• The Journal of Korean Philosophical History
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• no.25
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• pp.315-332
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• 2009

This research shows that Mozi setting forth a military thoughts will be discussed and lighted up in modern value. Mozi's theory on a military was formed in order to protect weak people from the strong in the basis of his philosophy of not attacking. It is different from the art of war for attack. There are some merits in Mozi's strategy of war that we have to take notice of. First of all, Mozi indicated that married women, the old and children was provided with specific roles in the war times. Especially, married women' parts will be equal to men. The old and children should usually be respected for the government to offer them important roles in the war time. Therefore, this paper will be showed in the respect of Mozi's thoughts that is considered as human being's fundamental rights in terms of ensuring the weak's rights. Furthermore, Mozi's theories on technology from loving the civil was applied to defensive fight and his advanced arms was arranged in the actual fight. However, his arms could be useful to the only defensive fight. This case was reflected as Mojia's theories that science is sincerely useful toward the civil. There will be some small or large wars in the future. Mozi's theories based on a military peaceful thoughts that everyone ought to be loved without discrimination should be highly valued when all wars will take place for the purpose of their own profits.

• The Journal of Bigdata
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• v.5 no.2
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• pp.201-213
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• 2020

Despite empirical research that the response to human risk is significantly influenced affective factors, the role of affective factors has been unexplored in information privacy research. This study aims to explore the privacy behaviors of location-based service (LBS) users from an affective point of view. Specifically, the study explored the relationship between three types of privacy threats (collection, hacking, secondary use), two affects (worry, anger), and a coping behavior (continuous use intentions). The structured survey was conducted with 552 users. In order to analyze the effect of the combination of perception of particular privacy threats and particular affects on the intention of continuous use, association rules, one of the data mining techniques, was employed. As a result, there was a difference in the intention to use according to the combination of the perception of risk and affect responses, and the most significant influence on the intention is when the second use of personal information was combined with anger. This study has significant theoretical contribution in that it includes affective factors in the research of information privacy users, complementing the biases of existing cognition-oriented approaches and providing a comprehensive understanding of privacy response behavior.

• Journal of the Korean Regional Science Association
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• v.37 no.1
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• pp.29-44
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• 2021

Particulate matter(PM) is one of the leading causes of lung cancer. Recognizing its considerable risk to human health, people change their behaviors when a concentration level of PM is high. The impact of particulate matter on urban activities may vary depending on the lasting days of PM and PM matter alerts. In addition, the level of averting behavior may vary depending on the types and physical characteristics of urban activities and the degree of vulnerability to PM among people. Although the way people respond to PM may vary depending on these various factors, previous research evidence on this is very insufficient. Therefore, this study multilaterally analyzed the impact of PM on the urban activities in Jongno-gu, one of the CBD areas of Seoul. For this, we linked SKT's mobile phone signal data to land use data to extract the daily number of active people by urban activity types and ages. According to multiple regression analysis, the averting behavior varies depending on the type of urban activity, the physical characteristics of the place of activity(inside and outdoor), the lasting days of PM, PM alerts and the age of people. The results of this study can be used as basic data to policy makers who establish policies for adapting to air pollution policies by providing various effects of PM on the urban activities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.59-66
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• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.575-584
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• 2022

The purpose of this study is to derive interaction types that appear variously in immersive contents through John Dewey's empirical theory, and to explore how the derived types are delivered in real cases. For this reason, two cases of the National Museum of Korea 'Immersive Digital Gallery' and 'Arte Museum Jeju' were analyzed through interaction types derived from the empirical theory. The interaction types derived based on the experience theory are the elements of 'multi-sensory', 'simultaneous experience' and 'sensory expansion'. In both cases, these types appear connected rather than grafted one by one in common. In one direction, 'multi-sensory' leads to 'sensory expansion', and in two directions, 'simultaneous experience' leads to 'sensory expansion'. As such, the core types of communication between technology and humans are not delivered one by one, but a cycle of interaction is formed in multiple ways. Therefore, it can be seen that the interaction type of immersive contents is expanded step by step by fusion of various senses and experiences in various fields, rather than a 1:1 partial delivery method. Based on this, it will be necessary to study how types are expanded and how viewers are affected when interaction is implemented in immersive contents in the future.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.231-245
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• 2022

Most humans live at work and continue their lives through work. This study aims to understand the influence of job crafting on job satisfaction and knowledge sharing behavior, innovation behavior, and smart work environment variables in the rapidly changing labor market. In addition, a study was conducted on the effect of job crafting on job satisfaction by dividing it into male and female groups. The study was conducted on 453 office workers who responded to the survey, and SPSS 26.0 and Amos 26.0 were used. Frequency analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, descriptive statistics, correlation, structural model analysis and Multi-group analysis were performed. Multiple mediating effects were analyzed and Process macro model 7 was used as the smart work environment effect. As a result of the analysis, first, job crafting had a significant effect on job satisfaction, and second, job crafting had a significant effect on knowledge sharing behavior and innovation behavior. Third, knowledge sharing behavior had a significant effect on job satisfaction, but innovation behavior did not affect job satisfaction. Fourth, for the parameters of knowledge sharing behavior and innovation behavior, knowledge sharing behavior had a partial mediating effect, but innovation behavior did not affect it. Fifth, it was found that the moderating effect of the smart work environment had no effect, and sixth, the results of a study on the effect of job crafting on job satisfaction divided into men and women had a greater effect than men.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.217-240
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• 2021

Upon the human exploration era of the Moon, this paper introduces lunar topography and geologic fundamentals to space scientists. The origin of scientific terminology for the lunar topography was briefly summarized, and the extension of the current Korean terminology is suggested. Specifically, we suggest the most representative lunar topography that are useful to laymen as 1 ocean (Oceanus Procellarum), 10 maria (Mare Imbrium, Mare Serenitatis, Mare Tranuillitatis, Mare Nectaris, Mare Fecundatis, Mare Crisium, Mare Vaporium, Mare Cognitum, Mare Humorum, Mare Nubium), 6 great craters (Tyco, Copernicus, Kepler, Aristachus, Stebinus, Langrenus). We also suggest Korean terms for highland, maria, mountains, crater, rille, rima, graben, dome, lava tube, wrinkle ridge, trench, rupes, and regolith. In addition, we introduce the standard model for the lunar interior and typical rocks. According to the standard model on the basis of historical impact events, the lunar geological eras are classified as Pre-Nectarian, Nectarian, Imbrian, Erathostenesian, and Copernican in chronologic order. Finally, we summarize the latest discovery records on the water on the Moon, and introduce the concept of water extraction from the lunar soil, which is to be developed by the Korea Institute of Geoscience and Mineral Resources (KIGAM).

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.461-469
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• 2022

It is very important to ensure the reliability of dam inflow data, which is critical in planning and managing the supply and demand of water resources in a basin. However, the simple water balance model sometimes results in negative inflows and does not consider the actual inflow characteristics. In this study, to address these issues, the existing water balance formula was modified by considering evaporation which is available for calculation among other outflows. The modified water balance formula was applied to the Soyang Dam. The results showed that the rate of negative inflows decreased in the re-evaluated dam inflow data and it was possible to secure consistency for the total inflow volume. In addition, investigating the water availability in the Soyang Dam watershed based on the water balance concept considering evaporation, it was found that direct water use in the human aspect was about 60%, and the indirect water use in the natural aspect was about 40%. In drought years, it was also confirmed that the proportion of indirect use of water resources increased.