• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 한국정보통신학회논문지
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• 제21권3호
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• pp.629-635
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• 2017

본 논문에서는 영상에 첨가된 가우시안 잡음과 임펄스 잡음의 영향을 완화하기 위하여 잡음의 종류에 따라 처리하는 비선형 합성 필터를 제안하였다. 잡음 판단을 통해 국부 마스크의 중심화가 가우시안 잡음으로 판단된 경우, 국부 마스크 내의 표본분산을 이용하여 공간 가중치 필터와 화소 변화에 따른 가중치 필터의 가중치를 다르게 적용하여 처리하고, 임펄스 잡음으로 판단된 경우, 국부 마스크의 잡음 밀도에 따라 국부 히스토그램 가중치 필터와 표준 메디안 필터의 가중치를 다르게 적용하여 처리하는 알고리즘을 제안하였다. 그리고 제안한 필터 알고리즘의 성능을 평가하기 위해 PSNR(peak signal to noise ratio)을 사용하여 기존의 방법들과 제안한 필터 알고리즘을 가우시안 잡음, 임펄스 잡음 및 두 잡음이 혼합된 복합잡음 환경에서 각각 비교하였다.

• 한국음향학회지
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• 제29권1호
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• pp.56-61
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• 2010

본 논문에서는 HMM 기반의 연속음성인식에서 음향모델의 최적화 기법을 논한다. 대부분의 음성인식 시스템에서 HMM 상태별로 동일한 개수의 가우시안 성분 (mixture component)을 사용해 왔다. 그러나, 음향 모델링에 사용되는 데이터 샘플의 개수는 HMM상태별로 다르므로 이에 따른 최적화를 수행할 경우 모델 파라미터의 개수를 효과적으로 줄일 수 있을 뿐 아니라, 디코딩 단계에서 음성인식기의 속도 및 인식 성능 개선이 기대된다. 본 연구에서 제안한 방법은 기존에 알려진 MDL (minimum description length) 기반의 음향모델 최적화 방법에서 가우시안 성분들의 통합과정에 가우시안 성분의 가중치 정보 (mixture weight)를 반영하도록 개선하였다. 인식 실험 결과, 제안한 방법은 가우시안 성분의 가중치를 반영하지 않는 기존 방법에 비해 향상된 최적화 성능을 보임을 확인할 수 있었다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 추계학술대회
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• pp.73-76
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• 2018

A Gaussian noise is caused by surrounding environment or channel interference when transmitting image. The noise reduces not only image quality degradation but also high-level image processing performance. The Non-Local Means (NLM) filter finds similarity in the neighboring sets of pixels to remove noise and assigns weights according to similarity. The weighted average is calculated based on the weight. The NLM filter method shows low noise cancellation performance and high complexity in the process of finding the similarity using weight allocation and neighbor set. In order to solve these problems, we propose an algorithm that shows an excellent noise reduction performance by using Summed Square Image (SSI) to reduce the complexity and applying the weighting function based on a cosine Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2431-2449
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• 2024

As problems such as water pollution and fish species depletion have become serious, a land-based fish farming is receiving a great attention for ensuring stable productivity. In the fish farming, it is important to determine the timing of shipments, as one of key factors to increase net profit on the aquaculture. In this paper, we propose a system for predicting net profit to support decision of timing of shipment using fish farming-related statistical data. The prediction system consists of growth and farm-gate price prediction models, a cost statistics table, and a net profit estimation algorithm. The Gaussian process regression (GPR) model is exploited for weight prediction based on the analysis that represents the characteristics of the weight data of cultured fish under the assumption of Gaussian probability processes. Moreover, the long short-term memory (LSTM) model is applied considering the simple time series characteristics of the farm-gate price data. In the case of GPR model, it allows to cope with data missing problem of the weight data collected from the fish farm in the time and temperature domains. To solve the problem that the data acquired from the fish farm is aperiodic and small in amount, we generate the corresponding data by adopting a data augmentation method based on the Gaussian model. Finally, the estimation method for net profit is proposed by concatenating weight, price, and cost predictions. The performance of the proposed system is analyzed by applying the system to the Korean flounder data.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• 스마트미디어저널
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• 제4권3호
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• pp.50-55
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• 2015

본 논문에서는 야간 영상 감시(night-time video surveillance)에 특화된 GMM(Gausssian mixture model)기반의 배경 모델링(background modeling)을 이용한 배경 차분(background subtraction)방법을 제안한다. 야간 영상에서는 낮 영상에 비해 배경과 객체의 구분이 뚜렷하지 않아 매우 흡사한 픽셀 값들을 이용하여 배경을 분리해야 한다. 이러한 문제점을 해결하기 위해 전처리 단계에서 조정된 범위의 히스토그램 스트레칭을 이용하여 입력 픽셀 값을 배경 모델링에 이로운 픽셀 값으로 변경해준다. 조정된 픽셀 값을 이용하여 가장 이상적인 배경을 찾기 위해 픽셀 단위로 GMM기반의 배경 모델링 방법을 적용한다. GMM을 기반으로 한 배경모델링 방법에서는 새로운 픽셀 값이 입력되었을 때 어떤 가우시안에도 속하지 않는다면 가장 낮은 가중치를 가진 가우시안 분포를 제거함으로써 이전의 축적된 배경의 정보를 무시하는 결과를 낳게 된다. 따라서 본 논문에서는 낮은 가중치의 가우시안을 제거하는 대신 기존 가우시안의 평균과 입력된 픽셀 값의 차를 이용하여 새로운 평균에 적용함으로써 기존의 쌓여진 정보를 고려한다. 실험결과 제안된 배경 모델링 방법이 기존 방법의 이점을 유지하면서 야간 영상 감지에 특화된 배경 차분 결과를 보였다.

• The Journal of the Acoustical Society of Korea
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• 제17권4E호
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• pp.3-10
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• 1998

This paper concerns the dynamical behavior, in probabilistic sense, of a feedforward neural network performing auto association for novelty. Networks of retinotopic topology having a one-to-one correspondence between and output units can be readily trained using back-propagation algorithm, to perform autoassociative mappings. A novelty filter is obtained by subtracting the network output from the input vector. Then the presentation of a "familiar" pattern tends to evoke a null response ; but any anomalous component is enhanced. Such a behavior exhibits a promising feature for enhancement of weak signals in additive noise. As an analysis of the novelty filtering, this paper shows that the probability density function of the weigh converges to Gaussian when the input time series is statistically characterized by nonsymmetrical probability density functions. After output units are locally linearized, the recursive relation for updating the weight of the neural network is converted into a first-order random differential equation. Based on this equation it is shown that the probability density function of the weight satisfies the Fokker-Planck equation. By solving the Fokker-Planck equation, it is found that the weight is Gaussian distributed with time dependent mean and variance.

• 한국정보통신학회논문지
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• 제25권9호
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• pp.1176-1182
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• 2021

최근 인공지능과 IoT 기술의 발달에 따라 다양한 분야에서 자동화와 무인화가 진행되고 있으며, AI 객체인식의 기반이 되는 영상처리에 대한 중요성이 높아지고 있다. 특히 세밀한 데이터 처리가 필요한 시스템에서는 전처리 단계로 잡음 제거를 사용하고 있으나, 기존 알고리즘은 영상의 잡음 수준을 고려하지 않아 필터링 과정에서 블러링 현상이 나타나는 단점을 가지고 있다. 따라서 본 논문에서는 영상의 잡음 수준을 판단하여 가중치를 결정하는 변형된 가우시안 필터를 제안한다. 제안한 알고리즘은 쿼드트리 분할을 사용하여 영상의 AWGN에 대한 잡음추정치를 구하여 가우시안 가중치와 화소가중치를 정하며, 로컬마스크와 컨벌루션하여 최종출력을 구한다. 제안한 알고리즘을 평가하기 위해 기존 방법과 비교하여 시뮬레이션하였으며, 기존 방법에 비해 우수한 성능을 확인하였다.

• ETRI Journal
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• 제32권5호
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• pp.795-800
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• 2010

Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.