• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.637-644
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• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.892-903
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• 2018

The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.813-820
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• 2006

The advance of computer and communication technologies enables the embedded systems to be equipped with the network communication interfaces. Their appearance in network leads to security issues on the embedded systems. An easy way to overcome the security problem is to adopt the packet filter that is implemented in the general computer systems. However, general packet filters designed for host computers are not suitable to embedded systems because of their complexity. In this paper, we propose a lightweight packet filter for embedded systems. The lightweight packet filter is implemented in the kernel code. And we have installed a Web-GUI interface for user to easily set the filtering policies at remote space. The experimental results show that the proposed packet filter decreases the packet delivery time compared to the packet filter designed for host computers and it is comparable to the systems without packet filter.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.592-595
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• 2003

In this paper, we have designed a Home Networking System using an embedded linux board. The system based on the World-Wide-Web is composed of three parts - a Server, a Client and a Simulator. The Home Networking Server is built in an embedded board using an embedded linux kernel. A web-server and Home Networking Server Seryice Demon programming with a Jaya-Language is included in the board. Clients can connect to the server board using a web-browser in the desktop computer, PDA or any other machines which include a web-browser. For this purpose, we made the client program using a Java-Applet. So, the clients who connect to the server for the control of the applications, download the class-file and execute the client-program in the web-browser. So, the clients don't need any other programs to control the applications from a remote place. The size of server board is very small (86.3$\times$74mm), which makes it very useful not only for the Home-Networking-System but also in many other fields, e.g., embedded robot control system, etc. Using an embedded board instead of a desktop computer is good for a simple network environment and it occupies only a small space to make the system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.710-716
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• 2008

This paper covers the performance evaluation of two flash memory-based file storages, NAND and NOR, which are the major flash types. To evaluate their performances, we set up separate file storages for the two types of flash memories on a PocketPC-based experimental platform. Using the platform, we measured and compared the I/O throughputs in terms of buffer size, amount of used space, and kernel-level write caching. According to the results from our experiments, the overall performance of the NAND-based storage is higher than that of NOR by up to 4.8 and 5.7 times in write and read throughputs, respectively. The experimental results show the relative strengths and weaknesses of the two schemes and provide insights which we believe assist in the design of flash memory-based file storages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1448-1455
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• 2008

This paper designs Shared Memory on the Dual Core system so that it operates a general System V IPC on the Linux O.S. Shared Memory is the technique that many processes can access to identical memory area. We treat Shared Memory in this paper among big two branches of Shared Memory which are SVR in a kernel step format. We design a share memory facility of Linux operating system on the Dual Core System. In this paper the suggesting design plan of share memory facility in Dual Core system is enhancing the performance in existing unity processor system as a dual core practical use. We attempt a performance enhance in each CPU for each process which uses a share memory.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.167-173
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• 2023

This paper investigates several methods of visualizing high-dimensional data in a low-dimensional space. At first, principal component analysis and multidimensional scaling are briefly introduced as linear approaches, and then kernel principal component analysis, self-organizing map, locally linear embedding, Isomap, Laplacian Eigenmaps, and local multidimensional scaling are introduced as nonlinear approaches. In particular, t-SNE, which is widely used but relatively unfamiliar in the field of statistics, is described in more detail. We also present a simple example for several methods, including t-SNE. Finally, we provide a review of several recent studies pointing out the limitations of t-SNE and discuss the future research problems presented.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.14-28
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• 2010

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.

• Journal of the Korea Computer Graphics Society
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• v.23 no.2
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• pp.29-39
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• 2017

This paper presents a unified framework to efficiently and realistically simulate surface and wave foams. The framework is designed to first project 3D water particles from an underlying water solver onto 2D screen space in order to reduce the computational complexity of determining where foam particles should be generated. Because foam effects are often created primarily in fast and complicated water flows, we analyze the acceleration and curvature values to identify the areas exhibiting such flow patterns. Foam particles are emitted from the identified areas in 3D space, and each foam particle is advected according to its type, which is classified on the basis of velocity, thereby capturing the essential characteristics of foam wave motions. We improve the realism of the resulting foam by classifying it into two types: surface foam and wave foam. Wave foam is characterized by the sharp wave patterns of torrential flow s, and surface foam is characterized by a cloudy foam shape even in water with reduced motion. Based on these features, we propose a technique to correct the velocity and position of a foam particle. In addition, we propose a kernel technique using the screen space density to efficiently reduce redundant foam particles, resulting in improved overall memory efficiency without loss of visual detail in terms of foam effects. Experiments convincingly demonstrate that the proposed approach is efficient and easy to use while delivering high-quality results.