• 전기학회논문지P
• /
• 제58권4호
• /
• pp.385-390
• /
• 2009

This paper presents a novel active noise control (ANC) system using least mean square (LMS) algorithm and neural network approach for decreasing acoustic noise signals inside high-speed trains. We construct a LMS framework as a nominal ANC system and additionally design an artificial single-layered perceptron model as an auxiliary ANC which is aimed to reduce real-time residuary noise due to its nonstationary and uncertain nature. Parameter vector of the hybrid ANC is determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. We achieve simulation experiment to demonstrate the proposed ANC system employing realistic acoustic noise signals measured in Korea Train eXpress (KTX).

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2004년도 춘계학술대회논문집
• /
• pp.410-415
• /
• 2004

The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2001년도 추계학술대회논문집 I
• /
• pp.427-432
• /
• 2001

This paper shows the optimum design of the fluid engine mount. The design has been modified by trial and error because there is many design parameters that can be varied in order to obtain resonant and notch frequencies, and notch depth. It seems to be a great application for optimal design for the mount. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination provides the lowest resonant peak and notch depth\ulcorner In this study, the enhanced artificial life algorithm is applied to get the desired notch frequency of a fluid mount and minimize transmissibility at the notch frequency. The present hybrid algorithm is the synthesis of an artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The results show that the performance of a conventional engine mount can be improved significantly compared with the optimized mount.

• 한국소음진동공학회논문집
• /
• 제13권10호
• /
• pp.767-776
• /
• 2003

The theoretical method is developed to Investigate the nitration characteristics of the combined cylindrical shells with an annular plate joined to the shell at any arbitrary axial position. The structural coupling between shell and plate is simulated using two types of artificial springs a translational spring is introduced for translational coupling and a rotational spring is used for rotational coupling. The springs are continuously distributed along circumferential direction. Using the Rayleigh-Ritz method the natural frequencies and mode shapes of the combined shell with an annular plate examine. The effect of Inner-to-outer radius ratio, axial position of annular plate and length-to-radius ratio of shell on vibration characteristics of combined cylindrical shells is studied. The theoretical results are verified by comparison with FEM results.

• The Journal of the Acoustical Society of Korea
• /
• 제15권3E호
• /
• pp.4-12
• /
• 1996

In this study, we examine the applicability of an artificial neural network(ANN) for filtering underwater random noise and for identifying underlying signals taken from noisy environment. The approach is to find a way of compressing the input data and then decompressing it using an ANN as in image compressing process. It is well known that random signal is hard to compress while ordered information is not. The use of a limited number of processing elements(PEs) in the hidden layer of an Ann ensures that some of the noise would be removed in the reconstruction process. Two types of the signals, synthesized and measured, are used to examine the effectiveness of the ANN-based filter. After training process is completed, the ANN successfully extracts the underlying signals form the synthesized or measured noisy signals. In particular, compared with the results form without filtering or moving averaged, the ANN-based filter gives much better spectrograms to identify underlying signals from the measured noisy data. This filtering process is achieved without using and kind of highly accurate signal processing technique. More experimentation needs to be followed to develop the ANN-based filtering technique to the level of complete understanding.

• 한국가시화정보학회지
• /
• 제2권2호
• /
• pp.3-7
• /
• 2004

In this paper, computational aeroacoustics (CAA) method is used for flow-noise analysis and flow-noise visualization. High order high resolution scheme of optimized high order compact is used to resolve the small acoustic quantities and large flow quantities at the same time. An adaptive nonlinear artificial dissipation model and generalized characteristic boundary condition are also used. Aeolion tone noise, cavity noise, and jet noise are investigated. The visualizations of flow-noise are successful and characteristics of noise are studied. It is observed that the propagation directivity of noise is different with that of flow. With the help of CAA method, the visualization of noise is possible.

• 한국어병학회지
• /
• 제34권2호
• /
• pp.243-248
• /
• 2021

Offshore wind power generation is an energy generation field that is rapidly developing owing to the increasing demand for clean energy. However, the physiological response of fish to the underwater noise generated during construction or operation of wind turbines is unclear. We confirmed the effects of sound pressures of 125, 135, 145, and 155 dB/µPa, including 140 dB/µPa (the standard sound pressure for noise damage recognition in South Korea), through serum analysis in rock bream (Oplegnathus fasciatus). High mortality induced by reduced immunity through artificial infection after stimulation was confirmed. These results suggest that rock bream is negatively affected by the noise generated during the construction of offshore wind power plants.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2000년도 추계학술대회논문집
• /
• pp.796-801
• /
• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• 로봇학회논문지
• /
• 제14권1호
• /
• pp.14-21
• /
• 2019

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

• 한국컴퓨터정보학회논문지
• /
• 제23권4호
• /
• pp.9-16
• /
• 2018

In this paper, we propose wide range of categorizes Artificial Intelligence technology as Learning, Inference, and Cognitive. Also, it analyzes 758 cases of open patents. For an analysis, target technologies were selected and categorized into specific areas to collect information about the patents. After removing noise, the patent information for each technology such as patent assignees and IPC code, was analyzed to evaluate the maturity of technology, the way ahead for research and development and the trends in core technology. This research presents directions of Artificial intelligence technology research and trend analysis of core Artificial Intelligent technology using quantitative analysis of patent information. Also Artificial intelligence technology requires technological development necessity through close cooperation in diverse fields.