• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.712-717
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• 2007

Using home network system including home server, home service robot, a variety of device, it is generally known that application of Multi Agent System for performing variously distributed process that can be occur in home environment, is efficient method. In this system, it is intelligent service robot that a key of human interface and physical service offer Therefore, using application of established multi agent system, we can defined Multi Agent Robot System. In 'open' home environment, between all agent data interaction and cooperation are needed for Multi Agent System offer to user that more efficient service. For this, we focus our attention on define as agent that can autonomic drive and offer to user that physical service robots and, design, suggest the simulator can display that between robot agents communication or between other agents, like home server, and robot agents communication information to user interface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.19-24
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• 2019

The development of ICT technology has a huge impact on the existing closed CCTV security equipment market. With the importance of video data particularly highlighted in areas such as self-driving cars, unmanned vehicles and smart cities, various technologies using video are emerging. In this paper, we proposed a method to transmit videos and metadata as a part of smart city integration, and to solve the traffic, environment and security problems caused in urban life by utilizing the metadata instead of using CCTV videos for simple recording purposes, and reverse tunneling technique was designed and implemented as a method for accessing CCTV videos for vehicles from remote locations. Integrated management of CCTV videos and metadata for vehicles that have been used only for limited purposes in closed environments will enable efficient operation of integrated centers in real time required by smart cities, such as vehicle status check, road conditions and facility management.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.1-9
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• 2019

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.151-158
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• 2019

Wireless communication technology (WAVE) for vehicles, which is the core technology behind the next-generation intelligent transport system (C-ITS), is used to deliver information about vehicles to prevent traffic accidents and traffic situations that may arise between vehicles and infrastructure. Similar traffic issues often arise in marine scenarios. Currently, AIS is being used as a means of transmitting information such as the status of relative vessels, but research is being carried out to solve problems with AIS such as overloading by applying wireless communication technology for vehicles to the sea. In this study, a collision warning system suitable for small-sized vessels was developed based on the marine application of WAVE for vehicles verified through prior research, and the adequacy of this collision warning system was reviewed through a practical test. It is expected that this system will contribute greatly to future e-Navigation applications or self-driving ships as well as to preventing marine accidents.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.77-82
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• 2019

AI is applied increasingly to overall industries such as living, medical, financial service, autonomous car, etc. thanks to rapid technology development. AI-leading countries are strengthening their competency to secure competitiveness since AI is positioned as the core technology in $4^{th}$ Industrial Revolution. Although Korea has the competitive IT infra and human resources, it lags behind traditional AI-leaders like United States, Canada, Japan and, even China which devotes all its might to develop intelligent technology-intentive industry. AI is the critical technology influencing on the national industry in the near future according to advancement of intelligent information society so that concentration of capability is required with national interest. Also, joint development with global AI-leading companies as well as development of own technology are crucial to prevent technology subordination. Additionally, regulatory reform and preparation of related law are very urgent.

• Smart Media Journal
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• v.10 no.2
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• pp.110-118
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• 2021

The Internet of Things (IoT) technology, a key growth engine of the 4th industrial revolution, has grown to a stage where it can autonomously communicate with each other and process data according to space and circumstances. Accordingly, the IT infrastructure becomes increasingly complex and the importance of the monitoring field for maintaining the system stably is increasing. Monitoring technology has been used in the past, but there is a need to find a flexible monitoring system that can respond to the rapidly changing ICT technology. This paper conducts research on designing and testing an open source-based Prometheus monitoring system. We builds a simple infrastructure based on IoT devices and collects data about devices through the Exporter. Prometheus collects data based on pull and then integrates into one dashboard using Grafana and visualizes data to monitor device information.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.544-552
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• 2020

This study investigated the effectiveness of "Healing Stay in Forest," a long-term stay forest therapy program implemented by National Center for Forest Therapy in 2019 with 49 participants by measuring pre- and post-questionnaire responses and physical changes, such as blood pressure, body composition, and HRV. Results confirmed statistically significant reduction in negative emotions and enhancement of autonomic nerve balance in the participants. Regarding the changes in the evaluation of physical fitness based on medical history, some significant differences were observed in grip strength and standing high jump, in which participants with no medical history showed better results than those with medical history. These findings are important for verifying the effectiveness of long-term stay forest therapy (6 nights and 7 days).

• Journal of Korean Public Health Nursing
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• v.34 no.3
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• pp.325-340
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• 2020

Purpose: This study was undertaken to identify the effects of a camp-type group counseling program based on the self-determination theory, for controlling smartphone usage amongst university students. The program consisted of smartphone overdependence, basic psychological needs, and adaptation to college life. Method: This was a quasi-experimental research with a non-equivalent control group design (pre-test and post-test design). A total of 38 freshmen (17 experimental and 21 control) participated in the study. Experimental subjects attended a two nights and three days camp, where the group counseling program included meetings for basic psychological needs (eg., autonomy, competence, and relatedness). Results: This study showed significant differences in basic psychological needs (Z=-2.91, p=.003) and adaptation to college life (Z=-3.47, p<.001). Although no significant difference was obtained in the degree of smartphone overdependence (p=.194), mean score of the experimental group pre-test and post-test changed from the level of potential risk users to general users. Conclusion: The current findings indicate that by considering the basic psychological needs and adaptation to college life, our program could be an effective intervention for controlling smartphone usage amongst college students. Therefore, we propose that a camp-type counseling program could be considered for application as group therapy for programs associated with preventing smartphone overdependence.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.13-20
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• 2019

Recently, object detection technology based on CNN has been actively studied. Object detection technology is used as an important technology in autonomous vehicles, intelligent image analysis, and so on. In this paper, we propose a rotation change robust object detection system by applying MI-FL (Moment Invariant-Feature Layer) to SSD (Single Shot Multibox Detector) which is one of CNN-based object detectors. First, the features of the input image are extracted based on the VGG network. Then, a total of six feature layers are applied to generate bounding boxes by predicting the location and type of object. We then use the NMS algorithm to get the bounding box that is the most likely object. Once an object bounding box has been determined, the invariant moment feature of the corresponding region is extracted using MI-FL, and stored and learned in advance. In the detection process, it is possible to detect the rotated image more robust than the conventional method by using the previously stored moment invariant feature information. The performance improvement of about 4 ~ 5% was confirmed by comparing SSD with existing SSD and MI-FL.