Dong-Chul Park

Myongji University

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Dong-Min Woo

Myongji University

Yun‐Sik Lee

Pusan National University

Chung Nguyen Tran

Universitat Autònoma de Barcelona

Hai-Nguyen Ho

Myongji University

Le-Anh Tran

Myongji University

Soo-Young Min

Korea Electronics Technology Institute

Thanh-Dat Nguyen

Kyung Hee University

My-Ha Le

Ho Chi Minh City University of Technology and Education

Do-Thanh Sang

Myongji University

Taikyeong Jeong

Hallym University

Cooperative Institutions

Myongji University

Korea Advanced Institute of Science and Technology

Seoul National University

Jeonbuk Development Institute

Ho Chi Minh City University of Technology and Education

Yonsei University

Korea Electronics Technology Institute

Samsung (South Korea)

Georgia Institute of Technology

Korea Atomic Energy Research Institute

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Standard Additive Model in Data Mining

Do-Thanh Sang Dong-Min Woo Dong-Chul Park

The habitual purpose of data mining is prediction, one of the most direct real-world applications. There are many technologies available to data mining in literature and they achieved some results with reasonable accuracies. This paper designs and implements an advanced model based on fuzzy inference system, namely Standard Additive Model (SAM) for forecasting the output of any record given the input variables only from the database, the age of abalone in particular. SAM offers an optimum solution for the prediction and can be definitely an alternative approach for conventional models such as neural networks. The experimental result comparison to multi-layer perceptron neural network (MLPNN) is provided in same context.

Multilayer perceptron

Fuzzy inference system

Perceptron

10.1109/cyberc.2010.16

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Reconstruction of 3D Building Model from Satellite Imagery Based on the Grouping of 3D Line Segments Using Centroid Neural Network

Korean Journal of Remote Sensing (2011)

Dong-Min Woo Dong-Chul Park Hai-Nguyen Ho Taehyun Kim

본 논문에서는 중심신경망을 이용하여 위성영상으로부터 직사각형 형태의 3차원 건물의 지붕모델을 재구성하는 방법을 연구하였다. 제안된 3차원 지붕모델 재구성 기법의 핵심은 3차원 선소의 군집화에 있다. 이를 위해 한 쌍의 스테레오 영상으로부터 구해진 DEM (Digital Elevation Map) 데이터와 2차원 선소에 의해서 3자원 선소를 발생하였다. 제안된 군집화 과정은 중심신경망을 이용한 방법에 의해 수행되며, 2단계로 구성된다. 첫 번째 단계에서는 선소 추출과정에서 끊어지거나, 중복된 3차원 선소를 건물을 이루는 주된 선소로 군집화하고, 두 번째 단계에서는 건물을 구성하는 주된 선소를 구하기 위해 서로 평행인 선소들의 군으로 군집화를 수행한다. 이 군집화 결과를 최종 클러스터링 과정을 통해 직사각형 형태의 지붕모델로 재구성하게 된다. 제안된 방법이 대전지역의 고해상도 IKONOS 위성영상에 의해 실험되었다. 재구성된 건물모델이 원래 건물의 위치와 형태를 대체로 정확히 반영하여, 본 논문에서 제안된 기법을 고해상도 위성영상에 적용하여 도시지역의 건물모델을 구축하는데 효과적으로 사용될 수 있음이 입증되었다. This paper highlights the reconstruction of the rectilinear type of 3D rooftop model from satellite image data using centroid neural network. The main idea of the proposed 3D reconstruction method is based on the grouping of 3D line segments. 3D lines are extracted by 2D lines and DEM (Digital Elevation Map) data evaluated from a pair of stereo images. Our grouping process consists of two steps. We carry out the first grouping process to group fragmented or duplicated 3D lines into the principal 3D lines, which can be used to construct the rooftop model, and construct the groups of lines that are parallel each other in the second step. From the grouping result, 3D rooftop models are reconstructed by the final clustering process. High-resolution IKONOS images are utilized for the experiments. The experimental result's indicate that the reconstructed building models almost reflect the actual position and shape of buildings in a precise manner, and that the proposed approach can be efficiently applied to building reconstruction problem from high-resolution satellite images of an urban area.

Centroid

Elevation (ballistics)

Line (geometry)

3D Reconstruction

Line segment

10.7780/kjrs.2011.27.2.121

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Distilled Pooling Transformer Encoder for Efficient Realistic Image Dehazing

arXiv (Cornell University) (2024)

Le-Anh Tran Dong-Chul Park

This paper proposes a lightweight neural network designed for realistic image dehazing, utilizing a Distilled Pooling Transformer Encoder, named DPTE-Net. Recently, while vision transformers (ViTs) have achieved great success in various vision tasks, their self-attention (SA) module's complexity scales quadratically with image resolution, hindering their applicability on resource-constrained devices. To overcome this, the proposed DPTE-Net substitutes traditional SA modules with efficient pooling mechanisms, significantly reducing computational demands while preserving ViTs' learning capabilities. To further enhance semantic feature learning, a distillation-based training process is implemented which transfers rich knowledge from a larger teacher network to DPTE-Net. Additionally, DPTE-Net is trained within a generative adversarial network (GAN) framework, leveraging the strong generalization of GAN in image restoration, and employs a transmission-aware loss function to dynamically adapt to varying haze densities. Experimental results on various benchmark datasets have shown that the proposed DPTE-Net can achieve competitive dehazing performance when compared to state-of-the-art methods while maintaining low computational complexity, making it a promising solution for resource-limited applications. The code of this work is available at https://github.com/tranleanh/dpte-net.

Pooling

10.48550/arxiv.2412.14220

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Short-Term Load Forecasting Using Multiscale BiLinear Recurrent Neural Network

Springer eBooks (2006)

Dong-Chul Park Chung Nguyen Tran Yun‐Sik Lee

Multilayer perceptron

Perceptron

10.1007/11801603_36

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CLUSTERING OF 3D LINE SEGMENTS USING CENTROID NEURAL NETWORK FOR BUILDING DETECTION

Journal of Circuits Systems and Computers (2014)

Dong-Chul Park Dong-Min Woo Changsun Kim Soo-Young Min

An efficient method for rectangular boundary extraction from aerial image data is proposed in this paper. A centroid neural network (CNN) with a metric utilizing line segments is adopted to group low-level line segments for detecting rectangular objects. The proposed method extracts rectangular boundaries for building rooftops from 3D edge images with various types of noises arising from the stereo matching process. In order to overcome the noises in 3D edge images including line segments of shadows, a clustering method utilizes the constraint where the heights of building rooftops are similar and the clustering process is performed with candidate 3D line segments with similar heights. Experiments are performed with a set of high-resolution satellite image data. The results show that the proposed method can remove noisy segments including shadow lines efficiently and thus find more accurate rectangular boundaries and building information.

Centroid

Line segment

Line (geometry)

10.1142/s0218126614500716

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Image Classification using Fast Learning Convolutional Neural Networks

Advanced science and technology letters (2015)

Keonhee Lee Dong-Chul Park

In this paper, we propose an image classification method for improving the learning speed of convolutional neural networks (CNN). Although CNN is widely used in multiclass image classification datasets, the learning speed remains slow for large amounts of data. Therefore, we attempted to improve the learning speed by applying an extreme learning machine (ELM). We propose a learning method combining a network of CNN with this ELM. First, we use an orthogonal bipolar vector (OBV) for studying different types of neural networks. After learning a limited epoch used in the CNN, we determine the output of the neural network using the ELM. To evaluate the performance, we compared the learning speed and classification rate of conventional CNN against the proposed method. Experimental results show that the proposed algorithm has a faster learning curve than the existing algorithm.

Extreme Learning Machine

Contextual image classification

Speedup

10.14257/astl.2015.113.11

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Satellite image classification using a classifier integration model

Dong-Chul Park Taekyung Jeong Yun‐Sik Lee Soo-Young Min

A new satellite image classification method using a classifier integration model(CIM)is proposed in this paper. CIM does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The CIM is applied to the problem of satellite image classification on a set of image data. The results demonstrate that the CIM scheme can enhance the classification accuracy of individual classifiers that use specific feature vector group.

Contextual image classification

Feature vector

Linear classifier

Margin classifier

Confusion

10.1109/aiccsa.2011.6126608

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POCS-based Clustering Algorithm

arXiv (Cornell University) (2022)

Le-Anh Tran Henock M. Deberneh Truong-Dong Do Thanh-Dat Nguyen My-Ha Le

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.

Data stream clustering

k-medians clustering

Single-linkage clustering

Clustering high-dimensional data

10.48550/arxiv.2208.08888

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BRAND SOUND OF PREMIUM SEDANS AND ITS IMAGE ANALYSIS BASED ON HUMAN ENGINEERING

한국소음진동공학회 국제학술발표논문집 (2008)

Kyung Hoon Lee Sang‐Kwon Lee Dong-Chul Park Tae Gyu Kim

Sound analysis

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