• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.510-511
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• 2017

In this paper, we have studied to minimize the lifetime and fault occurrence of LED fixtures, which have a short lifetime and frequent faults. In the current LED chip, the lifetime is semi-permanent, but compared to the lifetime of the LED chip, Drivers do not last long. In recent years, low-priced LED illuminators such as those from China have entered the market, and many consumers are perceiving LED illuminators. In order to solve these problems, we designed an LED illuminator of 25W class by using AC Direct IC, which has a longer lifetime than the conventional driver, by removing the electrolytic capacitor in the LED driving driver which is the cause of the failure.

• Journal of IKEEE
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• v.22 no.4
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• pp.1104-1108
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• 2018

IIoT applies IoT to industrial sites to monitor factors such as production, manufacturing, and safety, and it is a solution that allows the worker to easily manage the site. An important technology element in this IIoT is a technology that collects information on industrial sites and delivers reliable information to managers using sensors. Therefore, general industrial sites use wired network methods such as Ethernet and RS485 to deliver information. However, there are limitations to the problem of infrastructure costs and to the wide range of line constructions in network deployment. Therefore, in this paper, the network of IEEE 802.15.4 Ad-Hoc wireless sensors is deployed on production lines with machine tools. In addition, we describe the routing method considering machine tool layout and sensor node failure detection algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.117-125
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• 2008

With increase of concern about the Ubiquitous application, the necessity of the computer system which is miniaturized is becoming larger. The ARM processor is showing a high share from embedded system market. In this paper, ideal method for RTD-1000 controller construction and development is described using ARM microcontroller. Existing RTD-1000 measures distance of disconnection or defect of sensing casket by measuring receiving reflected wave which was sent via copper wire inside the leaking sensing rod. Using this RTD-1000, leakage and breakage of water and oil pipe can be sensed and it reports damage results to the networks. But, existing RTD-1000 wastes hardware resources much and costs a great deal to installation. Also, it needs a cooling device because the heating problem, and has some problem of the secondary memory unit such as the hard disk. So, long tenn maintenance has some problems in the outside install place. In this paper, for the resolving the problem of RTD-1000, RTD-1000A embedded system based on ARM is proposed and simulated.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.222-234
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• 2020

Autonomous vehicles are equipped with a wide rage of sensors such as GPS, RADAR, LIDAR, camera, IMU, etc. and are driven by recognizing and judging various transportation systems at intersections in the city. The accident ratio of the intersection of the autonomous vehicles is 88% of all accidents due to the limitation of prediction and judgment of an area outside the sensing distance. Not only research on non-signalized intersection collision avoidance strategies through V2V and V2I is underway, but also research on safe intersection driving in failure situations is underway, but verification and fragments through simple intersection scenarios Only typical V2V failures are presented. In this paper, we analyzed the architecture of the V2V module, analyzed the causal factors for each V2V module, and defined the failure mode. We presented intersection scenarios for various road conditions and traffic volumes. we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to analyze the risk of autonomous vehicle based on the simulation. We presented ASIL, which is the result of risk analysis, proposed a monitoring concept for each component of the V2V module, and presented monitoring coverage.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.581-584
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• 2008

무선 센서 네트워크는 최근 대두되고 있는 유비쿼터스 컴퓨팅의 연구에 힘입어, 광범위하게 설치되어 있는 유무선 네트워크 인프라에 상황인지를 위한 다양한 센서 디바이스를 결합하여 감지된 환경 데이터를 응용서비스와 연동하는 기술이다. 기본적으로 홈/빌딩 시큐리티, 사회기반 안전시설, 기계의 고장 진단, 의료 분야 등의 실세계의 다양한 분야에 응용될 수 있다. 본 논문에서 다루고자하는 실종 아동에 대한 문제는 어제 오늘의 이야기가 아니다. 미아 발생률은 꾸준히 증가하고 있으며 장기 미아의 발생 역시 증가하고 있다. 따라서 미아 방지를 위해 새로운 서비스들이 소개되었으나 이용자의 경제적 비용 부담 증가와 같은 문제점을 안고 있다. 본 논문에서 제안하는 미아 찾기 시스템은 무선 센서 네트워크 기술 응용 중의 하나로서 미아가 발생 시에 아동이 장기 미아가 되지 않도록 효율적이면서도 빠르게 미아를 찾을 수 있도록 하기 위해서 개발되었다. RFID 리더기가 장착된 고정형 센서 노드와 이동형 센서노드가 RFID 태그를 지닌 아동의 정보를 얻어서 중앙 제어 서버로 데이터를 넘기면 태그 정보와 전송한 센서 노드의 위치, 전송 시간을 바탕으로 아동의 위치를 파악할 수가 있다. 미아 찾기 시스템은 이용자가 태그만을 지니면 되기 때문에 경제적 부담은 줄어 들 수 있다. 또한 상기 시스템으로 미아 찾기만이 아니라 주변 환경 감시등의 여러 응용에도 적용 시킬 수가 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.983-990
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• 2008

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.3
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• pp.266-273
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• 2012

In this paper, the new open fault detection and tolerant operation method for 3 phase PWM rectifier is proposed. When open fault occurred on the inverter switches of 3 Phase PWM rectifier, the DC link voltage ripple is increased because the input current of the faulty phase is distorted. In this case, the quality of electric power would decrease, and the life time of DC link capacitor is decreased. The open fault is detected by a simple MRAS(Model Reference Adaptive System) without additional hardware sensors, and the tolerant operation carried out by turning on the opposite switch of the faulty switch without any redundancy. By the proposed method, the faulty phase input current can be controlled, so that 3-phase input current is balanced relatively under the faulty condition and the voltage ripple of DC link output is reduced. The validity of the proposed technique is proved on the 6kW 3-phase PWM rectifier system by simulation and experiment.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.525-534
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• 2016

The biological wastewater treatment plant, which uses microbial community to remove organic matter and nutrients in wastewater, is known as its nonlinear behavior and uncertainty to operate. Therefore, operation of the biological wastewater treatment process much depends on observation and knowledge of operators. The manual inspection of human operators is essential to manage the process properly, however, it is impossible to detect a fault promptly so that the process can be exposed to improper condition not securing safe effluent quality. Among various process faults, equipment malfunction is critical to maintain normal operational state. To detect equipment faults automatically, the dynamic time warping was tested using on-line oxidation-reduction potential (ORP) and dissolved oxygen (DO) profiles in a sequencing batch reactor (SBR), which is a type of wastewater treatment process. After one cycle profiles of ORP and DO were measured and stored, they were warped to the template profiles which were prepared already and the distance result, accumulated distance (D) values were calculated. If the D values were increased significantly, some kinds of faults could be detected and an alarm could be sent to the operator. By this way, it seems to be possible to make an early detecting of process faults.