• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.27-28
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• 2007

In this paper, the Wireless Sensor Network (WSN) based on ZigBee technology was devised and developed. Wireless communication was applied to petrochemical domain, like other industries. And sensor network of IEEE 802.15.4 protocol stack diagram was described. Then, by analyzing the protocol, the software systems included the communication Protocol and point-to-point network were implemented with Freescale Semiconductor's product MC13192-SARD DSK board. After that, the performance of this design system was evaluated, and finally, by using PC Graphic User Interface (GUI) and IDE CW08 V3.1 programming tool, the real time communication data and the curve function were displayed.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.29-34
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• 2002

This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.51-59
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• 1997

In this paper, we perform the position control of a DC servo motor using fuzzy neural controller. We use the Fuzzy controller for the position control, because the Fuzzy controller is designed simpler than other intelligent controller, but it is difficult to design for the triangle membership function format. Therefore we solve the problem using the BP learning method of neural network. The proposed Fuzzy neural network controller has been applied to the position control of various virtual plants. And the DC servo motor position control using the fuzzy neural network controller is performed as a real time experiment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.301-308
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• 2009

In the modern life, machines are used for various areas in industries as the advance of science and industrial development has proceeded. In many machines, the rotating machines play an important role in many processes. Therefore, the development of fault diagnosis and monitoring system for rotating machines is required. An ubiquitous sensor network (USN) is a combination of the key computer science and engineering area technology including the wireless network, embedded system hardware and software, communication, real-time system, etc. It collects environmental information to realize a variety of functions. In this work, a data reduction algorithm for USN based remote fault diagnostic system which can be easily applied to previously built factories is proposed. To verify the feasibility of the proposed scheme, some simulations and experiments are executed.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3159-3161
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• 1999

This paper aims at developing remote control system to control and monitor distributed various devices through internet or information communication network. SNMP (Simple Network Management Protocol) and UPS (Uninterruptible Power Supply) are adopted for network management protocol and applied device, respectively. For controlling and monitoring distributed devices in real-time, Java-environment software is constructed. Also, general-use interface controller between network device and applied device is proposed. Finally, serial communication such as RS-232 and RS-485 is used between controller and applied device.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.438-440
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• 2003

This paper resents an application of an Artificial Neural Network(ANN) technique to forecast the short-term system marginal price(SMP). The forecasting of SMP is a very important factor in an electricity market for the optimal biddings of market participants as well as for the market stabilization of regulatory bodies. The proposed neural network scheme is composed of three layers. In this process, input data are set up to reflect market conditions. And the $\lambda$ that is the coefficient of activation function is modified in order to give a proper signal to each neuron and improve the adaptability for a neural network. The reposed techniques are trained validated and tested with the historical real-world data from korea Power Exchange(KPX).

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.177-183
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• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.544-564
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• 2022

Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.