• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.467-474
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• 2018

To organize and document the contents of meetings and discussions is very important in various tasks. However, in the past, people had to manually organize the contents themselves. In this paper, we describe the development of a system that generates the meeting minutes automatically using the TextRank algorithm. The proposed system records all the utterances of the speaker in real time and calculates the similarity based on the appearance frequency of the sentences. Then, to create the meeting minutes, it extracts important words or phrases through a non-supervised learning algorithm for finding the relation between the sentences in the document data. Especially, we improved the performance by introducing the keyword weighting technique for the TextRank algorithm which reconfigured the PageRank algorithm to fit words and sentences.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.81-86
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• 2014

In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.349-354
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• 2019

s programming education becomes more important in recent years, it is necessary to learn how the source code written by students reflects Object-Oriented(OO) concepts. We present a tool called the Round-Trip Translator(RTT) that transforms the Unified Modeling Language(UML) class diagram and Java source code to provide a web-based environment that provides real-time synchronization of UML and source code. RTT was created by improving existing RTE and is a tool for students who are learning OO concepts to understand how their UML or source code reflects the concepts that user intended. This study compares the efficiency and user- friendliness of RTT with the existing Round-Trip Engineering-based tools. The results show that students have improved understanding of OO concepts through UML and source code translation by using the RTT. We also found out that students were satisfied with the use of the RTT, which provides more efficient and convenient user interface than the existing tools.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.4
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• pp.221-230
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• 2019

This paper aims to introduce the case of operating electronic document system and record management system as one system called i-Works at Korea Midland Power Company. i-Works combines intelligent services, such as artificial intelligence and a chatbot, as a supplementary tool for record management. As such, the preparation process and progress direction for the development of the record management system is introduced, an in-depth review of real-time transfer and utilization of the functional classification system to enhance the utilization of the current records is presented, and new technologies, such as artificial intelligence for an efficient management of the increasing number of electronic records, are established.

• Journal of Engineering Education Research
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• v.24 no.1
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• pp.34-45
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• 2021

This research is a case study about "Critical Thinking and Debate Education" class which was done for freshmen at the engineering college of "S" Women's University. Real time remote classes through LMS and ZOOM were the most effective tools under on-line circumstances, considering the fact that video lectures only cannot cultivate students' capabilities of critical thinking and communication. Throughout the analysis on students' self-reflection journals and lecture evaluations, this paper examined considerable future points and the pros and cons of "Critical Thinking and Debate Education" under online presentation and discussion situation. As research outputs, students told they could feel less nervousness and anxiety when they exercise and have a presentation because they could choose familiar space for them. In addition, students also told that they feel comfortable about both self-feedback and peer evaluation, repeatedly seeing the recorded video clip. However, on the contrary, sometimes students felt uncomfortable due to unstable internet connection through the online classes, and they also were regretful about the missing chances of interaction between a teacher and students and of intimate exchanges among students. They also told they had felt a kind of limit of enhancing their presentation skills just in front of the monitor. Considering these outcomes, this research paper points out that online education needs to be proceeded by strengthening multi layered feedback to students with the build-up of a non-face-to-face stable educational infrastructure, application of online instructional strategy, and utilization of YouTube platform and video contents. Through this research paper, I hope the new system of encompassing on/off line "Critical Thinking and Debate Education" and effective teaching and learning method can be developed soon by strengthening the strength of online education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.63-68
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• 2021

Recently there is sharply increasing interest in watching and creating video contents such as YouTube. However, creating such video contents without privacy protection technique can expose other people in the background in public, which is consequently violating their privacy rights. This paper seeks to remedy these problems and proposes a technique that identifies faces and protecting portrait rights by blurring the face. The key contribution of this paper lies on our deep-learning technique with low detection error and high computation that allow to protect portrait rights in real-time videos. To reduce errors, an efficient tracking algorithm was used in this system with face detection and face recognition algorithm. This paper compares the performance of the proposed system with and without the tracking algorithm. We believe this system can be used wherever the video is used.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.