• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.19-24
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• 2010

The number of automobiles are continuously increasing in Korea since 1990's and it causes frustrating commuting traffic and holyday traffic. Meanwhile, the obsolete traffic signal control system is still under static control based on the aggregated traffic statistics thus it is not sufficiently adaptive in real world traffic situation that changes in real time. Thus, in this paper, we propose an adaptive signal control system using fuzzy control technology that can react to real time traffic situations. The method computes the priority of signal phases based on the number of waiting automobiles and occupying time on intersection using fuzzy membership functions. The phase with highest priority obtains "proceed" signal. Also, the duration of this "proceed" signal is determined based on the ratio of number of waiting automobiles of given phase and total number of waiting automobiles on intersection. In experiment, we show that the proposed fuzzy control system is better than the static control system for all sorts of traffic congestion situations by simulation.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.15-29
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• 2018

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.135-144
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• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.1-6
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• 2021

Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study proposes a method in which we can easily design reinforcement learning agents and implement game play agents by applying PPO(Proximal Policy Optimization) algorithms. And we could identify the performance was increased about 44% than the conventional method. The tools we used are the Unity 3D game engine and Unity ML SDK. The experimental result shows that agents became to learn game rules and make better strategic decisions as experiments go on. On average, the puzzle gameplay agents implemented in this study played puzzle games better than normal people. It is expected that the designed agent could be used to speed up the game level design process.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.107-114
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• 2022

Delegation is a powerful mechanism that allocates access rights to users to provide flexible and dynamic access control decisions. It is also particularly useful in a distributed environment. Among the representative delegation models, the RBDM0 and RDM2000 models are role delegation as the user to user delegation. However, In RBAC, the concept of inheritance of the role class is not well harmonized with the management rules of the actual corporate organization. In this paper, we propose an Adding Attributes on Permission-Based Delegation Model (ABDM) that guarantees the permanence of delegated permissions. It does not violate the separation of duty and security principle of least privilege. ABDM based on RBAC model, supports both the role to role and user to user delegation with an attribute. whenever the delegator wants the permission can be withdrawn, and A delegator can give permission to a delegatee.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.51-62
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• 2022

This study suggests a design of predictive modeling for a hospital fall risk based on inpatients' posture. Inpatient's profile, medical history, and body measurement data along with basic information about a bed they use, were used to predict a fall risk and suggest an algorithm to determine the level of risk. Fall risk prediction is largely divided into two parts: a real-time fall risk evaluation and a qualitative fall risk exposure assessment, which is mostly based on the inpatient's profile. The former is carried out by recognizing an inpatient's posture in bed and extracting rule-based information to measure fall risk while the latter is conducted by medical staff who examines an inpatient's health status related to hospital fall risk and assesses the level of risk exposure. The inpatient fall risk is determined using a sigmoid function with recognized inpatient posture information, body measurement data and qualitative risk assessment results combined. The procedure and prediction model suggested in this study is expected to significantly contribute to tailored services for inpatients and help ensure hospital fall prevention and inpatient safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1148-1157
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• 2022

In 2019, COVID-19 changed many people's lives. Among them, metaverse supports non-face-to-face services through various methods, replacing daily tasks. This phenomenon was created and formed like a culture due to the prolonged COVID-19. In this paper, the fun elements used in the existing game were organized to find out the fun factors of the metaverse, and the items and contents were reclassified according to the metaverse with five experts. Classification was classified using reproducibility, sensory fun [graphic, auditory, text, manipulation, empathy, play, perspective], challenging fun [absorbedness, challenging, discovery, thrill, reward, problem-solving], imaginative fun [new story, love, freedom, agency, expectation, change], social fun[rules, competition, social behavior, status, cooperation, participation, exchange, belonging, currency transaction], interactive fun[decision making, communication sharing, hardware, empathy, nurturing, autonomy], realistic fun[sense of unity in reality, easy of learning, adaptation, intellectual problems solving, pattern recognition, sense of reality, community], and creative fun[application, creation, customizing, virtual world].

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.529-534
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• 2021

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.