• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.6
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• pp.1075-1082
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• 2016

In order to calculate the optimum installation interval between a speed hump and hump type crosswalk that are installed continuously in succession, this study examined the speed of a vehicle that passes different intervals between speed humps and hump type crosswalks from the approach section of a roundabout having a maximum speed limit of 30km/h; analyzed the effects of speed humps and hump type crosswalks installed continuously in succession on vehicle driving speed; and simulated the optimum installation height of hump type crosswalk. As a result, the following conclusion was drawn. First, it was found that the optimum interval between a speed hump and hump type crosswalk, which are the representative traffic calming techniques for reducing vehicle speed, to control vehicle speed under 30km/h is 30m. Second, as a result of comparing the deceleration of a vehicle that pass hump type crosswalks, it was found that if the installation interval is 65 m and above, a speed hump and hump type crosswalk had no effect. Therefore, it is desirable that the maximum installation interval between a speed hump and hump type crosswalk for controlling vehicle speed within a fixed road section should not exceed 65m. Third, the analysis showed that the optimum installation height of hump type crosswalk is 6-8cm in case vehicle speed at the approach section is 20km/h or lower, 8-10cm in case of 30km/h, and 10cm in case of 30km/h or higher, respectively. Fourth, even at a road section on which a speed hump and hump type crosswalk are installed, speed reduction effects may sometimes be insignificant due to a driver's studying effect, traffic conditions and so on. Thus, it is judged that speed reduction effects will be greater if several traffic calming techniques such as speed hump, chicane, and choker are applied at the same time. Therefore, in case of applying traffic calming techniques for the purpose of reducing vehicle speed in order to promote pedestrian safety, the composite application of several techniques should be considered.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of the Daesoon Academy of Sciences
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• v.28
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• pp.103-131
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• 2017

As the establishment of self-identity is based on 'Relationships of Mutual Beneficence,' the formation of 'Relationships of Mutual Beneficence' is the only road to the security and confirmation of self-existence. But given that our ordinary life almost entirely consists of actions objectifying others, the formation of 'Relationships of Mutual Beneficence' is by no means easy. The formation of 'Relationships of Mutual Beneficence' should be based on morality, controlling self-desire, and not objectifying others. Philosophy based on a priori reasoning describes self-control over selfdesire as the domination of the body through a priori reasoning. But this practical philosophy cannot present a self-evidential internal motivation behind moral actions. Due to this, the application of moral order given by a priori reasoning in response to reality is likely to be reinterpreted on basis of self-interest. With regards to this, the 'propensity towards sympathy' is given as new moral norm. The 'propensity towards sympathy' as emotion is direct and consistent given that feeling occurs prior to thinking. The 'propensity towards sympathy' is intuitive in the sense that it is an instinctual response preceeding a reasoned judgment. The 'propensity towards sympathy,' as a natural moral emotion, is self-validating given that all human beings know it and practice it. But previous studies on the 'propensity towards sympathy' have an obvious limitation because they adopt phenomenological approaches to the 'propensity towards sympathy' which eschew the investigation of morality. Though they present the 'propensity towards sympathy' as a natural emotion based on body rather than reason, they do not philosophically explain the 'propensity towards sympathy.' Thus the 'propensity towards sympathy' as a natural moral emotion is likely to be interpreted as a subjective and relative moral norm. This paper philosophically explains that the 'propensity towards sympathy' is a universal moral norm on the basis of Merleau-Ponty's 'flesh.' 'Flesh' is formed as the entanglement between oneself and others and presents the 'propensity towards sympathy' as its philosophical basis. In other words, 'flesh' formed as the mixture or entanglement between oneself and others is the material foundation upon which one can activate the 'propensity towards sympathy.' This paper's approach to the 'propensity of sympathy' can be desribed as a phenomenological approach to the 'propensity towards sympathy' as a universal moral norm.