• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.767-769
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• 2022

본 논문에서는 현대인들의 일상생활 속 누적된 스트레스를 완화하고 사용자의 편의를 고려한 "아로마테라피를 지원하는 지능형 샤워부스" 시스템을 제안한다. 제안하는 시스템의 주요 기능은 다음과 같다. 첫째, 적외선 온도 센서와 초음파 센서, 카메라를 통해 사용자의 신체 정보와 기분을 측정한다. 둘째, 측정된 사용자의 신체 정보를 반영하여 Linear actuator를 이용해 샤워기의 높낮이 및 수온을 자동으로 조절한다. 셋째, OpenCV와 앱 내에 만족도 평가를 통해 사용자의 기분에 따라 알맞은 아로마오일을 추천하고 이를 샤워기 필터에 주입한다. IoT기술과 연동된 샤워부스 시스템을 통해 사용자 컨디션에 맞춘 아로마테라피를 지원하여 현대인의 지친 심신 회복과 사용자 편의성이 증대될 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.827-829
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• 2022

본 논문은 코로나19로 인해 발생하는 우울증을 개선하고 태양광 패널을 이용하여 친환경적으로 재배할 수 있는 "태양광 에너지를 활용한 지능형 자율이동 채소재배기"를 제안한다. 본 논문이 제안하는 주요한 특징은 다음과 같다. 첫째, 태양광 패널을 이용하여 재배기에 전원을 공급한다. 둘째, 카메라와 OpenCV를 이용하여 채소의 상태를 매일 확인 후 LED 색상을 조절하여 최적의 채소 성장 환경을 만든다. 셋째, 수위 센서와 모터 펌프를 이용하여 자동으로 물이 공급될 수 있도록 하고, 수온과 수질을 주기적으로 체크하는 등 Human task를 감소시킨다. 넷째, DC모터를 이용하여 실내·외로 자율이동을 하고, 액추에이터를 이용하여 채소가 햇빛을 최대한 많이 받아 성장할 수 있도록 한다. 제안하는 시스템은 가정에서 채소를 재배하는 방식에 IoT기술을 활용하여 사용자의 편의성을 증가시키고, 녹색식물을 통해 '코로나 블루'를 해소하고자 하는 사람에게 필요한 "태양광 에너지를 활용한 지능형 자율이동 채소재배기"의 개발을 목표로 한다.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.135-140
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• 2020

Recently, due to the rapid diffusion of intelligent systems and IoT technologies, power saving techniques in real-time embedded systems has become important. In this paper, we propose P-GA (Parallel Genetic Algorithm), a scheduling algorithm aims at reducing the power consumption of real-time systems in multi-core hybrid memory environments. P-GA improves the Proportional-Fairness (PF) algorithm devised for multi-core environments by combining the dynamic voltage/frequency scaling of the processor with the nonvolatile memory technologies. Specifically, P-GA applies genetic algorithms for optimizing the voltage and frequency modes of processors and the memory types, thereby minimizing the power consumptions of the task set. Simulation experiments show that the power consumption of P-GA is reduced by 2.85 times compared to the conventional schemes.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.4 s.44
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• pp.55-66
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• 2005

The effectiveness of fuzzy supervisory control technique for the control of seismic responses of smart base isolation system is investigated in this study. To this end, first generation base isolated building benchmark problem is employed for the numerical simulation. The benchmark structure under consideration is an eight-story base isolated building having irregular plan and is equipped with low-damping elastometric bearings and magnetorheological (MR) dampers for seismic protection. Lower level fuzzy logic controllers (FLC) for far-fault or near-fault earthquakes are developed in order to effectively control base isolated building using multi-objective genetic algorithm. Four objectives, i.e. reduction of peak structural acceleration, peak base drift, RMS structural acceleration and RMS base drift, are used in multi-objective optimization process. When earthquakes are applied to benchmark building, each of low level FLCs provides different command voltage and supervisory fuzzy controller combines two command voltages io one based on fuzzy inference system in real time. Results from the numerical simulations demonstrate that base drift as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.5
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• pp.231-238
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• 2017

A Class B device in LoRaWAN periodically receives a Beacon message from a gateway for synchronization, and it also periodically activates a ping slot to receive data from the server. In this paper, we save energy consumption by controlling the beacon-less operation which is immediately started on the packet loss. In addition, we propose a method that the server inactivates the ping slots of a device according to the amount of messages, which have to be received from the server, to save energy consumption resulting from activated but unused empty slots. The experiment with the 20% of packet loss rate showed that the reduced beacon-less operation and the inactivation of the ping slot decrease the energy consumption by 96.7% and 60% as compared to the existing method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.53-56
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• 2019

본 논문에서는 화력을 이용하는 대형 파워 플랜트 설비의 미세먼지 발생량을 저감시키고 능동적으로 제어 할 수 있는 효율적인 시스템을 제안한다. 이 시스템은 기존의 고정형으로 설계된 집진기 방식의 고정부하량 한계점과 극복하고 초미세먼지 PM2.5, 미세먼지 PM10의 발생량에 따라 IoT센서 감지에 의해 지능형 알고리즘으로 효율적으로 저감 제어 처리량을 극대화하고, 미세먼지 발생량을 최소화한다. 또한 이 시스템의 차별성은 기존의 집진기에서 잡혀지지 않는 초미세먼지를 새로운 형태의 물질인 FAA(Fine-dust Adsorption Agent)를 통해 연료 연소 시 발생되는 초미세먼지 미세입자 자체를 크게 만들어 기존 설비 집진기 필터에 포집되게 하는 혁신적인 방식이다. 이번 연구를 통해 350도~1000도 열원에서 작용할 수 있는 화학물질 FAA 용액(Agent)을 개발 하였으며 지능형 AI 분사장치를 통해 연료에 첨가되어 연소 시 미세먼지를 20배~50배까지 볼륨을 확대시켜 기존 집진필터에 포집될 수 있게 동작된다. 이때, 기존 설계된 집진기의 한계(부하)용량에 상관없이 미세먼지 발생량을 상황인식 반응형 알고리즘(AI제어) 통해 분사량을 능동적으로 조절하여 미세먼지 발생량을 저감하는 진보적 혁신성을 지닌다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.83-92
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• 2022

In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/m³), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO₂, CO, and NO₂ in subway stations.