• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.587-594
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• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.5
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• pp.56-69
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• 1999

본 논문에서는 움직임 보상 차영상 ( motion compensated difference image; MCDI)을 대역 분할한 뒤, 움직임 정보를 이용하여 에너지가 큰 부분만을 선택하여 적응적으로 벡터 양자화를 행함으로써 효율적으로 동영상을 부호화 할 수 있는 기법을 제안하였다. 제안한 기법에서는 MCDI의 특성들을 고려하여 에너지가 낮은 부분은 부호화를 행하지 않고, 에너지가 높은 부분에 대하여서만 움직임 벡터의 크기에 따라 클래스를 분류한 뒤 각 클래스에 맞는 코드북을 설계하여 벡터 양자화를 행한다. 이를 위해서는 부호화 되는 에너지가 높은 부분에 대한 정보와 클래스 부분에 대한 정보가 필요하다. 본 논문에서는 이러한 부가 정보의 양을 움직임 벡터를 이용하여 최소화함으로써 비트율을 크게 줄이면서도 우수한 화질의 복원 영상을 얻을 수 있었다.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.159-161
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• 2000

본 논문에서는 부하제어 서비스 클래스와 같이 결정적인 수준의 보장을 지원할 수 없는 연결 구조를 이중화하여 실시간 RPC의 종료시한 만족도를 개선하는 기법을 제안하고 그 성능을 평가한다. 제안된 기법은 RPC의 여유시간에 따라 요청 혹은 응답 메시지 전송에 있어서 종료시한 만족가능성이 높은 연결을 선택하도록 함으로써 각 RPC 트랜잭션에 여유시간에 따른 우선순위를 부여할 수 있으며 분할 기준치의 효율적인 선택에 의해 성능의 향상을 기할 수 있다. 이를 위해 할당된 대역폭에 따른 최적의 분할 기준치를 통계적으로 추정하여 실험 결과와 비교하였다. SMPL을 기반으로 수행된 모의실험은 제안된 기법이 실험에서 주어진 인자값에 대해 실시간 RPC의 종료시한 만족도를 개선할 수 있으며 통계적으로 0.3% 이내에서 최적의 분할 기준치를 추정할 수 있음을 보인다.

• Journal of KIISE
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• v.45 no.1
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• pp.94-98
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• 2018

We propose Wide Inception ResNet (WIR Net) an optimized neural network architecture as a real-time semantic segmentation method for autonomous driving. The neural network architecture consists of an encoder that extracts features by applying a residual connection and inception module, and a decoder that increases the resolution by using transposed convolution and a low layer feature map. We also improved the performance by applying an ELU activation function and optimized the neural network by reducing the number of layers and increasing the number of filters. The performance evaluations used an NVIDIA Geforce GTX 1080 and TX1 boards to assess the class and category IoU for cityscapes data in the driving environment. The experimental results show that the accuracy of class IoU 53.4, category IoU 81.8 and the execution speed of $640{\times}360$, $720{\times}480$ resolution image processing 17.8fps and 13.0fps on TX1 board.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• The Transactions of the Korea Information Processing Society
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• v.2 no.4
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• pp.477-486
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• 1995

This paper describes and constructs object-oriented analysis models using Galois-lattices that we are always studying in discrete mathematics, shows fundamental approaches to maintain the models, analyzes the construction of object-oriented analysis models through good examples. Also, we define several properties of Galois-lattices that have binary relations between class objects, propose the incremental updating algorithms that can update the Galois-lattice whenever new classes are added. This proposal shows that in case of adding new class nodes the results from simulations can implement in constant time and have linearly the incremental structures in worst cases, and in that the growth rate of lattices is proportioned to class nodes in time complexity. This results can achieve the high understandability of object-oriented analysis models and the high traceability of maintenance models. Furthermore it is possible to make more efficient performances of class reusability in advantages of object-oriented systems and support truly the class hierarchical maintenances.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.