• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.664-671
• /
• 2018

Generally, conventional solar tracking systems employ irradiance sensors to track a sun position, which enables the system to generate maximum solar energy. The usage of irradiance sensors increases system costs and deteriorates the performance of systems from sensor malfunctions. In this paper, a new solar tracking system without irradiance sensors has been proposed in which the controller capable of controlling and monitoring remotely is based on Artik platform. The proposed system tracks the sun position by comparing the amount of currents from several solar panels, resulting in removing irradiance sensors. In order to verify the performance of the proposed solar tracking method, the 12[V]-20[W] prototype system is built and implemented. Since the proposed system has remote monitoring functions through the employment of Artik as the IoT platform, more advantages in installation, maintenance and expanded functionality can be obtained compared to the conventional solar tracking system.

• Electronics and Telecommunications Trends
• /
• v.30 no.4
• /
• pp.92-101
• /
• 2015

미래 전장은 정보 지식 기반의 첨단 전력체계를 확충하기 위해 향후 전력구조를 통합, 지휘통제통신(C4I) 체계와 생존성과 통합성이 향상된 전장의 네트워크중심전(NCW) 수행능력을 향상시킬 것이다. 사이버물리시스템(Cyber-Physical Systems: CPS)은 함정전투체계에 적용되고 있는 DDS를 포함하여 국방 M&S의 근간인 Live, Virture, Constructive(L-V-C) 체계의 큰 축을 형성하고 있다. 사물인터넷(Internet of Things: IoT) 기술은 센서네트워크, 통신, Radio Frequency Identification(RFID), Ubiquitous Sensor Network(USN), Machine to Machine(M2M), D2D 기술 및 상황인지, 지능서비스를 위한 정보수집/가공/융합/분석/예측기술을 포괄적으로 포함한 기술로서 미래산업을 이끌어 갈 차세대 선도 기술이며, 특히 군사적으로도 감시정찰 센서네트워크(USN), 견마형로봇, 경전투로봇과 무인기 기술 및 전술정보통신망체계(TICN) 등 첨단 통신네트워크 기술의 전력화 추세는 IoT 기술의 적용영역을 넓혀주고 있다. 감시정찰체계(Sensor)에서는 감시정찰 분야 영상정보 처리, 표적탐지 등과 관련된 IoT 기술 소요와 지휘통제통신(C4I) 체계의 상호운용성, 데이터링크, 지능형 통신체계 등 C4I 관련 IoT 기술 소요 및 타격체계(Shooter)의 내장형 SW 등 유 무인 무기체계 관련 IoT 기술의 소요가 증대될 것으로 예상된다. 본고는 CPS 및 IoT 기술의 군사적 활용방안 및 획득전략에 대한 적용기술 및 발전방향을 살펴본다.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.46-55
• /
• 2022

It is very important to first detect and remove defective fruits with scratches or bruised areas in the automatic fruit quality screening system. This paper proposes a method of detecting defective areas in fruits using the latest artificial intelligence technology, the Yolo V4 deep learning model in order to overcome the limitations of the method of detecting fruit's defective areas using the existing image processing techniques. In this study, a total of 2,400 defective fruits, including 1,000 defective apples and 1,400 defective fruits with scratch or decayed areas, were learned using the Yolo V4 deep learning model and experiments were conducted to detect defective areas. As a result of the performance test, the precision of apples is 0.80, recall is 0.76, IoU is 69.92% and mAP is 65.27%. The precision of pears is 0.86, recall is 0.81, IoU is 70.54% and mAP is 68.75%. The method proposed in this study can dramatically improve the performance of the existing automatic fruit quality screening system by accurately selecting fruits with defective areas in real time rather than using the existing image processing techniques.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.206-214
• /
• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.06a
• /
• pp.296-297
• /
• 2019

최근의 IoT 기술의 발전은 다양한 형상, 네트워크 특징 및 서비스 아키텍쳐를 가지는 IoT 기기, 서비스 및 단말을 활용한 서비스의 발전을 가져오고 있다. 특히 OneM2M, OCF 등의 표준기구등은 다양한 IoT 기기 및 서비스 아키텍쳐에 대한 정의를 최근 수년간 진행해 오고 있으며, 이러한 IoT 서비스는 단순히 기기의 원격 상태 확인 및 제어 뿐만 아니라, 클라우드 및 AI 기술과의 연계를 통하여 그 서비스 영역을 지속적으로 확장 중에 있다. 이 중 Internet of Media Things 표준은 다양한 미디어 기반 서비스를 Thing으로 표현하여 다양한 Thing과의 연계 방안을 제시하고 있다. 본 논문에서는 기존에 다양한 기법을 통하여 연구 및 구현되고 있는 영상 기반 서비스를 Internet of Media Things 표준 기반으로 구현하기 위한 방법을 제시한다. 기존 영상 분석 기술은 대부분 정확도의 향상에 그 목적을 가지고 있어 서비스 형태로 제공하고 타 기기와의 연계성을 제공하기 위한 추가적인 기술간 연계가 필요하다. 본 논문에서는 Yolo v3 기반의 Face Detection 기술에 대하여, 해당 기술을 Internet of Media Things 표준으로 표출하기 위한 요구사항을 파악하고 실제 구현하기 위한 방안에 대하여 검토한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.211-215
• /
• 2015

It comes into the spotlight as the new Blue Ocean in which the connected car industry in which the car and mobile communication technology is convergence. All sorts of infortainments services connecting with the portable electronic device(Smart phone, tablet PC, and MP3 player) and car are rapidly grown. The Connected car emphasizes the vehicle connectivity with the concept that the car has communication with the around on a real time basis and it provides the safety and expedience to the operator and using the thing of Internet (IoT) in the car and supports the application, presently, the entertainment service including the real-time Navigation, parking assistant function, not only the remote vehicle control and management service but also Email, multimedia streaming service, SNS and with the platform. Intelligent vehicle network is studied as the kind according to MANET(Mobile Ad Hoc Network) for the safety operation of the cars of the road and improving the efficiency of the driving. The intelligent vehicle network is comprised for the driving information offering changing rapidly of the communication(V2V: Vehicle to Vehicle) between the car and the car, communication(V2I : Vehicle to Infrastructure) between the infrastructure and the car, and V2X (Vehicle to Nomadic).

• Smart Media Journal
• /
• v.12 no.5
• /
• pp.73-80
• /
• 2023

Recently, intelligent control systems are developing rapidly in various application fields, and methods for utilizing technologies such as deep learning, IoT, and cloud computing for intelligent control systems are being studied. An important technology in an intelligent control system is recognizing and tracking objects in images. However, existing multi-object tracking technology has problems in accuracy and speed. In this paper, a real-time intelligent control system was implemented using YOLO v5 and YOLO v6 based on a one-shot architecture that increases the accuracy of object tracking and enables fast and accurate tracking even when objects overlap each other or when there are many objects belonging to the same class. The experiment was evaluated by comparing YOLO v5 and YOLO v6. As a result of the experiment, the YOLO v6 model shows performance suitable for the intelligent control system.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1406-1421
• /
• 2019

The Internet-of-Things (IoT) has been deployed in almost every facet of our day to day activities. This is made possible because sensing and data collection devices have been given computing and communication capabilities. The devices implement System-on-Chips (SoCs) that incorporate a lot of functionalities, yet they are severely constrained in terms of memory capacitance, hardware area, and power consumption. With the increase in the functionalities of sensing devices, there is a need for low-cost synthesizable processors to handle control, interfacing, and error processing. The first step in selecting a synthesizable processor core for low-cost devices is to examine the hardware resource utilization to make sure that it fulfills the requirements of the device. This paper gives an analysis of the hardware resource usage of ten synthesizable processors that implement the Reduced Instruction Set Computer Five (RISC-V) Instruction Set Architecture (ISA). All the ten processors are synthesized using Vivado v2018.02. The maximum frequency, area, and power reports are extracted and a comparison is made to determine which processor is ideal for low-cost hardware devices.

• Journal of Korea Multimedia Society
• /
• v.24 no.3
• /
• pp.396-405
• /
• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.97-100
• /
• 2023

차량 인터넷은 목적지까지 스스로 주행하는 자율 주행 자동차의 최적 경로 설정을 도와주는 차세대 네트워크이다. 자율 주행 자동차의 원활한 자율 주행을 위해서는 도로 위 객체 인지뿐만 아니라 실시간 교통 정보가 수신되어야 한다. 공격자는 자동차로 전달되는 메시지를 탈취하여 내용을 변경하거나 메시지를 제거하는 중간자 공격을 시도할 수 있다. 중간자 공격을 탐지하기 위해 MARINE 기법이 제안되었지만, 주행하는 자동차가 적은 환경에서 중간자 공격을 탐지하기 어렵다. 제안 방법은 이러한 문제를 해결하기 위해 교통 정보 센터에 축적된 교통 정보를 이용하여 자동차에 전달되는 메시지를 분석하고 중간자 공격을 탐지하는 방법을 제안한다.