• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.8
• /
• pp.1023-1028
• /
• 2010

Recently, an intelligent service robot is being developed for use in guiding and providing information to visitors about the building at public institutions. The intelligent robot has a sensor at the bottom to recognize its location. Four wheels, which are arranged in the form of a lozenge, support the robot. This robot cannot be operated on uneven ground because its driving parts are attached to its main body that contains the important internal components. Continuous impact with the ground can change the precise positions of the components and weaken the connection between each structural part. In this paper, the design of the suspension system for such a robot is described. The dynamic model of the robot is created, and the driving characteristics of the robot with the designed suspension system are simulated. Additionally, the suspension system is optimized to reduce the impact for the robot components.

• Journal of the Korean Applied Science and Technology
• /
• v.37 no.3
• /
• pp.473-483
• /
• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• The Journal of the Korea Contents Association
• /
• v.20 no.5
• /
• pp.653-664
• /
• 2020

The first purpose of this study is to understand the impact of the emotional intelligence of golf coaches on customer citizenship behavior. The second is to verify the moderating effects of gender roles in the relationship between emotional intelligence and customer citizenship behavior. In order to achieve the research purposes, 5 indoor and outdoor golf driving ranges were selected in Cheongju, and 318 customers were selected as the objects. SPSS 22.0 and AMOS 21.0 software was used. The following were identified as the result of the analysis. First, besides 'Emotional Control' and 'Helping Others' all four sub-factors of the golf coach's emotional intelligence had a noticeable influence on customer citizenship behavior. Second, the influence of emotional intelligence differed according to the gender of coaches. In the case of male coaches, emotional intelligence had an impact on all factors except transmission. In the case of women, it was found that feedback, helping others and emotional control had an impact on all factors except transmission. Therefore, these results show that in-depth research on the emotional intelligence of golf coaches is required. They also suggest that there will be a need to study how the gender of the coach affects customer citizenship behavior.

• Journal of the Korea Society for Simulation
• /
• v.24 no.1
• /
• pp.35-43
• /
• 2015

This research proposed a new method to evaluate the objective validity to launch smart transportation services that various stakeholders are complicatedly inter-connected. First of all, we have designed the fundamental business model to form the smart transportation services and defined the stakeholders taking part in the services. Also, the criteria to evaluate the economical validity has been proposed based on the relationship among stakeholders. Especially, in the case EV drivers and charging service providers, the economical validity depends on the scale of spreading. Therefore, we have compared the two extreme scenarios, the poor and stable level of EV spreading. According to the result, it may be said that EV drivers and charging service providers cannot be guaranteed the economical validity due to the burden of initial investment. On the contrary to this, suppliers of EV and charging gears may secure more than a certain level of profit. In addition, the government may have great profit due to reducing the CO2 emission and cost for importing energy sources. Therefore, it is needed to enhance the level of supporting EV drivers and charging service providers at the first stage. Also, the impact of the ratio of EV and charging service stations on the economical validity of smart transportation should be further investigated.

• Informatization Policy
• /
• v.25 no.1
• /
• pp.82-98
• /
• 2018

Internet overdependence has become a public health concern in Korea. It is well known that family and community level efforts could alleviate possible harms from Internet use. However, little research exists regarding smartphone overdependence. This study examines factors affecting smartphone overdependence and, specifically, help-seeking for smartphone overdependence among Korean adolescents. The study is based on parental mediation theory and uses a help-seeking framework. The results indicate that preventative education provided by school and community increases the levels of awareness of both the possible harms from smartphone use and the option of accessing the mental health service, while it was not effective in preventing smartphone overdependence. Parental mediation was a protective factor for smartphone overdependence but was negatively associated with the intention to use the mental health service. In sum, behavioral problems related to smartphone use require multidimensional preventative efforts from both the family and the community. It is suggested that effective preventive education methods are developed for parents and adolescents.

• Journal of Korean Society of Transportation
• /
• v.25 no.5
• /
• pp.23-32
• /
• 2007

Variable message sign (VMS), which is used for providing real-time information on traffic conditions and incidents, is one of the important components of intelligent transportation systems. VMS messages need to meet the requirements with the consideration of human factors that messages should be readable and understandable while driving. This study developed a legibility distance model for VMS messages using in-vehicle differential global positioning data (DGPS). Traffic conditions, highway geometric conditions, and VMS message characteristics were investigated for establishing the legibility model based on multiple linear regression analysis. The height of VMS characters, speed, and the number of lanes were identified as dominant factors affecting the variation of legibility distances. It is expected that the proposed model would play a significant role in designing VMS messages for providing more effective real-time traffic information.

• Journal of Korea Water Resources Association
• /
• v.54 no.10
• /
• pp.795-805
• /
• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.52 no.2
• /
• pp.96-109
• /
• 2024

The purpose of this study is to propose a method for evaluating the similarity of Show gardens using Deep Learning models, specifically VGG-16 and ResNet50. A model for judging the similarity of show gardens based on VGG-16 and ResNet50 models was developed, and was referred to as DRG (Deep Recognition of similarity in show Garden design). An algorithm utilizing GAP and Pearson correlation coefficient was employed to construct the model, and the accuracy of similarity was analyzed by comparing the total number of similar images derived at 1st (Top1), 3rd (Top3), and 5th (Top5) ranks with the original images. The image data used for the DRG model consisted of a total of 278 works from the Le Festival International des Jardins de Chaumont-sur-Loire, 27 works from the Seoul International Garden Show, and 17 works from the Korea Garden Show. Image analysis was conducted using the DRG model for both the same group and different groups, resulting in the establishment of guidelines for assessing show garden similarity. First, overall image similarity analysis was best suited for applying data augmentation techniques based on the ResNet50 model. Second, for image analysis focusing on internal structure and outer form, it was effective to apply a certain size filter (16cm × 16cm) to generate images emphasizing form and then compare similarity using the VGG-16 model. It was suggested that an image size of 448 × 448 pixels and the original image in full color are the optimal settings. Based on these research findings, a quantitative method for assessing show gardens is proposed and it is expected to contribute to the continuous development of garden culture through interdisciplinary research moving forward.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.150-156
• /
• 2017

In July, 2015, the Korean national assembly passed 'Act on Capital Markets and Financial Investments,' and therefore, it was expected that the crowd funding would be activated owing to a variety of fundraisings and investments. Hence, for the success of the crowd funding, this paper tried to identify the factors affecting the funding. In this study we analyzed the core variables of the Unified Theory of Acceptance and Use of Technology(UTAUT) and their perceived risks on the crowd funding participants' intentions as well as the mediating effects of the attitudes; the core variables of UTAUT were performance expectancy, perceived risk, facilitating conditions, social influence, and the like. As a result, it was found that such facilitating conditions as performance expectancy and social influence would affect crowd funding participants' intention positively, but that effort expectancy and perceived risk would not significantly affect their intention. On the other hand, as a result of testing the mediating effects of the attitudes, it was found that performance expectancy and social influence would have significant mediating effects on participants' intention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.533-535
• /
• 2021

The differences between this study and previous studies are as follows. First, by building a cloud-based system using IoT technology, the system was built to monitor the status of buildings in real time from anywhere with an internet connection. Second, a model for predicting the future was developed using artificial intelligence (LSTM) and statistical (ARIMA) methods for the measured time series sensor data, and the effectiveness of the proposed prediction model was experimentally verified using a scaled-down building model. Third, a method to analyze the condition of a building more three-dimensionally by visualizing the structural deformation of a building by convergence of multiple sensor data was proposed, and the effectiveness of the proposed method was demonstrated through the case of an actual earthquake-damaged building.