• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.116-121
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• 2014

This paper discusses computer-generated heuristics for motion planning. Planning with many degrees of freedom is a challenging task, because the complexity of most planning algorithms grows exponentially with the number of dimensions of the problem. A well-designed heuristic may greatly improve the performance of a planning algorithm in terms of the computation time. However, in recent years, with increasingly challenging high-dimensional planning problems, the design of good heuristics has itself become a complicated task. In this paper, we present an approach to algorithmically develop a heuristic for motion planning, which increases the efficiency of a planner in similar environments. To implement the idea, we generalize modern motion planning algorithms to an extent, where a heuristic is represented as a set of random variables. Distributions of the variables are then analyzed with computer learning methods. The analysis results are then utilized to generate a heuristic. During the experiments, the proposed approach is applied to several planning tasks with different algorithms and is shown to improve performance.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.92-98
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• 1995

The position control of flexible joint manipulator is investigated by applying the self-organizing fuzzy logic controller (SOC) proposed by Procyk and Mamdani. The SOC is a heuristic rule-based controller and a further extension of an ordinary fuzzy controller, which has a hierachy structrue which consists of an algorithm being identical to a fuzzy controller at the lower ollp and a learning algorithm accomodating the performance evalution and rule modification function at the upper ollp. This form of control can be used in those complex systems which have been too difficult to control or which in the past have had to rely on the experience of a human operator. Even though the significant dynamic coupling of the motors and links on the flexible joint manipulator, the performance of command-following is good by applying the proposed SOC.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.845-852
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• 2018

This study has developed a preventive launcher from smartphone addiction for the digital generation and the contents recommendation based on machine learning which used multiple and collective intelligence. This could provide convenient digital nurturing experience for the parents who fear their children's over use of digital devices and also suggest individually adaptive digital learning methods that enhance the learning efficiency and pleasurable and safe learning environment for the children. Suggested application is a kind of gamification launcher that protects children from harmful contents and from smartphone addiction with time limit settings. For parents who find difficulty choosing from various kinds of contents and applications for education, this suggested system could provide a learning analytic report based on big data after collecting and analyzing the data of their children's learning and activities and recommend contents necessary for their kids using recommended algorithm by collective intelligence.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.211-218
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• 2008

In this paper, we proposed a new image summarization algorithm designed for automatically summarizing user's snapshot photos taken in a virtual environment based on user's context information and educational contents, and then presenting a summarized photos shortly after user's virtual reality experience. While other image summarization algorithms used date, location, and keyword to effectively summarize a large amount of photos, this algorithm is intended to improve users' memory retention by recalling their interests and important educational contents. This paper first describes some criteria of extracting the meaningful images to improve learning effects and the identification rate calculations, followed by the system architecture that integrates the virtual environment and the viewer interface. It will also discuss a user study to model the algorithm's optimal identification rate and then future research directions.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.33-41
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• 2015

In this paper, the simplification procedure of Karnaugh Map, which is essential to design digital logic circuits, was implemented as web-based educational Java applets. The learners will be able to experience interesting learning process by executing the proposed Java applets. In addition, since the proposed Java applets were designed to contain educational technologies by step-by-step procedure, the maximization of learning efficiency can be obtained. The learners can make virtual experiments on the simplification of digital logic circuits by clicking on some buttons or filling out some text fields. Furthermore, the Boolean expression and its schematic diagram occurred in the simplification process will be displayed on the separate frame so that the learners can learn effectively. The schematic diagram enables them to check out if the logic circuit is correctly connected or not. Finally, since the simplification algorithm used in the proposed Java applet is based on the modified Quine-McCluskey minimization technique, the proposed Java applets will show more encouraging result in view of learning efficiency if it is used as assistants of the on-campus offline class.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.483-490
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• 2019

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• The Mathematical Education
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• v.53 no.3
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• pp.413-434
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• 2014

Logic lays the foundation of Mathematics and the development of Mathematics is dependent on critical thinking. So it is important that school mathematics helps students develop their logical and critical thinking ability for both mathematics learning and problem solving in general. MINDSTORMS, a LEGO based programming activity kit, is an effective teaching and learning tool that can be used to enhance logical and critical thinking in students. This study focused on measuring the growth of students' ability to think logically and critically when they used MINDSTORMS activities to learn programming. In addition, we investigated how the students' logical and critical thinking changed from the MINDSTORMS learning experience. The study confirmed that the programming activities using MINDSTORMS help to enhance logical and critical thinking in students. The students attitude about logical and critical thinking became more positive and the activities helped to engage students to think logically and critically. This type of programming activities should be valuable in mathematics education and it should be included in a general mathematics curriculum.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.