• 전기전자학회논문지
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• 제22권4호
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• pp.1242-1245
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• 2018

본 논문에서, Gaussian noise를 제거할 때 발생하는 over blurring 현상을 감소시키는 network를 구현하였다. 기존 filtering 방식은 원 영상을 blurring하여 noise를 제거함으로써, edge나 corner 같은 high frequency 성분도 함께 지워지는 것을 확인할 수 있다. CNN (Convolutional Neural Network)기반 denoiser의 경우도 사소한 edge, keypoint를 noise로 인식하여 이러한 정보를 잃게 된다. 우리는 CNN을 기반으로 denoising된 high frequency 성분만을 획득하여 기존 denoiser에 추가함으로써 denoising 성능을 유지하면서 over blurring을 완화하는 network 제안한다.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• Journal of Communications and Networks
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• 제18권5호
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• pp.773-783
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• 2016

Both coordinated multi-point transmission and frequency reuse are effective approaches to mitigate inter-cell interference and improve network coverage. The motivation of this work is to explore the manner to effectively utilize the spectrum resource by reasonably combining cooperation and frequency reuse. The $Mat{\acute{e}}rn$ cluster process, which is appropriate to model networks with hot spots, is used to model the spatial distribution of base stations. Two cooperative mechanisms, coherent and non-coherent joint transmission (JT), are analyzed and compared. We also evaluate the effect of multiple antennas and imperfect channel state information. The simulation reveals that the proposed approach to combine cooperation and frequency reuse is effective to improve the network coverage for users located at both the center and the boundary of the cooperative region.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 추계학술발표회
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• pp.199-201
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• 2003

The monitoring effectiveness not only depends on the effectiveness of the network, but also the costs of the network. Generally the costs of the monitoring network are mainly on the equipment and personnel; the implementation and maintenance; the observation and sample connection; the sample analysis; and the data storage and processing. The cost of the monitoring network can be expressed as a function of monitoring frequency because the monitoring method can be an automatic or a manual measurement. To determine the sampling frequency of subsidiary groundwater monitoring stations, time series data of national groundwater monitoring stations were used. The proposed optimal sampling frequency for subsidiary groundwater monitoring station is about 7 to 20 days and the average frequency is about 2 weeks.

• 한국정보전자통신기술학회논문지
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• 제14권1호
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• pp.43-47
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• 2021

본 논문에서는 전력선 통신 (PLC: Power Line Communication) 시스템의 네트워크 데이터 처리량 (throughput)을 구내 (In-building) 환경에서 연구하였다. 전력선 채널은 주파수 선택적 페이딩 주파수 응답을 가지므로 감쇠 및 잡음의 영향을 최소화하기 위해서 adaptive bit loading 방식을 적용한 OFDM (Orthogonal Frequency Division Multiplexing) 변조 방식을 사용하였다. 구내 네트워크의 게이트웨이/CPE (Customer Premise Equipment) 전력선 통신 모뎀 사이의 전력선 통신 구간에서 처리할 수 있는 최대 데이터 처리량을 측정하기 위해 iperf 네트워크 성능 측정 툴을 이용하였고, TCP (Transmission Control Protocol) 윈도우 사이즈별 throughput을 분석하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 춘계전력전자학술대회 논문집(1)
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• pp.299-303
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• 2003

This paper deals with the frequency sensing methods at HVDC system. The objects of frequency information in HVDC system areto fire a valve and to control a frequency of AC network. Conventionally, there are two methods to measure a frequency of AC network. The first method is to draw out from the synchronous machine and the second method is from AC network. Two methods have advantages and disadvantage each other. For the extreme case of a receiving system of zero inertia (no generation), synchronous machine is essential. In this situation, the frequency information received from the synchronous machine shaft. However, the speed of synchronous machine is oscillated when a disturbance in AC network occurs, and HVDC may be oscillated due to an oscillation speed. To solve this drawback, in this paper, new frequency sensing method is proposed. A proposed method that is use a modified curve-fitting algorithm, has a robust characteristics against a harmonics and unbalanced faults. Consequently, A proposed method is verified by PSCAD/EMTDC Program and experimental test.

• 한국통신학회논문지
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• 제34권5A호
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• pp.391-400
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• 2009

최근 이동 통신 네트워크는 2G에서 3G로 이동하고 있으며 주파수의 효율성을 추구하고자 한다. 인지 무선 통신(Cognitive radio) 기술은 secondary 네트워크와 primary 네트워크의 공존을 허용함으로써 주파수의 효율성을 달성할 수 있는 기술로 떠오르고 있다. 하지만, primary 네트워크의 주파수 재사용율을 고려하지 않는 기존 인지무선 통신 방식은 primary 네트워크와 secondary 네트워크를 포함한 전체 네트워크의 성능을 저하시키게 된다. 본 논문에서는 secondary 네트워크가 primary 네트워크의 파일럿 신호를 감지하여 최적의 가용 주파수를 선택하는 복잡도가 낮은 방식을 제안한다. 그리고, primary 네트워크의 간섭을 최소화하는 제약조건을 가지면서 업링크와 다운링크의 용량을 최대화하는 최적화 문제를 고려한다. 시뮬레이션을 통하여 제안방법과 기존방법의 성능을 비교하였으며, 제안방법이 기존방법보다 primary 네트워크의 주파수 재사용율이 작고 채널 사용 변화가 심할 때 특히 우수한 성능을 보임을 확인할 수 있었다.

• Journal of Advanced Marine Engineering and Technology
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• 제23권4호
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.