• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.263-269
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• 2020

In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.274-281
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• 2006

Sensor network applications need synchronized time extremely such as object tracking, consistent state updates, duplicate detection, and temporal order delivery. This paper describes reliable time synchronization protocol (RTSP) for wireless sensor networks. In the proposed method, synchronization error is decreased by creating hierarchical tree with lower depth and reliability is improved by maintaining and updating information of candidate parent nodes. The RTSP reduces recovery time and communication overheads comparing to TPSN when there are topology changes owing to moving of nodes, running out of energy and physical crashes. Simulation results show that RTSP has about 20% better performance than TPSN in synchronization accuracy. And the number of message in the RTSP is $20%{\sim}60%$ lower than that in the TPSN when nodes are failed in the network. In case of different transmission range of nodes, the communication overhead in the RTSP is reduced up to 40% than that in the TPSN at the maximum.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2719-2730
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• 2009

In this paper, we implement the measurement functionality for performance measurements of EDGE (Enhanced Data Rates for GSM Evolution) terminal by using software. Generally speaking, the receiving algorithms in normal MODEM cannot be used directly to a measurement system due to the lack of accuracy. Therefore, we propose a new receiver algorithm for precise EDGE signal measurements. In the proposed algorithm, 2-stage (coarse stage, fine stage) parameters estimation (symbol-timing, frequency offset, carrier phase) scheme is used. To improve the estimation accuracy, we increase the number of the received signal samples by interpolation. The proposed EDGE signal measurement algorithm can be used for verifying the hardware measurement system, and also can be used for the commercial systems through software optimization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2369-2378
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• 2009

In this paper, we implement measurement functionality for performance measurement of the GSM (Global System for Mobile Communication) terminal by using software. Generally speaking, the receiving algorithms in normal modems cannot be used directly to the measurement system due to the lack of the algorithm accuracy. In this paper, we propose the new receiver algorithm for precise GSM signal measurements. In the receiving algorithm, 2-stage (coarse stage, fine stage) parameters estimation (symbol-timing, frequency offset, carrier phase) scheme is used. To improve the estimation accuracy, we increase the number of the received signal samples by interpolation. The proposed GSM signal measurement algorithm can be used for verifying the hardware measurement system. In addition, the proposed algorithm can be used for the commercial system through code execution speed optimization.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.646-654
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• 2020

Objectives: Users of parks or children's play facilities have pointed to pets' bowel movements as the most serious problem when using them. In prior studies, a very low detection rate of parasites (eggs) in sand flooring materials has been found. Even though feces have been identified, no parasites (eggs) have been detected. Method: A standard solution of nitrate nitrogen was used to verify the reliability of a new nitrogen analysis method. The linearity, precision, and accuracy of the nitrate nitrogen analysis method were verified. Using this method, the pollution distribution of the sand flooring material and the degree of pollution at each point were investigated. Results: As a result of the verification of the nitrogen analysis method, the linearity was found to be good at r2=0.999 when distilled water is mixed in a standard substance solution. The standard substance additive solution r2=0.968 was found to be good. Precision represented 0.01 to 0.06% RSD for peak height. The recovery rate was 92.4 to 104.0 percent, indicating high accuracy. According to the same method of analysis, the flooring material sand at a general amusement facility with the largest number of concealed spaces was nitrate nitrogen 6.1 times higher than at the entrance of the playground. Also, in a comparison between clean sand and sandy flooring, the average nitrogen concentration of the sand flooring material was 24.4-167 times higher than pure sand. Conclusions: As such, no parasites (eggs) were detected at all points under investigation, but the sand flooring was exposed to animal fecal contamination. Therefore, the management of nitrogenous components should allow accurate identification of animal fecal contamination so that the timing of sand replacement can be managed hygienically and safely.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.357-369
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• 2003

Electromagnetic valve (EMV) actuation system is a new technology for improving fuel efficiency and at the same time reducing omissions in internal combustion engines. It can provide more flexibility in valve event control compared with conventional variable valve actuation devices. The electromagnetic valve actuator must be designed by taking the operating conditions and engine geometry limits of the internal combustion engine into account. To help develop a simple design method, this paper presents a procedure for determine the basic design parameters and dimensions of the actuator from the relations of the valve dynamics, electromagnetic circuit and thermal loading condition based on the lumped method. To verify the accuracy of the lumped method analysis, experimental study is also carried out on a prototype actuator. It is found that there is a relatively good agreement between the experimental data and the results of the proposed design procedure. Through the whole speed range, the actuator maintains proper performances in valve timing and event control.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.25-30
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• 2017

In this Paper, we Propose the Low-cost Obstacle Recognition System Utilizing the Ultrasonic Sensor. Developed Obstacle Recognition System can be used to Aid the Visually Impaired Person. The Existence of the Obstacle is Notified to the Person through the Embodied Electronic Vibration Motor. The Timing Difference from the Recognition to the Notification Indicates the Distance to the Obstacle. Pulsed Ultrasonic Signal Controlled by MCU is Utilized and the Reflected Pulse through the Obstacle gives the Developed System the Existence of the Obstacle and the Distance to the Object. Pulse is sent Repetitively to Improve the Detection Accuracy. Developed Apparatus gives 30 Degree of Detection Angle and 2cm-30cm of the Detection Range when the Apparatus is Tested under Normal Walking Environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.861-866
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• 2014

This study evaluates the modeling accuracy of the existing three phase shift models on which the time domain oscillator phase noise theories are based. For the evaluation, this study investigates how accurately the three models can model the phase shift impulse train response of a simple planar oscillator. Evaluation result reveals that Kaertner model most accurately reflects the oscillator's phase shift impulse train responses for five different impulse train inputs, whereas PP model exhibited the worst performance in modeling the phase shift impulse train responses.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.