-289
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• 2020

In this paper, a method is proposed for establishing an approximate prediction model of rotor-dynamics through modal testing. In particular, the proposed method is applicable to systems that cannot be established according to conventional methods owing to the absence of information regarding the dimensions and material of the rotor-bearing system. The proposed method is demonstrated by employing a motor dynamometer driven by a 1 MW class induction motor without dimension and material information. The proposed method comprises a total of seven steps, wherein an initial model is established by incorporating approximate dimensions and material information, and the model is improved on the basis of the natural frequency characteristics of the system. During model improvement, the modification factor is introduced for adjusting the elastic modulus and shear modulus of the system. Analysis of critical speed and imbalance response indicates that the separation margin is 67% and the maximum vibration amplitude is less than the amplitude limit of 0.032 mm under the API 611 standard, which means that the motor dynamometer can stably operate at a rated speed of 1800 rpm. Hence, the obtained results validate the feasibility of the proposed method. Furthermore, for broad usage, it is necessary to accordingly apply and validate the proposed method for various rotor-bearing systems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.9
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• pp.897-904
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• 2017

Optical equipment consists of various components, and a detector is mounted and operated on aircraft, tanks, and warships for target detection and classification. The structural stability and optical performance of aeronautical optical equipment operated at several kilometers of altitude are degraded owing to vibration generated in the aircraft. It is necessary to verify the structural stability and optical performance requirements of the equipment in vibration environment conditions during the design phase. In this study, vibration environment conditions were analyzed using a test standard and the measurements of the vibration generated in aircraft. The conditions were classified as endurance and operating vibration conditions for structural stability and optical performance verification, respectively. The structural stability was verified according to natural frequency analysis, response analysis for the endurance vibration condition, and static analysis. The optical performance was verified by applying the vibration response analysis results to the optical design/analysis program.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.215-223
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• 2019

The wave of the information age gradually accelerates, and fusion analysis solutions that can utilize these knowledge data according to accumulation of various forms of big data such as large capacity texts, sounds, movies and the like are increasing, Reduction in the cost of storing data accordingly, development of social network service (SNS), etc. resulted in quantitative qualitative expansion of data. Such a situation makes possible utilization of data which was not trying to be existing, and the potential value and influence of the data are increasing. Research is being actively made to present future-oriented education systems by applying these fusion analysis systems to the improvement of the educational system. In this research, we conducted a big data analysis on Twitter, analyzed the natural language of the data and frequency analysis of the word, quantitative measure of how domestic windows education problems and outcomes were done in it as a solution.

• Journal of Korean Medicine Rehabilitation
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• v.26 no.4
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• pp.85-96
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• 2016

Objectives The purpose of this study was to investigate the relationship between the effects of acupuncture treatment and heart rate variability (HRV) in pre-menopausal obese women. Methods Thirty-seven obese women who met the inclusion criteria were recruited. To estimate the effects of acupuncture, obesity indices, such as body weight (BW), waist circumference (WC), hip circumference (HC), and the waist-hip ratio (WHR), were measured before and after the treatment. The HRV test was conducted before treatment and analyzed using the frequency domain method. Results The lnLF/HF ratio (natural logarithm of low frequency power/high frequency power ratio of the HRV value) before treatments was negatively correlated with differences in WC, HC, and WHR during treatment. The correlation coefficients between the lnLF/HF ratio and the differences in WC, HC, and WHR were r=-0.459 (p<0.01), r=-0.327 (p<0.05), and r=-0.339 (p<0.05) respectively. Conclusions As the baseline ratio of sympathetic activity to parasympathetic activity decreases, WC, HC, and WHR reduction significantly increased during treatment. Further study is needed to uncover the relationship between obesity-related variables and the autonomic nervous system to predict the effect of acupuncture.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.349-359
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• 2016

In order to analytically evaluate buffeting responses, the analysis of wind characteristics such as turbulence intensity, turbulence length, gust, roughness coefficient, etc must be a priority. Static aerodynamic force coefficients, flutter coefficients, structural damping ratios, aerodynamic damping ratios and natural frequencies affect the analytical responses. The bridge interested in this paper has being been used for 32 years. As the time passes, current terrain conditions around the bridge are different markedly from the conditions it was built 32 years ago. Also, wind environments were considerably varied by the climate change. For this reason, it is necessary to evaluate the turbulence intensity, length, spectrum and roughness coefficient of the bridge site from full-scale measurements using the structural health monitoring system. The evaluation results indicate that wind characteristics of bridge site is analogous to that of open terrain although the bridge is located on the coastal area. To calculate buffeting responses, the analysis variables such as damping ratios, static aerodynamic force coefficients and natural frequency were evaluated from measured data. The analysis was performed with regard to 4 cases. The evaluated variables from measured data are applied to the first and second analysis cases. And the other analysis cases were performed based on Design Guidelines for Steel Cable Supported Bridges. The calculated responses of each analysis cases are compared with the buffeting response measured at less than 25m/s wind speed. It is verified that the responses by the numerical analysis applying the estimated variables based on full-scale measurements are well agreed with the measured actual buffeting responses under wind speed 25m/s. Also, the extreme wind speed corresponding to a recurrence interval 200 years is derived from Gumbel distribution. The derived wind speed for return period of 200 years is 45m/s. Therefore the buffeting responses at wind speed 45m/s is determined by the analysis applying the estimated variables.

• Korean Journal of Environment and Ecology
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• v.24 no.2
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• pp.186-193
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• 2010

In order to examine the traits of sluice gate water control, halophyte community formation and their inter-relations in Saemangeum, both water level condition and halophyte community formation were analyzed periodically and spatially on the topographic map with Surfer, Saemageum Spatial Analysis System, and related field reports. The traits of water level condition are that average water level in the growing period of halophytes was similar to annual average water level, annual low level and high level appeared in the growing period, and water level was usually maintained within a range of -1.0m~0.5m above mean sea level, but it has changed more frequently year by year. Routine water level control, natural disaster prevention, construction, and civil appeal took major percentages of the reasons for sluice gate's opening and shutting. Since 2007, not only the overall control frequency of sluice gate but also its control frequency for construction and natural disaster prevention have increased outstandingly. Halophyte community had formed at a rate of 1,209ha/year in the 4,315 ha land in 2008, 6.3 times larger than in 2005, and 2,382 ha above around 1.0m was estimated to be artificially vegetated, 89.1 % of the 2,673ha-size sown area. High water level was found to be a more possible determinant than average water level or low water level in halophyte community formation and it was thought to be secondary factors whether tillage was conducted or/and whether surface sealing formed.