• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Nuclear Engineering and Technology
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• 제25권1호
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• pp.154-165
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• 1993

연속시간 마코프프로세스를 이용한 한 통계적 방법에 의한 일차원 지하 핵종이동 모델이 제시되었다. 지하매질은 보편적으로 지하수속도, 분산계수 또는 지연계수 등 물리화학적 변수 등의 비균질성을 보여 일반적인 결정론적 이류분산모델로는 잘 기술되지 않는다. 통계적 모델에서의 최종결과는 시간에 따른 함수로서의 기대값과 그 기대값의 분산도를 보여주는 분산치다. 매질이 균질하다고 생각될 정도로 나뉘어진 구획에 대한 핵종의 농도 분포를 구하여 결정론적인 해석해에 의한 농도분포와 비교하여 비균질 매질, 또는 현저하게 구분되는 다층매질의 경우에 대해서 유용 할 것이라는 결론을 얻었다. 매질을 나눈 구획수가 수치적 분산에 민감한 것으로 나타났지만 해석적 모델에 의해 분산계수가 보정될 수 있었다.

• Nuclear Engineering and Technology
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• 제19권3호
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• pp.192-197
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• 1987

다량의 중수반사체를 가진 조밀한 노심에서는 핵분열시 발생하는ｒ선과 중수소와의 (ｒ,n) 반응에 의해 지발 광중성자가 다량 생성되므로 이러한 계통을 기술하기 위하여 광중성자와 그 모핵종의 공간적 분리에 역점을 두어 2-점노 운동방정식을 정립하였다. 여러 반응도를 주입하여 출력 천이를 모사계산하므로써 노심과 반사체사이의 관련 효과를 조사하였다. 이 모델에 의한 모사계산 결과와 공간 종속 운동방정식에 의한 계산결과를 비교하였다. 반사체 영역에서의 광중성자 효과가 포함되므로써, 이를 포함하지 않은 모델에 비해 출력 천이현상을 감소시켰다. 실제로 출력을 측정하는 계측기는 이러한 공간적 분리영 향을 제거하기 위하여 노심 내부에 위치하여야 한다.

• 한국컴퓨터정보학회논문지
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• 제14권3호
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• pp.173-182
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• 2009

본 논문은 한국어 8단모음을 인식하기 위한 효율적인 파라미터의 추출과 자동 독화 시스템의 구축에 관하여 연구한 것이다. 얼굴의 특징들은 다양한 칼라 공간에서 다양한 값으로 표현되는 것을 이용하여 각 표현 값들을 증폭하거나 또는 축소, 대비시켜 얼굴 요소들이 추출되도록 하였다. 눈과 코의 위치, 안쪽 입의 외곽선, 윗입술의 상단, 이의 외곽선을 특징 점으로 찾았으며, 이를 분석하여 안쪽 입의 면적, 안쪽 입의 높이와 폭, 이의 보임 비율 코와 윗입술 상단과의 거리를 파라미터로 사용하였다. 2400개의 영상으로 분석하였고 이 분석을 바탕으로 신경망 시스템을 구축한 후 인식 실험을 하였다. 정상인 5명이 동원되었고, 사람들 사이에 있는 관찰 오차를 정규화를 통하여 수정하였으며 실험하여 파라미터의 유용성 관점에서 좋은 결과를 얻었다.

• 한국음향학회지
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• 제26권2호
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• pp.75-79
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• 2007

본 논문은 SVM(Support Vector Machines)을 이용한 음성신호 기반의 효과적인 성별인식 시스템을 제안한다. 분별적 이진(binary) 패턴 분류기인 SVM은 특징 공간에서 비선형 경계를 찾아 분류하는 방법으로 우수한 성능을 보인다고 알려져 있다. 연구에서는 기존의 성별인식에서 널리 쓰이고 있는 MFCC(Mel Frequency Cepstral Coefficients)를 사용하여 SVM과 기존의 GMM(Gaussian Mixture Model) 알고리즘의 성별인식 성능을 비교하였고, 특히, 보다 향상된 SVM의 성별인식을 위해 MFCC와 Pitch를 이용한 결합 특징 벡터를 적용하였다. 실험결과 MFCC 파라미터를 사용했을 때 제안된 SVM이 GMM보다 우수한 성별인식 성능을 보였고, 제안된 결합 특징 벡터를 사용 했을 때 우수한 성능을 보였다.

• 한국컴퓨터정보학회논문지
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• 제29권3호
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• pp.83-90
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• 2024

본 연구에서는 멀티미디어 환경에서 상호작용 시스템 중 하나인, 음성 대화 기술에 대하여 근접 음성 대화 시스템을 적용하는 솔루션을 제안한다. 사용자 아바타들 간 거리에 따라 음성의 볼륨을 조절하고, 가청 거리를 벗어난 사용자에게는 음소거를 적용하는 방식으로 멀티미디어 공간에서 여러 사용자 간의 음성 대화 방식을 설계하였다. 본 연구의 가장 큰 특징은 경제적인 개발을 위해, 거리를 기반으로 먼 거리에 있는 사용자에게는 저음질의 음성을 전달하고, 비 가청 지역에 들어선 사용자에게는 음성 데이터를 전송하지 않게 하는, reliable UDP 기반 능동적 서버 시스템에 있다. 제안 시스템은 사전에 완성하였던 유니티 게임 엔진 기반 프로젝트에서 성능을 측정하였으며, 본 연구에서 제안한 시스템을 메타버스 콘텐츠, 실시간 대전 액션 게임과 같이 여러 사용자 간 상호작용을 제공하는 환경에서 적극적으로 이용되는 것을 기대할 수 있다.