• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 1부
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• pp.816-820
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• 2007

본 논문은 무선랜 기반의 UPnP(Universal Plug and Play) AV(Audio/Video) 장치들을 서로 가까이 근접시킴으로써, 자동으로 네트워크가 형성되고, AV 컨텐츠가 재생되는 기술을 제안한 것이다. 이를 지원하기 위해 무선랜 신호 강도에 의한 근접성 측정 알고리즘과 네트워크 파라메터 설정을 통한 자동 접속, UPnP AV 제어기와 연동하여 자동으로 AV 컨텐츠를 재생하는 방안을 포함한다. 또한 본 논문에서는 데스크톱 및 임베디드 리눅스 환경에서 이를 구현한 내용 및 결과를 기술한다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2219-2226
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• 2017

This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• 한국통신학회논문지
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• 제25권8A호
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• pp.1124-1133
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• 2000

본 논문은 서비스 제어 시스템(Service Control Point : SCP) 서비스 교환 시스템(Service Switching Point: SSP) 그리고 지능형 정보제공 시스템(Intelligent Peripheral : IP)을 물리적 구성요소로 하는 차세대 지능망(Advanced Intelligent Network : AIN)에서 특수자원 제공을 담당하는 지능형 정보제공 시스템에서의 지연 시간에 관한 특성을 분석하였다 지능망 호가 지능형 정보제공 시스템으로 입력되는 시점부터 지능망 서비스가 처리되어 종료되는 시점까지의 시그널링 지연 및 자원 제공 점유시간을 각 구성 요소들이 연동되어서 운용되는 현장환경하에서 실측을 통해 분석하였다 종합정보통신ad 사용자부 (Integrated Serviced Digital Network User Part: ISUP) 프로토콜 기반에서의 호 설정 및 해제 시간을 지연 유형별로 분석하였으며 지능망 응용부(Intelligent Network Application Protocol : INAP) 프로토콜 기반에서의 자원 제공 점유시간을 차세대 지능망에서 제공하는 서비스별로 분석하였다.)

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• 한국멀티미디어학회논문지
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• 제20권3호
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• pp.511-519
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• 2017

This Paper introduces the design and communication method between PLC (Programmable Logic Controller) and Arduino based on MODBUS Protocol. MODBUS connection can be established in a new or existing system very easily, therefore we used this protocol in our proposed system. In the field of automatic devices, multi-function serial port such as RS232, RS422, RS485, and so on creates a great convenience to the developer. This proposed system used RS485 as a key mediator for data exchanging on a connected network. We also believe that it will reduce the development cost in various automated industry because this system can be reused or can be implemented any such PLC installed machines. RS485 is used as a communication interface between PLC (as a slave) and Arduino (as a master), through which a reliable network is created for safe and fast communication. Furthermore, RS485 allows multiple devices(up to 32) to communicate at half duplex on a single pair of wires and provides a long connectivity area (up to 1200 meters) as compare to other device, which makes it a user-friendly for various devices in the automated industry. Moreover, Arduino can play as a mediator by connecting third party device and setup a communication network with PLC.