• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.1
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• pp.23-29
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• 2013

TCN(train communication network) standard was approved in 1999 by the IEC (IEC 61375-1) and IEEE (IEEE 1473-T) organizations to warrant a reliable train and equipment interoperability. TCN defines the set of communication vehicle buses and train buses. The MVB(multifunction vehicle bus) defines the data communication interface of equipment located in a vehicle and the WTB(wire train bus) defines the data communication interface between vehicles. The WTB and each MVB will be connected over a node acting as gateway. Also, to support applications demanding a high reliability, the standard defines a redundancy scheme in which the bus may be double-line and redundant-node implemented. In this paper we have presented protocol analysis platform for the WTB redundancy which is part of TCN system, to verify communication state of high-speed trains. As a confirmation of its validity, the technology described in this paper has been successfully applied to state monitoring and protocol verification of redundancy WTB based on TCN.

• IE interfaces
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• v.24 no.3
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• pp.187-195
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• 2011

The automatic radiopharmaceutical module consists of several 2-way valves, couple of syringes, gas supply unit, heating(cooling) unit and sensors to control the chemical reagents as well as to help the chemical reaction. In order to control the actuators of radiopharmaceutical module, the process is tabulated using spread sheet as like excel. Unlike the common program, a trivial error is too critical to allowed in the process because the error can lead to leak the radioactive reagent and to cause the synthesis equipment failure during synthesizing. Hence, the synthesis process has been validated using graphic simulation while the operator checks the whole process visually and undergoes trial and error. The verification of the synthesis process takes a long time and has a difficulty in finding the error. This study presents a methodology to verify the process algebraically while the radiopharmaceutical module is converted to the network model. The proposed method is validated using actual synthesis process.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.225-230
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• 2015

This study presents a method for generating IEC 61850 Sampled Values(SV) traffic by combining the emulation function of network simulator ns-3 with the actual communication equipment. For the SV traffic generation and reception, the emulation function of the network simulator ns-3 is used, while as a communication network, the actual communication equipment, switches are used. In addition, the SV traffic frames generated are analyzed, using Wireshark, and it is confirmed that the SV traffic frames are generated accurately. The method for the SV traffic generation proposed in the present study will be very useful when various SV traffics are generated under the environment of an actual substation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.96-97
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• 2011

In these days many korean people have much interested in oceanic sports and oceanic leisure equipment.. However, the current condition for the marine leisure in korea are not advanced even worse than wnderdeveloped countries.. Recently several suggestion for vitalization on the marine leisure industry and preparations of the laws & the regulations for marine leisure.. This paper suggest the new marina model using ubiquitous sensor network to reduce the risk of the policy and the investment in the filed og marine leisure industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.467-469
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• 2018

Tactical wireless communications often suffer from communication problems such as channel interference and communication disconnection due to the variability of the network topology and poor communication environment. Therefore, it is very important to overcome the problem of communication failure of the wireless communication network caused by the poor tactical environment and to improve the survivability. This paper describes the automatic control methodology of wireless communication equipment to identify and distinguish the situation of communication failure without user intervention and to implement more effective solution.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.9-15
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• 2022

This paper proposes a coordinated multi-point communication (CoMP) method with channel selection to improve performance of a macro user equipment (MUE) in a dense small-cell network environment in a building located within coverage of a macro base station (MBS). In the proposed CoMP method, in order to improve the performance of the MUE located in the building, A small-cell base station (SBS) selects a channel with lower interference to the neighboring MUE and transmits appropriate signals to the MUE requiring CoMP. Simulation results show that the proposed CoMP method improves the performance of the MUE by up to 164% and 51%, respectivley, compared to a random channel allocation based traditional SBS network and CoMP method.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.245-252
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• 2020

Unexpected equipment defects can cause a huge economic losses in the society at large. Although condition monitoring can provide solutions, the signal processing algorithms must be developed to predict mechanical failures using data acquired from various sensors attached to the equipment. The signal processing algorithms used in a condition monitoring requires high computing efficiency and resolution. To improve condition monitoring on a wireless sensor network(WSN), data visualization can maximize the expressions of the data characteristics. Thus, this paper proposes the extraction of visual feature from temperature data over time using condition monitoring based on a WSN to identify environmental conditions of equipment in a large-scale infrastructure. Our results show that time-frequency analysis can visually track temperature changes over time and extract the characteristics of temperature data changes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.