• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.149-156
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• 2011

This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.56-68
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• 2022

Recently, as the demand for real-time processing increases, studies on a dynamic graph that changes over time has been actively done. There is a connected components processing algorithm as one of the algorithms for analyzing dynamic graphs. GPUs are suitable for large-scale graph calculations due to their high memory bandwidth and computational performance. However, when computing the connected components of a dynamic graph using the GPU, frequent data exchange occurs between the CPU and the GPU during real graph processing due to the limited memory of the GPU. The proposed scheme utilizes the Weighted-Quick-Union algorithm to process large-scale graphs on the GPU. It supports fast connected components computation by applying the size to the connected component label. It computes the connected component by determining the parts to be recalculated and minimizing the data to be transmitted to the GPU. In addition, we propose a processing structure in which the GPU and the CPU execute asynchronously to reduce the data transfer time between GPU and CPU. We show the excellence of the proposed scheme through performance evaluation using real dataset.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.2 s.332
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• pp.23-38
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• 2005

The state of channel between two or more wireless terminals is changed frequently due to noise or multiple environmental conditions in wireless network. In this paper, we analyze packet transmission time and queue length in a time-varying channel of packet based Wireless Networks. To reflect the feature of the time-varying channel, we model the channel as two-state Markov model and three-state Markov model Which are transformed to SFG(Signal Flow Graph) model, and then the distribution of the packet transmission can be modeled as Gaussian distribution. If the packet is arrived with Poisson distribution, then the packet transmission system is modeled as M/G/1. The average transmission time and the average queue length are analyzed in the time-varying channel, and are verified with some simulations.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.162-170
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• 2015

Commute time embedding involves computing the spectral decomposition of the graph Laplacian. It requires the computational burden proportional to $o(n^3)$, not suitable for large scale dataset. Many methods have been proposed to accelerate the computational time, which usually employ the Nystr${\ddot{o}}$m methods to approximate the spectral decomposition of the reduced graph Laplacian. They suffer from the lost of information by dint of sampling process. This paper proposes to reduce the errors by approximating the spectral decomposition of the graph Laplacian using that of the affinity matrix. However, this can not be applied as the data size increases, because it also requires spectral decomposition. Another method called approximate commute time embedding is implemented, which does not require spectral decomposition. The performance of the proposed algorithms is analyzed by computing the commute time on the patch graph.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.209-221
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• 1994

Timers are commonly used in hardware design for time delays that are to be much longer than the system clock period. In this paper, we present a method by which we can synthesie a hardware containing timers that implement long time delays described in VHDL. Because, in general, timers require high hardware cost, they must be utilized as efficiently as possible. To solve this problem we define a graph model and propose an algorithm that uses the graph model to minimize number of timers. A preliminary experimental result show that the algorithm implements all required time delays using minimum number of timers.

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.219-226
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• 2019

One of the most widely studied protocols for tactical ad-hoc networks is Optimized Link State Routing Protocol (OLSR). As for OLSR research, most research work focus on reducing control traffic overhead and choosing relay point. In addition, because OLSR is mostly dependent on link detection and propagation, dynamic Hello timer become research challenges. However, different timer interval causes imbalance of link validity time by affecting link lifetime. To solve this problem, we propose a weighted topology graph model for constructing a robust network topology based on the link validity time. In order to calculate the link validity time, we use control message timer, which is set for each node. The simulation results show that the proposed mechanism is able to achieve high end-to-end reliability and low end-to-end delay in small networks.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• Physical Therapy Rehabilitation Science
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• v.9 no.4
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• pp.287-294
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• 2020

Objective: This study aimed to investigate the effects of an ultrasound-based bar graph proportional to the quadriceps muscle thickness as a real-time visual feedback training tool in the elderly. Design: Cross-sectional study. Methods: Twenty-four elderly persons participated in this study and were randomly divided into three groups: oral training group (n=8, group 1), ultrasound imaging group (n=8, group 2), and graph group (n=8, group 3). In the pre condition, all participants performed maximal voluntary isometric contraction (MVIC) of the quadriceps with knee extension three times with oral training. In the post condition, group 1 performed MVIC of the quadriceps with oral training, group 2 performed MVIC of the quadriceps with real-time visual feedback using ultrasound imaging, and group 3 performed MVIC of the quadriceps with real-time visual feedback using a bar graph proportional to the quadriceps muscle thickness, three times for all groups. Muscle thickness, activity (mean, peak), tone, stiffness, logarithmic decrement, relaxation, and creep were measured in both conditions in all participants. Results: Visual feedback with a bar graph showed significant effects on muscle thickness, mean muscle activity, and peak muscle activity compared with oral training and visual feedback with ultrasound imaging (p<0.05). Conclusions: Isometric training of the quadriceps with real-time visual feedback using a bar graph proportional to the quadriceps muscle thickness may be more effective than other methods in improving muscle thickness and muscle activity. This study presented a tool that can help increase muscle thickness in the elderly.