• Journal of Digital Convergence
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• v.12 no.1
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• pp.555-561
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• 2014

Artificial intelligence in the game is mainly used to determine patterns of behavior of NPC (Non Player Character) and the enemy, path finding. These artificial intelligence is implemented by FSM (Finite State Machine) or Flocking method. The number of NPC behavior in FSM method is limited by the number of FSM states. If the number of states is too small, then NPC player can know the behavior patterns easily. On the other hand, too many implementation cases make it complicated. The NPC behaviors in Flocking method are determined by the leader's decision. Therefore, players can know easily direction of movement patterns or attack pattern of NPCs. To overcome these problem, this paper proposes agonistic behaviors(attacks, threats, showing courtesy, avoidance, submission)in animals to apply for the NPC, and implements agonistic behaviors using Unity3D engine. This paper can help developing a real sense of the NPC artificial intelligence.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.25-42
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• 2008

The research was conducted to investigate factors that facilitate users' finding books by analyzing classifications in public library and bookstore. This research was based on the assumption that the users' needs and information behaviors are similar in both public library and bookstore. The main purpose of this study is not to recommend classifying public library collections the way a bookstore does, but to figure out what makes the users' book finding more convenient by analyzing the classifications. To carry out the research, users' book finding in public library and classifications of public library and bookstore are analyzed. Then, a survey was conducted to investigate users' book finding behaviors, degree of convenience in finding books according to different classifications and the causes of the convenience. The results of the research showed that bookstore's classification was more convenient for the users in finding books.

• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.35-45
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• 2013

MANET has a weak point because it allows access from not only legal nodes but also illegal nodes. Most of the MANET researches had been focused on attack on routing path or packet forwarding. Nevertheless, there are insuffcient studies on a comprehensive approach to detect various attacks on malicious nodes at packet forwarding processes. In this paper, we propose a technique, named DTecBC (detection technique of malicious node behaviors based on collaboration), which can handle more effciently various types of malicious node attacks on MANET environment. The DTecBC is designed to detect malicious nodes by communication between neighboring nodes, and manage malicious nodes using a maintain table. OPNET tool was used to compare with Watchdog, CONFIDANT, SRRPPnT for verifying effectiveness of our approach. As a result, DTecBC detects various behaviors of malicious nodes more effectively than other techniques.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.35-41
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• 2018

The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.2
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• pp.57-82
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• 2022

Today, despite the increase in professional knowledge-related information needs of citizens, the expansion of citizen participatory research in academia, and the provision of information services for the professional knowledge, there are still difficulties in access to scholarly information resources by laypeople. Focusing on this problem, this study investigates laypeople's scholarly information needs and behaviors through a questionnaire survey. By examining the search and use behaviors of scholarly information resources, and the perception of the need to support the utilization of them, this study analyzes the degree and pattern of social sharing of scholarly information resources beyond the scholarly community. This study is significant in that it expands the range of users in traditional scholarly communication and emphasizes the need to support them to access and use scholarly information resources.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.21-30
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• 2023

Objectives: To prevent the spread of coronavirus disease 2019 (COVID-19), behaviors such as mask-wearing, social distancing, decreasing mobility, and avoiding crowds have been suggested, especially in high-risk countries such as Indonesia. Unfortunately, the level of compliance with those practices has been low. This study was conducted to determine the predisposing, enabling, and reinforcing factors of COVID-19 prevention behavior in Indonesia. Methods: This cross-sectional study used a mixed-methods approach. The participants were 264 adults from 21 provinces in Indonesia recruited through convenience sampling. Data were collected using a Google Form and in-depth interviews. Statistical analysis included univariate, bivariate, and multivariate logistic regression. Furthermore, qualitative data analysis was done through content analysis and qualitative data management using Atlas.ti software. Results: Overall, 44.32% of respondents were non-compliant with recommended COVID-19 prevention behaviors. In multivariate logistic regression analysis, low-to-medium education level, poor attitude, insufficient involvement of leaders, and insufficient regulation were also associated with decreased community compliance. Based on in-depth interviews with informants, the negligence of the Indonesian government in the initial stages of the COVID-19 pandemic may have contributed to the unpreparedness of the community to face the pandemic, as people were not aware of the importance of preventive practices. Conclusions: Education level is not the only factor influencing community compliance with recommended COVID-19 prevention behaviors. Changing attitudes through health promotion to increase public awareness and encouraging voluntary community participation through active risk communication are necessary. Regulations and role leaders are also required to improve COVID-19 prevention behavior.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.259-277
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• 2022

This study aims to understand international students' web search behaviors. During the experiment, fifteen international students were asked to conduct three search tasks which includes six search questions. Depending on the characteristics of search task, there were differences in search performance and search behavior. It was commonly found that participants with higher Korean fluency showed higher search performance; however, prior knowledge about the search topic did not always affect the search performance. In the search tasks that required navigation through menus and links within one web domain, participants often overlooked the correct answers, even if they were at the webpages containing the correct answer. Also, some participants did not realized that they found wrong answers. For enhancing information seeking behaviors among foreigners in Korea, the followings were suggested: 1) to design websites which are easy for non-native speakers to navigate, and 2) to use social media as a means of interactive communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.327-333
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• 2020

The aims of this study was to verify the validity using group comparison method and test/retest reliability of the Communication Behavior Scale for Nurses Caring for People with Dementia(CBS-D). The subjects were nurses who have worked at elderly care facilities in D, U and Y cities(n = 67), and nurses who have worked at general hospitals in D and U cities. The scores of the communication behaviors of nurses working in elderly care facilities were significantly higher than those of nurses working in general hospitals(t=2.49, p=.014). The Intraclass Correlation Coefficients for the test-retest reliability test was .813. Therefore, it was confirmed that CBS-D is an appropriate evaluation tool for evaluating the level of the nurse's communication behavior through various logical analyzes, and it is expected that it can be used in various ways for nursing care for people with dementia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.264-284
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• 2021

As the next-generation network architecture, software-defined networking (SDN) has great potential. But how to forward data packets safely is a big challenge today. In SDN, packets are transferred according to flow rules which are made and delivered by the controller. Once flow rules are modified, the packets might be redirected or dropped. According to related research, we believe that the key to forward data flows safely is keeping the consistency of flow rules. However, existing solutions place little emphasis on the safety of flow rules. After summarizing the shortcomings of the existing solutions, we propose FRChain to ensure the security of SDN data forwarding. FRChain is a novel scheme that uses blockchain to secure flow rules in SDN and to detect compromised nodes in the network when the proportion of malicious nodes is less than one-third. The scheme places the flow strategies into blockchain in form of transactions. Once an unmatched flow rule is detected, the system will issue the problem by initiating a vote and possible attacks will be deduced based on the results. To simulate the scheme, we utilize BigchainDB, which has good performance in data processing, to handle transactions. The experimental results show that the scheme is feasible, and the additional overhead for network performance and system performance is less than similar solutions. Overall, FRChain can detect suspicious behaviors and deduce malicious nodes to keep the consistency of flow rules in SDN.