• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.548-549
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• 2016

노령인구와 사회 취약 계층을 지원하기 위해 사회복지에 대한 사회적 노력이 확대되고 있으며, 또한 사물인터넷 (Internet of Things: loT)이란 IT 기술의 발전을 통해 사회복지 체계를 효율적으로 지원할 수 있는 기회가 마련되었다. 사물인터넷은 사물들로부터 수집한 정보를 분석 및 처리함으로써 다양한 지능 서비스 제공이 가능하기 때문에 그 활용도가 점차 증대되고 있다. 그러나 기존 사물인터넷 기반 사회취약 계층 서비스들은 Healthcare 서비스에 초점을 맞추고 있으며, 이는 사회복지 전 분야에 대한 효율적인 서비스 제공에 어려움을 주게 된다. 따라서 사회복지 체계에 대한 효과적인 지원을 위해 IT 융합 기반 기술이 제공되어야 하며, 본 논문에서는 사회복지 체계를 효율적으로 지원할 수 있는 서비스를 제공하기 위한 소프트웨어 플랫폼을 제안한다. 제안된 플랫폼은 loT 장치와 스마트 폰으로부터 정보를 수집하고 처리하여 이를 기반으로 다양한 사회복지 서비스 지원한다.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.247-252
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• 2018

The problem of the environment is urgently coming to the world as a problem that humanity must solve. In particular, Korea is directly affected by air pollution and marine pollution due to its geopolitical position with China, and is also exposed to a great deal of pollution due to air, water, soil, and weather. In this situation, due to the disconnection between the management domain / service (system) related to the environment, the ability to quickly identify causes and cope with situations in the event of environmental pollution or disasters is weak, and duplication and investment are being faced. The development of a service platform for smart environment disaster management is designed to detect environmental disasters in an early stage through the management of smart environment disaster management at the national level, It will be a way to predict complex environmental disasters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.231-237
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• 2017

This paper presents an implementation of a smart flowerpot which can adjust humidity and illumination automatically after monitoring the temperature, humidity, and illumination. We made a container of the flowerpot with a 3D printer and embedded a NodeMCU micro controller in it. We attached a temperature sensor, a humidity sensor, an illumination sensor, and a water pump to the NodeMCU. We developed a control program that adjusts humidity and illumination and ran it on the NodeMCU. Also we developed an Android application and set up an MQTT server. Using the MQTT server, the NodeMCU and the Android application can exchange messages which keep sensor values and commands. Using the Android application. the user can send the proper temperature, humidity, and illumination to the smart flowerpot and monitor the sensor values.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.406-409
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• 2022

In recent years, the developments in technologies, such as AI systems, Blockchain, VR/AR, 3D printing, robotics, and nanotechnology, are reshaping the future of healthcare right before our eyes. And also, healthcare has seen a paradigm shift towards prevention-oriented medicine, with a focus on consumers requirements. The spread of infectious diseases such as Covid-19 have altered the definition of healthcare and treatment facilities, necessitating immediate action to redesign hospitals' physical environments, adapt communication models to address social distancing requirements, implement virtual health solutions, and establish new clinical protocols. Hospitals, which have traditionally served as the hub of healthcare systems, are pursuing or being forced to reestablish themselves against this landscape. Rather than only treating ailments, future healthcare is predicted to focus on wellness and prevention. In personalized care, long-term prevention strategies, remote monitoring, early diagnosis, and detection are critical. Given the growing interest in smart healthcare defined by these modern technologies, this study looked into the definitions and service kinds of smart healthcare. The background and technical aspects of smart hospitals were also explored through a literature review.

• Smart Media Journal
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• v.12 no.10
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• pp.47-54
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• 2023

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.545-571
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• 2016

The development of ICT brings a big change in manufacturing industries, and new information technology such as IoT, AR, and big data was applied on manufacturing process. As a result, the concept of smart factory has been introduced as a new manufacturing paradigm. In fact advanced countries like USA, Germany, and Japan have actively introduced smart factory in their manufacturing industries such as electronic, automobile, machinery, to improve production efficiency and quality. The manufacturing environment has been changed into flexible system, so that smart factory will be leading future manufacturing industries. Thes changes have more severe influence on Korean manufacturing industries. Mny industrial companies, have a strong interest in smart factory and they, particularly big enterprises, have been adopting smart factory to increase their manufacturing efficiencies. However, Korean small and medium-sized enterprises (SMEs) have many financial and technological difficulties so that the diffusion of smart factory in Korean SMEs has not been satisfiable up to present. However, smart factory is very important for enhancing their competitiveness in global market. Therefore, this study aims at identifying the standardization strategy of smart factory in so-called Korean 'roots industry' by presuming that the standardization will activate the diffusion of smart factory among Korean SMEs. For this purpose, first, this study examines the competitiveness of SMEs, especially in 'roots industry' and identifies the necessity of diffusion of smart factory among those SMEs. Second, based on the active review on the existing literature, this study identifies four factor groups that would influence the adoption or diffusion of standardized smart factory. They are technological, organizational, industrial and policy factors. Third, using those four factors, this study made two comprehensive case analyses on the adoption and diffusion of smart factory. These two companies belong to molding sector which is one of the important six sectors in 'root industry'. Finally, based on the theoretical and empirical analyse, this study suggests four strategies for activating the standardization of smart factory; international standardization, government-leading standardization, firm-leading standardization, and non-standardization.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.155-162
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• 2020

Among the fourth industrial revolution technologies, products related to healthcare using IoT and sensors are currently being developed. We design and develop an interaction system based on user standing posture recognition using multi-channel large area pressure sensors in this paper. To this end, first of all we investigate major sensor markets of the sensor industry and review technology trends and the current and future of smart healthcare. Based on this survey, we examine and compare cases developed at home and abroad for multi-channel large-area pressure sensors, which are key components of the system that we want to develop. We recognize the standing posture status of the user through the developed system and experiment with how effective it is actually in user posture calibration and apply the research results to various healthcare devices' medical fields based on this.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually