• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.287-295
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• 2017

The most detrimental pressure pulsations in high-head pump-turbines is caused by the rotor-stator interaction (RSI) between the guide vanes and runner blades. When the pump-turbine operates in the S-shaped region of the characteristic curves, the deteriorative flow structures may significantly strengthen RSI, causing larger pressure pulsations and stronger vibration with an increased risk of mechanical failure. CFD simulations were carried out to analyze the impacts of flow evolution on the pressure pulsations in the S-shaped region of a model pump-turbine. The results show that the reverse flow vortex structures (RFVS) at the runner inlet have regular development and transition patterns when discharge reduces from the best efficiency point (BEP). The RFVS first occur at the hub side, and then shift to the mid-span near the no-load point, which cause the strongest pressure pulsations. The locally distributed RFVS at hub side enhance the local RSI and makes the pressure fluctuations at the corresponding sections stronger than those at the rest sections along the spanwise direction. Under the condition of RFVS at the mid-span, the smaller flow rate make the smaller difference of pressure pulsation amplitudes in the spanwise direction. Moreover, the rotating stall, rotating at 35.7%-62.5% of the runner rotational frequency, make the low frequency components of pressure pulsations distribute unevenly along the circumference in the vaneless space. However, it have little influence on the distributions of high components.

• 동의생리병리학회지
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• 제17권2호
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• pp.263-274
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• 2003

The present condition of study on the wave-resonance for the bio-KI is observed. And the probroms of reception attendent on the stimulation of wave is observed centering around the sensory organ. The results is as follows: In the wave-resonant stand point, the tendency of studying on KI is showed in the several field all over the world. Because it is originated radionics, the wave-resonant tools of MRA insistenting minute-magnetism-resonance-apparatus need the more severe data in the side of electric circuit. The wave resonance apparatus according to the frequency occurance transmits low-frequency's vibration ratio to the electric stimulating aspect. The wave-water is considered on the application of wave-resonance transcription on the water, and needs the comprehension of torsion-field level. The wave-stimulation of the bio-KI and the reception of the sensory-organ is observed the connection of the sensory-organ and it's corresponding wave. The informations recorded in the wave are distinguished patternly. And the several shaping waves transmit the informations each other through the resonnance. The wave theory is explained the LEE(理) as the wave-pattern and the KI(氣) as the revelation of the patterned-wave in the LEE-KI theory(理氣論), moreover the SHIN(神) as the information of the wave-pattern and the KI as the energy of the wave and the JEUNG(精) as the material of the wave in the JEUNG-KI-SHIN theory(精氣神論). In this point, the study on the wave-resonance of bio-KI is thought that it is significantly in the study method for oriental medicine. The sum of the wave makes the moving body and forms the universe. It means that the several wave patterns gather and form new field. The pattern is a kind of the information and the information is not materially. The information of wave-pattern is the arrangement and the combination of the material source.

• International Journal of Fluid Machinery and Systems
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• 제8권4호
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• pp.264-273
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• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

• 한국자기학회지
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• 제20권2호
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• pp.61-67
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• 2010

근래 첨단 전동기 개발에 고성능 영구자석이 적용됨에 따라 소형 전동기의 고출력화가 가능해진 반면 전동기의 진동과 소음이 커져 그 원인이 되는 코깅 토크에 대한 연구가 필요하게 되었다. 영구자석 전동기의 코깅토크는 영구자석에 의한 자계의 분포에 기인하게 되는데, 전동기의 자계 분포는 코어의 형상에 기인하므로 코어의 형상을 적절하게 설계함으로써 코깅토크를 저감시킬수 있다. 본 논문에서는 영구자석 전동기의 코깅토크를 저감하기 위한 기존의 sub-slot 법을 개선하여 보다 단순화 한 형상으로 코어의 설계변수를 대폭 줄임으로 효과적으로 코깅토크를 저감할 수 있는 기법을 제안하고 이를 영구자석 전동기에 적용하여 기존의 대표적인 sub-slot 법과 비교하고 그 성능을 검증하였다.

• 한국전산구조공학회논문집
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• 제30권2호
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• pp.145-151
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• 2017

콘크리트 구조물 또는 암석의 해체 공정에서 소음, 진동, 분진 등의 발생을 초래하는 기존의 발파공법을 대신하여 비폭성 파쇄제의 이용을 고려할 수 있다. 본 연구는 유한한 정사각형 콘크리트 구조물을 대상으로 하여 비폭성 파쇄제의 팽창이 준정적으로 균열을 발생, 진전시키는 과정을 유한요소해석으로 예측하고 기존 문헌에 보고된 실험결과와 비교함으로써 해석결과의 정확성을 검증하였다. 또한 비폭성 파쇄제 및 천공 홀의 배치에 따른 영향을 최소요구팽창압 관점에서 분석하였다. 더 나아가서 비폭성 파쇄제가 채워지지 않은 천공 홀이 비폭성 파쇄 공정에 미치는 영향을 분석하고, 분리 조각의 수를 증가시켜 파쇄도를 향상시키기 위한 비폭성 파쇄제 및 천공 홀의 효과적인 배치에 대하여 고찰하였다.

• 제어로봇시스템학회논문지
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• 제12궈1호
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• pp.33-40
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• 2006

We present a new technological concept named RCP (Robotic Cellular Phone), which combines RT and CP. That is an ubiquitous robot. RCP consists of 3 sub-modules, RCP Mobility, RCP interaction, and RCP Integration. RCP Interaction is the main focus of this paper. It is an interactive emotion system which provides CP with multi-emotional signal receiving functionalities. RCP Interaction is linked with communication functions of CP in order to interface between CP and user through a variety of emotional models. It is divided into a tactile, an olfactory and a visual mode. The tactile signal receiving module is designed by patterns and beat frequencies which are made by mechanical-vibration conversion of the musical melody, rhythm and harmony. The olfactory signal receiving module is designed by switching control of perfume-injection nozzles which are able to give the signal receiving to the CP-called user through a special kind of smell according to the CP-calling user. The visual signal receiving module is made by motion control of DC-motored wheel-based system which can inform the CP-called user of the signal receiving through a desired motion according to the CP-calling user. In this paper, a prototype system is developed far multi-emotional signal receiving modes of CP. We describe an overall structure of the system and provide experimental results of the functional modules.

• 한국정보통신학회논문지
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• 제11권7호
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• pp.1387-1393
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• 2007

기존의 단층 퍼셉트론은 출력 노드가 선형 분리 가능한 패턴들만을 분류할 수 있고 XOR과 같은 비선형 문제에 대해서는 분류할 수 없는 단점이 있다. 퍼지 단층 퍼셉트론은 퍼지 소속 함수(Fuzzy Membership Function)를 적용하여 단층 구조로 XOR 문제와 같은 고전적인 문제를 개선하였다. 그러나 퍼지 단층 퍼셉트론은 기존의 단층 퍼셉트론과 마찬가지로 결정 경계선이 진동하는 경우가 생기며 초기 가중치의 범위와 학습률에 따라 수렴성이 매우 낮아지는 단점이 있다. 따라서 본 논문에서는 바이어스항을 도입하여 결정 경계선이 진동하는 것을 방지하여 수렴성을 개선시키고 선형 활성화 함수를 제안하고 학습률과 모멘텀 개념을 도입 한 개선된 델타규칙을 적용함으로써 학습 시간을 단축시키는 개선된 퍼지 단층 퍼셉트론 알고리즘을 제안한다. 제안된 방법과 퍼지 단층 퍼셉트론간의 학습 성능을 분석하기 위하여 인공 신경망에서 벤치마크로 사용되는 XOR 문제와 패턴 분류에 적용하여 Epoch 수와 수렴성을 비교한 결과, 제안된 방법이 기존의 퍼지 단층 퍼셉트론보다 학습 시간이 적게 소요되고 수렴성이 개선된 것을 확인하였다.

• Current Optics and Photonics
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• 제4권4호
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• pp.286-292
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• 2020

Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.

• 화약ㆍ발파
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• 제39권3호
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• pp.1-14
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• 2021

록버스트는 암석의 돌연하고도 격렬한 파괴를 일컫는 말이다. 이 파괴과정에서 초과에너지가 지진에너지로 방출되면, 주변의 암반 중에는 지반진동이 발생한다. 이렇게 생성된 지반진동의 수준은 리히터 로컬 척도로 규모 4.5 이상에 이를 수 있다. 이와 같은 록버스트는 인명에 위해를 가할 뿐 아니라 지하작업장과 지상구조물에까지 손상을 일으킬 수 있다. 본 논문에서는 캐나다 록버스트 종합연구 1단계 및 2단계 보고서를 토대로 록버스트의 발생기구를 분석하였다. 아울러 단순화된 LS-DYNA 모델을 작성하여 채광막장 암반에서 발파 직후 발생되는 인장균열의 발생양상을 분석하여 보았다. 이 단순화 모델의 개념은 록버스트의 발생기구를 파악하기 위해 실험실에서 수행되는 소규모 시험에 적용한다면 매우 유용할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.