• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1581-1587
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• 2021

As embedded system are used widely and variously, multi-edge system, which multiple edges gather and perform complex operations together, is actively operating. In a multi-edge system, it often occurs that an abnormal operation at one edge is transferred to another edge or the entire system goes down. It is necessary to determine and control edge anomalies in order to prevent system down, but this can be a heavy burden on the resource-limited edge. As a solution to this, we use power consumption data to check the state of the edge device and transmit it based on a QRcode to check and control errors at the server. The architecture proposed in this paper is implemented using 'chip-whisperer' to measure the power consumption of the edge and 'Raspberry Pi 3' to implement the server. As a result, the proposed architecture server showed successful data transmission and error determination without additional load appearing at the edge.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.57-64
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• 2023

In this paper, we propose a smart umbrella stand based on the Internet of Things (IoT) that helps people to pack their umbrellas more easily. The proposed smart umbrella stand offers three functions. First, it receives weather information, and a hidden umbrella handle will automatically become visible when it rains, making it easier to grab. Second, the smart umbrella stand is equipped with a heated air system, allowing it to effectively dry umbrellas that have become wet due to rain. This makes it more convenient for users to dry and store their rain-soaked umbrellas. Third, the smart umbrella stand enables users to monitor its current status, track weather conditions, and monitor the water level in the rain gutter through a dedicated application. The proposed smart umbrella stand aims to bring convenience to daily life by making it more convenient to carry an umbrella on rainy days or days with a high probability of rain.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.470-470
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• 2023

하천 및 저수지의 수량자료 수집을 위해 부자식 또는 레이더식 수위계 등의 계측기를 이용하여 수위의 변화를 측정한다. 그러나, 수량 모니터링을 위해 설치되는 계측기와 유지관리를 위한 인력소모 비용의 이유로 일부 지역에서만 제한적으로 수행되고 있어 정확한 수문 해석에 한계가 있다. 이에 본 연구에서는 저가의 센싱장비와 라즈베리파이를 활용한 ICT 기술을 수량 모니터링분야에 적용하여 수위 모니터링 기술을 개발하고자 한다. 라즈베리파이(Raspberry Pi 3 B Model)는 오픈소스 기반의 초소형 컴퓨터로 여러 센서를 연결하여 다양한 수문 인자(토양수분, 온도, 수위 등)들을 측정할 수 있으며, 통신 모듈이 기본 내장되어 있어 통신망 구축을 통해 측정데이터를 데이터베이스에 저장할 수 있다. 수위센서(eTape)와 초음파센서를 연결하여 실제 하천 유량 모니터링에 활용되고 있는 부자식 수위계 장비와 비교 분석을 통해 활용 가능성을 검증하고자 하였다. 검증방법은 인공강우실험을 위해 설치된 플륨관에 수위센서(eTape)와 초음파센서를 부착하여 기존 운영중인 부자식 수위계와 비교하였다. 부자식 수위계의 측정결과와 두 센서의 상관관계를 분석한 결과 결정계수(R²)가 0.94 이상으로 나타났다. 분석결과와 같이 기존 고가의 수위 관측장비들을 대체하여 저가의 센서 기반 모니터링 장비들의 실용화가 가능할 것이라 판단된다. 향후 연구에서는 다양한 센서(수위, 초음파, 카메라 등)를 이용하여 농업용수 물수지 분석에 활용할 계획이다. 농업용수는 수자원 총량 중 차지하는 비율이 높음에도 불구하고 정량적 데이터가 부족한 실정이며, 지역별 공급 특성이 다양하여 이를 계량화하기 위해서는 다수의 계측기가 필요하다. 따라서, 본 연구에서 개발중인 센서를 활용하여 물순환 과정의 정량적인 계측을 통해 농업용수의 효율적인 관리 및 농업용수의 하천기여도 평가 자료로 활용될 수 있을 것으로 판단된다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.107-116
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• 2024

In this paper, we analyze the performance of MicroVM when running AI applications on an edge computing environment and whether it can replace current container technology and traditional virtual machines. To achieve this, we set up Docker container, Firecracker MicroVM and KVM virtual machine environments on a Raspberry Pi 4 and executed representative AI applications in each environment. We analyze the inference time, total CPU usage and trends over time and file I/O performance on each environment. The results show that there is no significant performance difference between MicroVM and container when running AI applications. Moreover, on average, a stable inference time over multiple trials was observed on MicroVM. Therefore, we can confirm that executing AI applications using MicroVM instead of container or heavy-weight virtual machine is suitable for an edge computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.563-569
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• 2018

This paper is on the implementation of a system for automatically monitoring window farm hydroponics based on Arduino (utilizing Arduino's open source code) emerging as the icon of the Fourth Industrial Revolution. A window farm, which means window-type hydroponics, is offered as an alternative to fulfill the desires of people who want to grow plants aside from the busy daily life in the city. The system proposed in this paper was developed to automatically monitor a window farm hydroponics cultivation environment using the Arduino UNO board, a four-charmel motor shield, temperature and humidity sensors, illumination sensors, and a real-time clock module. Modules for hydroponics have been developed in various forms, but power consumption is high because most of them use general power and motors. Since it is not a system that is monitored automatically, there is a disadvantage in that an administrator always has to manage its operational state. The system is equipped with a water supply that is most suitable for a plant growth environment by utilizing temperature, humidity, and light sensors, which function as Internet of Things sensors. In addition, the real-time clock module can be used to provide a more appropriate water supply. The system was implemented with sketch code in a Linux environment using Raspberry Pi 3 and Arduino UNO.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1123-1132
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• 2018

In this paper, we design and implement a public bicycle sharing system based on smart phone application capable of distributing access codes via internet connection. When smartphone user uses the application to request a bicycle unlock code, server receives the request and sends an encrypted code, which is used to unlock the bicycle at the station and the same code is used to return the bicycle. The station's hardware prototypes were built on top of Internet devices such as raspberry pi, arduino, keypad, and motor driver, and smartphone application basically includes shared bike rental and return functionality. It also includes an additional feature of reservation for a certain time period. We tested the implemented system, and found that it is efficient because it shows the average of 3-4 seconds delay. The system can be implemented to manage multiple bikes with a single control box, and as the user can use a smartphone application, this makes the system more cost effective.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.453-453
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• 2021

도시지역 비점오염원관리, 물순환 회복, 침투 및 증발산량 증가, 열섬현상 저감을 위한 주요한 방안으로 저영향개발(low impact development, LID)과 그린인프라 기법의 적용되고 있다. LID 시설은 소규모 분산형 시설로써 넓은 지역에 많고 다양한 시설들이 적용되어 시설의 개수가 많으며, 수질 및 토양 내 기성제품에 대한 센서들의 가격은 고가로 형성되어 있어 기기의 경제성 및 유지관리 등 적용하는데 제한적이다. 따라서 과거 모니터링 자료를 기반으로 오염물질들과의 상관성 분석을 통하여 계측이 어려운 항목들을 계측가능한 항목들로부터 예측 가능하며, 선정된 항목들에 대한 비용효율적인 센서를 개발하여 실시간 LID 모니터링이 가능한 비용효율적 모니터링을 개발하였다. 공주대학교 천안캠퍼스의 LID 시설들은 2013년에 조성되어 현재까지 시설이 운영되고 있으며, 5년이상의 과거 강우시 모니터링 자료들을 이용하여 오염물질 상관성 분석을 수행가능 하기에 대상지로 선정하였다. 오염물질 상관성 분석은 2013년부터 2017년도에 침투도랑에서 수행된 강우시 모니터링 자료를 활용하여 각 오염물질들의 상관성을 분석을 수행하였다. 침투도랑 내 유입되는 평균 유입수는 TSS 286.1±318.3 mg/L, BOD 22.6±39.5 mg/L, TN 8.96±5.85 mg/L, TP 1.01±1.11 mg/L로 나타났다. 겨울철에 비해 여름철에서의 오염물질의 유입농도가 높은 것으로 분석되었다. 이는 여름철 고온건조로 인한 노면 내 차량의 주행으로 인한 중금속, 폐타이어 등과 장마철 강우 시 유출된 토사로 인하여 유입수의 농도가 높은 것으로 분석되었다. 오염물질 부하량은 TSS와 COD 0.66으로 유의성이 높은 것으로 나왔으며, COD와 TSS, TP, TN 등 유의성이 높은 것으로 분석되었다. Arduino와 Raspberry PI를 활용하여 저비용 센서와 LTE 모뎀통신과 데이터 베이스 연결하여 개발된 프로그램을 통해서 무선으로 LID 시설에 대한 모니터링을 침투화분2와 식생체류지에 조성하였다. 전력공급이 어려운 식생체류지의 경우 태양열(Solar system) 시스템과 보조 전력 배터리를 조성하여 장마철이나 장기적인 악천후로 인한 전력을 생산하지 못할 경우 보조전력배터리에서 전력을 제공하여 지속적인 모니터링이 이루어지도록 설계하였다. 토양함수량, 토양온도와 Conductivity 등 3종류의 센서를 적용하였으며, 프로그램은 현재 2단계를 통한 2차수정을 통하여 프로그램을 구축하였다. 오차, 오작동, 계측값에 대한 검·보정 작업이 필요하다. 또한 대기자료의 구축을 통해 보다 토양과 LID 시설에 대한 영향분석이 필요한 것으로 사료된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.