• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.23-32
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• 2018

Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.83-92
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• 2020

The current technology drain at small and medium-sized enterprises in Korea is very serious. According to the National Intelligence Service's survey data, 69 percent of technology leaks are made through employees of small and medium-sized enterprises. A document security system was introduced to compensate for the problem. However, small and medium-sized enterprises are not doing well due to their poor environment. Therefore, it proposes a document security system suitable for small businesses by developing a location information machine learning system that automatically creates a document security Green Zone through learning, and an ECM-based electronic document leakage prevention system that manages generated Green Zone information by reflecting it into the document authority system. And step by step, propose a universal solution through cloud services..

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.141-157
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• 2023

The present work aimed to present the development of 'Funt', the augmented reality-based digital app as an educational tool for foreign language learning. Our work further evaluated the learning efficacy of the tool by the assessment of the three dependent measures including learning motivation, learning satisfaction, and learning involvement. With a learning app of 'Funt', students can use AR app to access recognition-based or location-based experiences such that any objects, artifacts, or media appear to be in the app. Students are then able to interact with the digital content by manipulating it to learn more about it. Students's engagement should also increase when they create their own experience in AR to demonstrate their understanding of a particular concept or words. Learning effects were evaluated on survey data collected from a hundred respondents aging six to nine years. One-group design for pre-test and post-test was utilized to examine the differences of learning efficacy by comparing the non-'Funt' group and the Funt group scores. A pairwise t-Test was performed for pairwise comparisons between two learning groups. The results indicate that the 'Funt' group scored significantly higher than the non-'Funt' group in the measures of learning motivation, learning satisfaction, and learning involvement. Overall, our results suggest that 'Funt' attracted the students' attention, provided them with a fun context to learn English vocabulary, and develop positive motivation and satisfaction towards vocabulary learning through AR technology.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Broadcasting and Media Magazine
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• v.25 no.1
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• pp.75-82
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• 2020

디지털 트윈이나 증강현실, 가상현실, 자율주행 등과 같이 현실 좌표계의 위치를 다루거나 현실과 가상세계를 융합하는 기술들에 있어 측위 기술은 상당히 주요하게 작용한다. 측위 기술은 그 목적과 타겟 디바이스에 따라 매우 다양하게 존재하며, 기존 측위 기술들에 인공지능을 융합하여 정밀도와 측위 주기를 개선시키는 등 다양한 연구가 진행되고 있는 분야이다. 본 고에서는 기존의 다양한 측위 기술들의 동향과 인공지능을 융합하여 성능을 높인 사례들에 대해 설명한다.

• Smart Media Journal
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• v.11 no.5
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.90-96
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• 2024

In this paper, we propose a technique to model high resolution seafloor topography with 1m intervals using actual water depth data near the east coast of the Korea with 1.6km distance intervals. Using a feature point extraction algorithm that harris corner based on deep learning, the location of the center of seafloor mountain was calculated and the surrounding topology was modeled. The modeled high-resolution seafloor topography based on deep learning was verified within 1.1m mean error between the actual warder dept data. And average error that result of calculating based on deep learning was reduced by 54.4% compared to the case that deep learning was not applied. The proposed algorithm is expected to generate high resolution underwater topology for the entire Korean peninsula and be used to establish a path plan for autonomous navigation of underwater vehicle.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.377-385
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• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.91-98
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• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.