• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.19-26
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• 2019

The masonry infill walls are one of the most popular components that are used for dividing and arranging spaces in building construction. In spite of the fact that the masonry infills have many advantages, the system needs to be used with caution when the earthquake load is to be considered. The infills tend to develop diagonal compression struts during earthquake and increase the demand in surrounding RC frames. If there are openings in the infill walls, the loading path gets even complicated and the engineering judgements are required for designing the system. In this study, a masonry infill system was investigated through finite element analysis (FEA) and the results were compared with the current design standard, ASCE 41. It is noted that the equivalent width of the compression strut estimated by ASCE 41 could be 32% less than that using detailed FEA. The global load resisting capacity was also estimated by 28% less when ASCE 41 was used compare to the FEA case. Rather than using expensive FEA, the adapting ASCE 41 for the analysis and design of the masonry infills with openings would provide a good estimation by about 25% conservatively.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.435-445
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• 2022

In this study, the reverberation of a bistatic sonar operated in southeastern coast in the East Sea in July 2020 was analyzed. The reverberation sensor data were collected through an LFM sound source towed by a research vessel and a horizontal line array receiver 1 km to 5 km away from it. The reverberation sensor data was analyzed by various methods including geo-plot after signal processing. Through this, it was confirmed that the angle reflected from the sound source through the scatterer to the receiver has a dominant influence on the distribution of the reverberation sound, and the probability distribution characteristics of bistatic sonar reverberation varies for each beam. In addition, parametric factors of K distribution and Rayleigh distribution were estimated from the sample through moment method estimation. Using the Kolmogorov-Smirnov test at the confidence level of 0.05, the distribution probability of the data was analyzed. As a result, it could be observed that the reverberation follows a Rayleigh probability distribution, and it could be estimated that this was the effect of a low reverberation to noise ratio.

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.61-73
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• 2007

As a part of Load and Resistance Factor Design(LRFD) code development in Korea, in this paper an intensive reliability analysis was performed to evaluate reliability levels of the two static bearing capacity methods for driven steel pipe piles adopted in Korean Standards for Structure Foundations by the representative reliability methods of First Order Reliability Method(FORM) and Monte Carlo Simulation(MCS). The resistance bias factors for the two static design methods were evaluated by comparing the representative measured bearing capacities with the design values. In determination of the representative bearing capacities of driven steel pipe piles, the 58 data sets of static load tests and soil property tests were collected and analyzed. The static bearing capacity formula and the Meyerhof method using N values were applied to the calculation of the expected design bearing capacity of the piles. The two representative reliability methods(FORM, MCS) based computer programs were developed to facilitate the reliability analysis in this study. Mean Value First Order Second Moment(MVFOSM) approach that provides a simple closed-form solution and two advanced methods of FORM and MCS were used to conduct the intensive reliability analysis using the resistance bias factor statistics obtained, and the results were then compared. In addition, a parametric study was conducted to identify the sensibility and the influence of the random variables on the reliability analysis under consideration.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.324-331
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• 2024

The height of large car and truck carriers from the keel to the wheel house is 44 ~ 46 m, and as the car-carriers increases in size, it exhibits the 'top heavy' characteristic, where the upper section is heavier than the lower section. This study aims to estimate the maximum outward heel angle of the Golden Ray car-carrier (G-ship) during turning maneuvers for accident investigation and the prevention of similar accidents. The theoretically calculated maximum outward heel is 7.5° (at 19 kn, rudder angle 35°) with a GM of +3.0 m or higher, and 16.7° with a GM of +1.85 m. Meanwhile the experimentally modified maximum outward heel is 10.5° (at 19 kn, rudder angle 35°) with a GM of +3.0 m or higher, and 23.3° with a GM of +1.85 m. The G-ship is maneuvered during an accident at a speed of 13 kn, at starboard rudder angle of 10° to 20°, it changes course from 038°(T) to 105°(T) based on the instructions of the on-board pilot. At this time, the maximum outward heel is estimated to be between 7.8° and 10.9° at the port side, which is 2.2 times higher than the normal outward heel. In the IS code, cargo ships are required to exhibit a minimum GoM of +0.15 m or more. The maneuvered G-ship exhibits a GoM of +1.72 m. It is not maneuvered because it fails to satisfy the international GoM criteria and because its GoM is insufficient to counteract the heeling moment during the maneuver. This study is performed based on accident-investigation results from the Korea Maritime Safety Tribunal and the USCG.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.697-707
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• 2007

In this study, we estimated the PMP(Probable Maximum Precipitation) and its transition in case of the typhoon Rusa which happened the biggest damage of all typhoons in the Korea. Specially, we analysed the moisture maximizing rate under the consideration of meteorological condition based on the orographic property when it hits in Gangneung region. The PMP is calculated by the rate of the maximum persisting 12 hours 1000 hPa dew points and representative persisting 12 hours 1000 hPa dew point. The former is influenced by the moisture inflow regions. These regions are determined by the surface wind direction, 850 hPa moisture flux and streamline, which are the critically different aspects compared to that of previous study. The latter is calculated using statistics program (FARD2002) provided by NIDP(National Institute for Disaster Prevention). In this program, the dew point is calculated by reappearance period 50-year frequency analysis from 5% of the level of significant when probability distribution type is applied extreme type I (Gumbel distribution) and parameter estimation method is used the Moment method. So this study indicated for small basin$(3.76km^2)$ the difference the PMP through new method and through existing result of established storm transposition and DAD(Depth-Area-Duration). Consequently, the moisture maximizing rate is calculated in the moisture inflow regions determined by meteorological fields is higher $0.20{\sim}0.40$ range than that of previous study. And the precipitation is increased $16{\sim}31%$ when this rate is applied for calculation.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.27-57
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• 2008

Many consumers enjoy the challenge of purchasing a brand that matches well with their own values and personalities (for example, Ko et al., 2008; Ko et al., 2006). Therefore, the personalities of consumers can impact on the final selection of a brand and its brand personality in two ways: first, the consumers may incline to purchase a brand or a product that reflects their own personalities; second, consumers tend to choose a company that has similar brand personalities to those brands that are being promoted. Therefore, the objectives of this study are following: 1. Is there any empirical relationship between a consumer's personality and the personality of a brand that he or she chooses? 2. Can a corporate brand be differentiated by the brand personality? In short, consumers are more likely to hold favorable attitudes towards those brands that match their own personality and will most probably purchase those brands matching well with their personality. For example, Matzler et al. (2006) found that extraversion and openness were positively related to hedonic product value; and that the personality traits directly (openness) and indirectly (extraversion, via hedonic value) influenced brand effects, which in turn droved attitudinal and purchase loyalty. Based on the above discussion, the following hypotheses are proposed: Hypothesis 1: the personality of a consumer is related to the brand personality of a product/corporate that he/she purchases. Kuksov (2007) and Wernerfelt (1990) argued that brands as a symbolic language allowed consumers to communicate their types to each other and postulated that consumers had a certain value of communicating their types to each other. Therefore, how brand meanings are established, and how a firm communicate with consumers about the meanings of the brand are interesting topics for research (for example, Escalas and Bettman, 2005; McCracken, 1989; Moon, 2007). Hence, the following hypothesis is proposed: Hypothesis 2: A corporate brand identity is differentiated by the brand personality. And there are significant differences among companies. A questionnaire was developed for collecting empirical measures of the Big-Five personality traits and brand personality variables. A survey was conducted to the online access panel members through the Internet during December 2007 in Korea. In total, 500 respondents completed the questionnaire, and considered as useable. Personality constructs were measured using the Five-factor Inventory (NEO-FFI) scale and a total of 30 items were actually utilized. Brand personality was measured using the five-dimension scale developed by Aaker (1997). A total of 17 items were actually utilized. The seven-point Likert-type scale was the format of responses, for example, from 1 indicating strongly disagreed to 7 for strongly agreed. The Analysis of Moment Structures (AMOS) was used for an empirical testing of the model, and the Maximum Likelihood Estimation (MLE) was applied to estimate numerical values for the components in the model. To diagnose the presence of distribution problems in the data and to gauge their effects on the parameter estimates, bootstapping method was used. The results of the hypothesis-1 test empirically show that there exit certain causality relationship between a consumer's personality and the brand personality of the consumer's choice. Thus, the consumer's personality has an impact on consumer's final selection of a brand that has a brand personality matches well with their own personalities. In other words, the consumers are inclined to purchase a brand that reflects their own personalities and tend to choose a company that has similar brand personalities to those of the brand being promoted. The results of this study further suggest that certain dimensions of the brand personality cause consumers to have preference to certain (corporate) brands. For example, the conscientiousness, neuroticism, and extraversion of the consumer personality have positively related to a selection of "ruggedness" characteristics of the brand personality. Consumers who possess that personality dimension seek for matching with certain brand personality dimensions. Results of the hypothesis-2 test show that the average "ruggedness" attributes of the brand personality differ significantly among Korean automobile manufacturers. However, the result of ANOVA also indicates that there are no significant differences in the mean values among manufacturers for the "sophistication," "excitement," "competence" and "sincerity" attributes of the corporate brand personality. The tight link between what a firm is and its corporate brand means that there is far less room for marketing communications than there is with products and brands. Consequently, successful corporate brand strategies must position the organization within the boundaries of what is acceptable, while at the same time differentiating the organization from its competitors.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.