• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.155-162
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• 2020

Recently, with the development of computer vision and deep learning technology, research on human action recognition has been actively conducted for video analysis, video surveillance, interactive multimedia, and human machine interaction applications. Diverse techniques have been introduced for human action understanding and classification by many researchers using RGB image, depth image, skeleton and inertial data. However, skeleton-based action discrimination is still a challenging research topic for human machine-interaction. In this paper, we propose an end-to-end skeleton joints mapping of action for generating spatio-temporal image so-called dynamic image. Then, an efficient deep convolution neural network is devised to perform the classification among the action classes. We use publicly accessible UTD-MHAD skeleton dataset for evaluating the performance of the proposed method. As a result of the experiment, the proposed system shows better performance than the existing methods with high accuracy of 97.45%.

• The Korean Journal of Physiology and Pharmacology
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• v.17 no.4
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• pp.299-306
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• 2013

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been widely used as a treatment for the movement disturbances caused by Parkinson's disease (PD). Despite successful application of DBS, its mechanism of therapeutic effect is not clearly understood. Because PD results from the degeneration of dopamine neurons that affect the basal ganglia (BG) network, investigation of neuronal responses of BG neurons during STN DBS can provide informative insights for the understanding of the mechanism of therapeutic effect. However, it is difficult to observe neuronal activity during DBS because of large stimulation artifacts. Here, we report the observation of neuronal activities of the globus pallidus (GP) in normal and PD model rats during electrical stimulation of the STN. A custom artifact removal technique was devised to enable monitoring of neural activity during stimulation. We investigated how GP neurons responded to STN stimulation at various stimulation frequencies (10, 50, 90 and 130 Hz). It was observed that activities of GP neurons were modulated by stimulation frequency of the STN and significantly inhibited by high frequency stimulation above 50 Hz. These findings suggest that GP neuronal activity is effectively modulated by STN stimulation and strongly dependent on the frequency of stimulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.263-272
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• 1998

This paper presents the new control strategy that can minimizes the torque ripple by considering the magnetic nonlinearity and phase torque averlapping intervals, and describes the whole SRM drive system using proposed control method implemented by DSP(Digital Signal Processor). To do this, inductance and torque are, at first, measured according to the variation of rotor position angle while current is kept constant at predetermined several values. From these measured values, the entire inductance and torque for any current and rotor position are inferred by using neural network. And the waveform of the reference phase torque is determined for the torque ripple to be minimized considering the torque overlap between phases. The controller is designed for the actual torque obtained by the inferred torque look-up table using measured current and rotor position angle to track the predetermined reference phase torque by delta modulation technique. To perform a real time processing and ensure the reliability of the controller, DSP is implemented.

• Korean Journal of Cognitive Science
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• v.9 no.1
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• pp.1-11
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• 1998

In this paper, the features which are thought to be used by humans based on the psychological experiment of human information processing are applied to character recognition problem. Man will deal with a little large area information as well as pixel by pixel information. Therefore we define the feature that represents a little wide region I information called region feature, and combine the features derived from region feature and pixel by pixel features that have been used by now. The features we used are the result of region feature based preanalysis, mesh with region attributes, cross distance difference and gradient. The training and test data in the experiment are handwritten Korean alphabets, digits and English alphabets, which are trained on neural network using back propagation algorithm and recognition results are 90.27-93.25%, 98.00% and 79.73-85.75%, respectively Experimental results show that the feature we are suggesting in this paper is 1-2% better than UDLRH feature similar in attribute to region feature, and the tendency of misrecognition is more easily acceptable by humans.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1447-1451
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• 2008

저수지 군 연계 운영을 위한 각 댐에서의 방류량을 결정하기 위해서는 대개 각 댐의 초기 저수량, 유역 상 하류 댐의 총 저수량, 수요량, 기간별 발전 목표 달성 정도, 그리고 예상되는 미래유입량 등이 추정되어야 한다. 본 연구에서는 댐 군 연계운영을 위한 일별 최적화 모형인 CoMOM(Coordinated Multi-reservoir Operating Model, 4.2)의 상위 단계의 더 큰 단위 기간에 활용될 댐 군 연계 운영 기본 가이드라인을 신경망 기법을 활용하여 도출할 수 있을 지를 실험해 보고자 한다. 이 방법은 기본적으로 CoMOM이 제시하는 일별 운영 계획의 결과가 최선의 정책일것이라는 가정에 근거하고 있다. 즉, 주어진 상황에서 일별 CoMOM이 제시하는 결과를 교사 신호로 하여 신경망 학습을 수행하고, 이 결과를 통해 규칙(Rule)을 생성하는 과정으로 요약할 수 있다. 신경망 분석은 CoMOM이 이수기 모형인 점을 고려하여 이수기만을 대상으로 실험하였으며, 단위 분석기간을 10일로 택하여 미래 10일간의 방류량을 결정하는 것을 목표로 하였다. 신경망 모형의 입력요소로는 각 댐의 초기 유효 저수량, 유역 상 하류 댐의 총 저수량, 10일간의 수요량, 그리고 향후 한달 동안의 예상 유입량을 적용하였고, 출력요소로는 CoMOM에서 제시한 방류량 결과를 사용하였다. 모형의 유효성을 검증하기 위해 한강수계의 이수기를 대상으로 과거의 유입량 자료가 재현된다고 가정하고, 모의운영을 통하여 적합성을 분석하였다. 이를 위해 매일 단위의 실제 댐 군 연계 운영의 상황을 모의할 수 있는 실시간 시뮬레이션을 적용하였으며, 신경망 모형의 운영 기준에 의해 결정된 향후 10일 동안의 총 방류량이 해당기간 동안 동일한 양으로 나누어 방류된다는 가정 하에 모의 운영하였다. 그리고 도출된 운영 결과는 최종적으로 실적과의 평균저수량, 발전량, 여수로 방류량 비교를 통해 평가하였다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.35-42
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• 1998

Finding a technique to achieve high machining precision and high productivity is an important issue for CNC machining. One of the solutions to meet better performance of machining is feedrate control. In this paper we present an adaptive feedrate neuro-control method for high precision and high speed machining. The adaptive neuro-control architecture consists of a neural network identifier(NNI) and an iterative learning control algorithm with inversion of the NNI. The NNI is an identifier for the nonlinear characteristics of feedrate and contour error, which is utilized in iterative learning for adaptive feedrate control with specified contour error tolerance. The proposed neuro-control method has been successfully evaluated for machining circular, corner and involute contours by computer simulations.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.965-971
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• 2011

As the liver cancer in our country cancerous occurrence frequency to be the gastric cancer in the common cancer, If the case which will be discovered in early rising the treatment record was considered seriously about under the early detection. The system which it sees with the system for the early detection of the liver cancer reacts the blood of the control group other than the patient who is confirmed as the liver cancer and the liver cancer to the biochip and aptamer protein biochip profiles mechanical studying leads and it is a system which it classifies. 1149 each other it reacted blood samples of the control group other than the liver cancer patient who is composed of the total 85 samples and the liver cancer which is composed of 310 samples to the biochip which is composed with different oligo from the present paper and it was a data which it makes acquire worker the neural network it led and it analyzes the classification efficiency of the result 95.38 ~ 97.95% which it was visible.

• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.11-20
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• 2019

With the development of deep learning technology, research is being actively carried out on user-friendly interfaces that are suitable for use in virtual reality or augmented reality applications. To support the interface using the user's hands, this paper proposes a deep learning-based fingertip detection method to enable the tracking of fingertip coordinates to select virtual objects, or to write or draw in the air. After cutting the approximate part of the corresponding fingertip object from the input image with the Grad-CAM, and perform the convolution neural network with Atrous Convolution for the cut image to detect fingertip location. This method is simpler and easier to implement than existing object detection algorithms without requiring a pre-processing for annotating objects. To verify this method we implemented an air writing application and showed that the recognition rate of 81% and the speed of 76 ms were able to write smoothly without delay in the air, making it possible to utilize the application in real time.