• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1126-1130
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• 2010

Research on intelligent robot is in active progress as the next generation IT education area. Since intelligent robots are closely related to the real human world, they should provide human behaviors or judging ability as their functions. For this reason, research is recently done not only on diverse hardware of robot education but also on service component architecture which includes various functions. In this paper we propose a study on learning to creative solve problems using RDS(Robotics Developer Studio). RDS is a software tool to control or program intelligence robot as a software module. Using service component framework which considers standardization of the integrated development of intelligent robot, we expect to provide 3-dimensional visual simulation environment, and save time and costs in education the environment for the intelligence robot experiment.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.153-161
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• 2015

In the present study, as part of the technology development (Quality of Life Technology, QoLT) to improve the socio-economic status of people with disabilities as an extension of these studies, we propose the development of the smart wheelchair system and navigation technology for stable and safe driving in various environments. For the disabled and the elderly make driving easy and convenient with manual/autonomous driving condition, we firstly develop the user-oriented smart wheelchair system with optimized sensors for environment recognition, and then we propose a navigation framework of a hierarchical structure to ensure real-time response, as well as driving stability when traveling to various environmental changes, and to enable a more efficient operation. From the result of several independent experiments, we ensure efficiency and safety of smart wheelchair and its navigation system.

• Atmosphere
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• v.31 no.3
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.155-161
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• 2019

Electroencephalography (EEG), a representative method of identifying temporal and spatial changes in brain activity, is a voluntary electrical activity measurable in the human scalp. Various interface technologies have been provided to control EEG activity, and it is possible to operate a machine such as a wheelchair or a robot through brainwaves. The characteristics of EEG data are collected in various types of channels in real time, and a server system for analyzing them is required to have an independent and lightweight system for the platform. In these days, the Spring platform is used as a large business server as an independent, lightweight server system. In this paper, we propose an EEG analysis system using the Spring server system. Using the proposed system, the reliability of EEG control can be enhanced, and analysis and control interface expansion can be provided in various aspects such as game and medical areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.195-200
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• 2021

With the development of transportation and mobiles demand for tourism travel is increasing and related industries are also developing significantly. The combination of augmented reality and tourism contents one of the areas of digital media technology, is also actively being studied, and artificial intelligence is already combined with the tourism industry in various directions, enriching tourists' travel experiences. In this paper, we propose a system that scans miniature models produced by reducing tourist areas, finds the relevant tourist sites based on models learned using deep learning in advance, and provides relevant information and 3D models as AR services. Because model learning and object detection are carried out using YOLOv3 neural networks, one of various deep learning neural networks, object detection can be performed at a fast rate to provide real-time service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1909-1928
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• 2021

At present, the Colombian government shares information on threats or vulnerabilities in the area of cybersecurity and cyberdefense, from other government agencies or departments, on an ad-hoc basis but not in real time, with the surveillance entities of the Government of the Republic of Colombia such as the Joint Command of Cybernetic Operations (CCOCI) and the Cybernetic Emergencies Response Team of Colombia (ColCERT). This research presents the MS-CSIRT (Management System Computer Security Incident Response Teams) methodology, that is used to unify the guidelines of a CSIRT towards a joint communication command in cybersecurity for the surveillance of Information Technology (IT), Technological Operations (TO), Internet Connection Sharing (ICS) or Internet of Things (IoT) infrastructures. This methodology evaluates the level of maturity, by means of a roadmap, to establish a CSIRT as a reference framework for government entities and as a guide for the areas of information security, IT and TO to strengthen the growth of the industry 4.0. This allows the organizations to draw a line of cybersecurity policy with scope, objectives, controls, metrics, procedures and use cases for the correct coordination between ColCERT and CCOCI, as support entities in cybersecurity, and the different companies (ICS, IoT, gas and energy, mining, maritime, agro-industrial, among others) or government agencies that use this methodology.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.168-177
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• 2020

In recent years, importance of blockchain systems has been grown after success of bitcoin. Distributed consensus algorithm is used to achieve an agreement, which means the same information is recorded in all nodes participating in blockchain network. Various algorithms were suggested to resolve blockchain trilemma, which refers conflict between decentralization, scalability, security. An algorithm based on Byzantine Agreement among Decentralized Agents (BADA) were designed for the same manner, and it used limited committee that enables an efficient consensus among considerable number of nodes. In addition, election of committee based on Proof-of-Nonce guarantees decentralization and security. In spite of such prominence, application of BADA in actual blockchain system requires further researches about performance and essential features affecting on the performance. However, performance assessment committed in real systems takes a long time and costs a great deal of budget. Based on this motivation, we designed and implemented a simulator for measuring performance of BADA. Specifically, we defined a simulation framework including three components named simulator Command Line Interface, transaction generator, BADA nodes. Furthermore, we carried out response surface analysis for revealing latent relationship between performance measure and design parameters. By using obtained response surface models, we could find an optimal configuration of design parameters for achieving a given desirable performance level.

• Convergence Security Journal
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• v.20 no.5
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• pp.3-10
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• 2020

Recently, the Internet and networks are regarded as essential infrastructures that constitute society, and security threats have been constantly increased. However, the network switch that actually transmits packets in the network can cope with security threats only through firewall or network access control based on fixed rules, so the effective defense for the security threats is extremely limited in the network itself and not actively responding as well. In this paper, we propose an in-network security framework using the high-level data plane programming language, P4 (Programming Protocol-independent Packet Processor), to deal with DDoS attacks and IP spoofing attacks at the network level by monitoring all flows in the network in real time and processing specific security attack packets at the P4 switch. In addition, by allowing the P4 switch to apply the network user's or administrator's policy through the SDN (Software-Defined Network) controller, various security requirements in the network application environment can be reflected.