• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.286-293
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• 2022

Recently, the importance of elemental technologies constituting smart factories is increasing due to the 4th Industrial Revolution, and simulation is widely used as a tool to learn these technologies. In particular, PID control is an automatic control technique used in various fields, and most of them analyze mathematical models in certain situations or research on application development with built-in controllers. In actual educational environment requires PID simulator training as well as PID control principles. In this paper, we propose a model that enables education and practice of various PID controls through 3D simulation. The proposed model implemented virtual balls and Fan and implemented PID control by configuring a system so that the force can be lifted by the air pressure generated in the Fan. At this time, the height of the ball was expressed in a graph according to each gain value of the PID controller and then compared with the actual system, and through this, satisfactory results sufficiently applicable to the actual class were confirmed. Through the proposed model, it is expected that the rapidly increasing elemental technology of smart factories can be used in various ways in a remote classroom environment.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.223-230
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• 2024

The purposes of this study is to find out how to use Generative AI by class stage and class model so that classes can be planned using various Generative AI in elementary and secondary education. To this end, contents of using Generative AI according to general instructional stages and instructional models by school level and subject were developed, and revised and supplemented through review by 13 field experts. As for the method of using Generative AI by class stage, general class stages were divided into three stages: 'class preparation', 'in class', and 'class arrangement', and the subject of using Generative AI at each stage, the contents of using it, and the types of Generative AI that can be used are summarized. As a method of using Generative AI according to the class model, eight class contents were developed based on teaching and learning models according to the characteristics of each school level and subject. In order to expand the use of Generative AI in elementary and secondary classes, it is necessary to develop more diverse class contents by school level and subject and distribute them in the field. It is also necessary to develop educational materials on matters to consider when using Generative AI in class.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.5
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• pp.339-350
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• 2024

In construction sites, the engine of heavy machinery is tested by practitioners who manually adjust engine settings and directly measure the output. This process has consistently raised concerns regarding time costs and the risk of incidents. To address these issues, simulations of heavy equipment are conducted using Speedgoat and the Simulink API. However, due to the varying compatibility of different versions of Speedgoat hardware and Simulink API, engineers need to have a comprehensive understanding of various Simulink APIs. It is practically challenging for engineers, who must have a deep understanding of heavy equipment structures, to also possess programming skills including API usage. Thus, this paper proposes a tool that allows inputting configuration values for heavy equipment simulation and visually outputs and logs the simulation results. The proposed tool provides functionalities to deliver configuration values, such as engine settings of heavy equipment, to the simulator model and to monitor and log the resulting simulation outputs. These functionalities have been validated through scenarios. By using the developed tool, engineers are expected to reduce the burden of learning Simulink API and focus more on understanding the structure of heavy equipment. Additionally, it is anticipated that this tool will provide a more efficient and safer working environment for heavy equipment testing on construction sites.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.69-75
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• 2024

Noise caused by adverse weather conditions in data collected during autonomous driving can lead to object recognition errors, potentially resulting in critical accidents. While this risk is widely acknowledged, there is a lack of research that quantitatively and systematically analyzes it. Therefore, this study aims to examine and quantify the extent to which noise affects object detection in autonomous driving environments. To this end, we utilized the YOLO v5 model trained on unprocessed datasets. The test data were divided into noise ratios of 0% (Original), 20%, 40%, 60%, and 80%, and the detection results were evaluated by constructing a Confusion Matrix. Experimental results show that as the noise ratio increases, the True Positive (TP) rate decreases, and the F1-score also significantly drops across all noise levels, specifically from 0.69 to 0.47, 0.29, 0.18, and 0.14. These findings are expected to contribute to enhancing the stability of autonomous driving technology. Future research will focus on collecting real datasets that include naturally occurring noise and developing more effective noise removal techniques.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.201-214
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• 2020

Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.34-42
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• 2006

As art analytic method to uncover interesting patterns hidden under a large volume of data, data mining research has been actively done so far in various fields. However, current state-of-the-arts in data mining research have several challenging problems such as being too ad-hoc. The existing techniques are mostly the ones designed for individual problems, so there is no unifying theory applicable for more general data mining problems. In this paper, we address the problem of classification, which is one of significant data mining tasks. Specifically, our objective is to evaluate radial basis function (RBF) model for classification tasks and investigate its usefulness. For evaluation, we analyze the popular Monk's problems which are well-known datasets in data mining research. First, we develop RBF models by using the representational capacity based learning algorithm, and then perform a comparative assessment of the results with other models generated by the existing techniques. Through a variety of experiments, it is empirically shown that the RBF model has not only the superior performance on the Monk's problems but also its modeling process can be controlled in a systematic way, so the RBF model with RC-based algorithm might be a good candidate to handle the current ad-hoc problem.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.7-16
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• 2009

A robot must be able to generate various skills to achieve given tasks intelligently and reasonably. The robot must first learn affordances to generate the skills. An affordance is defined as qualities of objects or environments that induce actions. Affordances can be usefully used to generate skills. Most tasks require sequential and goal-oriented behaviors. However, it is usually difficult to accomplish such tasks with affordances alone. To accomplish such tasks, a skill is constructed with an affordance and a soft behavioral motivation switch for reflecting goal-oriented elements. A skill calculates a behavioral motivation as a combination of both presently perceived information and goal-oriented elements. Here, a behavioral motivation is the internal condition that activates a goal-oriented behavior. In addition, a robot must be able to execute sequential behaviors. We construct skill networks by using generated skills that make action selection feasible to accomplish a task. A robot can select sequential and a goal-oriented behaviors using the skill network. For this, we will first propose a method for modeling and learning Bayesian networks that are used to generate affordances. To select sequential and goal-oriented behaviors, we construct skills using affordances and soft behavioral motivation switches. We also propose a method to generate the skill networks using the skills to execute given tasks. Finally, we will propose action-selection-mechanism to select sequential and goal-oriented behaviors using the skill network. To demonstrate the validity of our proposed methods, "Searching-for-a-target-object", "Approaching-a-target-object", "Sniffing-a-target-object", and "Kicking-a-target-object" affordances have been learned with GENIBO (pet robot) based on the human teaching method. Some experiments have also been performed with GENIBO using the skills and the skill networks.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.263-274
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• 2014

The objectives of this research are to develop information education framework and derive detail IT curriculums in University Liberal education, which is essential to effective learning all special knowledge and a base skill in university education. In order to achieve these research objectives, first this study theoretically derives three categories of IT education area based on comprehensive review of the previous research including IT Fluency, Information Literacy and Computational Thinking concepts, and explicates concrete items for each category. And then, with respect to each of these items, we empirically investigate the degree of necessity measured by the gap between the required level of knowledge and skills which student should have for effective studying of major curriculum and the present level of them which they really have. Field survey is employed for the data collection: 350 questionnaires are distributed to the students, and 313 questionnaires are collected in useful condition and are analyzed. The findings of this research shows that three dimensions of IT Liberal Education are empirically derived by factor analysis as following: (1) Foundational Concepts of IT, (2) Utilization Capabilities of IT, (3) Intellectual Capabilities of IT. And the results of this study can provide the theoretical basis for constructing the IT education. Also they can be used as a practical guideline in developing and promoting specific University IT education programs in Liberal Education.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.