• 한국정보디스플레이학회:학술대회논문집
• /
• 2004.08a
• /
• pp.707-710
• /
• 2004

We have proposed on dual-drive & -emission (DDE) panel based on organic light-emitting diodes (OLEDs). The device is composed on independent operation of two OLED structures with two transparent electrodes for data signals and an intermediate reflective electrode for common scan signal. Typical device structure is ITO / organic electroluminescent layer (1) /intermediate reflective electrode / organic electroluminescent layer (2) /transparent electrode. Symmetric bright emission could be obtained by applying AlNd as the intermediate reflective electrode and $MoO_3$ as a hole injection layer for upper device structure. The proposed panel is useful for emissive face-to-face panel exhibited for different images.

• Proceedings of the KIEE Conference
• /
• 1991.11a
• /
• pp.39-42
• /
• 1991

The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
• /
• 2001.11a
• /
• pp.42-42
• /
• 2001

The rate of coating consolidation influences the operation of several coating methods and the final quality of the coating layer. The rate of coating consolidation is characterized with a dynamic gloss meter at short times for a thin coating layer applied to the base sheet of interest. During the coating consolidation process, the laser gloss meter response curve exhibits two critical turning points that indicate the two coating immobilization points defined by the traditional methods. Five base sheets with several different coating suspensions are characterized. A model is proposed to estimate the rate of consolidation based on physical properties of the coating suspension, the base paper, and the liquid phase of the coating. The paper properties, especially the contact angle, are found to be an important factor in determining rate of consolidation. The model predicts the correct trends for the different coating suspensions and base sheets. The test method, along the model, can be used to determine the filtercake resistance of the coating layer for a thin and rapidly formed filtercake.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09b
• /
• pp.13-16
• /
• 2003

본 논문에서는 인터넷의 기능을 이용하여 인터넷이 연결된 어느 장소에서라도 로봇을 원격으로 제어하거나 로봇으로부터 받은 정보를 인터넷으로 전송 받아 처리할 수 있는 알고리즘을 구현하였다. 빠른 영상정보 전송을 위해 JPEG 압축 기술을 이용하였다. 인터넷을 통한 원격 제어에서의 가장 큰 문제점은 전송 지연 또는 데이터 손실과 같은 통신 장애이다. 이러한 상황에서도 로봇이 스스로 자신을 보호하고 원하는 작업을 수행하도록 하기 위해 자율이동 기능이 필수적인데, 이를 위해 본 논문에서는 2-layer fuzzy controller를 이용하였다. 2-layer fuzzy controller를 이용하여 로봇은 장애물을 회피하면서 원활하게 이동할 수 있다. 마지막으로 실제 구현 및 실험을 통해 그 효율성을 평가한다.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.746-751
• /
• 2004

Demands for higher productivity in container terminal environments continues to escalate consideration of equipment upgrades. And then transportation of containers using the automated container crane becomes more and more important for productivity enhancements. Introducing a hybrid control architecture to the container crane, it provides a effective means to the automated operation of the container crane. This paper addresses the methodology for automation of container cranes. In addition, this paper proposes a new control architecture for the automated container crane and explains each component of that architecture. The control architecture is composed of a deliberative control layer, a sequencing layer, and a reactive control layer. The proposed architecture is applied to a dual-hoist double-trolley container crane.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.772-777
• /
• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2009.05a
• /
• pp.335-337
• /
• 2009

Internal Pressure Maintenance Equipment absorbs internal pressure variation for it's storage has developed. The bladder ; main item of Internal Pressure Maintenance Equipment, has developed with multi-layer to prevent a penetration and to improve durability. And durability test and environment test was carried out to verify the performance in the condition that could be happened in storage or operation.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.7 no.5
• /
• pp.209-217
• /
• 2012

In the present, Wireless Sensor Network(WSN) has been used for the purpose of the military operation with surveillance systems and for collecting useful information from the natural environment. Basically, low-power, easy deployment and low cost are the most important factors to be deployed for WSNs. Lots of researches have been studied to meet those requirements, especially on the node capacity and battery lifetime improvements. Recently, the study of wireless mesh networks applied into the surveillance systems has been proceeded as a solution of easy deployment. In this paper, we proposed large-scale intelligent multi-layer surveillance systems based on QoS assuring Wireless Mesh Networks and implemented them in the real testbed environment. The proposed system explains functions and operations for each subsystem as well as S/W and H/W architectures. Experimental results are shown for the implemented subsystems and the performance is satisfactory for the surveillance system. We can identify the possibility of the implemented multi-layer surveillance system to be used in practice.

• Proceedings of the IEEK Conference
• /
• 2003.07b
• /
• pp.1065-1068
• /
• 2003

For the first time, high quality ultra-thin strained Si/SiGe on Insulator (SGOI) substrate with total SGOI thickness( $T_{Si}$ + $T_{SiGe}$) of 13 nm is developed to combine the device benefits of strained silicon and SOI. In the case of 6- 10 nm-thick top silicon, 100-110 % $I_{d,sat}$ and electron mobility increase are shown in long channel nFET devices. However, 20-30% reduction of $I_{d,sat}$ and electron mobility are observed with 3 nm top silicon for the same long channel device. These results clearly show that the FETs operates with higher performance due to the strain enhancement from the insertion of SiGe layer between the top silicon layer and the buried oxide(BOX) layer. The performance degradation of the extremely thin( 3 nm ) top Si device can be attributed to the scattering of the majority carriers at the interfaces.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.137-142
• /
• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.