• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1216-1221
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• 2022

Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.522-524
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• 2019

In spite of a rapid development in the quality of built-in mobile cameras, their some physical restrictions hinder them to achieve the satisfactory results of digital single lens reflex (DSLR) cameras. In this work we propose an end-to-end deep learning method to translate ordinary images by mobile cameras into DSLR-quality photos. The method is based on the framework of generative adversarial networks (GANs) with several improvements. First, we combined the U-Net with DenseNet and connected dense block (DB) in terms of U-Net. The Dense U-Net acts as the generator in our GAN model. Then, we improved the perceptual loss by using the VGG features and pixel-wise content, which could provide stronger supervision for contrast enhancement and texture recovery.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.114-116
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• 2022

The advent of the Multi-access Edge Computing (MEC) paradigm allows mobile users to offload resource-intensive and delay-stringent services to nearby servers, thereby significantly enhancing the quality of experience. Due to erratic roaming of mobile users in the network environment, maintaining maximum quality of experience becomes challenging as they move farther away from the serving edge server, particularly due to the increased latency resulting from the extended distance. The services could be migrated, under policies obtained using Deep Reinforcement Learning (DRL) techniques, to an optimal edge server, however, this operation incurs significant costs in terms of service downtime, thereby adversely affecting service quality of experience. Thus, this study addresses the service mobility problem of deciding whether to migrate and where to migrate the service instance for maximized migration benefits and marginal service downtime.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.344-347
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• 2016

유비쿼터스 환경 시대에 맞춰 현재 스마트 디바이스의 발달과 시장의 확대로 스마트 미디어 기기의 보급이 급속도로 확산되고 있으며 많은 교육용 어플리케이션 또한 개발되고 있는 중이다. 이러한 교육용 어플리케이션들은 지식기반사회의 학습도구로서 지식접근 및 창출에 중요한 요소인 인터넷과 웹을 활용하게 되고 이동성과 편의성을 추구하는 모바일기기를 통해 학습이 가능하도록 지원하는 프로그램이다. 본 연구는 모바일러닝 기반의 '창의적 문제해결(CPS, Creative Problem Solving)' 모형을 활용하는 교육용 앱에 대한 설계방안을 제시하고자 하며 연구의 목적은 대학 교육에서 학생들의 창의적 사고와 문제해결능력 향상을 돕는 모바일러닝 기반의 학습환경을 설계하는 데 있다. 제안하는 학습지원 도구는 모바일 앱 형태로 제작되며 학습활동에 있어 다양한 창의적 사고과정 활동과 표현방식, 상호작용성 등의 기능을 통해 학습자의 고차원적인 사고능력을 향상시키는 인지적 도구로서 활용될 수 있도록 설계하였다. 개발환경은 안드로이드용 앱 인벤터 프로그램을 사용하였으며 앱 인벤터 서버환경에서 제공되는 컴포넌트와 이벤트 핸들러를 이용하여 교수자모드와 학습자모드의 앱을 각각 설계하였다. 교수자 앱은 학습활동 제공을 의미하는 프로바이더(Provider) 모드 기능이 포함되며 학습자 앱은 CPS 활동을 위한 학습활동 모드 기능이 설계된다. 대부분의 창의성 관련 앱들은 그래픽작업을 통한 두뇌활동 향상 프로그램, 체험활동 위주의 프로그램 등으로 출시가 되어 있으나 교수-학습 활동을 위한 창의적 문제해결활동 관련 앱은 존재하지 않는 상태이므로 수업활동에서의 활용측면에서 가치가 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1410-1413
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• 2012

최근 스마트 시대에 디지털 컨버젼스(digital convergence)의 대표기기로 대두되고 있는 태블릿 PC는 휴대전화와 컴퓨터의 기능을 바탕으로 장소의 제한 없이 네트워크에 접속할 수 있다. 이는, 개인의 일상생활에서 큰 영향을 미치고 있는 실정이다. 10년 이상 e러닝이 주도해 온 IT교육시장에서 스마트러닝으로의 변화는 새로운 플랫폼을 구축하는 그 이상의 의미를 가진다. 스마트 러닝은 기존의 수직적인 학습방식을 수평적, 참여적, 지능적, 그리고 상호작용적인 방식으로 전환하여 학습의 효과를 높였다. 이러한 트랜드를 반영하여 스마트러닝의 장점을 극대화 시킬 수 있는 학습자 중심의 컨버젼스 러닝시스템(learning system)을 구현하고자 하였다. 또한, 영어의 중요성이 대두되면서 영어 인증시험에 대한 관심이 날로 커지고 있다. 그리하여 바쁜 일상생활 중에서 시간과 장소에 구애 받지 않고 태블릿 PC를 통하여 영어 인증시험을 공부할 수 있는 어플리케이션을 기획하였다. 본 LEMON(Learn English Mobile ON-air) 앱(application)은 영어 학습 시간이 충분하지 않은 대학생 및 직장인 등을 대상으로 TOEIC, TOEFL, TOEIC SPEAKING 영어 인증시험에 대한 학습이 가능하도록 구현하였다.

• Journal of Fashion Business
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• v.19 no.6
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• pp.82-100
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• 2015

Mobile fashion apps present much opportunity for marketers to engage consumers, however not all apps provide enough functions for their targeted audience. This study aims to determine how mobile fashion apps can be used to build brand community with consumer engagement. Qualitative data on fashion mobile apps were collected from the Apple app store and Android market during the spring and summer of 2015. A total of 110 fashion mobile apps were collected;, 50 apps were identified as apparel brands that either manufacture or sell apparel to consumers, which we categorized as "brand" fashion apps, and the remaining 60 were categorized as "non-brand" fashion apps. The result of the study can be summarized as below. The 60 non-brand fashion apps were grouped into 5 app types: shopping, searching, sharing, organizational, and informational. The main functions are for informational use and shopping needs, since at least half (31 apps) are used for either retrieving information or for shopping. However, in contrast, social networking and location were infrequent and not commonly utilized by these apps. The most common type of non-brand fashion apps available were shopping apps;, many shopping apps enable users to shop from several different websites and save their items into one universal shopping cart so that they only check out once. Most of these apps are informational and help consumers make more informed decisions on purchases;, in addition many offer location services to help consumers find these items in store. While these apps perform several functions, they do not link to social media. The 50 brand apps were grouped into 5 brand types: athletic, casual, fast fashion, luxury, and retailer. These apps were also checked for attributes to determine their functionality. The result shows that the main functions of brand fashion apps are for information (82% of the 50 apps) as well as location searching (72% of 50 apps). Conversely, these apps do not offer any photo sharing, and very few have organizational or community functions. Fashion mobile apps and m-marketing elements: To build brand community, mobile apps can be designed to motivate consumer's engagement with brands. The motivations of fashion mobile apps are useful in developing fashion mobile apps. Entertainment motives can be fulfilled with multimedia attributes, functionality motives are satisfied with organizational and location-based features, information motives with informational service, socialization with community and social network, learning and intellectual stimulation from informational attributes, and trend following through photo sharing. The 8 key attributes of mobile apps can correspond to the 4 m-marketing elements (i.e., Informative content, multimedia, interactions, and product promotions) that are further intertwined with m-branding elements. App Attributes and M-Marketing aim to Build Brand Community;, the eight key attributes can impact on 4 m-branding elements, which further contribute to building brand community by affecting consumers' perceptions of brands preference and advocacy, and their likelihood to be loyal.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.465-467
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• 2022

Mobile phones have become an essential item nowadays since it provides access to online platform and service fast and easy. Coming to these platforms such as Social Network Service (SNS) for shopping have been a go-to option for many people. However, searching for a specific fashion item in the picture is challenging, where users need to try multiple searches by combining appropriate search keywords. To tackle this problem, we propose a system that could provide immediate access to websites related to fashion items. In the framework, we also propose a deep learning model for an automatic analysis of image contexts using instance segmentation. We use transfer learning by utilizing Deep fashion 2 to maximize our model accuracy. After segmenting all the fashion item objects in the image, the related search information is retrieved when the object is clicked. Furthermore, we successfully deploy our system so that it could be assessable using any web browser. We prove that deep learning could be a promising tool not only for scientific purpose but also applicable to commercial shopping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.